1, dashed line - experiment # 2, short-dashed lines - experiment # 3 (adapted from

(Markushin *et al.*, 2009))

20

LIBS intensity, a.u.

30

20

30

LIBS intensity, a.u.

spectrum around 259.9 mn Fe line (b). Solid line – empty filter, dot-dashed line - experiment

287.5 288.0 288.5

Wavelength, nm

Si

259 260 261 262

Wavelength, nm

The two-element coded composite micro-particles were prepared by allowing the iron oxide biotinylated particles to interact with the silicon particles modified by avidin (experiment # 2). We monitored the amount of aggregates by taking 140 laser shots at the surface of 5 µm pore size centrifuge filters. After overnight incubation the filtrate with unbound microparticles was removed by centrifuging over 5 µm pore size filters, then the top part of the test tube was cut off and the bottom part with the particles being left on a filter (residue particles) was checked by LIBS for the presence of Fe and Si elements (dashed line on Fig. 3a, 3b). The presence of both Fe (259.9 nm) and Si (288.1 nm) emission lines in the same sample proves that we generated the two-element coded composite micro-particles. Thus, the ability of LIBS to detect the presence and composition of the micro-particles is demonstrated. Further, we suggest that this technique can be employed as a detection method for the future element coded assay development.

Sensitivity is a key factor of any analytical method. To determine the sensitivity of the elementcoded approach, we used the model protein avidin. We performed detection and quantification of avidin molecules using LIBS based iron oxide micro-particle assay (Fig. 4). The details of the experiment can be found elsewhere (Markushin *et al.*, 2009, Rock *et al.*, 2008). 1.5 µm iron oxide micro-particles coated with biotin were purchased from Bangs Laboratories and their aggregation was induced upon the addition of avidin. We monitored the amount of aggregates by using 140 laser shots at the surface of 5 µm pore size centrifuge filters after removing the filtrate with non-bound micro-particles. Figure 4 shows the avidin concentration dependence of the LIBS based intensity of Fe emission line at 259.9 nm integrated over the filter surface. The iron oxide micro-particle assay demonstrated limit-of-detection about 30 ppb of avidin. This Figure has a significant maximum at about 155 ppb and is discussed below.

Fig. 4. Avidin concentration dependence of the micro- particles aggregation (adapted from (Markushin *et al.*, 2009))

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

caused by non-specific interaction of micro-particles.

line - 250 U/ml, pink line – 1000 U/ml.

experiments.

Using Tag-Laser Induced Breakdown Spectroscopy 161

To obtain better sensitivity, we employed the magnetizing type of assay. In this approach, following the incubation, the unbound molecules, the single silicon particles, the single iron oxide particles and particle aggregates were separated using strong magnets. After completing steps of magnetizing and pipetting, the residue particles left on the filters were analyzed using LIBS for the presence of silicon. Fig. 5 shows the fragment of LIBS spectra around 288.1 nm silicon emission line obtained by the two-element (Si and Fe) Tag-LIBS assay for detection of CA 125 biomarker. The control lowest black line on Fig. 5 was obtained from the empty filter. The red line curve is a LIBS spectrum of control sample where instead of CA 125 the buffer was added. Other lines represent various concentrations of CA 125 in a solution (see Fig. 5). The presence of some Si in the control sample is possibly

Fig. 5. The fragment of LIBS spectra around 288.1 nm silicon emission line in the two-

element (Si and Fe) Tag-LIBS assay for detection of CA 125. The black line represents LIBS of the empty filter. Other lines represent various concentrations of CA 125 in a solution: red line – control sample with no CA 125, green line – 10 U/ml, blue line – 50 U/ml, light blue

Figure 6 shows the experimental results of the ovarian biomarker CA 125 detection by the two-element (Fe and Si) Tag-LIBS assay in a blood mimicking PBS buffer. The curve on Fig. 6 represents the CA 125 concentration dependence of the intensity of Si emission line at about 288.1 nm obtained by Tag-LIBS assay. The experimental curve has a maximum at about 10 U/ml and will be discussed later in comparison with results of other

We employed the element coded LIBS based approach for the detection of biomarker CA 125. For this particular measurement, we express the concentration of CA 125 in International Units (IU or U) per milliliter. Typically these units are used for quantification of biologically active substances (e.g. CA 125) instead of grams or moles (World Health Organization (WHO) Expert Committee on Biological Standardization, n.d.) According to the WHO Expert Committee on Biological Standardization, an International Unit is the specific biological activity of a substance, i.e. the quantity of a biologically active molecules required to produce a defined response. The use of such internationally accepted units ensures that biologically active substances with the same measured response will contain the same quantity expressed in International Units.

We also used the two-element micro-particle coding method to detect ovarian cancer biomarker CA 125. To do this, we prepared two bio-suspensions for groups A and B IgG antibody tagging (Fig. 1). In suspension B the biotinylated group A antibodies were immobilized to the silicon oxide micro particles with streptavidin. In suspension A the group B antibodies were immobilized through their Fc (fragment crystallizable region) fragments to iron oxide micro particles modified with immunoglobulin-binding protein G. Protein G mediates attachment of antibodies to the micro-particles and ensures the proper orientation of IgG molecules for the better immuno-assay efficiency (Bjorck & Kronvall, 1984). The buffer used contained 5% of BSA to mimic blood conditions (Majoor, 1946).

The following is a step-by-step description of the two-element micro-particle coding assay. To perform the analysis on the presence of CA 125 in a solution we follow the following procedure:


Unbound silicon oxide particles and dissolved CA 125 molecules were separated from unbound iron oxide particles and aggregates of iron oxide and silicon oxide particles by using strong magnets (residual flux density about 14.5-14.8 KGs (K&J Magnetics, Inc. website, n.d.). The separation-washing cycles were repeated 3 times.

After completing magnetizing and pipetting, the top part of the test tubes was cut off and the bottom filters with particles deposited on it (residue particles) were checked by LIBS for the presence of Si elements.

We employed the element coded LIBS based approach for the detection of biomarker CA 125. For this particular measurement, we express the concentration of CA 125 in International Units (IU or U) per milliliter. Typically these units are used for quantification of biologically active substances (e.g. CA 125) instead of grams or moles (World Health Organization (WHO) Expert Committee on Biological Standardization, n.d.) According to the WHO Expert Committee on Biological Standardization, an International Unit is the specific biological activity of a substance, i.e. the quantity of a biologically active molecules required to produce a defined response. The use of such internationally accepted units ensures that biologically active substances with the same measured response will contain

We also used the two-element micro-particle coding method to detect ovarian cancer biomarker CA 125. To do this, we prepared two bio-suspensions for groups A and B IgG antibody tagging (Fig. 1). In suspension B the biotinylated group A antibodies were immobilized to the silicon oxide micro particles with streptavidin. In suspension A the group B antibodies were immobilized through their Fc (fragment crystallizable region) fragments to iron oxide micro particles modified with immunoglobulin-binding protein G. Protein G mediates attachment of antibodies to the micro-particles and ensures the proper orientation of IgG molecules for the better immuno-assay efficiency (Bjorck & Kronvall, 1984). The buffer used contained 5% of BSA to mimic blood conditions (Majoor, 1946). The following is a step-by-step description of the two-element micro-particle coding assay. To perform the analysis on the presence of CA 125 in a solution we follow the following

Step 1. the monoclonal antibodies M86429M were biotinylated prior to doing assay. EZ-

Step 2. we prepared suspension B: the 1 µm silicon oxide particles (Bangs Laboratories,

Step 4. CA 125 solutions of defined concentrations were added to mixtures of suspension A

Unbound silicon oxide particles and dissolved CA 125 molecules were separated from unbound iron oxide particles and aggregates of iron oxide and silicon oxide particles by using strong magnets (residual flux density about 14.5-14.8 KGs (K&J Magnetics, Inc.

After completing magnetizing and pipetting, the top part of the test tubes was cut off and the bottom filters with particles deposited on it (residue particles) were checked by LIBS for

temperature and overnight at 4°C then were stored at 4° C.

website, n.d.). The separation-washing cycles were repeated 3 times.

Link Sulfo-NHS-Biotinylation Kit (Pierce Biotechnology, Inc.) was used for this purpose. PBS buffer used for dilutions contained 5% of BSA to mimic blood

Inc.) modified with streptavidin were added to the biotinylated monoclonal antibodies M86429M solution for overnight incubation at 4° C (Fig 1 b). Following incubation, unbound IgG molecules were washed away by three washcentrifugation cycles using spin-filters with a pore size about 100 nm (Millipore). Step 3. we prepared suspension A by adding the 1.5 µm iron oxide particles (Polysciences,

Inc.) modified with protein G to the monoclonal antibodies M86306M solution for

and suspension B taken in equal volumes and were incubated and shaken 4 hours at room temperature and overnight at 4° C, then were stored at 4° C. For the control experiment the PBS buffer with 5% of BSA (no CA 125) was added to a mixture of suspension A and suspension B and were incubated and shaken 4 hours at room

the same quantity expressed in International Units.

procedure:

conditions (Fig 1 a).

the presence of Si elements.

overnight incubation at 4° C (Fig 1 c).

To obtain better sensitivity, we employed the magnetizing type of assay. In this approach, following the incubation, the unbound molecules, the single silicon particles, the single iron oxide particles and particle aggregates were separated using strong magnets. After completing steps of magnetizing and pipetting, the residue particles left on the filters were analyzed using LIBS for the presence of silicon. Fig. 5 shows the fragment of LIBS spectra around 288.1 nm silicon emission line obtained by the two-element (Si and Fe) Tag-LIBS assay for detection of CA 125 biomarker. The control lowest black line on Fig. 5 was obtained from the empty filter. The red line curve is a LIBS spectrum of control sample where instead of CA 125 the buffer was added. Other lines represent various concentrations of CA 125 in a solution (see Fig. 5). The presence of some Si in the control sample is possibly caused by non-specific interaction of micro-particles.

Fig. 5. The fragment of LIBS spectra around 288.1 nm silicon emission line in the twoelement (Si and Fe) Tag-LIBS assay for detection of CA 125. The black line represents LIBS of the empty filter. Other lines represent various concentrations of CA 125 in a solution: red line – control sample with no CA 125, green line – 10 U/ml, blue line – 50 U/ml, light blue line - 250 U/ml, pink line – 1000 U/ml.

Figure 6 shows the experimental results of the ovarian biomarker CA 125 detection by the two-element (Fe and Si) Tag-LIBS assay in a blood mimicking PBS buffer. The curve on Fig. 6 represents the CA 125 concentration dependence of the intensity of Si emission line at about 288.1 nm obtained by Tag-LIBS assay. The experimental curve has a maximum at about 10 U/ml and will be discussed later in comparison with results of other experiments.

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

LIBS intensity, a.u.

Using Tag-Laser Induced Breakdown Spectroscopy 163

Fig. 7. Detection of ovarian cancer biomarker Leptin by two-element (Si and Fe) microparticle Tag-LIBS assay in two independent experiments (squares and diamonds) with an interval of one month. Solid line is obtained by the linear least squares approximation.

filtered over centrifugal filter with relatively big pore size about 5 µm.

likely to compete for the binding sites, which yields enhanced detection limit.

To ensure the applicability of the Tag-LIBS approach to the clinical environment the human fluid with bio-molecules of interest has to be checked. Blood is an extremely complex solution composed of plasma with dissolved proteins and blood cells (Cohn, 1948). Therefore, human blood plasma and model molecular pair avidin-biotin were chosen to test the Tag-LIBS approach for future clinical applications. To maintain homogeneity and equivalent conditions for all samples the human blood plasma after thawing has been

Leptin concentration, µg/ml

Two types of particles were used for the assay, i.e. 50 nm gold nano-particles and 1.5 µm iron oxide micro-particles. Due to significant differences in size and taking into account the densities of gold and iron (we used density of iron instead of combined iron and iron oxide density of particle for simplicity), every microgram of gold particles counted for approximately 1.4 109 nano-particles and every microgram of iron oxide particles included about 1.8 105 micro-particles. Furthermore, the total surface area of 1 µg of gold particles was about 6.2 106 µm2 in comparison with about 7.1 105 µm2 total surface area of 1 µg of iron oxide particles. Thus, to balance the surface areas of the two types of particles, we took about 10 times more iron oxide particles (by weight) than gold particles. In addition, the concentration of biotin molecules attached to the nano-particles was chosen to be greater than the concentration of avidin. Under these conditions, the molecules of avidin are less

Results shown in Fig. 8 demonstrate the ability of the Tag-LIBS approach to detect model molecules avidin in human blood plasma. Tag-LIBS analysis has been performed with a series of dilutions with the following final concentrations of avidin: 0 ppb, 6 ppb, 64 ppb, 322 ppb, 644 ppb, 1483 ppb, 2321 ppb, 3224 ppb, and 6448 ppb (curves 0 – 8, Fig. 8). The

Fig. 6. Detection of ovarian cancer biomarker CA 125 by two-element (Si and Fe) microparticle Tag-LIBS assay. The curve represents the LIBS intensity of Si emission line in the two-element Tag-LIBS assay at the various concentrations of CA 125.

The two-element (Si-Fe) coded Tag-LIBS assay has been used to analyze the ovarian cancer biomarker Leptin. Leptin, IgG monoclonal antibodies H86901M (Group A) and IgG H86412M (Group B) monoclonal antibodies to Leptin were purchased from Biodesign International. Monoclonal antibodies H86901M and H86412M were biotinylated prior to doing assay. EZ-Link Sulfo-NHS-Biotinylation Kit (Pierce Biotechnology, Inc.) was used for this purpose.

Equal amount of 3 µm silicon oxide particles (Kisker Biotech GmbH) and 1.5 µm iron oxide particles (Bangs Laboratories, Inc.) were added to pre-mixed solution of defined concentration of Leptin and equal concentrations of IgG monoclonal antibodies H86901M and IgG monoclonal antibodies H86412M for 3 hour incubation at room temperature and overnight at 4° C. Using strong magnets and residual particles on filters assayed, single and aggregated particles were separated. We monitored the amount of aggregates in a similar way like it was described earlier.

The intensity of the silicon emission line at about 288.1 nm as a function of the concentration of Leptin is shown in Fig. 7 for two independent Tag-LIBS experiments (marked with squares and diamonds) performed over with a one-month interval. The reproducibility is about 20-30%. The trend line of linear least-square approximation is shown on the figure and so is the R-squared coefficient, which is about 0.949. The ascending part of the Tag-LIBS assay curve was from 10 to ~ 400 µg/ml. In contrast with CA 125 Tag-LIBS assay (Fig. 6) the local maximum feature of the calibration curve for Leptin assay has not been determined in the range of concentrations investigated. The presence of Leptin at the level of 11µg/ml has been detected by Tag-LIBS approach.

Fig. 6. Detection of ovarian cancer biomarker CA 125 by two-element (Si and Fe) microparticle Tag-LIBS assay. The curve represents the LIBS intensity of Si emission line in the

The two-element (Si-Fe) coded Tag-LIBS assay has been used to analyze the ovarian cancer biomarker Leptin. Leptin, IgG monoclonal antibodies H86901M (Group A) and IgG H86412M (Group B) monoclonal antibodies to Leptin were purchased from Biodesign International. Monoclonal antibodies H86901M and H86412M were biotinylated prior to doing assay. EZ-Link Sulfo-NHS-Biotinylation Kit (Pierce Biotechnology, Inc.) was used for

CA 125 concentration, U/ml

Equal amount of 3 µm silicon oxide particles (Kisker Biotech GmbH) and 1.5 µm iron oxide particles (Bangs Laboratories, Inc.) were added to pre-mixed solution of defined concentration of Leptin and equal concentrations of IgG monoclonal antibodies H86901M and IgG monoclonal antibodies H86412M for 3 hour incubation at room temperature and overnight at 4° C. Using strong magnets and residual particles on filters assayed, single and aggregated particles were separated. We monitored the amount of aggregates in a similar

The intensity of the silicon emission line at about 288.1 nm as a function of the concentration of Leptin is shown in Fig. 7 for two independent Tag-LIBS experiments (marked with squares and diamonds) performed over with a one-month interval. The reproducibility is about 20-30%. The trend line of linear least-square approximation is shown on the figure and so is the R-squared coefficient, which is about 0.949. The ascending part of the Tag-LIBS assay curve was from 10 to ~ 400 µg/ml. In contrast with CA 125 Tag-LIBS assay (Fig. 6) the local maximum feature of the calibration curve for Leptin assay has not been determined in the range of concentrations investigated. The presence of Leptin at the level of 11µg/ml has

two-element Tag-LIBS assay at the various concentrations of CA 125.

this purpose.

LIBS intensity, a.u.

way like it was described earlier.

been detected by Tag-LIBS approach.

Fig. 7. Detection of ovarian cancer biomarker Leptin by two-element (Si and Fe) microparticle Tag-LIBS assay in two independent experiments (squares and diamonds) with an interval of one month. Solid line is obtained by the linear least squares approximation.

To ensure the applicability of the Tag-LIBS approach to the clinical environment the human fluid with bio-molecules of interest has to be checked. Blood is an extremely complex solution composed of plasma with dissolved proteins and blood cells (Cohn, 1948). Therefore, human blood plasma and model molecular pair avidin-biotin were chosen to test the Tag-LIBS approach for future clinical applications. To maintain homogeneity and equivalent conditions for all samples the human blood plasma after thawing has been filtered over centrifugal filter with relatively big pore size about 5 µm.

Two types of particles were used for the assay, i.e. 50 nm gold nano-particles and 1.5 µm iron oxide micro-particles. Due to significant differences in size and taking into account the densities of gold and iron (we used density of iron instead of combined iron and iron oxide density of particle for simplicity), every microgram of gold particles counted for approximately 1.4 109 nano-particles and every microgram of iron oxide particles included about 1.8 105 micro-particles. Furthermore, the total surface area of 1 µg of gold particles was about 6.2 106 µm2 in comparison with about 7.1 105 µm2 total surface area of 1 µg of iron oxide particles. Thus, to balance the surface areas of the two types of particles, we took about 10 times more iron oxide particles (by weight) than gold particles. In addition, the concentration of biotin molecules attached to the nano-particles was chosen to be greater than the concentration of avidin. Under these conditions, the molecules of avidin are less likely to compete for the binding sites, which yields enhanced detection limit.

Results shown in Fig. 8 demonstrate the ability of the Tag-LIBS approach to detect model molecules avidin in human blood plasma. Tag-LIBS analysis has been performed with a series of dilutions with the following final concentrations of avidin: 0 ppb, 6 ppb, 64 ppb, 322 ppb, 644 ppb, 1483 ppb, 2321 ppb, 3224 ppb, and 6448 ppb (curves 0 – 8, Fig. 8). The

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

ongoing.

further employed (Vance *et al.*, 2010).

**4. Conclusions** 

improvement of the Tag-LIBS detection limits for biomarkers.

Using Tag-Laser Induced Breakdown Spectroscopy 165

applications level 2.5 ng/ml. To achieve the goal we plan to use more sensitive gated spectrometer (Andor ME 5000 Echelle Spectrograph) with a better spectral resolution. Preliminary experiments (data not shown) demonstrated about 20 times improved sensitivity for detection of iron oxide micro-particles. Thus, we can expect significant

Figures 4, 6 and 7 show that the binding curves have a local maximum on the calibration curves for avidin (Fig. 4) and CA 125 (Fig. 6), but has not been observed for Leptin (Fig. 7). In these experiments, three different proteins and protein molecules were investigated. These have different molecular weights (mw). The molecule avidin is a tetrameric biotinbinding protein with molecular weight about 67 KD (UniProt protein database, n.d., a), ovarian cancer biomarker CA 125 (also known as mucin 16 or MUC16 (HUGO Gene Nomenclature Committee database, n.d.)) has a molecular weight about 2,353 KD (UniProt protein database, n.d., b), and Leptin has molecular weight about 18.6 KD (UniProt protein database, n.d., c). Proteins with higher molecular weight usually have bigger size (i.e. hydrodynamic radius) (Armstrong *et al.*, 2004). Therefore, during the cross-linking process the bigger proteins may occupy larger areas and steric effects may take place especially for larger proteins (Connolly *et al.*, 2001). This is in agreement with observed Tag-LIBS data (Fig. 4, 6, and 7). The presence of local maximum on the calibration curves detected for molecules with bigger molecular weight avidin and CA 125 but not for Leptin might be in part the result of steric effects and constraints of bio-molecule access to unbound binding sites on micro-particles (Connolly *et al.*, 2001) or due to the cross linking critical concentration of protein molecules in a solution (Heidelberger, 1933). It is also possible that the differences in the experimental protocols (see description above) used for different proteins played some role. It is important to mention that the matrix effects accompanying LIBS may play a role (Cremers & Radziemski, 2006, B). Further studies are currently

Laser-induced breakdown spectroscopy yields integrated data about atomic composition of a sample. LIBS can yield more than ten thousand spectral data points for each sample. Several methods do exist to reduce the massive amount of data up to the reasonable point. The traditional way of analysis involves taking into account the emission spectral amplitudes (or areas under the spectral lines) related to few chosen elements of interest omitting all other spectral data (Cremers & Radziemski, 2006, B). This leads to plotting standard calibration curves to be compared with unknown sample. Another way to reduce the amount of experimental data involves the use of the principal component analysis (PCA) (Labbe *et al.*, 2008). PCA converts the multi-dimensional set of experimental data into the new typically less dimensional set of principal components with higher variance. Several classification algorithms such as k-Nearest Neighbor and Support vector machines can be

Clearly, it is important to develop novel diagnostic methods for higher throughput screening of human samples. Known types of assays for detection of the disease biomarkers include enzyme, fluorescence, chemiluminescence, nephelometric and radio- immunoassays (Koivunen & Krogsrud, 2006). Encoded particle assays are attracting more attention due to their inherent ability for easier scaling-up from the single analyte to highly parallel multianalyte systems (Rubenstein, 2010). Such particle based assays are sometimes called virtual

spectrum of the empty filter was subtracted from the sample spectra. To enhance the clarity and viewing of the gold emission peak intensities at 280.2 nm, the sample spectra are slightly shifted along the Y-axis (Fig. 8). Data of three Tag-LIBS experiments were averaged to plot the control curve (curve 0, Fig. 8). The lowest 6 ppb concentration of model protein avidin was measured by Tag-LIBS approach in human blood plasma with about 8:1 signalto-noise ratio (curve 1, Fig. 8).

Fig. 8. The fragment of background subtracted LIBS spectra around 280.2 nm Gold emission line in the two-element (Au and Fe) Tag-LIBS assay for detection of avidin in human blood plasma. Concentration of avidin: 0 – 0 ppb (control sample), 1 – 6 ppb, 2 – 64 ppb, 3 – 322 ppb, 4 – 644 ppb, 5 – 1483 ppb, 6 – 2321 ppb, 7 – 3224 ppb, 8 – 6448 ppb.

Clearly it is important to maintain, and possibly enhance, the analytical sensitivity of an assay while at the same time obtain detection limits that are comparable or better than the current attainable. For the CA 125 biomarker the normal level has been previously determined, e.g. Niloff *et al.* (Niloff *et al.*, 1985) have demonstrated that the elevation of serum CA 125 concentration over 35 Units/ml (the upper limit of normal) correlates with cancer disease progression. For the Leptin biomarker the level below 2.5 ng/ml (the lower limit of normal) typically correlates with the presence of cancer (Mor *et al.*, 2005).

In the proof-of-principle experiments with ovarian cancer biomarkers we **achieved about 1 U/ml detection limit for CA 125 and about 11µg/ml for Leptin**. The typical minimum detectable concentration of C A125 for solid phase enzyme-linked immunosorbent assays (ELISA) was estimated to be from 5 U/ml (Alpha Diagnostic Intl. instruction manual, n.d.) to 7 U/ml (Thomas *et al.*, 1995). Therefore, the current Tag-LIBS sensitivity for CA 125 is comparable to existing commercial ELISA assays. For ovarian biomarker Leptin more work has to be done to improve sensitivity of the current method up to necessary for clinical

spectrum of the empty filter was subtracted from the sample spectra. To enhance the clarity and viewing of the gold emission peak intensities at 280.2 nm, the sample spectra are slightly shifted along the Y-axis (Fig. 8). Data of three Tag-LIBS experiments were averaged to plot the control curve (curve 0, Fig. 8). The lowest 6 ppb concentration of model protein avidin was measured by Tag-LIBS approach in human blood plasma with about 8:1 signal-

Fig. 8. The fragment of background subtracted LIBS spectra around 280.2 nm Gold emission line in the two-element (Au and Fe) Tag-LIBS assay for detection of avidin in human blood plasma. Concentration of avidin: 0 – 0 ppb (control sample), 1 – 6 ppb, 2 – 64 ppb, 3 – 322

Clearly it is important to maintain, and possibly enhance, the analytical sensitivity of an assay while at the same time obtain detection limits that are comparable or better than the current attainable. For the CA 125 biomarker the normal level has been previously determined, e.g. Niloff *et al.* (Niloff *et al.*, 1985) have demonstrated that the elevation of serum CA 125 concentration over 35 Units/ml (the upper limit of normal) correlates with cancer disease progression. For the Leptin biomarker the level below 2.5 ng/ml (the lower

In the proof-of-principle experiments with ovarian cancer biomarkers we **achieved about 1 U/ml detection limit for CA 125 and about 11µg/ml for Leptin**. The typical minimum detectable concentration of C A125 for solid phase enzyme-linked immunosorbent assays (ELISA) was estimated to be from 5 U/ml (Alpha Diagnostic Intl. instruction manual, n.d.) to 7 U/ml (Thomas *et al.*, 1995). Therefore, the current Tag-LIBS sensitivity for CA 125 is comparable to existing commercial ELISA assays. For ovarian biomarker Leptin more work has to be done to improve sensitivity of the current method up to necessary for clinical

ppb, 4 – 644 ppb, 5 – 1483 ppb, 6 – 2321 ppb, 7 – 3224 ppb, 8 – 6448 ppb.

limit of normal) typically correlates with the presence of cancer (Mor *et al.*, 2005).

to-noise ratio (curve 1, Fig. 8).

applications level 2.5 ng/ml. To achieve the goal we plan to use more sensitive gated spectrometer (Andor ME 5000 Echelle Spectrograph) with a better spectral resolution. Preliminary experiments (data not shown) demonstrated about 20 times improved sensitivity for detection of iron oxide micro-particles. Thus, we can expect significant improvement of the Tag-LIBS detection limits for biomarkers.

Figures 4, 6 and 7 show that the binding curves have a local maximum on the calibration curves for avidin (Fig. 4) and CA 125 (Fig. 6), but has not been observed for Leptin (Fig. 7). In these experiments, three different proteins and protein molecules were investigated. These have different molecular weights (mw). The molecule avidin is a tetrameric biotinbinding protein with molecular weight about 67 KD (UniProt protein database, n.d., a), ovarian cancer biomarker CA 125 (also known as mucin 16 or MUC16 (HUGO Gene Nomenclature Committee database, n.d.)) has a molecular weight about 2,353 KD (UniProt protein database, n.d., b), and Leptin has molecular weight about 18.6 KD (UniProt protein database, n.d., c). Proteins with higher molecular weight usually have bigger size (i.e. hydrodynamic radius) (Armstrong *et al.*, 2004). Therefore, during the cross-linking process the bigger proteins may occupy larger areas and steric effects may take place especially for larger proteins (Connolly *et al.*, 2001). This is in agreement with observed Tag-LIBS data (Fig. 4, 6, and 7). The presence of local maximum on the calibration curves detected for molecules with bigger molecular weight avidin and CA 125 but not for Leptin might be in part the result of steric effects and constraints of bio-molecule access to unbound binding sites on micro-particles (Connolly *et al.*, 2001) or due to the cross linking critical concentration of protein molecules in a solution (Heidelberger, 1933). It is also possible that the differences in the experimental protocols (see description above) used for different proteins played some role. It is important to mention that the matrix effects accompanying LIBS may play a role (Cremers & Radziemski, 2006, B). Further studies are currently ongoing.

Laser-induced breakdown spectroscopy yields integrated data about atomic composition of a sample. LIBS can yield more than ten thousand spectral data points for each sample. Several methods do exist to reduce the massive amount of data up to the reasonable point. The traditional way of analysis involves taking into account the emission spectral amplitudes (or areas under the spectral lines) related to few chosen elements of interest omitting all other spectral data (Cremers & Radziemski, 2006, B). This leads to plotting standard calibration curves to be compared with unknown sample. Another way to reduce the amount of experimental data involves the use of the principal component analysis (PCA) (Labbe *et al.*, 2008). PCA converts the multi-dimensional set of experimental data into the new typically less dimensional set of principal components with higher variance. Several classification algorithms such as k-Nearest Neighbor and Support vector machines can be further employed (Vance *et al.*, 2010).

### **4. Conclusions**

Clearly, it is important to develop novel diagnostic methods for higher throughput screening of human samples. Known types of assays for detection of the disease biomarkers include enzyme, fluorescence, chemiluminescence, nephelometric and radio- immunoassays (Koivunen & Krogsrud, 2006). Encoded particle assays are attracting more attention due to their inherent ability for easier scaling-up from the single analyte to highly parallel multianalyte systems (Rubenstein, 2010). Such particle based assays are sometimes called virtual

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

(November 2007), pp. 1907 – 1916

(October 1999), pp. 1259-1267

1986), pp. 149-184

pp. 969-974

Totowa

*International.* 02.03.2011, Available from

http://www.4adi.com/objects/catalog/product/extras/1820.pdf

*Journal, Vol.* 87, No.6, (December 2004), pp. 4259–4270

proteins. *Blood,* Vol.3, No.5, (May 1948), pp. 471 – 485

In: *OSCAR website.* 07.13.2011. Available from http://www.creosa.desu.edu/resources/libs.html

(September 1997), No.9, pp. 1749–1756

*Institute,* Vol.96, No.11, (June 2004), pp. 816-818

Using Tag-Laser Induced Breakdown Spectroscopy 167

Alpha Diagnostic International. (n.d.). Instruction manual No. M-1820, In: *Alpha Diagnostic* 

Aragon, C.; Aguilera, J.A. & Penalba, F. (1999). Improvements in Quantitative Analysis of

Armstrong, J.K.; Wenby, R.B.; Meiselman, H.J. & Fisher, T.C. (2004). The hydrodynamic

Barber, H.R.K. (1986). Ovarian cancer. *Cancer Journal for Clinicians,* Vol. 36, No.3, (May-June

Bjorck, L. & Kronvall, G. (1984). Purification and some properties of streptococcal protein G,

Brambilla, C.; Fievet, F.; Jeanmart, M.; de Fraipont, F.; Lantuejoul, S.; Frappat, V.; Ferretti, G.;

biomarkers. *Europian Respiratory Journal*, Vol.21, (January 2003), pp. 36s-44s Cohn, E.J. (1948). The chemical specificity of the interaction of diverse human plasma

Connolly, S.; Cobbe, S. & Fitzmaurice, D. (2001). Effects of Ligand−Receptor Geometry and

Empirical LIBS database. (n.d.) Laser-Induced Breakdown Spectroscopy Elemental Spectra.

Farbman-Yogev, I.; Bohbot-Raviv, Y. & Ben-Shaul, A. (1998). A Statistical Thermodynamic

Freeman, R.G.; Raju, P.A.; Norton, S.M.; Walton, I.D.; Smith, P.C.; He, L.; Natan, M.J.; Sha,

Fulton, R.J.; McDade, R.L.; Smith, P.L.; Kienker, L.J. & Kettman, J.R. Jr. (1997). Advanced

Garber, K. (2004) Debate Rages Over Proteomic Patterns. *Journal of the National Cancer* 

Palleschi, & I. Schechter (Eds.), 1-39. University Press, Cambridge

*Chemistry A,* Vol.102, No.47, (September 1998), pp. 9586-9592

approach to quantitative proteomics. *Molecular & Cellular Proteomics,* Vol.6, No.11,

Steel Composition by Laser-Induced Breakdown Spectroscopy at Atmospheric Pressure Using an Infrared Nd:YAG Laser. *Applied Spectroscopy* Vol. 53, No.10,

radii of macromolecules and their effect on red blood cell aggregation. *Biophysical* 

a novel IgG-binding reagent. *Journal of Immunology,* Vol.133, No.2, (August 1984),

Brichon, P.Y. & Moro-Sibilot, D. (2003). Early detection of lung cancer: role of

Stoichiometry on Protein-Induced Aggregation of Biotin-Modified Colloidal Gold. *Journal of Physical Chemistry B,* Vol. 105, No.11, (February 2001), pp. 2222-2226 Cremers, B. D. & Radziemski, L. J. (2006). A. In: *Handbook of Laser-Induced Breakdown Spectroscopy,* B. D. Cremers & L. J. Radziemski, 23-168, Wiley, New York Cremers, D.A. & Radziemski, L.J. (2006). B. History and fundamentals of LIBS. In: In: *Laser-*

*induced breakdown spectroscopy (LIBS): fundamentals and applications,* A. Miziolek, V.

Model for Cross-Bridge Mediated Condensation of Vesicles. *Journal of Physical* 

M.Y. & Penn, S.G. (2005). Use of Nanobarcodes Particles in Bioassays. In: *Methods in Molecular Biology*, S.J. Rosenthal, D.W. Wright (Eds.), 73-84, Humana Press Inc,

multiplexed analysis with the FlowMetrix system. *Clinical Chemistry,* Vol.43,

arrays to be distinguished from more conventional positional arrays (Rubenstein, 2010). For capture entities, the currently employed encoded particle assays use typically antibodies or oligo-nucleotides coded by nucleotide sequences or set of dyes (Rubenstein, 2010). Metals, non-metals, their alloys and composites have not been used extensively despite the fact that they can offer some advantages over traditional coding entities. First, they are chemically and biologically inert. They are stable, not harmful for patients, have longer shelf-time and relatively easier to handle.

The choice of proteins to detect is paramount to the diagnostic of cancers (Petricoin *et al.*, 2002; Mor *et al.*, 2005; Wagner, 2003; Garber, 2004). By employing the multi-element coded technique, we offer a novel approach to **detect many proteins in serum simultaneously**. The fundamental question that needs careful attention is: which proteins should be detected and monitored? Therefore, the screening for the potential cancer markers becomes crucial for the successful diagnostic development. The use of multi-element micro- and nanoparticles labels for possible biomarkers can help in the multiplex biomarker panel development.

Compared with the numerous elemental analytical techniques available, LIBS provides many advantages. LIBS method requires much smaller sample volumes and minimal sample preparation. It provides real-time spectra, does not require the use of time-of-flight devices and is easy to implement. In addition, elements analyzed by LIBS have extremely narrow emission bandwidths and characterization of each chemical element, as defined by a unique series of emission lines, is highly specific. As a result, LIBS is one of the most effective techniques for multi-element analysis of samples. For the Tag-LIBS application the identification code is the multi-elemental composition of nano- and micro-particles.

Using micro-particles for the detection of biomarkers has several advantages compared to ELISA micro-titer plates type assays. First, the throughput of the particle assay can be greater because of the larger surface-to-volume ratio. Second, the use of mixtures particles coated by individual capture molecules allows development of multi-analyte assays. Nontoxic and inexpensive labels with minimal interference of background signal and improved specific activity, with unique signature appeared to be the most desirable label technologies from the viewpoint of industry and academia (Harma, 2002).

We suggest that the use of LIBS after tagging specific proteins provides a novel approach for inexpensive, robust and accurate diagnosis of EOC. In principle, the proposed method is applicable for different types of cancer (i.e. ovarian, prostate or lung cancer (Brambilla *et al.*, 2003)) provided we can identify specific biomarkers.

#### **5. Acknowledgments**

This research was supported by the Center for Research and Education in Optical Sciences and Applications funded by a National Science Foundation Center of Research Excellence in Science and Technology (award # 0630388). We thank Dr. D. Connolly of the Fox Chase Cancer Center, PA for numerous discussions and the Blood Bank of Delmarva for providing human blood plasma for research.

#### **6. References**

Ahrends, R.; Pieper, S.; Kuhn, A.; Weisshoff, H.; Hamester, M.; Lindemann, T.; Scheler, C.; Lehmann, K.; Taubner, K. & Linscheid, M.W. (2007). A metal-coded affinity tag

arrays to be distinguished from more conventional positional arrays (Rubenstein, 2010). For capture entities, the currently employed encoded particle assays use typically antibodies or oligo-nucleotides coded by nucleotide sequences or set of dyes (Rubenstein, 2010). Metals, non-metals, their alloys and composites have not been used extensively despite the fact that they can offer some advantages over traditional coding entities. First, they are chemically and biologically inert. They are stable, not harmful for patients, have longer shelf-time and

The choice of proteins to detect is paramount to the diagnostic of cancers (Petricoin *et al.*, 2002; Mor *et al.*, 2005; Wagner, 2003; Garber, 2004). By employing the multi-element coded technique, we offer a novel approach to **detect many proteins in serum simultaneously**. The fundamental question that needs careful attention is: which proteins should be detected and monitored? Therefore, the screening for the potential cancer markers becomes crucial for the successful diagnostic development. The use of multi-element micro- and nanoparticles labels for possible biomarkers can help in the multiplex biomarker panel

Compared with the numerous elemental analytical techniques available, LIBS provides many advantages. LIBS method requires much smaller sample volumes and minimal sample preparation. It provides real-time spectra, does not require the use of time-of-flight devices and is easy to implement. In addition, elements analyzed by LIBS have extremely narrow emission bandwidths and characterization of each chemical element, as defined by a unique series of emission lines, is highly specific. As a result, LIBS is one of the most effective techniques for multi-element analysis of samples. For the Tag-LIBS application the

Using micro-particles for the detection of biomarkers has several advantages compared to ELISA micro-titer plates type assays. First, the throughput of the particle assay can be greater because of the larger surface-to-volume ratio. Second, the use of mixtures particles coated by individual capture molecules allows development of multi-analyte assays. Nontoxic and inexpensive labels with minimal interference of background signal and improved specific activity, with unique signature appeared to be the most desirable label

We suggest that the use of LIBS after tagging specific proteins provides a novel approach for inexpensive, robust and accurate diagnosis of EOC. In principle, the proposed method is applicable for different types of cancer (i.e. ovarian, prostate or lung cancer (Brambilla *et al.*,

This research was supported by the Center for Research and Education in Optical Sciences and Applications funded by a National Science Foundation Center of Research Excellence in Science and Technology (award # 0630388). We thank Dr. D. Connolly of the Fox Chase Cancer Center, PA for numerous discussions and the Blood Bank of Delmarva for providing

Ahrends, R.; Pieper, S.; Kuhn, A.; Weisshoff, H.; Hamester, M.; Lindemann, T.; Scheler, C.;

Lehmann, K.; Taubner, K. & Linscheid, M.W. (2007). A metal-coded affinity tag

identification code is the multi-elemental composition of nano- and micro-particles.

technologies from the viewpoint of industry and academia (Harma, 2002).

2003)) provided we can identify specific biomarkers.

**5. Acknowledgments** 

**6. References** 

human blood plasma for research.

relatively easier to handle.

development.

approach to quantitative proteomics. *Molecular & Cellular Proteomics,* Vol.6, No.11, (November 2007), pp. 1907 – 1916

Alpha Diagnostic International. (n.d.). Instruction manual No. M-1820, In: *Alpha Diagnostic International.* 02.03.2011, Available from

http://www.4adi.com/objects/catalog/product/extras/1820.pdf


Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

No.21, (May 2005), pp. 7677–7682

http://www.creosa.desu.edu/

ex\_Assays/Sample\_Pages.pdf

*(UniProtKB).* 07.05.2011. Available from http://www.uniprot.org/uniprot/Q8WXI7

(March 2006), pp. 231-244

1995), pp. 211-216

2010), pp. 96-111

pp. 572-577

Using Tag-Laser Induced Breakdown Spectroscopy 169

Niloff, J.M.; Bast, R.C.; Schaetzl, E.M. & Knapp, R.C. (1985). Predictive value of CA 125

OSCAR website. (n.d.) Optical Science Center for Applied Research at Delaware State

Petricoin, E.F.; Ardekani, A.M.; Hitt, B.A.; Levine, P.J.; Fusaro, V.A.; Steinberg, S.M.; Mills,

Rock, S.; Marcano, A.; Markushin, Y.; Sabanayagam, C. & Melikechi, N. (2008). Elemental

database. *Applied Optics*, Vol.47, No.31, (September 2008), pp. G99-G104 Rubenstein, K. (2010). Multiplex Assays: Evolving Technologies, Applications and Future

http://www.insightpharmareports.com/uploadedFiles/Reports/Reports/Multipl

Smith, A.M.; Dave, S.; Nie, S.; True, L. & Gao, X. (2006). Multicolor quantum dots for

Thomas, C.M.; Massuger, L.F.; Segers, M.F.; Schijf, C.P.; Doesburg, W.H. & Wobbes, T.

UniProtKB/Swiss-Prot. protein database. A. (n.d.). P02701, In: *Protein Knowledgebase* 

UniProtKB/Swiss-Prot. protein database. B. (n.d.). Q8WXI7, In: *Protein Knowledgebase* 

UniProtKB/Swiss-Prot. protein database. C. (n.d.). P41159, In: *Protein Knowledgebase* 

Vance, T.; Pokrajac, D.; Lazarevic, A.; Marcano, A.; Markushin, Y.; McDaniel, S. & Melikechi,

Wagner, L., (2003) A test before its time? FDA stalls distribution process of proteomic test. *Journal of the National Cancer Institute,* Vol.96, No.7, (April 2003), pp.500-501 WHO Expert Committee on Biological Standardization. (n.d.). Biological methods. In :

Zal, T, & Gascoignea, N.R. (2004). Photobleaching-Corrected FRET Efficiency Imaging of Live Cells. *Biophysical Journal*, Vol.86, No.6, (June 2004), pp. 3923-3939

*Antibiotics-microbiological assay.* 07.12.2011. Available from http://whqlibdoc.who.int/publications/1979/9241541504\_p143-223.pdf

Directions. In: *Insight Pharma Reports,* 07.13.2010. Available from

*Obstetrics & Gynecology,* Vol.151, No.7, (April 1985), pp. 981-986

University. In: *OSCAR website.* 07.12.2011. Available from

*Proceedings of the National Academy of Sciences of the United States of America*, Vol.102,

antigen levels in second-look procedures for ovarian cancer. *American Journal of* 

G.B.; Simone, C.; Fishman, D.A.; Kohn, E.C. & Liotta L.A. (2002). Use of proteomic patterns in serum to identify ovarian cancer. *The Lancet,* Vol. 359, (February, 2002),

analysis of laser induced breakdown spectroscopy aided by an empirical spectral

molecular diagnostics of cancer. *Expert Review of Molecular Diagnostics,* Vol.6, No.2,

(1995). Analytical and clinical performance of improved Abbott IMx CA 125 assay: comparison with Abbott CA 125 RIA. *Clinical Chemistry*, Vol.41, No.2, (February

*(UniProtKB).* 07.05.2011. Available from http://www.uniprot.org/uniprot/P02701

*(UniProtKB).* 07.05.2011. Available from http://www.uniprot.org/uniprot/P41159

N. (2010) Classification of LIBS Protein Spectra Using Multilayer Perceptrons. *Transactions on Mass-Data Analysis of Images and Signals,* Vol.2, No.1, (September


http://www.genenames.org/data/hgnc\_data.php?hgnc\_id=15582


Gosling, J.P. (1990). A decade of development in immunoassay methodology. *Clinical* 

Harma, H. (2002). 02.03.2011. Particle technologies in diagnostics. In : *Tekes. National* 

Heidelberger, M.; Kendall, F.E. & Soo Hoo, C.M. (1933). Quantitative Studies on the

Hugo Gene Nomenclature Committee database. (n.d.) MUC16. In: *HUGO Gene Nomenclature* 

Ingraham, F.S. & Wooley, D. F. (1964) Polyvinyldene fluoride. *Industrial and Engineering* 

K&J Magnetics, Inc. website. (n.d.) In: *K&J Magnetics, Inc. website.* 02.07.2011. Available from

Koivunen, M.E. & Krogsrud, R.L. (2006). Principles of Immunochemical Techniques Used in Clinical Laboratories. *Labmedicine,* Vol.37, No.8, (August 2006), pp.490-497 Labbe, N.; Swamidoss, I.M.; Andre, N.; Martin, M.Z.; Young, T.M.; & Rials, T.G. (2008).

Majoor, C.L.H. (1946). The possibility of detecting individual proteins in blood serum by

Markushin Y., Marcano A., Rock S. & Melikechi N. (2010). Determination of protein

*Analytical Atomic Spectrometry*, Vol.25, No.2, (December 2010), pp. 148-149 Markushin, Y.; Melikechi, N.; Marcano, A.; Rock, S.; Henderson, E. & Connolly, D. (2009).

McGuire, V.; Whittemore, A.S.; Norris, R. & Oakley-Girvan, I. (2000). Survival in epithelial

Melikechi, N; and Markushin, Y, (2011) Mono and Multi Coded LIBS Assays and methods,

Mirkin, C.A.; Jwa-Min Nam & Thaxon, S. (2005). Bio-barcode based detection of target

Miziolek, A.W., Palleschi, V., Schechter, I., *Laser-induced breakdown spectroscopy: fundamentals* 

Mor, G.; Visintin, I.; Lai, Y.; Zhao, H.; Schwartz, P.; Rutherford, T.; Yue, L.; Bray-Ward, P. &

*Journal of Biology and Medicine,* Vol. 18, (May 1946), pp. 419–441

pp. 719015-1-719015-6, 07.13.2011. Available from

http://www.freepatentsonline.com/y2005/0037397.html

*and applications*, Cambridge University Press, 2006

Vol.152, No.6, (September 2000), pp. 528-532

US patent US20110171636 (pending)

http://dx.doi.org/10.1117/12.810247

Extraction of information from laser-induced breakdown spectroscopy spectral data by multivariate analysis. *Applied Optics*, Vol.47, No.31, (November 2008), pp.

differentiation of solubility curves in concentrated sodium sulfate solutions. *Yale* 

hydrogen composition by laser-induced breakdown spectroscopy. *Journal of* 

LIBS-based multi-element coded assay for ovarian cancer application, *Proceedings of the Society of Photographic Instrumentation Engineers (SPIE)*, Vol. 7190, (January 2009),

ovarian cancer patients with prior breast cancer. *American Journal of Epidemiology,*

analytes. In: *United States patent # US2005/0037397 A1. 07.13.2011.* Available from

Ward, D.C. (2005). Serum protein markers for early detection of ovarian cancer.

Precipitin Reaction: Antibody Production in Rabbits Injected with an Azo Protein.

*Chemistry*, Vol.36, No.8, (August 1990), pp. 1408-1427

www.tekes.fi/fi/document/43342/particle\_pdf

*Committee database,* 02.03.2011. Available from

http://www.kjmagnetics.com/specs.asp

G158-G165

*Technology Agency. Technology Review 126/2002.* Available from

*Journal of Experimental Medicine* 58, No. 2, (July 1933), pp. 137-152

http://www.genenames.org/data/hgnc\_data.php?hgnc\_id=15582

*Chemistry,* Vol.56, No.9, (September, 1964), pp. 53–55

*Proceedings of the National Academy of Sciences of the United States of America*, Vol.102, No.21, (May 2005), pp. 7677–7682


http://www.uniprot.org/uniprot/Q8WXI7


**10** 

*USA* 

**Homeobox Genes and Their Functional** 

**Significance in Ovarian Tumorigenesis** 

It is widely recognized that many pathways that control normal embryonic patterning are deregulated in human cancers. Mutations or aberrant expression of components of the Wnt, Hedgehog and Notch signaling pathways have been demonstrated to play pivotal roles in tumorigenesis. Homeobox genes constitute an evolutionarily conserved gene super-family that represents another important class of patterning regulators. These genes encode transcription factors that are essential for controlling cell differentiation and specification of the body plan during embryonic development. Although many homeobox genes have been reported to be aberrantly expressed in ovarian cancer, the functional significance of these genes in ovarian tumorigenesis has only emerged in recent years. This chapter discusses recent research studies that demonstrate that homeobox genes have diverse functions in the biology of ovarian cancer. These functions include specifying patterns of histologic differentiation of ovarian cancers, controlling growth and survival of tumor cells, and promoting tumor angiogenesis, cell-cell interactions and tumor cell invasiveness. This chapter discusses how studies of homeobox genes provide insights into our understanding of the cell-of-origin of ovarian cancers, the striking morphologic heterogeneity of these

Homeobox genes were first discovered in *Drosophila* by their mutations that caused homeotic transformation, a phenomenon in which body segments form in inappropriate locations (Gehring & Hiromi, 1986; McGinnis & Krumlauf, 1992). A classic example of a homeotic transformation in *Drosophila* is the formation of legs rather than antennae caused by ectopic expression of the *Antennapedia* gene (Schneuwly et al., 1987). Homeobox genes play essential roles in defining the unique identities of specific organs and body regions during embryonic development. Distinct sets of homeobox genes control skeletal patterning, limb formation, craniofacial morphogenesis, development of the central nervous system and other organ systems including the gastrointestinal tract and urogenital organs (Capecchi, 1997; Beck, 2002; Panganiban & Rubenstein, 2002; Christensen et al., 2008). Homeobox genes also control cell renewal and tissue regeneration processes in adults such as hematopoiesis, angiogenesis, spermatogenesis and endometrial remodeling (Gorski & Walsh, 2000; Argiropoulos & Humphries, 2007; Vitiello et al., 2007; Maclean & Wilkinson, 2010).

tumors, and the unique clinical behavior of ovarian cancer.

**2. Overview of homeobox genes** 

**1. Introduction** 

Bon Quy Trinh and Honami Naora

*University of Texas MD Anderson Cancer Center* 

Zhu, W.; Wang, X.; Ma, Y.; Rao, M.; Glimm, J. & Kovach, J.S. (2003). Detection of cancerspecific markers amid massive mass spectral data. *Proceedings of the National Academy of Sciences of the United States of America*, Vol.100, No.25, (December, 2003) pp. 14666–14671

## **Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis**

Bon Quy Trinh and Honami Naora *University of Texas MD Anderson Cancer Center USA* 

#### **1. Introduction**

170 Ovarian Cancer – Basic Science Perspective

Zhu, W.; Wang, X.; Ma, Y.; Rao, M.; Glimm, J. & Kovach, J.S. (2003). Detection of cancer-

pp. 14666–14671

specific markers amid massive mass spectral data. *Proceedings of the National Academy of Sciences of the United States of America*, Vol.100, No.25, (December, 2003)

> It is widely recognized that many pathways that control normal embryonic patterning are deregulated in human cancers. Mutations or aberrant expression of components of the Wnt, Hedgehog and Notch signaling pathways have been demonstrated to play pivotal roles in tumorigenesis. Homeobox genes constitute an evolutionarily conserved gene super-family that represents another important class of patterning regulators. These genes encode transcription factors that are essential for controlling cell differentiation and specification of the body plan during embryonic development. Although many homeobox genes have been reported to be aberrantly expressed in ovarian cancer, the functional significance of these genes in ovarian tumorigenesis has only emerged in recent years. This chapter discusses recent research studies that demonstrate that homeobox genes have diverse functions in the biology of ovarian cancer. These functions include specifying patterns of histologic differentiation of ovarian cancers, controlling growth and survival of tumor cells, and promoting tumor angiogenesis, cell-cell interactions and tumor cell invasiveness. This chapter discusses how studies of homeobox genes provide insights into our understanding of the cell-of-origin of ovarian cancers, the striking morphologic heterogeneity of these tumors, and the unique clinical behavior of ovarian cancer.

#### **2. Overview of homeobox genes**

Homeobox genes were first discovered in *Drosophila* by their mutations that caused homeotic transformation, a phenomenon in which body segments form in inappropriate locations (Gehring & Hiromi, 1986; McGinnis & Krumlauf, 1992). A classic example of a homeotic transformation in *Drosophila* is the formation of legs rather than antennae caused by ectopic expression of the *Antennapedia* gene (Schneuwly et al., 1987). Homeobox genes play essential roles in defining the unique identities of specific organs and body regions during embryonic development. Distinct sets of homeobox genes control skeletal patterning, limb formation, craniofacial morphogenesis, development of the central nervous system and other organ systems including the gastrointestinal tract and urogenital organs (Capecchi, 1997; Beck, 2002; Panganiban & Rubenstein, 2002; Christensen et al., 2008). Homeobox genes also control cell renewal and tissue regeneration processes in adults such as hematopoiesis, angiogenesis, spermatogenesis and endometrial remodeling (Gorski & Walsh, 2000; Argiropoulos & Humphries, 2007; Vitiello et al., 2007; Maclean & Wilkinson, 2010).

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 173

are also mediated by additional conserved motifs that are present in the different families. PAX proteins contain an additional DNA-binding domain called the paired box (Robson et al., 2006). HOX proteins contain a hexapeptide motif that mediates interactions with PBX cofactors (Chang et al., 1995). MEIS proteins also act as co-factors for HOX proteins (Shanmugam et al., 1999). Furthermore, target specificity and functional diversity of homeoproteins are achieved via interactions with other transcription factors (Chariot et al., 1999). Whereas homeoproteins have highly selective functions *in vivo*, they exhibit relatively promiscuous DNA-binding *in vitro* (Biggin & McGinnis, 1997). As a consequence, only few *bona fide* target genes have been identified. Several homeoproteins also have intriguing nontranscriptional functions. The *Drosophila* homeoprotein Bicoid represses translation of *caudal*  mRNA by directly binding to the 3' untranslated region of *caudal* mRNA (Dubnau & Struhl, 1996). HOXA9 has been reported to bind the translation initiation factor eIF4E and to stimulate eIF4E-dependent export of *cyclin D1* mRNA (Topisirovic et al., 2005). HOXB7

In the past 15 years, there has been increasing evidence that many homeobox genes are aberrantly expressed in a variety of malignancies. Much of the pioneering work has come from the hematopoietic field, where overexpression of various *HOX* genes has been found to promote leukemogenesis (Thorsteinsdottir et al., 1997; Kroon et al., 1998; Fischbach et al., 2005). Expression patterns of homeobox genes in solid tumors can be divided into three broad categories (Abate-Shen, 2002; Samuel & Naora, 2005). Firstly, homeobox genes that are normally expressed in differentiated adult tissues are often down-regulated in tumors. Two examples are *NKX3.1* and *HOXA10* that control morphogenesis of the prostate and uterus respectively, and are expressed in these tissues during development and in the adult (Bhatia-Gaur et al., 1999; Benson et al., 1996). *NKX3.1* is frequently deleted in prostate cancers (He et al., 1997), whereas *HOXA10* is often silenced by methylation in high-grade endometrial cancers (Yoshida et al., 2006). Secondly, homeobox genes can be re-expressed in tumors derived from tissues in which these genes are normally expressed during embryonic development. *PAX2,* a regulator of urogenital patterning, is normally expressed in the developing kidney and is reactivated in renal cancers (Dressler et al., 1990; Gnarra & Dressler, 1995). A third, less common, category includes homeobox genes that are expressed in tumors derived from a lineage in which the particular gene is not expressed during development. An example is *PAX5* that is expressed in medulloblastoma but not in neonatal cerebellum (Kozmik et al., 1995). Loss or gain of homeobox gene expression in tumors therefore often reflects an inappropriate recapitulation of embryonic pathways and, in many but not all cases, this misexpression can be indicative of the cell-of-origin of the tumor.

Whereas other types of tumors often exhibit 'loss' of the specialized features of the tissue from which they derive, many ovarian cancers exhibit specialized features of non-ovarian lineages. Epithelial ovarian cancers have been thought to originate from the simple monolayered epithelium that lines the ovarian surface (OSE) or from post-ovulatory inclusion cysts that arise from invaginations of the ovarian surface (Feeley & Wells, 2001). However, the major subtypes of ovarian cancer (serous, endometrioid, mucinous) exhibit

binds Ku proteins and stimulates DNA repair (Rubin et al., 2007).

**2.3 Misexpression of homeobox genes in tumors** 

**3. Homeobox genes and the origin of ovarian cancers** 

Mutations in homeobox genes cause a spectrum of complex developmental disorders. For example, mutations in the *HOXA13* gene cause hand-foot-genital syndrome, an autosomal dominant trait characterized by distal limb and genitourinary malformations (Mortlock & Innis, 1997). *SIX1* mutations cause branchio-oto-renal syndrome, a disorder characterized by hearing loss and renal abnormalities (Ruf et al., 2004).

#### **2.1 Organization of mammalian homeobox genes**

There are approximately 200 homeobox genes in the human genome (Tupler et al., 2001) and these are categorized into several different families named after their homologs in the fly (Banerjee-Basu & Baxevanis, 2001). For example, members of the mammalian *PAX, MSX* and *CDX* gene families are related to the *Drosophila* genes *paired*, *muscle segment* and *caudal,*  respectively. Whereas most homeobox genes are scattered throughout the genome, the members of the mammalian *HOX* and *DLX* gene families are organized in clusters. The *HOX* family is related to the *Drosophila HOM-C* cluster and comprises 39 genes. *HOX* genes are organized in four clusters located on different chromosomes and are aligned into 13 paralogous groups (Figure 1). The six members of the *DLX* family are related to the *Drosophila distal-less* (*dll*) gene and are organized in bigene clusters located upstream of *HOX* clusters (Figure 1). These gene clusters are thought to have arisen from gene duplication during evolution (Sumiyama et al., 2003; Lemons & McGinnis, 2006). A striking feature of *HOX* genes is their temporal and spatial colinearity. This phenomenon describes the coupling of the timing and location of expression of *HOX* genes along the anterior-posterior body axis to their relative position in the gene clusters. *HOX* genes that are located at the 3' end of the clusters tend to be expressed early in development and in anterior body regions, whereas those at the 5' end of clusters are generally expressed later and in more posterior body regions (McGinnis & Krumlauf, 1992; Pearson et al., 2005) (Figure 1).

Fig. 1. Organization of *HOX* and *DLX* gene clusters in *Drosophila* and mammals.

#### **2.2 Structural features and mechanisms**

Homeobox genes encode transcription factors, often called 'homeoproteins' that are characterized by a 61 amino acid DNA-binding domain termed the homeodomain. The homeodomain forms a helix-turn-helix structure that binds DNA elements containing a TAAT core motif (Gehring et al., 1994). Although the three-dimensional structure of the homeodomain is highly conserved among homeoproteins, diversity in the amino acid residues gives rise to different DNA-binding specificities (Gehring et al., 1994; Biggin & McGinnis, 1997). Binding affinity and selectivity of homeoproteins for target gene promoters

Mutations in homeobox genes cause a spectrum of complex developmental disorders. For example, mutations in the *HOXA13* gene cause hand-foot-genital syndrome, an autosomal dominant trait characterized by distal limb and genitourinary malformations (Mortlock & Innis, 1997). *SIX1* mutations cause branchio-oto-renal syndrome, a disorder characterized by

There are approximately 200 homeobox genes in the human genome (Tupler et al., 2001) and these are categorized into several different families named after their homologs in the fly (Banerjee-Basu & Baxevanis, 2001). For example, members of the mammalian *PAX, MSX* and *CDX* gene families are related to the *Drosophila* genes *paired*, *muscle segment* and *caudal,*  respectively. Whereas most homeobox genes are scattered throughout the genome, the members of the mammalian *HOX* and *DLX* gene families are organized in clusters. The *HOX* family is related to the *Drosophila HOM-C* cluster and comprises 39 genes. *HOX* genes are organized in four clusters located on different chromosomes and are aligned into 13 paralogous groups (Figure 1). The six members of the *DLX* family are related to the *Drosophila distal-less* (*dll*) gene and are organized in bigene clusters located upstream of *HOX* clusters (Figure 1). These gene clusters are thought to have arisen from gene duplication during evolution (Sumiyama et al., 2003; Lemons & McGinnis, 2006). A striking feature of *HOX* genes is their temporal and spatial colinearity. This phenomenon describes the coupling of the timing and location of expression of *HOX* genes along the anterior-posterior body axis to their relative position in the gene clusters. *HOX* genes that are located at the 3' end of the clusters tend to be expressed early in development and in anterior body regions, whereas those at the 5' end of clusters are generally expressed later and in more posterior

body regions (McGinnis & Krumlauf, 1992; Pearson et al., 2005) (Figure 1).

Fig. 1. Organization of *HOX* and *DLX* gene clusters in *Drosophila* and mammals.

Homeobox genes encode transcription factors, often called 'homeoproteins' that are characterized by a 61 amino acid DNA-binding domain termed the homeodomain. The homeodomain forms a helix-turn-helix structure that binds DNA elements containing a TAAT core motif (Gehring et al., 1994). Although the three-dimensional structure of the homeodomain is highly conserved among homeoproteins, diversity in the amino acid residues gives rise to different DNA-binding specificities (Gehring et al., 1994; Biggin & McGinnis, 1997). Binding affinity and selectivity of homeoproteins for target gene promoters

**2.2 Structural features and mechanisms** 

hearing loss and renal abnormalities (Ruf et al., 2004).

**2.1 Organization of mammalian homeobox genes** 

are also mediated by additional conserved motifs that are present in the different families. PAX proteins contain an additional DNA-binding domain called the paired box (Robson et al., 2006). HOX proteins contain a hexapeptide motif that mediates interactions with PBX cofactors (Chang et al., 1995). MEIS proteins also act as co-factors for HOX proteins (Shanmugam et al., 1999). Furthermore, target specificity and functional diversity of homeoproteins are achieved via interactions with other transcription factors (Chariot et al., 1999). Whereas homeoproteins have highly selective functions *in vivo*, they exhibit relatively promiscuous DNA-binding *in vitro* (Biggin & McGinnis, 1997). As a consequence, only few *bona fide* target genes have been identified. Several homeoproteins also have intriguing nontranscriptional functions. The *Drosophila* homeoprotein Bicoid represses translation of *caudal*  mRNA by directly binding to the 3' untranslated region of *caudal* mRNA (Dubnau & Struhl, 1996). HOXA9 has been reported to bind the translation initiation factor eIF4E and to stimulate eIF4E-dependent export of *cyclin D1* mRNA (Topisirovic et al., 2005). HOXB7 binds Ku proteins and stimulates DNA repair (Rubin et al., 2007).

#### **2.3 Misexpression of homeobox genes in tumors**

In the past 15 years, there has been increasing evidence that many homeobox genes are aberrantly expressed in a variety of malignancies. Much of the pioneering work has come from the hematopoietic field, where overexpression of various *HOX* genes has been found to promote leukemogenesis (Thorsteinsdottir et al., 1997; Kroon et al., 1998; Fischbach et al., 2005). Expression patterns of homeobox genes in solid tumors can be divided into three broad categories (Abate-Shen, 2002; Samuel & Naora, 2005). Firstly, homeobox genes that are normally expressed in differentiated adult tissues are often down-regulated in tumors. Two examples are *NKX3.1* and *HOXA10* that control morphogenesis of the prostate and uterus respectively, and are expressed in these tissues during development and in the adult (Bhatia-Gaur et al., 1999; Benson et al., 1996). *NKX3.1* is frequently deleted in prostate cancers (He et al., 1997), whereas *HOXA10* is often silenced by methylation in high-grade endometrial cancers (Yoshida et al., 2006). Secondly, homeobox genes can be re-expressed in tumors derived from tissues in which these genes are normally expressed during embryonic development. *PAX2,* a regulator of urogenital patterning, is normally expressed in the developing kidney and is reactivated in renal cancers (Dressler et al., 1990; Gnarra & Dressler, 1995). A third, less common, category includes homeobox genes that are expressed in tumors derived from a lineage in which the particular gene is not expressed during development. An example is *PAX5* that is expressed in medulloblastoma but not in neonatal cerebellum (Kozmik et al., 1995). Loss or gain of homeobox gene expression in tumors therefore often reflects an inappropriate recapitulation of embryonic pathways and, in many but not all cases, this misexpression can be indicative of the cell-of-origin of the tumor.

#### **3. Homeobox genes and the origin of ovarian cancers**

Whereas other types of tumors often exhibit 'loss' of the specialized features of the tissue from which they derive, many ovarian cancers exhibit specialized features of non-ovarian lineages. Epithelial ovarian cancers have been thought to originate from the simple monolayered epithelium that lines the ovarian surface (OSE) or from post-ovulatory inclusion cysts that arise from invaginations of the ovarian surface (Feeley & Wells, 2001). However, the major subtypes of ovarian cancer (serous, endometrioid, mucinous) exhibit

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 175

*Hoxa7* in transformed mouse OSE cells promoted the abilities of *Hoxa9, Hoxa10* and *Hoxa11*

The study of Cheng *et al* (2005) cannot be interpreted to conclusively demonstrate that the OSE is the cell-of-origin of ovarian cancers. However, the findings of this study suggest an intriguing model in which OSE-derived tumors acquire Müllerian phenotypes through homeotic transformation. The finding that colinearity of *AbdB HOX* expression (i.e. HOXA9, HOXA10, HOXA11) is recapitulated in ovarian cancers is striking, as it might explain the relative abundance of the ovarian cancer subtypes (i.e. serous> endometrioid> mucinous). The capability of OSE cells to acquire features of different lineages could stem from the intrinsically 'uncommitted' or embryonic-like phenotype of adult OSE cells (Auersperg et al., 2001; Naora, 2007). Unlike most other adult epithelia, the OSE lacks specialized features and expresses little or no E-cadherin (Maines-Bandiera & Auersperg, 1997). The OSE expresses both fibroblast markers and markers characteristic of simple epithelium (Auersperg et al., 1994), and also highly expresses stem cell maintenance genes (Bowen et al., 2009). This plasticity of the OSE is likely to be important for post-ovulatory repair (Auersperg et al., 2001). Both the OSE and Müllerian ducts derive from the coelomic epithelium, and the predominance of Müllerian phenotypes in ovarian cancers could reflect the close primordial relationship between the OSE and Müllerian ducts (Auersperg et al., 2001). More recently, it has been reported that the OSE and tubal fimbria are anatomically contiguous and that these tissues are parts of a transitional epithelium (Auersperg, 2011). On the other hand, less common subtypes of ovarian cancers such as clear-cell and transitional-cell tumors have features resembling those of renal and urothelial tissues whose

More recently, expression of other homeobox genes that control urogenital patterning has been studied in ovarian cancers. *Pax2* is expressed in the developing kidneys, Wolffian ducts and Müllerian ducts (Dressler et al., 1990; Torres et al., 1995). *Pax8* is also expressed in the developing kidney and Müllerian ducts (Plachov et al., 1990). Female *Pax2* homozygous mutant mice lack the entire reproductive tract (Torres et al., 1995). Female *Pax8* null mice do not develop a functional uterus whereas development of the oviduct, cervix and vagina is unaffected (Mittag et al., 2007). PAX2 and PAX8 are normally expressed in tubal, endometrial and endocervical epithelia (Tong et al., 2007; Tong et al., 2011). PAX2 has also been detected in secondary Müllerian structures such as endometriosis, endosalpingiosis and rete ovarii (Tong et al., 2007). PAX2 and PAX8 have been detected in 64 to 100% of nonmucinous ovarian cancers, and in 74 to 90% of primary and metastatic renal cell carcinomas (Bowen et al., 2007; Tong et al., 2007; Nonaka et al., 2008; Chivukula et al., 2009; Zhai et al., 2010; Laury et al., 2011; Tacha et al., 2011). The absence or rareness of PAX2 and PAX8 in many other types of cancers such as colorectal carcinomas and mesotheliomas has raised the possibility that these proteins could be useful markers for differential diagnosis (Tong et al., 2007; Zhai et al., 2010; Laury et al., 2011; Tacha et al., 2011), but this depends on the appropriate setting. Ovarian metastasis from renal cell carcinoma and renal metastasis from ovarian carcinoma are rare. However, ovarian cancer commonly involves the uterus and omentum. PAX2 and PAX8 are frequently expressed in endometrial carcinomas (56 to 98%) (Sharma et al., 2010; Laury et al., 2011; Tacha et al., 2011), but have been detected at low frequency (<10%) in mucinous ovarian cancers that closely resemble colorectal carcinomas (Muratovska et al., 2003; Bowen et al., 2007; Nonaka et al., 2008). On the other hand, PAX8

to induce tumor differentiation along their respective pathways (Cheng et al., 2005).

embryonic relationship to the OSE is more distant.

**3.2 PAX expression and differential diagnosis** 

morphologic features that resemble those of the epithelia of the reproductive tract that derive from the Müllerian ducts (*viz.* fallopian tube, endometrium, endocervix, respectively). Mucinous ovarian cancers also exhibit intestinal-like features. The OSE origin has been supported by several mouse genetic models in which tumors were induced by introducing specific oncogenic alterations into the OSE (Orsulic et al., 2002; Connolly et al., 2003; Wu et al., 2007). On the other hand, various histopathologic and genetic studies have supported origins in primary Müllerian derivatives such as the tubal fimbria (Lee et al., 2007) and in secondary Müllerian structures such as endometriosis (Prowse et al., 2006). Detailed discussions of these studies are beyond the scope of this chapter and these are elegantly reviewed in several articles (Dubeau, 2008; Jarboe et al., 2008; Cho & Shih, 2009).

#### **3.1** *HOX* **genes and the Müllerian phenotype**

One argument against the OSE as the origin of ovarian cancers has been the lack of evidence that demonstrates the capability of OSE cells to differentiate along multiple Müllerian lineages. Differentiation of the Müllerian ducts is controlled by several sets of homeobox genes. These include the tandemly arranged *Hoxa9, Hoxa10, Hoxa11* and *Hoxa13* genes that are related to the *Drosophila* abdominal patterning gene *Abdominal-B* (*AbdB*) (Benson et al., 1996; Hsieh-Li et al., 1995; Zhao & Potter, 2001) (Figure 1). Targeted mutagenesis of *AbdB HOX* genes results in region-specific defects along the reproductive tract. For example, *Hoxa10*-deficient female mice exhibit homeotic transformation of the anterior segment of the uterus into oviductal-like structures (Benson et al., 1996). Replacement of the homeobox of the *Hoxa11* gene with that of *Hoxa13* in mice causes homeotic transformation of the uterus into cervical/vaginal-like structures (Zhao & Potter, 2001). The *AbdB HOX* genes are uniformly expressed along the axis of the Müllerian ducts early in embryonic development. As the ducts differentiate, *Hoxa9, Hoxa10, Hoxa11* and *Hoxa13* become expressed in the primordia of the fallopian tubes, uterus, lower uterine segment/cervix, and upper vagina, respectively (Taylor et al., 1997). This colinear *HOX* expression is maintained in the adult tract with sharply defined anterior boundaries of expression and tapered posterior expression. We have found that the *AbdB HOX* genes are not expressed in normal human OSE whereas their colinear expression patterns in Müllerian epithelia are recapitulated in the major subtypes of ovarian cancers according to the pattern of Müllerian-like differentiation of these tumors (Cheng et al., 2005). HOXA9 was found to be expressed in serous tumors and also in endometrioid and mucinous tumors. In contrast, HOXA10 was strongly expressed in endometrioid and mucinous but not serous tumors, whereas HOXA11 was mostly restricted to mucinous tumors (Table 1). Clear-cell ovarian carcinomas have features that overlap with those of serous and endometrioid tumors, and were found to express HOXA9 and HOXA10. This recapitulation of the *AbdB HOX* gene program in ovarian cancers could be interpreted to reflect Müllerian origins. However, by ectopically expressing *AbdB HOX* genes in undifferentiated, transformed mouse OSE cells and propagating transfected cells in the peritoneum of female mice, we demonstrated that OSEderived cells acquire features of different Müllerian lineages. Mouse OSE cells that expressed *Hoxa9* formed papillary tumors that resembled high-grade serous ovarian carcinoma, whereas expression of *Hoxa10* and *Hoxa11* induced formation of high-grade endometrioid-like and mucinous-like tumors, respectively (Cheng et al., 2005). We also found that the *Hoxa7* gene, located 3' of *Hoxa9*, is expressed in normal Müllerian epithelia and in differentiated ovarian tumors irrespective of their histologic subtype. Expression of

morphologic features that resemble those of the epithelia of the reproductive tract that derive from the Müllerian ducts (*viz.* fallopian tube, endometrium, endocervix, respectively). Mucinous ovarian cancers also exhibit intestinal-like features. The OSE origin has been supported by several mouse genetic models in which tumors were induced by introducing specific oncogenic alterations into the OSE (Orsulic et al., 2002; Connolly et al., 2003; Wu et al., 2007). On the other hand, various histopathologic and genetic studies have supported origins in primary Müllerian derivatives such as the tubal fimbria (Lee et al., 2007) and in secondary Müllerian structures such as endometriosis (Prowse et al., 2006). Detailed discussions of these studies are beyond the scope of this chapter and these are elegantly reviewed in several articles (Dubeau, 2008; Jarboe et al., 2008; Cho & Shih, 2009).

One argument against the OSE as the origin of ovarian cancers has been the lack of evidence that demonstrates the capability of OSE cells to differentiate along multiple Müllerian lineages. Differentiation of the Müllerian ducts is controlled by several sets of homeobox genes. These include the tandemly arranged *Hoxa9, Hoxa10, Hoxa11* and *Hoxa13* genes that are related to the *Drosophila* abdominal patterning gene *Abdominal-B* (*AbdB*) (Benson et al., 1996; Hsieh-Li et al., 1995; Zhao & Potter, 2001) (Figure 1). Targeted mutagenesis of *AbdB HOX* genes results in region-specific defects along the reproductive tract. For example, *Hoxa10*-deficient female mice exhibit homeotic transformation of the anterior segment of the uterus into oviductal-like structures (Benson et al., 1996). Replacement of the homeobox of the *Hoxa11* gene with that of *Hoxa13* in mice causes homeotic transformation of the uterus into cervical/vaginal-like structures (Zhao & Potter, 2001). The *AbdB HOX* genes are uniformly expressed along the axis of the Müllerian ducts early in embryonic development. As the ducts differentiate, *Hoxa9, Hoxa10, Hoxa11* and *Hoxa13* become expressed in the primordia of the fallopian tubes, uterus, lower uterine segment/cervix, and upper vagina, respectively (Taylor et al., 1997). This colinear *HOX* expression is maintained in the adult tract with sharply defined anterior boundaries of expression and tapered posterior expression. We have found that the *AbdB HOX* genes are not expressed in normal human OSE whereas their colinear expression patterns in Müllerian epithelia are recapitulated in the major subtypes of ovarian cancers according to the pattern of Müllerian-like differentiation of these tumors (Cheng et al., 2005). HOXA9 was found to be expressed in serous tumors and also in endometrioid and mucinous tumors. In contrast, HOXA10 was strongly expressed in endometrioid and mucinous but not serous tumors, whereas HOXA11 was mostly restricted to mucinous tumors (Table 1). Clear-cell ovarian carcinomas have features that overlap with those of serous and endometrioid tumors, and were found to express HOXA9 and HOXA10. This recapitulation of the *AbdB HOX* gene program in ovarian cancers could be interpreted to reflect Müllerian origins. However, by ectopically expressing *AbdB HOX* genes in undifferentiated, transformed mouse OSE cells and propagating transfected cells in the peritoneum of female mice, we demonstrated that OSEderived cells acquire features of different Müllerian lineages. Mouse OSE cells that expressed *Hoxa9* formed papillary tumors that resembled high-grade serous ovarian carcinoma, whereas expression of *Hoxa10* and *Hoxa11* induced formation of high-grade endometrioid-like and mucinous-like tumors, respectively (Cheng et al., 2005). We also found that the *Hoxa7* gene, located 3' of *Hoxa9*, is expressed in normal Müllerian epithelia and in differentiated ovarian tumors irrespective of their histologic subtype. Expression of

**3.1** *HOX* **genes and the Müllerian phenotype** 

*Hoxa7* in transformed mouse OSE cells promoted the abilities of *Hoxa9, Hoxa10* and *Hoxa11* to induce tumor differentiation along their respective pathways (Cheng et al., 2005).

The study of Cheng *et al* (2005) cannot be interpreted to conclusively demonstrate that the OSE is the cell-of-origin of ovarian cancers. However, the findings of this study suggest an intriguing model in which OSE-derived tumors acquire Müllerian phenotypes through homeotic transformation. The finding that colinearity of *AbdB HOX* expression (i.e. HOXA9, HOXA10, HOXA11) is recapitulated in ovarian cancers is striking, as it might explain the relative abundance of the ovarian cancer subtypes (i.e. serous> endometrioid> mucinous). The capability of OSE cells to acquire features of different lineages could stem from the intrinsically 'uncommitted' or embryonic-like phenotype of adult OSE cells (Auersperg et al., 2001; Naora, 2007). Unlike most other adult epithelia, the OSE lacks specialized features and expresses little or no E-cadherin (Maines-Bandiera & Auersperg, 1997). The OSE expresses both fibroblast markers and markers characteristic of simple epithelium (Auersperg et al., 1994), and also highly expresses stem cell maintenance genes (Bowen et al., 2009). This plasticity of the OSE is likely to be important for post-ovulatory repair (Auersperg et al., 2001). Both the OSE and Müllerian ducts derive from the coelomic epithelium, and the predominance of Müllerian phenotypes in ovarian cancers could reflect the close primordial relationship between the OSE and Müllerian ducts (Auersperg et al., 2001). More recently, it has been reported that the OSE and tubal fimbria are anatomically contiguous and that these tissues are parts of a transitional epithelium (Auersperg, 2011). On the other hand, less common subtypes of ovarian cancers such as clear-cell and transitional-cell tumors have features resembling those of renal and urothelial tissues whose embryonic relationship to the OSE is more distant.

#### **3.2 PAX expression and differential diagnosis**

More recently, expression of other homeobox genes that control urogenital patterning has been studied in ovarian cancers. *Pax2* is expressed in the developing kidneys, Wolffian ducts and Müllerian ducts (Dressler et al., 1990; Torres et al., 1995). *Pax8* is also expressed in the developing kidney and Müllerian ducts (Plachov et al., 1990). Female *Pax2* homozygous mutant mice lack the entire reproductive tract (Torres et al., 1995). Female *Pax8* null mice do not develop a functional uterus whereas development of the oviduct, cervix and vagina is unaffected (Mittag et al., 2007). PAX2 and PAX8 are normally expressed in tubal, endometrial and endocervical epithelia (Tong et al., 2007; Tong et al., 2011). PAX2 has also been detected in secondary Müllerian structures such as endometriosis, endosalpingiosis and rete ovarii (Tong et al., 2007). PAX2 and PAX8 have been detected in 64 to 100% of nonmucinous ovarian cancers, and in 74 to 90% of primary and metastatic renal cell carcinomas (Bowen et al., 2007; Tong et al., 2007; Nonaka et al., 2008; Chivukula et al., 2009; Zhai et al., 2010; Laury et al., 2011; Tacha et al., 2011). The absence or rareness of PAX2 and PAX8 in many other types of cancers such as colorectal carcinomas and mesotheliomas has raised the possibility that these proteins could be useful markers for differential diagnosis (Tong et al., 2007; Zhai et al., 2010; Laury et al., 2011; Tacha et al., 2011), but this depends on the appropriate setting. Ovarian metastasis from renal cell carcinoma and renal metastasis from ovarian carcinoma are rare. However, ovarian cancer commonly involves the uterus and omentum. PAX2 and PAX8 are frequently expressed in endometrial carcinomas (56 to 98%) (Sharma et al., 2010; Laury et al., 2011; Tacha et al., 2011), but have been detected at low frequency (<10%) in mucinous ovarian cancers that closely resemble colorectal carcinomas (Muratovska et al., 2003; Bowen et al., 2007; Nonaka et al., 2008). On the other hand, PAX8

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 177

expression libraries with cancer patient sera and has been termed SEREX (serologic identification of antigens by recombinant expression cloning) (Sahin et al., 1995). We have identified the HOXA7 and HOXB7 homeoproteins as ovarian tumor antigens by using the SEREX approach (Naora et al., 2001a, 2001b). Serum antibodies to HOXA7 were detected in 16 of 24 (67%) patients with differentiated ovarian carcinomas and in 0/30 (0%) healthy women (Naora et al., 2001a). Antibodies to HOXA7 were also detected in 13 of 19 (68%) women with cystadenomas, but in only one of 24 (4%) patients with poorly differentiated ovarian carcinoma (Naora et al., 2001a). This serologic reactivity reflected the prevalence of HOXA7 expression in cystadenomas and differentiated ovarian carcinomas as compared to poorly differentiated carcinomas (Naora et al., 2001a). Whereas HOXA7 is absent from normal OSE, HOXB7 was detected in normal OSE and at higher levels in ovarian carcinomas irrespective of the type or degree of differentiation (Naora et al., 2001b). Serum antibodies to HOXB7 were detected in only one of 29 (3%) healthy women and in 13 of 39 (33%) ovarian cancer patients (Naora et al., 2001b). Although this frequency is not high, the application of Bayesian modeling to multiplexed assays of serum antibodies to multiple ovarian tumor antigens has found that assaying serum antibodies to HOXB7, p53 and the antigen NY-CO-8 is the most effective combination for discriminating between ovarian cancer patients and healthy women (Erkanli et al., 2006). Widschwendter *et al.* (2009) reported that methylation of the *HOXA9* and *HOXA11* genes in normal endometrium can discriminate between premenopausal women with ovarian cancer and age-matched healthy women. Although the biological significance of these findings is unclear, this study raises the intriguing possibility that the methylation status of specific *HOX* genes in the

endometrium might be useful for predicting risk of ovarian cancer.

Table 1. Homeobox gene expression in histologic subtypes of ovarian cancer.

Given their essential developmental functions, it is not surprising that many homeobox genes are misexpressed in different types of cancers. In some cases, this aberrant expression reflects changes in cell differentiation in tumors and could occur as a consequence of tumorigenesis. On the other hand, there is increasing evidence that anomalous expression of homeobox genes plays causal roles in tumorigenesis (Abate-Shen, 2002; Samuel & Naora, 2005; Robson et al., 2006). Overexpression of several *HOX* genes in bone marrow cells leads to acute myeloid leukemia (Thorsteinsdottir et al., 1997; Kroon et al., 1998; Fischbach et al., 2005). Conversely, loss or down-regulation of a homeobox gene that is normally expressed in adult tissues can predispose cells for transformation. Inactivation of *Nkx3.1* in mice leads to the development of lesions that resemble prostate intraepithelial neoplasia (Kim et al.,

(\* mostly restricted to differentiated tumors)

**4. Homeobox genes and the hallmarks of cancer** 

has been reported to have comparable sensitivity to the Wilms tumor gene product WT1 in detecting serous ovarian cancer cells in fluid cytologic specimens and superior specificity to WT1 in distinguishing tumor cells from mesothelial cells (McKnight et al., 2010).

One interpretation of the frequency of PAX2 and PAX8 expression in ovarian cancers is that it implicates Müllerian origins of these tumors (Tong et al., 2007; Tong et al., 2011). However, there are several observations that challenge this notion. Whereas most studies have not detected PAX2 or PAX8 in normal OSE, these proteins have been detected in inclusion cysts (Bowen et al., 2007; Chivukula et al., 2009; Zhai et al., 2010; Auersperg, 2011). PAX8 has also been detected in peritoneal serous carcinomas (Tong et al., 2011). These tumors originate from the peritoneal mesothelium, a coelomic epithelial derivative to which the OSE is very closely related. Furthermore, PAX2 and PAX8 have been detected in tumors derived from non-urogenital lineages such as Kaposi's sarcoma (Buttiglieri et al., 2004) and thymic tumors (Laury et al., 2011). These cases might fall within the third category of anomalously expressed homeobox genes described above.

#### **3.3** *CDX2* **and the intestinal phenotype**

Another homeoprotein that has been extensively studied in differential diagnosis is CDX2. *Cdx2* controls intestinal differentiation and is expressed in the gut during development and in the adult (James et al., 1994). In contrast to PAX2 and PAX8, CDX2 is more frequently detected in mucinous ovarian carcinomas (64 to 93%) than in non-mucinous subtypes (0 to 7%) (Fraggetta et al., 2003; Werling et al., 2003; Groisman et al., 2004). CDX2 has been detected at lower frequency in mucinous ovarian cystadenomas and borderline tumors in keeping with the decreased prevalence of intestinal differentiation in these tumors (Werling et al., 2003). The most common secondary tumor to mimic an ovarian primary tumor is metastatic colorectal adenocarcinoma. Distinguishing primary mucinous ovarian carcinoma from metastatic colorectal adenocarcinoma is essential for clinical management but can be very difficult given their similar morphologic features. CDX2 alone is unsuitable for distinguishing primary from secondary mucinous ovarian tumors, as it is expressed in 90% of colorectal carcinomas metastatic to the ovary (Tornillo et al., 2004). However, several studies have reported promising predictive values when CDX2 is combined with other markers. These include cytokeratin 7 and mucin 5AC that are more frequently expressed in cancers of ovarian rather than lower gastrointestinal origin, and mucin 2 and carcinoembryonic antigen that are more frequently expressed in cancers of gastrointestinal rather than ovarian origin (Groisman et al., 2004; Park et al., 2007).

#### **3.4 Other diagnostic applications of homeoproteins**

The studies discussed above indicate that expression of several homeobox genes in ovarian cancers is associated with specific patterns of differentiation (Table 1), and raise the possibility that homeoproteins could serve as markers for differential diagnosis when used in appropriate settings and in combination with other tissue-specific markers. Misexpressed homeoproteins might also be useful for early detection. A significant limitation of assaying molecules that are shed by tumor cells is that their levels might not be detected in body fluids particularly when tumors are small. On the other hand, cancer patients often generate antibodies to molecules that are expressed in tumors and not in normal tissues and to selfantigens that are overexpressed in tumors. These circulating antibodies can be regarded as 'signals' that are amplified by the immune system and could serve as biomarkers for early cancer detection. One approach of identifying tumor antigens is to screen tumor cDNA

has been reported to have comparable sensitivity to the Wilms tumor gene product WT1 in detecting serous ovarian cancer cells in fluid cytologic specimens and superior specificity to

One interpretation of the frequency of PAX2 and PAX8 expression in ovarian cancers is that it implicates Müllerian origins of these tumors (Tong et al., 2007; Tong et al., 2011). However, there are several observations that challenge this notion. Whereas most studies have not detected PAX2 or PAX8 in normal OSE, these proteins have been detected in inclusion cysts (Bowen et al., 2007; Chivukula et al., 2009; Zhai et al., 2010; Auersperg, 2011). PAX8 has also been detected in peritoneal serous carcinomas (Tong et al., 2011). These tumors originate from the peritoneal mesothelium, a coelomic epithelial derivative to which the OSE is very closely related. Furthermore, PAX2 and PAX8 have been detected in tumors derived from non-urogenital lineages such as Kaposi's sarcoma (Buttiglieri et al., 2004) and thymic tumors (Laury et al., 2011). These cases might fall within the third category of

Another homeoprotein that has been extensively studied in differential diagnosis is CDX2. *Cdx2* controls intestinal differentiation and is expressed in the gut during development and in the adult (James et al., 1994). In contrast to PAX2 and PAX8, CDX2 is more frequently detected in mucinous ovarian carcinomas (64 to 93%) than in non-mucinous subtypes (0 to 7%) (Fraggetta et al., 2003; Werling et al., 2003; Groisman et al., 2004). CDX2 has been detected at lower frequency in mucinous ovarian cystadenomas and borderline tumors in keeping with the decreased prevalence of intestinal differentiation in these tumors (Werling et al., 2003). The most common secondary tumor to mimic an ovarian primary tumor is metastatic colorectal adenocarcinoma. Distinguishing primary mucinous ovarian carcinoma from metastatic colorectal adenocarcinoma is essential for clinical management but can be very difficult given their similar morphologic features. CDX2 alone is unsuitable for distinguishing primary from secondary mucinous ovarian tumors, as it is expressed in 90% of colorectal carcinomas metastatic to the ovary (Tornillo et al., 2004). However, several studies have reported promising predictive values when CDX2 is combined with other markers. These include cytokeratin 7 and mucin 5AC that are more frequently expressed in cancers of ovarian rather than lower gastrointestinal origin, and mucin 2 and carcinoembryonic antigen that are more frequently expressed in cancers of gastrointestinal

The studies discussed above indicate that expression of several homeobox genes in ovarian cancers is associated with specific patterns of differentiation (Table 1), and raise the possibility that homeoproteins could serve as markers for differential diagnosis when used in appropriate settings and in combination with other tissue-specific markers. Misexpressed homeoproteins might also be useful for early detection. A significant limitation of assaying molecules that are shed by tumor cells is that their levels might not be detected in body fluids particularly when tumors are small. On the other hand, cancer patients often generate antibodies to molecules that are expressed in tumors and not in normal tissues and to selfantigens that are overexpressed in tumors. These circulating antibodies can be regarded as 'signals' that are amplified by the immune system and could serve as biomarkers for early cancer detection. One approach of identifying tumor antigens is to screen tumor cDNA

WT1 in distinguishing tumor cells from mesothelial cells (McKnight et al., 2010).

anomalously expressed homeobox genes described above.

rather than ovarian origin (Groisman et al., 2004; Park et al., 2007).

**3.4 Other diagnostic applications of homeoproteins** 

**3.3** *CDX2* **and the intestinal phenotype** 

expression libraries with cancer patient sera and has been termed SEREX (serologic identification of antigens by recombinant expression cloning) (Sahin et al., 1995). We have identified the HOXA7 and HOXB7 homeoproteins as ovarian tumor antigens by using the SEREX approach (Naora et al., 2001a, 2001b). Serum antibodies to HOXA7 were detected in 16 of 24 (67%) patients with differentiated ovarian carcinomas and in 0/30 (0%) healthy women (Naora et al., 2001a). Antibodies to HOXA7 were also detected in 13 of 19 (68%) women with cystadenomas, but in only one of 24 (4%) patients with poorly differentiated ovarian carcinoma (Naora et al., 2001a). This serologic reactivity reflected the prevalence of HOXA7 expression in cystadenomas and differentiated ovarian carcinomas as compared to poorly differentiated carcinomas (Naora et al., 2001a). Whereas HOXA7 is absent from normal OSE, HOXB7 was detected in normal OSE and at higher levels in ovarian carcinomas irrespective of the type or degree of differentiation (Naora et al., 2001b). Serum antibodies to HOXB7 were detected in only one of 29 (3%) healthy women and in 13 of 39 (33%) ovarian cancer patients (Naora et al., 2001b). Although this frequency is not high, the application of Bayesian modeling to multiplexed assays of serum antibodies to multiple ovarian tumor antigens has found that assaying serum antibodies to HOXB7, p53 and the antigen NY-CO-8 is the most effective combination for discriminating between ovarian cancer patients and healthy women (Erkanli et al., 2006). Widschwendter *et al.* (2009) reported that methylation of the *HOXA9* and *HOXA11* genes in normal endometrium can discriminate between premenopausal women with ovarian cancer and age-matched healthy women. Although the biological significance of these findings is unclear, this study raises the intriguing possibility that the methylation status of specific *HOX* genes in the endometrium might be useful for predicting risk of ovarian cancer.


(\* mostly restricted to differentiated tumors)

Table 1. Homeobox gene expression in histologic subtypes of ovarian cancer.

#### **4. Homeobox genes and the hallmarks of cancer**

Given their essential developmental functions, it is not surprising that many homeobox genes are misexpressed in different types of cancers. In some cases, this aberrant expression reflects changes in cell differentiation in tumors and could occur as a consequence of tumorigenesis. On the other hand, there is increasing evidence that anomalous expression of homeobox genes plays causal roles in tumorigenesis (Abate-Shen, 2002; Samuel & Naora, 2005; Robson et al., 2006). Overexpression of several *HOX* genes in bone marrow cells leads to acute myeloid leukemia (Thorsteinsdottir et al., 1997; Kroon et al., 1998; Fischbach et al., 2005). Conversely, loss or down-regulation of a homeobox gene that is normally expressed in adult tissues can predispose cells for transformation. Inactivation of *Nkx3.1* in mice leads to the development of lesions that resemble prostate intraepithelial neoplasia (Kim et al.,

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 179

have overlapping functions. For example, both DLX4 and DLX5 induce c-Myc expression (Xu and Testa 2009; Trinh et al., 2011). On the other hand, MSX1 induces expression of growth arrest genes such as GADD153 and inhibits proliferation of ovarian cancer cells (Park et al., 2001), whereas MSX2 promotes ovarian cancer growth (Zhai et al., 2011). As discussed earlier, diversity in amino acid residues in the homeodomain and other motifs of family members gives rise to different DNA-binding specificities and can result in different phenotypes. The function or mechanism of a homeoprotein cannot therefore be inferred

A second important hallmark of cancer cells is their ability to circumvent signals that inhibit cell growth (Hanahan & Weinberg, 2000). Whereas TGF- induces G1 arrest in most types of normal cells, many tumors are resistant to the anti-proliferative effect of TGF- (Siegel & Massagué, 2003). The gene responses that are central to the TGF cytostatic program are activation of the cyclin-dependent kinase inhibitors p15Ink4B and p21WAF1/Cip1 and repression of c-myc and ID transcription factors. This cytostatic program is tightly regulated by a network of transcription factors that include Smad proteins, Sp1 and c-myc (Feng et al., 2000; 2002; Gartel et al., 2001). Resistance to the anti-proliferative effect of TGF- has been attributed to TGF- receptor or Smad4 mutations in several types of tumors, particularly those of gastrointestinal origin (Markowitz et al., 1995; Hahn et al., 1996). TGF- receptor mutations have been detected in 12 to 31% of ovarian cancers, but many TGF--resistant ovarian cancers have been found to express functional receptors and rarely have Smad4 mutations (Yamada et al., 1999; Wang et al., 2000; Francis-Thickpenny et al., 2001). We have found that DLX4 blocks the anti-proliferative effect of TGF- by inactivating transcriptional control of the TGF- cytostatic program through three distinct but integrated mechanisms (Trinh et al., 2011). Firstly, DLX4 directly binds Smad4 and prevents Smad4 from forming transcriptional complexes with Smad2 and Smad3. Secondly, DLX4 binds the DNA-binding domain of Sp1 and impairs the DNAbinding ability of Sp1. In addition, DLX4 induces expression of c-Myc, a repressor of p15*Ink4B* and p21*WAF1/Cip1* transcription (Trinh et al., 2011). An important outcome of our finding that DLX4 disables key transcriptional control mechanisms of the TGF- cytostatic program is that it explains why tumors that lack TGF- receptor or Smad mutations

Cancer cells encounter many physiologic stresses that trigger cell death and have evolved adaptive strategies to circumvent cell death programs. One important selective advantage is evasion of anoikis. A significant proportion of ovarian cancer cells in ascites exist as multicellular aggregates (Burleson et al., 2004). We have found that overexpression of HOXA10 in OSE-derived cells promotes homophilic cell adhesion and enables these cells to escape anoikis (Ko et al., 2010). Another selective advantage is the ability to survive under conditions where levels of growth factors are limited. We have found that DLX4 enables ovarian cancer cells to escape apoptosis induced by withdrawal of exogenous growth factors. This effect was due at least in part to induction of FGF-2 expression by DLX4 in tumor cells (Hara et al., 2007). In addition, DLX4 (also known as BP1 and DLX7) has been reported to induce bcl-2 and GATA-1 expression and to promote survival of leukemic and

from studies of its related family members.

become resistant to the anti-proliferative effect of TGF-.

**4.3 Resistance to cell death** 

**4.2 Evasion of growth-suppressors** 

2002a). Inactivation of *Nkx3.1* cooperates with loss-of-function of *Pten* to induce carcinoma (Kim et al., 2002b). *Cdx2* heterozygous mutant mice develop adenomatous intestinal polyps (Chawengsaksophak et al., 1997). *Cdx2* inactivation enhances the sensitivity of mice to chemically induced colon carcinogenesis (Bonhomme et al., 2003). Re-expression of *Nkx3.1* and *Cdx2* in prostate and colon cancer cells, respectively, inhibits cell proliferation (Kim et al., 2002a; Mallo et al., 1998). On the other hand, re-expression of *Hoxa10* in endometrial cancer cells does not alter proliferation but inhibits invasive behavior (Yoshida et al., 2006). Up- or down- regulation of homeobox genes in tumors, depending on their context, can therefore significantly modulate different hallmark capabilities of cancer.

#### **4.1 Sustained proliferative signaling**

A well-established hallmark of cancer cells is their ability to sustain chronic proliferation (Hanahan and Weinberg, 2000). One important growth factor that promotes autocrine cell growth and that is frequently overexpressed in ovarian cancers is fibroblast growth factor-2 (FGF-2) (Le Page et al., 2006). The *FGF-2* gene is a transcriptional target of HOXB7 (Caré et al., 1996). Enforced expression of *HOXB7* in OSE cells induces FGF-2 expression and cell proliferation (Naora et al., 2001b). The homeoprotein DLX4 is absent from most normal adult tissues and is expressed in >50% of ovarian cancers (Hara et al., 2007). We have found that overexpression of DLX4 in ovarian cancer cells induces FGF-2 expression, increases clonogenicity *in vitro* and promotes tumor growth *in vivo* (Hara et al., 2007), but it is not known whether DLX4 directly activates *FGF-2* transcription. We recently found that DLX4 also induces expression of c-Myc (Trinh et al., 2011). This induction occurs by two mechanisms. We identified that DLX4 prevents transforming growth factor-TGF- mediated repression of c-*myc* transcription, and also induces c-*myc* promoter activity independently of TGF-/Smad signaling (Trinh et al., 2011). DLX5, another member of the *DLX* family, has also been found to directly activate c-*myc* transcription in lung cancer cells (Xu and Testa, 2009). It has been reported that DLX5 is overexpressed in ovarian cancers and that inhibiting DLX5 expression by RNA interference attenuates AKT signaling and inhibits growth of ovarian cancer cells (Tan et al., 2010). Furthermore, *DLX5* cooperates with activated *HRAS* in transformation of human OSE cells. This growth-stimulatory effect of DLX5 has been attributed in part to its ability to activate transcription of the gene encoding insulin receptor substrate 2, an oncogenic signaling adaptor protein (Tan et al., 2010).

The studies discussed above demonstrate that activation of specific sets of homeobox genes in ovarian cancers drives tumor cell proliferation by inducing transcription of genes that encode multiple, different components of signaling pathways. In several cases, a homeobox gene promotes proliferation by the same mechanism in cells of different lineages. HOXB7 induces FGF-2 expression in OSE cells, breast cancers and melanomas, and stimulates growth of these cell types (Caré et al., 1996; 1998; Naora et al., 2001b). Overexpression of *SIX1* stimulates proliferation of breast and ovarian cancer cells by inducing cyclin A1 expression (Coletta et al., 2004; Behbakht et al., 2007). On the other hand, the effect of a homeobox gene can be cell type-specific. For example, overexpression of HOXA10 in myelomonocytic cells activates transcription of the gene encoding the cyclin-dependent kinase inhibitor p21WAF1/Cip1 and induces cell cycle arrest in G1 phase (Bromleigh & Freedman, 2000). In contrast, we have found that overexpression of HOXA10 in OSEderived cells has no effect on cell proliferation (Ko et al., 2010). Because homeoproteins of a given family share regions of extensive homology, it is not surprising that family members have overlapping functions. For example, both DLX4 and DLX5 induce c-Myc expression (Xu and Testa 2009; Trinh et al., 2011). On the other hand, MSX1 induces expression of growth arrest genes such as GADD153 and inhibits proliferation of ovarian cancer cells (Park et al., 2001), whereas MSX2 promotes ovarian cancer growth (Zhai et al., 2011). As discussed earlier, diversity in amino acid residues in the homeodomain and other motifs of family members gives rise to different DNA-binding specificities and can result in different phenotypes. The function or mechanism of a homeoprotein cannot therefore be inferred from studies of its related family members.

#### **4.2 Evasion of growth-suppressors**

178 Ovarian Cancer – Basic Science Perspective

2002a). Inactivation of *Nkx3.1* cooperates with loss-of-function of *Pten* to induce carcinoma (Kim et al., 2002b). *Cdx2* heterozygous mutant mice develop adenomatous intestinal polyps (Chawengsaksophak et al., 1997). *Cdx2* inactivation enhances the sensitivity of mice to chemically induced colon carcinogenesis (Bonhomme et al., 2003). Re-expression of *Nkx3.1* and *Cdx2* in prostate and colon cancer cells, respectively, inhibits cell proliferation (Kim et al., 2002a; Mallo et al., 1998). On the other hand, re-expression of *Hoxa10* in endometrial cancer cells does not alter proliferation but inhibits invasive behavior (Yoshida et al., 2006). Up- or down- regulation of homeobox genes in tumors, depending on their context, can

A well-established hallmark of cancer cells is their ability to sustain chronic proliferation (Hanahan and Weinberg, 2000). One important growth factor that promotes autocrine cell growth and that is frequently overexpressed in ovarian cancers is fibroblast growth factor-2 (FGF-2) (Le Page et al., 2006). The *FGF-2* gene is a transcriptional target of HOXB7 (Caré et al., 1996). Enforced expression of *HOXB7* in OSE cells induces FGF-2 expression and cell proliferation (Naora et al., 2001b). The homeoprotein DLX4 is absent from most normal adult tissues and is expressed in >50% of ovarian cancers (Hara et al., 2007). We have found that overexpression of DLX4 in ovarian cancer cells induces FGF-2 expression, increases clonogenicity *in vitro* and promotes tumor growth *in vivo* (Hara et al., 2007), but it is not known whether DLX4 directly activates *FGF-2* transcription. We recently found that DLX4 also induces expression of c-Myc (Trinh et al., 2011). This induction occurs by two mechanisms. We identified that DLX4 prevents transforming growth factor-TGF- mediated repression of c-*myc* transcription, and also induces c-*myc* promoter activity independently of TGF-/Smad signaling (Trinh et al., 2011). DLX5, another member of the *DLX* family, has also been found to directly activate c-*myc* transcription in lung cancer cells (Xu and Testa, 2009). It has been reported that DLX5 is overexpressed in ovarian cancers and that inhibiting DLX5 expression by RNA interference attenuates AKT signaling and inhibits growth of ovarian cancer cells (Tan et al., 2010). Furthermore, *DLX5* cooperates with activated *HRAS* in transformation of human OSE cells. This growth-stimulatory effect of DLX5 has been attributed in part to its ability to activate transcription of the gene encoding

insulin receptor substrate 2, an oncogenic signaling adaptor protein (Tan et al., 2010).

The studies discussed above demonstrate that activation of specific sets of homeobox genes in ovarian cancers drives tumor cell proliferation by inducing transcription of genes that encode multiple, different components of signaling pathways. In several cases, a homeobox gene promotes proliferation by the same mechanism in cells of different lineages. HOXB7 induces FGF-2 expression in OSE cells, breast cancers and melanomas, and stimulates growth of these cell types (Caré et al., 1996; 1998; Naora et al., 2001b). Overexpression of *SIX1* stimulates proliferation of breast and ovarian cancer cells by inducing cyclin A1 expression (Coletta et al., 2004; Behbakht et al., 2007). On the other hand, the effect of a homeobox gene can be cell type-specific. For example, overexpression of HOXA10 in myelomonocytic cells activates transcription of the gene encoding the cyclin-dependent kinase inhibitor p21WAF1/Cip1 and induces cell cycle arrest in G1 phase (Bromleigh & Freedman, 2000). In contrast, we have found that overexpression of HOXA10 in OSEderived cells has no effect on cell proliferation (Ko et al., 2010). Because homeoproteins of a given family share regions of extensive homology, it is not surprising that family members

therefore significantly modulate different hallmark capabilities of cancer.

**4.1 Sustained proliferative signaling** 

A second important hallmark of cancer cells is their ability to circumvent signals that inhibit cell growth (Hanahan & Weinberg, 2000). Whereas TGF- induces G1 arrest in most types of normal cells, many tumors are resistant to the anti-proliferative effect of TGF- (Siegel & Massagué, 2003). The gene responses that are central to the TGF cytostatic program are activation of the cyclin-dependent kinase inhibitors p15Ink4B and p21WAF1/Cip1 and repression of c-myc and ID transcription factors. This cytostatic program is tightly regulated by a network of transcription factors that include Smad proteins, Sp1 and c-myc (Feng et al., 2000; 2002; Gartel et al., 2001). Resistance to the anti-proliferative effect of TGF- has been attributed to TGF- receptor or Smad4 mutations in several types of tumors, particularly those of gastrointestinal origin (Markowitz et al., 1995; Hahn et al., 1996). TGF- receptor mutations have been detected in 12 to 31% of ovarian cancers, but many TGF--resistant ovarian cancers have been found to express functional receptors and rarely have Smad4 mutations (Yamada et al., 1999; Wang et al., 2000; Francis-Thickpenny et al., 2001). We have found that DLX4 blocks the anti-proliferative effect of TGF- by inactivating transcriptional control of the TGF- cytostatic program through three distinct but integrated mechanisms (Trinh et al., 2011). Firstly, DLX4 directly binds Smad4 and prevents Smad4 from forming transcriptional complexes with Smad2 and Smad3. Secondly, DLX4 binds the DNA-binding domain of Sp1 and impairs the DNAbinding ability of Sp1. In addition, DLX4 induces expression of c-Myc, a repressor of p15*Ink4B* and p21*WAF1/Cip1* transcription (Trinh et al., 2011). An important outcome of our finding that DLX4 disables key transcriptional control mechanisms of the TGF- cytostatic program is that it explains why tumors that lack TGF- receptor or Smad mutations become resistant to the anti-proliferative effect of TGF-.

#### **4.3 Resistance to cell death**

Cancer cells encounter many physiologic stresses that trigger cell death and have evolved adaptive strategies to circumvent cell death programs. One important selective advantage is evasion of anoikis. A significant proportion of ovarian cancer cells in ascites exist as multicellular aggregates (Burleson et al., 2004). We have found that overexpression of HOXA10 in OSE-derived cells promotes homophilic cell adhesion and enables these cells to escape anoikis (Ko et al., 2010). Another selective advantage is the ability to survive under conditions where levels of growth factors are limited. We have found that DLX4 enables ovarian cancer cells to escape apoptosis induced by withdrawal of exogenous growth factors. This effect was due at least in part to induction of FGF-2 expression by DLX4 in tumor cells (Hara et al., 2007). In addition, DLX4 (also known as BP1 and DLX7) has been reported to induce bcl-2 and GATA-1 expression and to promote survival of leukemic and

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 181

cancer stems from its propensity for aggressive intraperitoneal dissemination, with 70% of patients presenting with advanced-stage disease. FGF-2 stimulates cell migration, and advanced-stage ovarian cancers express a gene signature associated with FGF-2 signaling (De Cecco et al., 2004). HOXB7 induces FGF-2 expression in OSE-derived cells (Naora et al., 2001b) and inhibiting *HOXB7* expression in ovarian cancer cells inhibits invasiveness (Yamashita et al., 2006). Invasiveness of ovarian cancer cells is also inhibited when *HOXB13* expression is suppressed (Yamashita et al., 2006). Overexpression of *SIX1* increases metastasis of rhabdomyosarcoma by inducing expression of the cytoskeletal protein ezrin (Yu et al., 2004), but it is not known whether *SIX1* promotes ovarian cancer dissemination by the same mechanism. Conversely, *BARX2* inhibits invasiveness of ovarian cancer cells and loss of *BARX2* in ovarian cancers is associated with adverse survival (Sellar et al., 2001). The tumor-suppressive property of *BARX2* has been attributed in part to its ability to induce

Functions of several other homeobox genes that have been implicated in ovarian tumor progression are more complex. In addition to its anti-proliferative effect, TGF- is wellknown to induce epithelial-to-mesenchymal transition (EMT) and metastasis (Siegel and Massagué, 2003). We have found that DLX4 not only blocks the anti-proliferative effect of TGF- by sequestering Smad4, but also partially inhibits TGF--induced EMT (Trinh et al., 2011). The ability of DLX4 to inhibit TGF--induced EMT could explain the reported association of DLX4 with favorable prognosis in lung cancer patients and its metastasissuppressive activity (Tomida et al., 2007). On the other hand, we have found that DLX4 expression in ovarian cancers is strongly associated with disease progression (Hara et al., 2007). This association is likely to be due to the ability of DLX4 to stimulate other tumorpromoting processes via its induction of c-Myc, FGF-2 and vascular endothelial growth factor (VEGF) (Hara et al., 2007; Trinh et al., 2011). Another example of a homeobox gene with paradoxical functions is *HOXA4*. Whereas *HOXA4* is more highly expressed in invasive than in non-invasive ovarian cancers, *HOXA4* inhibits ovarian cancer cell migration (Klausen et al., 2009). These authors have speculated that increased *HOXA4* expression in

In contrast to many other types of cancers, ovarian cancer rarely spreads by hematogenous routes. Ovarian cancer cells typically disseminate by intraperitoneal 'seeding' whereby exfoliated tumor cells are transported throughout the pelvic cavity by the peritoneal fluid and frequently implant onto the mesothelial linings of the cavity wall and omentum (Naora & Montell, 2005). Attachment of ovarian cancer cells to mesothelial surfaces is mediated in part by interactions between ECM proteins and integrins (Heyman et al., 2008). We have found that HOXA10 stimulates attachment of OSE-derived cells to omental mesothelial cells by inducing expression of v3 integrin (Ko et al., 2010). The *ITGB3* gene that encodes 3 integrin has also been reported to be a transcriptional target of HOXA10 in endometrial cells (Daftary et al., 2002). However, comparison of our studies of HOXA10 in ovarian and endometrial cancers reveals striking differences as well as similarities. We have found that gain of *HOXA10* expression in endometrioid ovarian carcinomas is associated with endometrial-like differentiation (Cheng et al., 2005), whereas *HOXA10* down-regulation in endometrial carcinomas correlates with loss of glandular differentiation (Yoshida et al., 2006). Consistent with these observations, *HOXA10* promoted homophilic cell adhesion in both endometrial cancer cells and OSE-derived cells (Yoshida et al., 2006; Ko et al., 2010). However, whereas *HOXA10* expression in endometrial cancer cells inhibited invasiveness

expression of the cell adhesion molecule cadherin-6 (Sellar et al., 2001).

invasive cancers might constitute a homeostatic response.

breast cancer cells (Shimamoto et al., 1997; Stevenson et al., 2007). PAX2 has also been reported to promote survival of ovarian cancer cells and various other cell types such as bladder cancer cells, Kaposi's sarcoma and renal cell carcinoma cells (Gnarra & Dressler, 1995; Muratovska et al., 2003; Buttiglieri et al., 2004), but the underlying mechanism of the anti-apoptotic effect of PAX2 is not known.

Chemoresistance is a major challenge in the clinical management of ovarian cancer. *BARX2* is a homeobox gene that has been strongly implicated in modulating sensitivity of tumor cells to platinum. A study by Sellar *et al.* (2002) investigated isogenic ovarian cancer cell lines that were established from patients' tumors before and after platinum therapy. It was found that *BARX2* expression was down-regulated in tumor cell lines that were established upon tumor recurrence after platinum therapy and that transfection of *BARX2* into platinum-resistant cells reversed platinum-resistance. There has been significant interest in studying platinum-resistance in stem cell-like cell populations in ovarian cancers, and the homeoprotein Nanog has been used as a stem cell marker in these studies (Zhang et al., 2008). Furthermore, some homeobox genes confer resistance to cell death induced by other agents or signals. Expression of *HOXB13* in ovarian cancer cells has been reported to confer resistance to tamoxifen-mediated apoptosis (Miao et al., 2007). On the other hand, *SIX1* overexpression renders ovarian cancer cells resistant to tumor necrosis factor-related apoptosis inducing ligand (TRAIL)-mediated apoptosis (Behbakht et al., 2007).

#### **4.4 DNA repair and genomic instability**

Most agents that are commonly used to treat ovarian cancer induce cell death by causing DNA damage. The DNA double-strand break (DSB) is the most dangerous type of DNA damage. DSBs are induced by ionizing radiation and topoisomerase II inhibitors such as etoposide (Helleday et al., 2008). The inability of a cell to properly respond to DSBs leads to genomic instability. Genomic instability has been described as an 'enabling' characteristic of cancer cells (Hanahan & Weinberg, 2011). The primary mechanisms that repair DSBs are homologous recombination (HR) and non-homologous end-joining (NHEJ). The latter is the dominant DSB repair pathway in mammalian cells and is error-prone (Lieber et al., 2003). Both deficiencies and increases in NHEJ activity contribute to DNA repair infidelity and genomic instability (Difilippantonio et al., 2000; Brady et al., 2003). Several homeoproteins have been implicated in DNA repair and genomic instability. HOXB7 has been reported to stimulate NHEJ-mediated DNA repair and to confer resistance to ionizing radiation (Rubin et al., 2007). This activity was associated with the ability of HOXB7 to bind Ku proteins. Ku proteins form a complex that binds to the ends of DSBs (Lieber et al., 2003). On the other hand, DLX4 has been reported to inhibit expression of BRCA1, a component of the HRmediated DNA repair pathway (Kluk et al., 2010). Overexpression of SIX1 has been found to lead to genomic instability by attenuating the G2-M DNA damage checkpoint (Coletta et al., 2008). In these studies, the functions of HOXB7, DLX4 and SIX1 were studied in breast cancer cells. However, these homeoproteins are also overexpressed in ovarian cancers (Naora et al., 2001b, Hara et al., 2007; Behbakht et al., 2007), and might potentially contribute to DNA repair infidelity and genomic instability in ovarian cancer cells.

#### **4.5 Invasion and metastasis**

The ability of tumor cells to invade adjacent tissues and colonize distant sites is another well-established hallmark of cancer (Hanahan & Weinberg, 2000). The lethality of ovarian

breast cancer cells (Shimamoto et al., 1997; Stevenson et al., 2007). PAX2 has also been reported to promote survival of ovarian cancer cells and various other cell types such as bladder cancer cells, Kaposi's sarcoma and renal cell carcinoma cells (Gnarra & Dressler, 1995; Muratovska et al., 2003; Buttiglieri et al., 2004), but the underlying mechanism of the

Chemoresistance is a major challenge in the clinical management of ovarian cancer. *BARX2* is a homeobox gene that has been strongly implicated in modulating sensitivity of tumor cells to platinum. A study by Sellar *et al.* (2002) investigated isogenic ovarian cancer cell lines that were established from patients' tumors before and after platinum therapy. It was found that *BARX2* expression was down-regulated in tumor cell lines that were established upon tumor recurrence after platinum therapy and that transfection of *BARX2* into platinum-resistant cells reversed platinum-resistance. There has been significant interest in studying platinum-resistance in stem cell-like cell populations in ovarian cancers, and the homeoprotein Nanog has been used as a stem cell marker in these studies (Zhang et al., 2008). Furthermore, some homeobox genes confer resistance to cell death induced by other agents or signals. Expression of *HOXB13* in ovarian cancer cells has been reported to confer resistance to tamoxifen-mediated apoptosis (Miao et al., 2007). On the other hand, *SIX1* overexpression renders ovarian cancer cells resistant to tumor necrosis factor-related

apoptosis inducing ligand (TRAIL)-mediated apoptosis (Behbakht et al., 2007).

to DNA repair infidelity and genomic instability in ovarian cancer cells.

The ability of tumor cells to invade adjacent tissues and colonize distant sites is another well-established hallmark of cancer (Hanahan & Weinberg, 2000). The lethality of ovarian

Most agents that are commonly used to treat ovarian cancer induce cell death by causing DNA damage. The DNA double-strand break (DSB) is the most dangerous type of DNA damage. DSBs are induced by ionizing radiation and topoisomerase II inhibitors such as etoposide (Helleday et al., 2008). The inability of a cell to properly respond to DSBs leads to genomic instability. Genomic instability has been described as an 'enabling' characteristic of cancer cells (Hanahan & Weinberg, 2011). The primary mechanisms that repair DSBs are homologous recombination (HR) and non-homologous end-joining (NHEJ). The latter is the dominant DSB repair pathway in mammalian cells and is error-prone (Lieber et al., 2003). Both deficiencies and increases in NHEJ activity contribute to DNA repair infidelity and genomic instability (Difilippantonio et al., 2000; Brady et al., 2003). Several homeoproteins have been implicated in DNA repair and genomic instability. HOXB7 has been reported to stimulate NHEJ-mediated DNA repair and to confer resistance to ionizing radiation (Rubin et al., 2007). This activity was associated with the ability of HOXB7 to bind Ku proteins. Ku proteins form a complex that binds to the ends of DSBs (Lieber et al., 2003). On the other hand, DLX4 has been reported to inhibit expression of BRCA1, a component of the HRmediated DNA repair pathway (Kluk et al., 2010). Overexpression of SIX1 has been found to lead to genomic instability by attenuating the G2-M DNA damage checkpoint (Coletta et al., 2008). In these studies, the functions of HOXB7, DLX4 and SIX1 were studied in breast cancer cells. However, these homeoproteins are also overexpressed in ovarian cancers (Naora et al., 2001b, Hara et al., 2007; Behbakht et al., 2007), and might potentially contribute

anti-apoptotic effect of PAX2 is not known.

**4.4 DNA repair and genomic instability** 

**4.5 Invasion and metastasis** 

cancer stems from its propensity for aggressive intraperitoneal dissemination, with 70% of patients presenting with advanced-stage disease. FGF-2 stimulates cell migration, and advanced-stage ovarian cancers express a gene signature associated with FGF-2 signaling (De Cecco et al., 2004). HOXB7 induces FGF-2 expression in OSE-derived cells (Naora et al., 2001b) and inhibiting *HOXB7* expression in ovarian cancer cells inhibits invasiveness (Yamashita et al., 2006). Invasiveness of ovarian cancer cells is also inhibited when *HOXB13* expression is suppressed (Yamashita et al., 2006). Overexpression of *SIX1* increases metastasis of rhabdomyosarcoma by inducing expression of the cytoskeletal protein ezrin (Yu et al., 2004), but it is not known whether *SIX1* promotes ovarian cancer dissemination by the same mechanism. Conversely, *BARX2* inhibits invasiveness of ovarian cancer cells and loss of *BARX2* in ovarian cancers is associated with adverse survival (Sellar et al., 2001). The tumor-suppressive property of *BARX2* has been attributed in part to its ability to induce expression of the cell adhesion molecule cadherin-6 (Sellar et al., 2001).

Functions of several other homeobox genes that have been implicated in ovarian tumor progression are more complex. In addition to its anti-proliferative effect, TGF- is wellknown to induce epithelial-to-mesenchymal transition (EMT) and metastasis (Siegel and Massagué, 2003). We have found that DLX4 not only blocks the anti-proliferative effect of TGF- by sequestering Smad4, but also partially inhibits TGF--induced EMT (Trinh et al., 2011). The ability of DLX4 to inhibit TGF--induced EMT could explain the reported association of DLX4 with favorable prognosis in lung cancer patients and its metastasissuppressive activity (Tomida et al., 2007). On the other hand, we have found that DLX4 expression in ovarian cancers is strongly associated with disease progression (Hara et al., 2007). This association is likely to be due to the ability of DLX4 to stimulate other tumorpromoting processes via its induction of c-Myc, FGF-2 and vascular endothelial growth factor (VEGF) (Hara et al., 2007; Trinh et al., 2011). Another example of a homeobox gene with paradoxical functions is *HOXA4*. Whereas *HOXA4* is more highly expressed in invasive than in non-invasive ovarian cancers, *HOXA4* inhibits ovarian cancer cell migration (Klausen et al., 2009). These authors have speculated that increased *HOXA4* expression in invasive cancers might constitute a homeostatic response.

In contrast to many other types of cancers, ovarian cancer rarely spreads by hematogenous routes. Ovarian cancer cells typically disseminate by intraperitoneal 'seeding' whereby exfoliated tumor cells are transported throughout the pelvic cavity by the peritoneal fluid and frequently implant onto the mesothelial linings of the cavity wall and omentum (Naora & Montell, 2005). Attachment of ovarian cancer cells to mesothelial surfaces is mediated in part by interactions between ECM proteins and integrins (Heyman et al., 2008). We have found that HOXA10 stimulates attachment of OSE-derived cells to omental mesothelial cells by inducing expression of v3 integrin (Ko et al., 2010). The *ITGB3* gene that encodes 3 integrin has also been reported to be a transcriptional target of HOXA10 in endometrial cells (Daftary et al., 2002). However, comparison of our studies of HOXA10 in ovarian and endometrial cancers reveals striking differences as well as similarities. We have found that gain of *HOXA10* expression in endometrioid ovarian carcinomas is associated with endometrial-like differentiation (Cheng et al., 2005), whereas *HOXA10* down-regulation in endometrial carcinomas correlates with loss of glandular differentiation (Yoshida et al., 2006). Consistent with these observations, *HOXA10* promoted homophilic cell adhesion in both endometrial cancer cells and OSE-derived cells (Yoshida et al., 2006; Ko et al., 2010). However, whereas *HOXA10* expression in endometrial cancer cells inhibited invasiveness

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 183

inhibited in cells is by using a cell-penetrating peptide that blocks interactions between HOX and PBX proteins. This peptide has been reported to inhibit growth of ovarian cancer cells (Morgan et al., 2010). However, it should be noted that many different HOX proteins are expressed in normal cells as well as in tumors and utilize PBX proteins as co-factors (Chang et al., 1995; Shanmugam et al., 1999). On the other hand, the studies to date indicate that distinct sets of homeoproteins control cell cycle progression and cell survival. Homeoproteins might therefore be useful as markers for predicting responsiveness to

As discussed above, studies of *NKX3.1* and *CDX2* have demonstrated that misexpression of homeobox genes can induce pre-neoplastic lesions or predispose cells to transformation (Kim et al., 2002a; 2002b; Chawengsaksophak et al., 1997). Studying how homeobox genes are deregulated in tumors could therefore provide important insights into cancer risk. However, the mechanisms that cause aberrant expression of homeobox genes in solid tumors are poorly understood. Mutations in homeobox genes are associated with many developmental abnormalities (Mortlock and Innis, 1997; Ruf et al., 2004), but have rarely been detected in solid tumors. Deregulation of many *HOX* genes in leukemias and some *PAX* genes in sarcomas has been attributed to chromosomal translocations (Samuel & Naora, 2005; Argiropoulos & Humphries, 2007). A few homeobox genes localize to 'hotspots' that undergo loss of heterozygosity (LOH) or are amplified in tumors. The *HOXB*  gene cluster and *DLX4* map to the 17q21.3-q22 region, a 'hot-spot' that is amplified in ~10% of breast and ovarian cancers (Watanabe et al., 2001; Hyman et al., 2002; Hirasawa et al., 2003). However, overexpression of *HOXB7* and *DLX4* occurs in >50% of breast and ovarian cancers (Naora et al., 2001b; Man et al., 2005; Wu et al., 2006; Hara et al., 2007), indicating that gene amplification is not the sole mechanism underlying the overexpression of these genes. On the other hand, *NKX3.1* maps to 8p21, a region that is deleted in ~80% of prostate cancers (He et al., 1997). *BARX2* is located at 11q24-q25, within a minimal region that is associated with frequent LOH and adverse survival in ovarian cancer (Gabra et al., 1996). It is interesting to note that *BARX2* is the only homeobox gene with tumor-suppressive properties that has been identified to be lost in ovarian cancer. In contrast, other homeobox genes have been found to be overexpressed in ovarian cancers (Table 2). In this regard, the pattern of misexpression of homeobox genes in ovarian cancers is remarkably more similar

chemotherapeutic agents.

**5.1 Developmental signals** 

**5. Mechanisms of homeobox gene deregulation in tumors** 

to that in hematologic malignancies rather than in other solid tumors.

Little is known about the signaling pathways that control expression of homeobox genes in tumors. However, studies from the developmental biology field can provide important insights. Cross-regulatory interactions have been reported between bone morphogenetic proteins (BMPs) and *DLX* genes during normal cell differentiation. For example, BMP-2 activates *Dlx3* transcription (Park & Morasso, 2002), whereas Smad6, an antagonist of BMP signaling, inhibits DLX3 transcriptional activity (Berghorn et al., 2006). We have observed that levels of DLX4 protein decrease in cells following TGF- stimulation (Trinh et al., 2011). This raises the possibility that DLX4 is a component of a regulatory loop that blocks TGF signaling and is conversely regulated by TGF-. There is considerable evidence that

and metastasis (Yoshida et al., 2006), *HOXA10* activation in OSE-derived tumor cells lead to increased numbers of peritoneal implants by enabling tumor cells to escape anoikis and stimulating their attachment to mesothelial surfaces (Ko et al., 2010). These studies indicate that cellular behavior induced by a homeobox gene can differ depending on the cell type and context, and highlight fundamental differences between intraperitoneal seeding of ovarian cancer and 'classic' metastasis of endometrial and many other types of carcinomas.

#### **4.6 Angiogenesis**

Angiogenesis is a well-established hallmark of cancer that has been extensively studied in ovarian cancer. The angiogenic factors VEGF, FGF-2 and IL-8 are overexpressed in ovarian cancers and tumor microvessel density is a strong predictor of outcomes (Hollingsworth et al., 1995; Yoneda et al., 1998). VEGF is also the causative factor of ascites (Zhang et al., 2002). We have found that DLX4 expression in ovarian cancers is strongly associated with ascites and reduced overall survival in patients (Hara et al., 2007). Furthermore, we have demonstrated that overexpression of DLX4 in ovarian cancer cells promotes ascites and increases tumor microvessel density in mouse xenograft models. This activity of DLX4 was attributed to its induction of FGF-2 and VEGF expression (Hara et al., 2007). HOXB7 has also been found to induce FGF-2 and VEGF expression in breast cancer cells (Caré et al., 2001), and might stimulate angiogenesis in ovarian cancer by the same mechanism.

#### **4.7 Implications for therapy**

To date, functions of homeobox genes have not been described in replicative immortality or in emerging hallmarks and enabling characteristics of cancer such as deregulated cellular energetics, inflammation and evasion of immune destruction (Hanahan & Weinberg, 2011). Because homeoproteins control expression of numerous genes in different cell types and in response to different cellular signals, it is likely that misexpressed homeoproteins also modulate tumor pathogenesis by regulating one or more of these other hallmark capabilities. One central finding that has emerged from recent studies is that misexpression of an individual homeoprotein can promote multiple hallmark capabilities (Table 2).


Table 2. Implicated functions of up- (↑) and down- (↓) regulated homeobox genes in ovarian cancer.

This raises the possibility that homeoproteins could be attractive therapeutic targets. The most significant challenge to effectively inhibiting an overexpressed homeoprotein in tumors is specificity. As discussed earlier, different homeoproteins particularly within a family have highly conserved domains. One approach by which HOX protein activity can be

and metastasis (Yoshida et al., 2006), *HOXA10* activation in OSE-derived tumor cells lead to increased numbers of peritoneal implants by enabling tumor cells to escape anoikis and stimulating their attachment to mesothelial surfaces (Ko et al., 2010). These studies indicate that cellular behavior induced by a homeobox gene can differ depending on the cell type and context, and highlight fundamental differences between intraperitoneal seeding of ovarian cancer and 'classic' metastasis of endometrial and many other types of carcinomas.

Angiogenesis is a well-established hallmark of cancer that has been extensively studied in ovarian cancer. The angiogenic factors VEGF, FGF-2 and IL-8 are overexpressed in ovarian cancers and tumor microvessel density is a strong predictor of outcomes (Hollingsworth et al., 1995; Yoneda et al., 1998). VEGF is also the causative factor of ascites (Zhang et al., 2002). We have found that DLX4 expression in ovarian cancers is strongly associated with ascites and reduced overall survival in patients (Hara et al., 2007). Furthermore, we have demonstrated that overexpression of DLX4 in ovarian cancer cells promotes ascites and increases tumor microvessel density in mouse xenograft models. This activity of DLX4 was attributed to its induction of FGF-2 and VEGF expression (Hara et al., 2007). HOXB7 has also been found to induce FGF-2 and VEGF expression in breast cancer cells (Caré et al., 2001),

To date, functions of homeobox genes have not been described in replicative immortality or in emerging hallmarks and enabling characteristics of cancer such as deregulated cellular energetics, inflammation and evasion of immune destruction (Hanahan & Weinberg, 2011). Because homeoproteins control expression of numerous genes in different cell types and in response to different cellular signals, it is likely that misexpressed homeoproteins also modulate tumor pathogenesis by regulating one or more of these other hallmark capabilities. One central finding that has emerged from recent studies is that misexpression of an individual homeoprotein can promote multiple hallmark capabilities (Table 2).

Table 2. Implicated functions of up- (↑) and down- (↓) regulated homeobox genes in ovarian

This raises the possibility that homeoproteins could be attractive therapeutic targets. The most significant challenge to effectively inhibiting an overexpressed homeoprotein in tumors is specificity. As discussed earlier, different homeoproteins particularly within a family have highly conserved domains. One approach by which HOX protein activity can be

and might stimulate angiogenesis in ovarian cancer by the same mechanism.

**4.6 Angiogenesis** 

**4.7 Implications for therapy** 

cancer.

inhibited in cells is by using a cell-penetrating peptide that blocks interactions between HOX and PBX proteins. This peptide has been reported to inhibit growth of ovarian cancer cells (Morgan et al., 2010). However, it should be noted that many different HOX proteins are expressed in normal cells as well as in tumors and utilize PBX proteins as co-factors (Chang et al., 1995; Shanmugam et al., 1999). On the other hand, the studies to date indicate that distinct sets of homeoproteins control cell cycle progression and cell survival. Homeoproteins might therefore be useful as markers for predicting responsiveness to chemotherapeutic agents.

#### **5. Mechanisms of homeobox gene deregulation in tumors**

As discussed above, studies of *NKX3.1* and *CDX2* have demonstrated that misexpression of homeobox genes can induce pre-neoplastic lesions or predispose cells to transformation (Kim et al., 2002a; 2002b; Chawengsaksophak et al., 1997). Studying how homeobox genes are deregulated in tumors could therefore provide important insights into cancer risk. However, the mechanisms that cause aberrant expression of homeobox genes in solid tumors are poorly understood. Mutations in homeobox genes are associated with many developmental abnormalities (Mortlock and Innis, 1997; Ruf et al., 2004), but have rarely been detected in solid tumors. Deregulation of many *HOX* genes in leukemias and some *PAX* genes in sarcomas has been attributed to chromosomal translocations (Samuel & Naora, 2005; Argiropoulos & Humphries, 2007). A few homeobox genes localize to 'hotspots' that undergo loss of heterozygosity (LOH) or are amplified in tumors. The *HOXB*  gene cluster and *DLX4* map to the 17q21.3-q22 region, a 'hot-spot' that is amplified in ~10% of breast and ovarian cancers (Watanabe et al., 2001; Hyman et al., 2002; Hirasawa et al., 2003). However, overexpression of *HOXB7* and *DLX4* occurs in >50% of breast and ovarian cancers (Naora et al., 2001b; Man et al., 2005; Wu et al., 2006; Hara et al., 2007), indicating that gene amplification is not the sole mechanism underlying the overexpression of these genes. On the other hand, *NKX3.1* maps to 8p21, a region that is deleted in ~80% of prostate cancers (He et al., 1997). *BARX2* is located at 11q24-q25, within a minimal region that is associated with frequent LOH and adverse survival in ovarian cancer (Gabra et al., 1996). It is interesting to note that *BARX2* is the only homeobox gene with tumor-suppressive properties that has been identified to be lost in ovarian cancer. In contrast, other homeobox genes have been found to be overexpressed in ovarian cancers (Table 2). In this regard, the pattern of misexpression of homeobox genes in ovarian cancers is remarkably more similar to that in hematologic malignancies rather than in other solid tumors.

#### **5.1 Developmental signals**

Little is known about the signaling pathways that control expression of homeobox genes in tumors. However, studies from the developmental biology field can provide important insights. Cross-regulatory interactions have been reported between bone morphogenetic proteins (BMPs) and *DLX* genes during normal cell differentiation. For example, BMP-2 activates *Dlx3* transcription (Park & Morasso, 2002), whereas Smad6, an antagonist of BMP signaling, inhibits DLX3 transcriptional activity (Berghorn et al., 2006). We have observed that levels of DLX4 protein decrease in cells following TGF- stimulation (Trinh et al., 2011). This raises the possibility that DLX4 is a component of a regulatory loop that blocks TGF signaling and is conversely regulated by TGF-. There is considerable evidence that

Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 185

homeoproteins and the mechanisms that cause aberrant homeobox gene expression in tumors need to be identified. It is also important to determine whether a given homeobox gene controls a cellular process by the same mechanism in cells of different lineages, or has cell type-specific effects. Studies from the developmental biology field have provided powerful insights into the regulation, functions and mechanisms of homeobox genes in human cancers. Stronger integration between the developmental and cancer biology fields will be instrumental for furthering our understanding of the functional significance of

Studies by H. Naora are supported by a grant from the National Institutes of Health (R01 CA141078) and by a University of Texas MD Anderson Cancer Center Institutional Research

Abate-Shen, C. (2002) Deregulated homeobox gene expression in cancer: cause or consequence? *Nat Rev Cancer,* Vol. 2, No. 10, pp. 777-785, ISSN 1474-175X. Andikyan V. & Taylor H.S. (2009) WT1 represses *HOX* gene expression in the regulation of

Argiropoulos, B. & Humphries, R.K. (2007) *Hox* genes in hematopoiesis and leukemogenesis. *Oncogene,* Vol. 26, No. 47, pp. 6766-6776, ISSN 0950-9232. Auersperg, N., Maines-Bandiera, S.L., Dyck, H.G., Kruk, P.A. (1994) Characterization of

Auersperg, N. (2011) The origin of ovarian carcinomas: A unifying hypothesis. *Int J Gynecol* 

Banerjee-Basu, S. & Baxevanis, A.D. (2001) Molecular evolution of the homeodomain family

Beck, F. (2002) Homeobox genes in gut development. *Gut,* Vol. 51, No. 3, pp. 450-454, ISSN

Behbakht, K., Qamar, L., Aldridge, C.S., Coletta, R.D., Davidson, S.A., Thorburn, A., Ford,

Benson, G.V., Lim, H., Paria, B.C., Satokata, I., Dey, S.K., Maas, R.L. (1996) Mechanisms of

binding. *J Biol Chem,* Vol. 281, No. 29, pp. 20357-20367, ISSN 0021-9258.

gynaecologic tumour histologic type. *J Cell Mol Med,* Vol. 13, No. 11-12, pp. 4522-

cultured human ovarian surface epithelial cells: phenotypic plasticity and premalignant changes. *Lab Invest,* Vol. 71, No. 4, pp. 510-518, ISSN 0023-6837. Auersperg, N., Wong, A.S., Choi, K.C., Kang, S.K., Leung, P.C. (2001) Ovarian surface

epithelium: biology, endocrinology, and pathology. *Endocr Rev,* Vol. 22, No. 2, pp.

of transcription factors. *Nucl Acids Res*, Vol. 29, No. 15, pp. 3258-3269, ISSN 0305-

H.L. (2007) Six1 overexpression in ovarian carcinoma causes resistance to TRAILmediated apoptosis and is associated with poor survival. *Cancer Res,* Vol. 67, No. 7,

reduced fertility in *Hoxa-10* mutant mice: uterine homeosis and loss of maternal *Hoxa-10* expression. *Development,* Vol. 122, No. 9, pp. 2687-2696, ISSN 0950-1991. Berghorn, K.A., Clark-Campbell, P.A., Han, L., McGrattan, M., Weiss, R.S., Roberson, M.S.

(2006) Smad6 represses Dlx3 transcriptional activity through inhibition of DNA

homeobox genes in ovarian cancer.

4531, ISSN 1582-1838.

255-288, ISSN 0163-769X.

pp. 3036-3042, ISSN 0008-5472.

1048.

0017-5749.

*Pathol,* Vol. 30, No. 1, pp. 12–21, ISSN 0277-1691.

Grant. Our work is dedicated to the memory of Sean Patrick.

**7. Acknowledgement** 

**8. References** 

patterning of the reproductive tract is controlled by a regulatory network of distinct sets of Wnts and homeobox genes (Kobayashi & Behringer, 2003). *MSX2* is a transcriptional target of -catenin/TCF and *MSX2* expression is increased in endometrioid ovarian carcinomas with deregulated -catenin (Zhai et al., 2011). Expression of *AbdB HOX* genes in the endometrium is also tightly regulated by estrogen and progesterone (Ma et al., 1998). WT1 is a transcription factor that is used as a marker of serous ovarian cancer and reportedly represses *HOXA10* expression (Andikyan & Taylor, 2009). WT1-mediated repression could explain why many serous ovarian cancers do not express *HOXA10* (Cheng et al., 2005).

#### **5.2 Epigenetic mechanisms**

DNA methylation is the most commonly identified mechanism that silences expression of homeobox genes in solid tumors such as breast and lung cancers (Novak et al., 2006; Rauch et al., 2007). We have found that *HOXA10* down-regulation in high-grade endometrial carcinomas is due to promoter methylation (Yoshida et al., 2006). DNA methyltransferases that methylate DNA are recruited by Polycomb repressive complexes (Mills, 2010). Polycomb and Trithorax group proteins form multi-protein complexes that contain histone methyltransferase activity and dynamically alter chromatin structure by modifying specific residues in histone tails. Polycomb group proteins keep *HOX* genes repressed, whereas Trithorax group proteins counteract Polycomb-mediated silencing and maintain *HOX* expression (Soshnikova & Duboule, 2009). Polycomb and Trithorax group proteins are aberrantly expressed in different types of cancers (Mills, 2010), but it is unclear whether altered expression of these proteins causes *HOX* activation in ovarian cancers. A striking aspect of *HOX* gene clusters is the presence of long noncoding RNAs and microRNAs in the intergenic regions. These non-coding RNAs control transcription of *HOX* genes through a variety of *cis-* and *trans*- acting mechanisms (Lemons & McGinnis, 2006; Yekta et al., 2008). One intriguing example is the long non-coding RNA *HOTAIR*. *HOTAIR* is located in the *HOXC* locus and interacts with and targets the Polycomb repressive complex 2 (PRC2) to the *HOXD* locus located on a different chromosome (Rinn et al., 2007). *HOTAIR* expression in primary breast tumors has been found to be a strong predictor of metastasis (Gupta et al., 2010). Enforced expression of *HOTAIR* in cancer cells increased metastasis by inducing genome-wide re-targeting of PRC2 to an occupancy pattern that resemble that of embryonic fibroblasts (Gupta et al., 2010). Almost all homeobox genes that have been studied in ovarian cancer are overexpressed (Tables I,II), and their activation in tumors might arise from down-regulation of non-coding RNAs. Indeed, microRNA-185 has been reported to target *Six1* and is expressed at decreased levels in ovarian cancers (Imam et al., 2010).

#### **6. Conclusions**

In conclusion, the functional significance of homeobox genes in ovarian cancer is rapidly emerging as an intriguing research area that provides new molecular insights into the histogenesis of the different subtypes of ovarian cancer and the progression of this disease. The studies to date raise the possibility that specific sets of homeoproteins might serve as diagnostic or predictive markers in the appropriate settings and in combination with other markers. However, more mechanistic studies are essential to further develop our understanding of the functions of homeobox genes in ovarian cancer biology and to translate this research into clinical applications. In particular, the target genes of homeoproteins and the mechanisms that cause aberrant homeobox gene expression in tumors need to be identified. It is also important to determine whether a given homeobox gene controls a cellular process by the same mechanism in cells of different lineages, or has cell type-specific effects. Studies from the developmental biology field have provided powerful insights into the regulation, functions and mechanisms of homeobox genes in human cancers. Stronger integration between the developmental and cancer biology fields will be instrumental for furthering our understanding of the functional significance of homeobox genes in ovarian cancer.

#### **7. Acknowledgement**

Studies by H. Naora are supported by a grant from the National Institutes of Health (R01 CA141078) and by a University of Texas MD Anderson Cancer Center Institutional Research Grant. Our work is dedicated to the memory of Sean Patrick.

#### **8. References**

184 Ovarian Cancer – Basic Science Perspective

patterning of the reproductive tract is controlled by a regulatory network of distinct sets of Wnts and homeobox genes (Kobayashi & Behringer, 2003). *MSX2* is a transcriptional target of -catenin/TCF and *MSX2* expression is increased in endometrioid ovarian carcinomas with deregulated -catenin (Zhai et al., 2011). Expression of *AbdB HOX* genes in the endometrium is also tightly regulated by estrogen and progesterone (Ma et al., 1998). WT1 is a transcription factor that is used as a marker of serous ovarian cancer and reportedly represses *HOXA10* expression (Andikyan & Taylor, 2009). WT1-mediated repression could explain why many serous ovarian cancers do not express *HOXA10* (Cheng et al., 2005).

DNA methylation is the most commonly identified mechanism that silences expression of homeobox genes in solid tumors such as breast and lung cancers (Novak et al., 2006; Rauch et al., 2007). We have found that *HOXA10* down-regulation in high-grade endometrial carcinomas is due to promoter methylation (Yoshida et al., 2006). DNA methyltransferases that methylate DNA are recruited by Polycomb repressive complexes (Mills, 2010). Polycomb and Trithorax group proteins form multi-protein complexes that contain histone methyltransferase activity and dynamically alter chromatin structure by modifying specific residues in histone tails. Polycomb group proteins keep *HOX* genes repressed, whereas Trithorax group proteins counteract Polycomb-mediated silencing and maintain *HOX* expression (Soshnikova & Duboule, 2009). Polycomb and Trithorax group proteins are aberrantly expressed in different types of cancers (Mills, 2010), but it is unclear whether altered expression of these proteins causes *HOX* activation in ovarian cancers. A striking aspect of *HOX* gene clusters is the presence of long noncoding RNAs and microRNAs in the intergenic regions. These non-coding RNAs control transcription of *HOX* genes through a variety of *cis-* and *trans*- acting mechanisms (Lemons & McGinnis, 2006; Yekta et al., 2008). One intriguing example is the long non-coding RNA *HOTAIR*. *HOTAIR* is located in the *HOXC* locus and interacts with and targets the Polycomb repressive complex 2 (PRC2) to the *HOXD* locus located on a different chromosome (Rinn et al., 2007). *HOTAIR* expression in primary breast tumors has been found to be a strong predictor of metastasis (Gupta et al., 2010). Enforced expression of *HOTAIR* in cancer cells increased metastasis by inducing genome-wide re-targeting of PRC2 to an occupancy pattern that resemble that of embryonic fibroblasts (Gupta et al., 2010). Almost all homeobox genes that have been studied in ovarian cancer are overexpressed (Tables I,II), and their activation in tumors might arise from down-regulation of non-coding RNAs. Indeed, microRNA-185 has been reported to

target *Six1* and is expressed at decreased levels in ovarian cancers (Imam et al., 2010).

In conclusion, the functional significance of homeobox genes in ovarian cancer is rapidly emerging as an intriguing research area that provides new molecular insights into the histogenesis of the different subtypes of ovarian cancer and the progression of this disease. The studies to date raise the possibility that specific sets of homeoproteins might serve as diagnostic or predictive markers in the appropriate settings and in combination with other markers. However, more mechanistic studies are essential to further develop our understanding of the functions of homeobox genes in ovarian cancer biology and to translate this research into clinical applications. In particular, the target genes of

**5.2 Epigenetic mechanisms** 

**6. Conclusions** 


Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 187

Chang, C.-P., Shen, W-F., Rozenfeld, S., Lawrence, H.J., Largman, C., Cleary, M.L. (1995) Pbx

Chawengsaksophak, K., James, R., Hammond, V.E., Kontgen, F., Beck F. (1997) Homeosis

Cheng, W., Liu, J., Yoshida, H., Rosen, D., Naora, H. (2005) Lineage infidelity of epithelial

reproductive tract. *Nat Med,* Vol. 11, No. 5, pp. 531-537, ISSN 1078-8956. Chivukula, M., Dabbs, D.J., O'Connor, S., Bhargava, R. (2009) PAX 2: a novel Müllerian

carcinoma. *Int J Gynecol Pathol,* Vol. 28, No. 6, pp. 570-578, ISSN 0277-1691. Cho, K.R. & Shih, I.M. (2009) Ovarian cancer. *Annu Rev Pathol,* Vol. 4, pp. 287-313, ISSN

Christensen, K.L., Patrick, A.N., McCoy, E.L., Ford, H.L. (2008) The *Six* family of homeobox

Coletta, R.D., Christensen, K., Reichenberger, K.J.*,* Lamb, J., Micomonaco, D., Huang, L.,

Coletta, R.D., Christensen, K.L., Micalizzi, D.S., Jedlicka, P., Varella-Garcia, M., Ford, H.L.

Connolly, D.C., Bao, R., Nikitin, A.Y., Stephens, K.C., Poole, T.W., Hua, X., Harris, S.S.,

ovarian cancer. *Cancer Res*, Vol. 63, No. 6, pp. 1389-1397, ISSN 0008-5472. Daftary, G.S., Troy, P.J., Bagot, C.N., Young, S.L., Taylor, H.S. (2002) Direct regulation of

De Cecco, L., Marchionni, L., Gariboldi, M., Reid, J.F., Lagonigro, M.S., Caramuta, S.,

Difilippantonio, M.J., Zhu, J., Chen, H.T., Meffre, E., Nussenzweig, M.C., Max, E.E., Ried, T.,

*Acad Sci USA,* Vol. 101, No. 17, pp. 6478-6483, ISSN 0027-8424.

*Endocrinol,* Vol. 16, No. 3, pp. 571-579, ISSN 0888-8809.

2. *Oncogene,* Vol. 23, No. 49, pp. 8171-8183, ISSN 0950-9232.

Hox proteins. *Genes Dev*, Vol. 9, No. 6, pp. 663–674, ISSN 0890-9369. Chariot, A., Gielen, J., Merville, M.P., Bours, V. (1999) The homeodomain-containing

12, pp. 1851-1857, ISSN 0006-2952.

ISSN 0028-0836.

1553-4006.

ISSN 0008-5472.

ISSN 0028-0836.

230X.

proteins display hexapeptide-dependent cooperative DNA binding with a subset of

proteins. An update on their interacting partners. *Biochem Pharmacol,* Vol. 58, No.

and intestinal tumours in *Cdx2* mutant mice. *Nature,* Vol. 386, No. 6620, pp. 84–87,

ovarian cancers is controlled by *HOX* genes that specify regional identity in the

marker for serous papillary carcinomas to differentiate from micropapillary breast

genes in development and cancer. *Adv Cancer Res,* Vol. 101, pp. 93-126, ISSN 0065-

Wolf, D.M., Müller-Tidow, C., Golub, T.R., Kawakami, K., Ford, H.L. (2004) The Six1 homeoprotein stimulates tumorigenesis by reactivation of cyclin A1. *Proc Natl* 

(2008) Six1 overexpression in mammary cells induces genomic instability and is sufficient for malignant transformation. *Cancer Res,* Vol. 68, No. 7, pp. 2204-2213,

Vanderhyden, B.C., Hamilton, T.C. (2003) Female mice chimeric for expression of the simian virus 40 TAg under control of the MISIIR promoter develop epithelial

beta3-integrin subunit gene expression by HOXA10 in endometrial cells. *Mol* 

Ferrario, C., Bussani, E., Mezzanzanica, D., Turatti, F., Delia, D., Daidone, M.G., Oggionni, M., Bertuletti, N., Ditto, A., Raspagliesi, F., Pilotti, S., Pierotti, M.A., Canevari, S., Schneider, C. (2004) Gene expression profiling of advanced ovarian cancer: characterization of a molecular signature involving fibroblast growth factor

Nussenzweig, A. (2000) DNA repair protein Ku80 suppresses chromosomal aberrations and malignant transformation. *Nature,* Vol. 404, No. 6777, pp. 510-514,


Bhatia-Gaur, R., Donjacour, A.A., Sciavolino, P.J., Kim, M., Desai, N., Young, P., Norton,

Biggin, M.D. & McGinnis, W. (1997) Regulation of segmentation and segmental identity by

specificity. *Development,* Vol. 124, No. 22, pp. 4425-4433, ISSN 0950-1991. Bonhomme, C., Duluc, I., Martin, E., Chawengsaksophak, K., Chenard, M.P., Kedinger, M.,

development. *Gynecol Oncol*. Vol. 104, No. 2, pp. 331-337, ISSN 0090-8258. Bowen N.J., Walker L.D., Matyunina L.V., Logani S., Totten K.A., Benigno B.B., McDonald

initiating cells. *BMC Med Genomics,* Vol. 2, pp. 71–85, ISSN 1755-8794. Brady, N., Gaymes, T.J., Cheung, M., Mufti, G.J., Rassool, F.V. (2003) Increased error-prone

Bromleigh, V.C. & Freedman, L.P. (2000) *p21* is a transcriptional target of HOXA10 in

Burleson, K.M., Casey, R.C., Skubitz, K.M., Pambuccian, S.E., Oegema, T.R., Skubitz, A.P.

Buttiglieri, S., Deregibus, M.C., Bravo, S., Cassoni, P., Chiarle, R., Bussolati, B., Camussi, G.

sarcoma cells. *J Biol Chem,* Vol. 279, No. 6, pp. 4136–4143, ISSN 0021-9258. Capecchi, M.R. (1997) *Hox* genes and mammalian development. *Cold Spring Harb Symp* 

Caré, A., Silvani, A., Meccia, E., Mattia, G., Stoppacciaro, A., Parmiani, G., Peschle, C.,

in melanomas. *Mol Cell Biol,* Vol. 16, No. 9, pp. 4842–4851, ISSN 0270-7306. Caré, A., Silvani, A., Meccia, E., Mattia, G., Peschle, C., Colombo, M.P. (1998) Transduction

Caré, A., Felicetti, F., Meccia, E., Bottero, L., Parenza, M., Stoppacciaro, A., Peschle, C.,

*Quant Biol*, Vol. 62, pp. 273-281, ISSN 0091-7451.

*Oncogene,* Vol. 16, No. 25, pp. 3285–3289, ISSN 0950-9232.

*Cancer Res,* Vol. 61, No. 17, pp. 6532–6539, ISSN 0008-5472

966-977, ISSN 0890-9369.

1798-1805, ISSN 0008-5472.

170–181, ISSN 0090-8258.

ISSN 0890-9369.

C.R., Gridley, T., Cardiff, R.D., Cunha, G.R., Abate-Shen, C., Shen, M.M. (1999) Roles for *Nkx3.1* in prostate development and cancer. *Genes Dev,* Vol. 13, No. 8, pp.

*Drosophila* homeoproteins: the role of DNA binding in functional activity and

Beck, F., Freund, J.N., Domon-Dell, C. (2003) The *Cdx2* homeobox gene has a tumour suppressor function in the distal colon in addition to a homeotic role during gut development. *Gut,* Vol. 52, No. 10, pp. 1465–1471, ISSN 0017-5749. Bowen, N.J., Logani, S., Dickerson, E.B., Kapa, L.B., Akhtar, M., Benigno, B.B., McDonald,

J.F. (2007) Emerging roles for PAX8 in ovarian cancer and endosalpingeal

J.F. (2009) Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotential and capable of serving as ovarian cancer

NHEJ activity in myeloid leukemias is associated with DNA damage at sites that recruit key nonhomologous end-joining proteins. *Cancer Res*, Vol. 63, No. 8, pp.

differentiating myelomonocytic cells. *Genes Dev,* Vol. 14, No. 20, pp. 2581-2586,

(2004) Ovarian carcinoma ascites spheroids adhere to extracellular matrix components and mesothelial cell monolayers. *Gynecol Oncol,* Vol. 93, No. 1, pp.

(2004) Role of Pax2 in apoptosis resistance and proinvasive phenotype of Kaposi's

Colombo, M.P. (1996) HOXB7 constitutively activates basic fibroblast growth factor

of the SkBr3 breast carcinoma cell line with the *HOXB7* gene induces bFGF expression, increases cell proliferation and reduces growth factor dependence.

Colombo, M.P. (2001) HOXB7: a key factor for tumor-associated angiogenic switch.


Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 189

Gorski, D.H. & Walsh, K. (2000). The role of homeobox genes in vascular remodeling and

Groisman, G.M., Meir, A., Sabo, E. (2004) The value of Cdx2 immunostaining in

the ovaries. *Int J Gynecol Pathol*, Vol*.* 23, No. 1, pp. 52-57, ISSN 0277-1691. Gupta, R.A., Shah, N., Wang, K.C., Kim, J., Horlings, H.M., Wong, D.J., Tsai, M.C., Hung, T.,

Hahn, S.A., Schutte, M., Hoque, A.T., Moskaluk, C.A., da Costa, L.T., Rozenblum, E.,

Hanahan, D. & Weinberg, R.A. (2000) The hallmarks of cancer. *Cell*, Vol. 100, No. 1, pp. 57-

Hanahan, D. & Weinberg, R.A. (2011) Hallmarks of cancer: The next generation. *Cell,* Vol.

Hara, F., Samuel, S., Liu, J., Rosen, D., Langley, R.R., Naora, H. (2007) A homeobox gene

He, W.W., Sciavolino, P.J., Wing, J., Augustus, M., Hudson, P., Meissner, P.S., Curtis, R.T.,

Helleday, T., Petermann, E., Lundin, C., Hodgson, B., Sharma, R.A. (2008) DNA repair

Heyman, L., Kellouche, S., Fernandes, J., Dutoit, S., Poulain, L., Carreiras, F. (2008)

Hirasawa, A., Saito-Ohara, F., Inoue, J., Aoki, D., Susumu, N., Yokoyama, T., Nozawa, S.,

Hollingsworth, H.C., Kohn, E.C., Steinberg, S.M., Rothenberg, M.L., Merino, M.J. (1995)

Hsieh-Li, H.M., Witte, D.P., Weinstein, M., Branford, W., Li, H., Small, K., Potter, S.S. (1995)

female fertility. *Development*, Vol. 121, No. 5, pp. 1373-1385, ISSN 0950-1991.

differentiating primary ovarian carcinomas from colonic carcinomas metastatic to

Argani, P., Rinn, J.L., Wang, Y., Brzoska, P., Kong, B., Li, R., West, R.B., van de Vijver, M.J., Sukumar, S., Chang, H.Y. (2010) Long non-coding RNA *HOTAIR*  reprograms chromatin state to promote cancer metastasis. *Nature*, Vol*.* 464, No.

Weinstein, C.L., Fischer, A., Yeo, C.J., Hruban, R.H., Kern, S.E. (1996) *DPC4,* a candidate tumor suppressor at human chromosome 18q21.1. *Science*, Vol. 271, No.

related to *Drosophila Distal-less* promotes ovarian tumorigenicity by inducing expression of vascular endothelial growth factor and fibroblast growth factor-2. *Am* 

Shell, B.K., Bostwick, D.G., Tindall, D.J., Gelmann, E.P., Abate-Shen, C., Carter, K.C. (1997) A novel human prostate-specific, androgen-regulated homeobox gene (*NKX3.1*) that maps to 8p21, a region frequently deleted in prostate cancer.

pathways as targets for cancer therapy. *Nat Rev Cancer*, Vol. 8, No. 3, pp. 193-204,

Vitronectin and its receptors partly mediate adhesion of ovarian cancer cells to peritoneal mesothelium *in vitro. Tumor Biol*, Vol. 29, No. 4, pp. 231-244, ISSN 1010-

Inazawa, J., Imoto, I. (2003) Association of 17q21-q24 gain in ovarian clear cell adenocarcinomas with poor prognosis and identification of *PPM1D* and *APPBP2* as likely amplification targets. *Clin Cancer Res*, Vol. 9, No. 6, pp. 1995-2004, ISSN 1078-

Tumor angiogenesis in advanced stage ovarian carcinoma. *Am J Pathol*, Vol. 147,

*Hoxa 11* structure, extensive antisense transcription, and function in male and

angiogenesis. *Circ Res*, Vol. 87, No. 10, pp. 865-872, ISSN 0009-7330.

7291, pp. 1071-1076, ISSN 0028-0836.

5247, pp. 350-353, ISSN 0036-8075.

144, No. 5, pp. 646-674, ISSN 0092-8674.

*J Pathol*, Vol *.* 170, No. 5, pp. 1594-1606, ISSN 0002-9440.

*Genomics*, Vol. 43, No. 1, pp. 69-77, ISSN 0888-7543.

70, ISSN 0092-8674.

ISSN 1474-175X.

No. 1, pp. 33-41, ISSN 0002-9440.

4283.

0432.


Dressler, G.R., Deutsch, U., Chowdhury, K., Nornes, H.O., Gruss, P. (1990) *Pax2,* a new

Dubeau, L. (2008) The cell of origin of ovarian epithelial tumors. *Lancet Oncol,* Vol. 9, No. 12,

Dubnau, J. & Struhl, G. (1996) RNA recognition and translational regulation by a homeodomain protein. *Nature*, Vol. 379, No. 6567, pp. 694-699, ISSN 0028-0836. Erkanli, A., Taylor, D.D., Dean, D., Eksir, F., Egger, D., Geyer, J., Nelson, B.H., Stone, B.,

Feeley, K.M. & Wells, M. (2001) Precursor lesions of ovarian epithelial malignancy.

Feng, X.H., Lin, X., Derynck, R. (2000) Smad2, Smad3 and Smad4 cooperate with Sp1 to

Feng, X.H., Liang, Y.Y., Liang, M., Zhai, W., Lin, X. (2002) Direct interaction of c-Myc with

Fischbach, N.A., Rozenfeld, S., Shen, W., Fong, S., Chrobak, D., Ginzinger, D., Kogan, S.C.,

Fraggetta, F., Pelosi, G., Cafici, A., Scollo, P., Nuciforo, P., Viale, G. (2003) CDX2

Francis-Thickpenny, K.M., Richardson, D.M., van Ee, C.C., Love, D.R., Winship, I.M.,

Gabra, H., Watson, J.E., Taylor, K.J., Mackay, J., Leonard, R.C., Steel, C.M., Porteous, D.J.,

Gartel, A.L., Ye, X., Goufman, E., Shianov, P., Hay, N., Najmabadi, F., Tyner, A.L. (2001)

Gehring, W.J. & Hiromi, Y. (1986) Homeotic genes and the homeobox. *Annu Rev Genet*, Vol.

Gehring, W.J., Qian, Y.Q., Billeter, M., Furukubo-Tokunaga, K., Schier, A.F., Resendez-Perez,

Gnarra, J.R. & Dressler, G.R. (1995) Expression of Pax-2 in human renal cell carcinoma and

system. *Development*, No. 109, No. 4, pp. 787-795, ISSN 0950-1991.

*Cancer Res,* Vol. 66, No. 3, pp. 1792-1798, ISSN 0008-5472.

*Histopathology*, Vol. 38, No. 2, pp. 87-95, ISSN 0309-0167.

p15Ink4B*. Mol Cell,* Vol. 9, No. 1, pp. 133-143, ISSN 1097-2765.

*in vivo*. *Blood,* Vol. 105, No. 4, pp. 1456-1466, ISSN 0006-4971.

*Arch,* Vol. 443, No. 6, pp. 782-786, ISSN 0945-6317.

*Res*, Vol*.* 56, No. 5*,* pp. 950–954, ISSN 0008-5472.

*Cell*, Vol. 78, No. 2, pp. 211-223, ISSN 0092-8674.

*Sci USA*, Vol*.* 98, No. 8, pp. 4510-4515, ISSN 0027-8424.

pp. 1191-1197, ISSN 1470-2045.

5178-5193, ISSN 0261-4189.

687-691, ISSN 0007-0920.

20, pp. 147-173, ISSN 0066-4197.

4092–4098, ISSN 0008-5472.

murine paired-box-containing gene and its expression in the developing excretory

Fritsche, H.A., Roden, R.B. (2006) Application of Bayesian modeling of autologous antibody responses against ovarian tumor-associated antigens to cancer detection.

induce p15*Ink4B* transcription in response to TGF-. *EMBO J,* Vol*.* 19, No. 19, pp.

Smad2 and Smad3 to inhibit TGF--mediated induction of the CDK inhibitor

Radhakrishnan, A., Le Beau, M.M., Largman, C., Lawrence, H.J. (2005) *HOXB6* overexpression in murine bone marrow immortalizes a myelomonocytic precursor *in vitro* and causes hematopoietic stem cell expansion and acute myeloid leukemia

immunoreactivity in primary and metastatic ovarian mucinous tumours*. Virchows* 

Baguley, B.C., Chenevix-Trench, G., Shelling, A.N. (2001) Analysis of the TGF functional pathway in epithelial ovarian carcinoma. *Br J Cancer,* Vol. 85, No. 5, pp.

Smyth, J.F. (1996) Definition and refinement of a region of loss of heterozygosity at 11q23.3-q24.3 in epithelial ovarian cancer associated with poor prognosis. *Cancer* 

Myc represses the p21*WAF1/Cip1* promoter and interacts with Sp1/Sp3. *Proc Natl Acad* 

D., Affolter, M., Otting, G., Wüthrich, K. (1994) Homeodomain-DNA recognition.

growth inhibition by antisense oligonucleotides. *Cancer Res*, Vol. 55, No. 18, pp.


Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 191

Lemons, D. & McGinnis, W. (2006) Genomic evolution of *Hox* gene clusters. *Science*, Vol. 313,

Le Page, C., Ouellet, V., Madore, J., Hudson, T.J., Tonin, P.N., Provencher, D.M., Mes-

Lieber, M.R., Ma, Y., Pannicke, U., Schwarz, K. (2003) Mechanism and regulation of human

Ma L., Benson G.V., Lim H., Dey S.K., Maas R.L. (1998) *Abdominal B (AbdB) Hoxa* genes:

MacLean, J.A. & Wilkinson, M.F. (2010) The *Rhox* genes. *Reproduction.* 140(2), pp. 195-213,

Maines-Bandiera, S.L. & Auersperg, N. (1997) Increased E-cadherin expression in ovarian

Mallo, G.V., Soubeyran, P., Lissitzky, J.C., André, F., Farnarier, C., Marvaldi, J., Dagorn, J.C.,

Man, Y.G., Fu, S.W., Schwartz, A., Pinzone, J.J., Simmens, S.J., Berg, P.E. (2005) Expression of

McGinnis, W. & Krumlauf, R. (1992) Homeobox genes and axial patterning. *Cell*, Vol. 68,

McKnight, R., Cohen, C., Siddiqui, M.T. (2010) Utility of paired box gene 8 (PAX8)

Miao, J., Wang, Z., Provencher, H., Muir, B., Dahiya, S., Carney, E., Leong, C.O., Sgroi, D.C.,

Mills, A.A. (2010) Throwing the cancer switch: reciprocal roles of polycomb and trithorax proteins. *Nat Rev Cancer*, Vol. 10, No. 10, pp. 669-682, ISSN 1474-175X. Mittag, J., Winterhager, E., Bauer, K., Grümmer, R. (2007) Congenital hypothyroid female

*USA*, Vol. 104, No. 43, pp. 17093-17098, ISSN 0027-8424.

*Endocrinology,* Vol. 148, No. 2, pp. 719–725, ISSN 0013-7227.

*J Pathol*, Vol. 211, No. 1, pp. 26-35, ISSN 0022-3417.

*Cancer,* Vol. 118, No. 7, pp. 1750–1758, ISSN 0020-7136.

No. 5795, pp. 1918-1922, ISSN 0036-8075.

No. 2, pp. 141–154, ISSN 0012-1606.

14030–14036, ISSN 0021-9258.

No. 2, pp. 283-302, ISSN 0092-8674.

298-302, ISSN 1934-662X.

Vol. 16, No. 3, pp. 250-255, ISSN 0277-1691.

ISSN 1471-0072.

ISSN 1470-1626.

candidate precursor to serous carcinoma that originates in the distal fallopian tube.

Masson, A.M. (2006) From gene profiling to diagnostic markers: IL-18 and FGF-2 complement CA125 as serum-based markers in epithelial ovarian cancer. *Int J* 

non-homologous DNA end-joining. *Nat Rev Mol Cell Biol*, Vol. 4, No. 9, pp. 712-720,

regulation in adult uterus by estrogen and progesterone and repression in müllerian duct by the synthetic estrogen diethylstilbestrol (DES). *Dev Biol*, Vol. 197,

surface epithelium: an early step in metaplasia and dysplasia? *Int J Gynecol Pathol,* 

Iovanna J.L. (1998) Expression of the *Cdx1* and *Cdx2* homeotic genes leads to reduced malignancy in colon cancer-derived cells. *J Biol Chem*, Vol. 273, No. 22, pp.

*BP1,* a novel homeobox gene, correlates with breast cancer progression and invasion. *Breast Cancer Res Treat*, Vol. 90, No. 3, pp. 241-247, ISSN 0167-6806. Markowitz, S., Wang, J., Myeroff, L., Parsons, R., Sun, L., Lutterbaugh, J., Fan, R.S.,

Zborowska, E., Kinzler, K.W., Vogelstein, B., Brattain, M., Willson, J.K. (1995) Inactivation of the type II TGF-beta receptor in colon cancer cells with microsatellite instability. *Science,* Vol. 268, No. 5215, pp. 1336-1338, ISSN 0036-8075.

expression in fluid and fine-needle aspiration cytology: an immunohistochemical study of metastatic ovarian serous carcinoma. *Cancer Cytopathol,* Vol*.* 118, No. 5, pp.

Orsulic, S. (2007) *HOXB13* promotes ovarian cancer progression. *Proc Natl Acad Sci* 

*Pax8-*deficient mice are infertile despite thyroid hormone replacement therapy.


Hyman, E., Kauraniemi, P., Hautaniemi, S., Wolf, M., Mousses, S., Rozenblum, E., Ringnér,

Imam, J.S., Buddavarapu, K., Lee-Chang, J.S., Ganapathy, S., Camosy, C., Chen, Y., Rao,

James, R., Erler, T., Kazenwadel, J. (1994) Structure of the murine homeobox gene *cdx-2*.

Jarboe E.A., Folkins A.K., Drapkin R., Ince T.A., Agoston E.S., Crum C.P. (2008) Tubal and

Kim, M.J., Bhatia-Gaur, R., Banach-Petrosky, W.A.*,* Desai, N., Wang, Y., Hayward, S.W.,

Kim M.J., Cardiff R.D., Desai N., Banach-Petrosky W.A., Parsons R., Shen M.M., Abate-Shen

Klausen, C., Leung, P.C., Auersperg, N. (2009) Cell motility and spreading are suppressed

Kluk, B.J., Fu, Y., Formolo, T.A., Zhang, L., Hindle, A.K., Man, Y-G., Siegel, R.S., Berg, P.E.,

Ko, S.Y., Lengyel, E., Naora, H. (2010) The Müllerian *HOXA10* gene promotes growth of

Kobayashi, A. & Behringer, R.R. (2003) Developmental genetics of the female reproductive tract in mammals. *Nat Rev Genet,* Vol. 4, No. 12, pp. 969-980, ISSN 1471-0056. Kozmik, Z., Sure, U., Rüedi, D., Busslinger, M., Aguzzi, A. (1995). Deregulated expression of

Kroon, E., Krosl, J., Thorsteinsdottir, U., Baban, S., Buchberg, A.M., Sauvageau, G. (1998)

Lee, Y., Miron, A., Drapkin, R., Nucci, M.R., Medeiros, F., Saleemuddin, A., Garber, J., Birch,

*Res*, Vol. 62, No. 21, pp. 6240-6245, ISSN 0008-5472.

*Histopathology*, Vol. 53, No. 2, pp. 127-138, ISSN 0309-0167.

*Cancer Res*, Vol. 7, No. 9, pp. 1425-1437, ISSN 1541-7786.

*Cell Endocrinol*, Vol. 317, No. 1-2, pp. 112-119, ISSN 0303-7207.

21, pp. 15229–15237, ISSN 0021-9258.

2999-3004, ISSN 0008-5472.

pp. 513-524, ISSN 1449-2288.

pp. 816-826, ISSN 0147-5185.

ISSN 0027-8424.

0950-9232.

0027-8424.

M., Sauter, G., Monni, O., Elkahloun, A., Kallioniemi, O.P., Kallioniemi, A. (2002) Impact of DNA amplification on gene expression patterns in breast cancer. *Cancer* 

M.K. (2010) MicroRNA-185 suppresses tumor growth and progression by targeting the *Six1* oncogene in human cancers. *Oncogene*, Vol. 29, No. 35, pp. 4971–4979, ISSN

Expression in embryonic and adult intestinal epithelium. *J Biol Chem*, Vol. 269, No.

ovarian pathways to pelvic epithelial cancer: a pathologic perspective.

Cunha, G.R., Cardiff, R.D., Shen, M.M., Abate-Shen, C. (2002a) *Nkx3.1* mutant mice recapitulate early stages of prostate carcinogenesis. *Cancer Res*, Vol*.* 62, No. 11, pp.

C. (2002b) Cooperativity of *Nkx3.1* and *Pten* loss of function in a mouse model of prostate carcinogenesis. *Proc Natl Acad Sci USA*, Vol. 99, No. 5, pp. 2884–2889, ISSN

by HOXA4 in ovarian cancer cells: possible involvement of beta1 integrin. *Mol* 

Deng, C., McCaffey, T.A., Fu, S.W. (2010). BP1, an isoform of DLX4 homeoprotein, negatively regulates *BRCA1* in sporadic breast cancer. *Int J Biol Sci*, Vol. 6, No. 5,

ovarian surface epithelial cells by stimulating epithelial-stromal interactions. *Mol* 

*PAX5* in medulloblastoma. *Proc Natl Acad Sci USA*, Vol. 92, No. 12, pp. 5709–5713,

HoxA9 transforms primary bone marrow cells through specific collaboration with Meis1a but not Pbx1b. *EMBO J*, Vol. 17, No. 13, pp. 3714–3725, ISSN 0261-4189. Laury, A.R., Perets, R., Piao, H., Krane, J.F., Barletta, J.A., French, C., Chirieac, L.R., Lis, R.,

Loda, M., Hornick, J.L., Drapkin, R., Hirsch, M.S. (2011) A comprehensive analysis of PAX8 expression in human epithelial tumors. *Am J Surg Pathol*, Vol. 35, No. 6,

C., Mou, H., Gordon, R.W., Cramer, D.W., McKeon, F.D., Crum, C.P. (2007) A

candidate precursor to serous carcinoma that originates in the distal fallopian tube. *J Pathol*, Vol. 211, No. 1, pp. 26-35, ISSN 0022-3417.


Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 193

Plachov, D., Chowdhury, K., Walther, C., Simon, D., Guenet, J.L., Gruss, P. (1990) *Pax8*, a

Prowse, A.H., Manek, S., Varma, R., Liu, J., Godwin, A.K., Maher, E.R., Tomlinson, I.P.,

of ovarian cancer. *Int J Cancer,* Vol. 119, No. 3, pp. 556-562, ISSN 0020-7136. Rauch, T., Wang, Z., Zhang, X., Zhong, X., Wu, X., Lau, S.K., Kernstine, K.H., Riggs, A.D.,

Rinn, J.L., Kertesz, M., Wang, J.K., Squazzo, S.L., Xu, X., Brugmann, S.A., Goodnough, L.H.,

Robson, E.J., He, S-J., Eccles, M.R. (2006) A panorama of *PAX* genes in cancer and development. *Nat Rev Cancer,* Vol. 6, No. 1, pp. 52-62, ISSN 1474-175X. Rubin, E., Wu, X., Zhu, T., Cheung, J.C., Chen, H., Lorincz, A, Pandita, R.K., Sharma, G.G.,

Ruf, R.G., Xu, P.X., Silvius, D., Otto, E.A., Beekmann, F., Muerb, U.T., Kumar, S., Neuhaus,

Sahin, U., Türeci, O., Schmitt, H., Cochlovius, B., Johannes, T., Schmits, R., Stenner, F., Luo,

Samuel, S. & Naora, H. (2005). Homeobox gene expression in cancer: insights from

Schneuwly, S., Klemenz, R., Gehring, W.J. (1987). Redesigning the body plan of *Drosophila*

Sellar, G.C., Li., L., Watt, K.P., Nelkin, B.D., Rabiasz, G.J., Stronach, E.A., Miller, E.P.,

Sellar, G.C., Watt, K.P., Li, L., Nelkin, B.D., Rabiasz, G.J., Porteous, D.J., Smyth, J.F, Gabra,

*Mol Cell Biol*, Vol. 19, No. 11, pp. 7577-7588, ISSN 0270-7306.

gland. *Development,* Vol. 110, No. 2, pp. 643–651, ISSN 0950-1991.

*Natl Acad Sci USA,* Vol. 104, No. 13, pp. 5527-5532, ISSN 0027-8424.

Vol. 129, No. 7, pp. 1311–1323, ISSN 0092-8674.

1527-1535, ISSN 0008-5472.

pp. 8090–8095, ISSN 0027-8424.

2437, ISSN 0959-8049.

pp. 816-818, ISSN 0028-0836.

No. 25, pp. 11810–11813, ISSN 0027-8424.

No. 19, pp. 6977–6981, ISSN 0008-5472.

murine paired box gene expressed in the developing excretory system and thyroid

Kennedy, S.H. (2006) Molecular genetic evidence that endometriosis is a precursor

Pfeifer, G.P. (2007) Homeobox gene methylation in lung cancer studied by genomewide analysis with a microarray-based methylated CpG island recovery assay. *Proc* 

Helms, J.A., Farnham, P.J., Segal, E., Chang, H.Y. (2007) Functional demarcation of active and silent chromatin domains in human *HOX* loci by noncoding RNAs. *Cell,*

Ha, H.C., Gasson, J., Hanakahi, L.A., Pandita, T.K., Sukumar, S. (2007) A role for the HOXB7 homeodomain protein in DNA repair. *Cancer Res,* Vol. 67, No. 4, pp.

T.J., Kemper, M.J., Raymond, R.M., Brophy, P.D., Berkman, J., Gattas, M., Hyland, V., Ruf, E.M., Schwartz, C., Chang, E.H., Smith, R.J., Stratakis, C.A., Weil, D., Petit, C., Hildebrandt, F. (2004) *SIX1* mutations cause branchio-oto-renal syndrome by disruption of EYA1-SIX1-DNA complexes. *Proc Natl Acad Sci USA,* Vol. 101, No. 21,

G., Schobert, I. & Pfreundschuh, M. (1995) Human neoplasms elicit multiple specific immune responses in the autologous host. *Proc Natl Acad Sci USA,* Vol. 92,

developmental regulation and deregulation. *Eur J Cancer,* Vol. 41, No. 16, pp. 2428-

by ectopic expression of the homeotic gene *Antennapedia*. *Nature,* Vol. 325, No. 6107,

Porteous, D.J., Smyth, J.F., Gabra, H. (2001) BARX2 induces cadherin 6 expression and is a functional suppressor of ovarian cancer progression. *Cancer Res,* Vol. 61,

H. (2002) The homeobox gene *BARX2* can modulate cisplatin sensitivity in human epithelial ovarian cancer. *Int J Oncol,* Vol*.* 21, No. 5, pp. 929-933, ISSN 1019-6439. Shanmugam, K., Green, N.C., Rambaldi, I., Saragovi, H.U., Featherstone, M.S. (1999) PBX

and MEIS as non-DNA-binding partners in trimeric complexes with HOX proteins.


Morgan, R., Plowright, L., Harrington, K.J., Michael, A., Pandha, H.S. (2010) Targeting HOX

Mortlock, D.P. & Innis, J.W. (1997) Mutation of *HOXA13* in hand-foot-genital syndrome. *Nat* 

Muratovska, A., Zhou, C., He, S., Goodyer, P., Eccles, M.R. (2003) Paired-Box genes are

Naora, H. & Montell, D.J. (2005) Ovarian cancer metastasis: Integrating studies from

Naora, H., Montz, F.J., Chai, C.Y., Roden, R.B. (2001a) Aberrant expression of homeobox

Naora, H., Yang, Y.Q., Montz, F.J., Seidman, J.D., Kurman, R.J., Roden, R.B. (2001b) A

Naora, H. (2007) The heterogeneity of epithelial ovarian cancers: reconciling old and new paradigms. *Expert Rev Mol Med,* Vol. 9, No. 13, pp. 1-12, ISSN 1462-3994. Nonaka, D., Chiriboga, L., Soslow, R.A. (2008) Expression of pax8 as a useful marker in

Novak, P., Jensen, T., Oshiro, M.M., Wozniak, R.J., Nouzova, M., Watts, G.S., Klimecki,

Orsulic, S., Li, Y., Soslow, R.A., Vitale-Cross, L.A., Gutkind, J.S., Varmus, H.E. (2002)

Panganiban, G. & Rubenstein, J.L. (2002) Developmental functions of the *Distal-less*/*Dlx*  homeobox genes. *Development,* Vol. 129, No. 19, pp. 4371-4386, ISSN 0950-1991. Park, J., Park, K., Kim, S., Lee, J.H. (2001) *Msx1* gene overexpression induces G1 phase cell

Park, G.T. & Morasso, M.I. (2002) Bone morphogenetic protein-2 (BMP-2) transactivates

keratinocytes. *Nucl Acids Res,* Vol. 30, No. 2, pp. 515-522, ISSN 0305-1048. Park, S.Y., Kim, B.H., Kim, J.H., Lee, S., Kang, G.H. (2007) Panels of immunohistochemical

Pearson, J.C., Lemons, D., McGinnis, W. (2005) Modulating *Hox* gene functions during

system. *Cancer Cell,* Vol. 1, No. 1, pp. 53-62. ISSN 1535-6108.

*Lab Med,* Vol*.* 131, No. 10, pp. 1561-1567, ISSN 0003-9985.

*Genet,* Vol. 15, No. 2, pp. 179-180, ISSN 1061-4036.

Vol. 32, No. 10, pp. 1566-1571, ISSN 0147-5185.

Vol*.* 281, No. 5, pp. 1234-1240, ISSN 0006-291X.

*Oncogene*, Vol. 22, No. 39, pp. 7989–7997, ISSN 0950-9232.

*Acad Sci USA,* Vol*.* 98, No. 26, pp. 15209-15214, ISSN 0027-8424.

ISSN 1471-2407.

4065, ISSN 0027-8424.

175X.

5472.

and PBX transcription factors in ovarian cancer. *BMC Cancer,* Vol. 10, No. 1, pp. 89,

frequently expressed in cancer and often required for cancer cell survival.

disparate model organisms. *Nat Rev Cancer,* Vol. 5, No. 5, pp. 355-366, ISSN 1474-

gene *HOXA7* is associated with müllerian-like differentiation of epithelial ovarian tumors and the generation of a specific autologous antibody response. *Proc Natl* 

serologically identified tumor antigen encoded by a homeobox gene promotes growth of ovarian epithelial cells. *Proc Natl Acad Sci USA,* Vol. 98, No. 7, pp. 4060-

distinguishing ovarian carcinomas from mammary carcinomas. *Am J Surg Pathol,* 

W.T., Kim, C., Futscher, B.W. (2006) Epigenetic inactivation of the *HOXA* gene cluster in breast cancer. *Cancer Res,* Vol*.* 66, No. 22, pp. 10664-10670, ISSN 0008-

Induction of ovarian cancer by defined multiple genetic changes in a mouse model

arrest in human ovarian cancer cell line OVCAR3. *Biochem Biophys Res Commun,* 

Dlx3 through Smad1 and Smad4: alternative mode for Dlx3 induction in mouse

markers help determine primary sites of metastatic adenocarcinoma. *Arch Pathol* 

animal body patterning. *Nat Rev Genet,* Vol. 6, No. 12, pp. 893-904, ISSN 1471-0056.


Homeobox Genes and Their Functional Significance in Ovarian Tumorigenesis 195

Topisirovic, I., Kentsis, A., Perez, J.M., Guzman, M.L., Jordan, C.T., Borden, K.L. (2005)

multiple levels. *Mol Cell Biol,* Vol. 25, No. 3, pp. 1100-1112, ISSN 0270-7306. Tornillo, L,, Moch, H., Diener, P.A., Lugli, A., Singer, G. (2004) CDX-2 immunostaining in

Torres, M., Gómez-Pardo, E., Dressler, G.R., Gruss, P. (1995) *Pax-2* controls multiple steps of

Trinh, B.Q., Barengo, N., Naora, H. (2011) Homeodomain protein DLX4 counteracts key

Tupler, R., Perini, G., Green, M.R. (2001) Expressing the human genome. *Nature,* Vol. 409,

Vitiello, D., Kodaman, P.H., Taylor, H.S. (2007) *HOX* genes in implantation. *Semin Reprod* 

Wang, D., Kanuma, T., Mizunuma, H., Takama, F., Ibuki, Y., Wake, N., Mogi, A., Shitara, Y.,

Watanabe, T., Imoto, I., Kosugi, Y., Ishiwata, I., Inoue, S., Takayama, M., Sato, A., Inazawa, J.

Werling, R.W., Yaziji, H., Bacchi, C.E., Gown, A.M. (2003) CDX2, a highly sensitive and

Widschwendter, M., Apostolidou, S., Jones, A.A., Fourkala, E.O., Arora, R., Pearce, C.L.,

Wu, X., Chen, H., Parker, B., Rubin, E., Zhu, T., Lee, J.S., Argani, P., Sukumar, S. (2006)

Wu, R., Hendrix-Lucas, N., Kuick, R., Zhai, Y., Schwartz, D.R., Akyol, A., Hanash, S., Misek,

643, ISSN 0021-9746.

No. 6822, pp. 832-833, ISSN 0028-0836.

*Med,* Vol*.* 25, No. 6, pp. 431-436, ISSN 1526-4564.

Vol. 60, No. 16, pp. 4507-4512, ISSN 0008-5472.

1991.

0950-9232.

ISSN 0090-8258.

pp. 303-310, ISSN 0147-5185.

2218, ISSN 0020-7136.

pp. 321-333, ISSN 1535-6108.

0008-5472.

Eukaryotic translation initiation factor 4E activity is modulated by HOXA9 at

primary and secondary ovarian carcinomas. *J Clin Pathol,* Vol. 57, No. 6, pp. 641-

urogeneital development. *Development,* Vol. 121, No. 12, pp. 4057-4065, ISSN 0950-

transcriptional control mechanisms of the TGF- cytostatic program and blocks the anti-proliferative effect of TGF-. *Oncogene*, Vol. 30, No. 24, pp. 2718–2729, ISSN

Takenoshita, S. (2000) Analysis of specific gene mutations in the transforming growth factor- signal transduction pathway in human ovarian cancer. *Cancer Res,*

(2001) A novel amplification at 17q21-23 in ovarian cancer cell lines detected by comparative genomic hybridization. *Gynecol Oncol,* Vol. 81, No. 2, pp. 172-177,

specific marker of adenocarcinomas of intestinal origin: an immunohistochemical survey of 476 primary and metastatic carcinomas. *Am J Surg Pathol,* Vol. 27, No. 3,

Frasco, M.A., Ayhan, A., Zikan, M., Cibula, D., Iyibozkurt, C.A., Yavuz, E., Hauser-Kronberger, C., Dubeau, L., Menon, U., Jacobs, I.J. (2009) *HOXA* methylation in normal endometrium from premenopausal women is associated with the presence of ovarian cancer: a proof of principle study. *Int J Cancer,* Vol*.* 125, No. 9, pp. 2214-

HOXB7, a homeodomain protein, is overexpressed in breast cancer and confers epithelial-mesenchymal transition. *Cancer Res,* Vol. 66, No. 19, pp. 9527-9534, ISSN

D.E., Katabuchi, H., Williams, B.O., Fearon, E.R., Cho, K.R. (2007) Mouse model of human ovarian endometrioid adenocarcinoma based on somatic defects in the Wnt/beta-catenin and PI3K/Pten signaling pathways. *Cancer Cell,* Vol. 11, No. 4,


Sharma, S.G., Gokden, M., McKenney, J.K., Phan, D.C., Cox, R.M., Kelly, T., Gokden, N.

Shimamoto, S., Nakamura, S., Bollekens, J., Ruddle, F.H., Takeshita, K. (1997) Inhibition of

Siegel, P.M. & Massagué, J. (2003) Cytostatic and apoptotic actions of TGF- in homeostasis and cancer. *Nat Rev Cancer.* Vol. 3, No. 11, pp. 807-820, ISSN 1474-175X. Soshnikova, N. & Duboule, D. (2009) Epigenetic regulation of vertebrate *Hox* genes. A dynamic equilibrium. *Epigenetics*, Vol. 4, No. 8, pp. 537-540, ISSN 1559-2294. Stevenson, H.S., Fu, S.W., Pinzone, J.J., Rheey, J., Simmens, S.J., Berg, P.E. (2007) BP1

breast cancer cells. *Breast Cancer Res*, Vol. 9, No. 5, pp. R60, ISSN 1465-5411. Sumiyama, K., Irvine, S.Q., Ruddle, F.H. (2003) The role of gene duplication in the evolution

Tacha, D., Zhou, D., Cheng, L. (2011) Expression of PAX8 in normal and neoplastic tissues:

Tan, Y., Cheung, M., Pei, J., Menges, C.W., Godwin, A.K., Testa, J.R. (2010) Upregulation of

Thorsteinsdottir, U., Sauvageau, G., Hough, M.R., Dragowska, W., Lansdorp, P.M.,

Tomida, S., Yanagisawa, K., Koshikawa, K., Yatabe, Y., Mitsudomi, T., Osada, H., Takahashi,

Tong, G.X., Chiriboga, L., Hamele-Bena, D., Borczuk, A.C. (2007) Expression of PAX2 in

Tong, G.X., Devaraj, K., Hamele-Bena, D., Yu, W.M., Turk, A., Chen, X., Wright, J.D.,

effusions. *Diagn Cytopathol,* Vol. 39, No. 8, pp. 567-574, ISSN 1097-0339.

signaling. *Cancer Res,* Vol. 70, No. 22, pp. 9197–9206, ISSN 0008-5472. Taylor, H.S., Vanden Heuvel, G.B., Igarashi, P. (1997) A conserved *Hox* axis in the mouse

Vol. 3, No. 1-4, pp. 151-159, ISSN 1345-711X.

Vol. 19, No. 4, pp. 293-299, ISSN 1533-4058.

No. 1, pp. 4600-4608, ISSN 0950-9232.

856-863, ISSN 0893-3952.

ISSN 1533-4058.

8424.

0006-3363.

0270-7306.

(2010) The utility of PAX-2 and renal cell carcinoma marker immunohistochemistry in distinguishing papillary renal cell carcinoma from nonrenal cell neoplasms with papillary features. *Appl Immunohistochem Mol Morphol,* Vol. 18, No. 6, pp. 494-498,

*DLX-7* homeobox gene causes decreased expression of *GATA-1* and c-*myc* genes and apoptosis. *Proc Natl Acad Sci USA,* Vol*.* 94, No. 7, pp. 3245-3249, ISSN 0027-

transcriptionally activates bcl-2 and inhibits TNFalpha-induced cell death in MCF7

and function of the vertebrate *Dlx/distal-less* bigene clusters. *J Struct Funct Genomics*,

A comprehensive immunohistochemical study. *Appl Immunohistochem Mol Morphol,*

DLX5 promotes ovarian cancer cell proliferation by enhancing IRS-2-AKT

and human female reproductive system: late establishment and persistent adult expression of the *Hoxa* cluster genes. *Biol Reprod,* Vol. 57, No. 6, pp. 1338-1345, ISSN

Lawrence, H.J., Largman, C., Humphries, R.K. (1997) Overexpression of *HOXA10*  in murine hematopoietic cells perturbs both myeloid and lymphoid differentiation and leads to acute myeloid leukemia. *Mol Cell Biol*, Vol. 17, No. 1, pp. 495–505, ISSN

T. (2007) Identification of a metastasis signature and the DLX4 homeobox protein as a regulator of metastasis by a combined transcriptome approach. *Oncogene,* Vol. 26,

papillary serous carcinoma of the ovary: immunohistochemical evidence of fallopian tube or secondary Müllerian system origin? *Mod Pathol,* Vol*.* 20, No. 8, pp.

Greenebaum, E. (2011) PAX8: A marker for carcinoma of Müllerian origin in serous


**11** 

*1,2USA 3China* 

**Transcriptomic Analysis of Human** 

Juan Cui1, Ying Xu1,2,3 and David Puett1,\* *1Department of Biochemistry and Molecular Biology* 

**Ovarian Cancer Cells: Changes Mediated** 

*2The Institute of Bioinformatics, University of Georgia, Athens, Georgia, 3College of Computer Science and Technology, Jilin University, Changchun,* 

**by Luteinizing Hormone Receptor Activation** 

According to the American Cancer Society (American Cancer Society, 2011), there were approximately 21,880 new cases of ovarian cancer in the US in 2010, representing ~3% of all cases of newly diagnosed cancer. It is estimated that 13,850 women died from the disease in the same year, thus ranking ovarian cancer as the fifth leading cause of cancer death in the US, only behind lung, breast, colorectal, and pancreatic. These five cancers account for over 60% of cancer deaths in the US. The high mortality rate associated with ovarian cancer is attributable largely to its diagnosis at later stages of progression (Choi et al., 2007) when treatment options are limited and often ineffectual. In the early stages of the disease, most

Epidemiological evidence has established that risk factors for ovarian cancer include a family history of ovarian or breast cancer, often arising from mutations in the BRCA1 or the BRCA2 gene, occurrence of breast cancer, again often due to the same mutations, high body weight, and the use of just estrogen without added progesterone for postmenopausal hormone therapy. Some protection appears to arise from oral contraceptives, pregnancy,

The etiology of ovarian cancer is overall poorly understood. There is, however, a prevailing theory that the pituitary gonadotropins, luteinizing hormone (LH) and follicle-stimulating hormone (FSH), may be contributory to the development or progression of the disease. The gonadotropins are heterodimeric glycoprotein hormones characterized by a common αsubunit and a hormone specific β-subunit. Both LH and FSH have been shown to have numerous effects on cultured ovarian carcinoma cells (Choi et al., 2007; Leung & Choi, 2007; Mandai et al., 2007; Lau et al., 2010). Moreover, the G protein-coupled receptors for LH (LHR) and FSH (FSHR) are expressed in the epithelial cells of the ovary (Choi et al., 2007). In the postmenopausal years, serum concentrations of LH and FSH are high due to the lack of negative feedback to the hypothalamus and pituitary concomitant with cessation of ovarian

patients are asymptomatic or exhibit rather non-specific symptoms or discomfort.

tubal ligation, and perhaps hysterectomy (American Cancer Society, 2011).

**1. Introduction** 

 \*

Corresponding Author


## **Transcriptomic Analysis of Human Ovarian Cancer Cells: Changes Mediated by Luteinizing Hormone Receptor Activation**

Juan Cui1, Ying Xu1,2,3 and David Puett1,\*

*1Department of Biochemistry and Molecular Biology 2The Institute of Bioinformatics, University of Georgia, Athens, Georgia, 3College of Computer Science and Technology, Jilin University, Changchun, 1,2USA 3China* 

#### **1. Introduction**

196 Ovarian Cancer – Basic Science Perspective

Xu, J. & Testa, J.R. (2009) *DLX5* (distal-less homeobox 5) promotes tumor cell proliferation

Yamada, S.D., Baldwin, R.L., Karlan, B.Y. (1999) Ovarian carcinoma cell cultures are

Yamashita, T., Tazawa, S., Yawei, Z., Katayama, H., Kato, Y., Nishiwaki, K., Yokohama, Y.,

Yekta, S., Tabin, C.J., Bartel, D.P. (2008) MicroRNAs in the *Hox* network: an apparent link to posterior prevalence. *Nat Rev Genet*, Vol. 9, No. 10, pp. 789-796, ISSN 1471-0056. Yoneda, J., Kuniyasu, H., Crispens, M.A., Price, J.E., Bucana, C.D., Fidler, I.J. (1998)

Yoshida, H., Broaddus, R., Cheng, W., Xie, S., Naora, H. (2006) Deregulation of the *HOXA10*

Yu Y., Khan J., Khanna C., Helman L., Meltzer P.S., Merlino G. (2004) Expression profiling

Zhai, Q.J., Ozcan, A., Hamilton, C., Shen, S.S., Coffey, D., Krishnan, B., Truong, L.D. (2010)

Zhai, Y., Iura, A., Yeasmin, S., Wiese, A.B., Wu, R., Feng, Y., Fearon, E.R., Cho, K.R. (2011)

Zhang, S., Balch, C., Chan, M.W., Lai, H.C., Matei, D., Schilder, J.M., Yan, P.S., Huang, T.H.,

Zhao, Y. & Potter, S. S. (2001) Functional specificity of the *Hoxa13* homeobox. *Development,* 

transition. *Cancer Res,* Vol. 66, No. 2, pp. 889-897, ISSN 0008-5472.

*Morphol,* Vol. 18, No. 4, pp. 323-332, ISSN 1533-4058.

Vol. 128, No. 16, pp. 3197–3207, ISSN 0950-1991.

2295-2309, ISSN 0002-9440.

0008-5472.

receptors. *Gynecol Oncol,* Vol. 75, No. 1, pp. 72-77, ISSN 0090-8258.

ISSN 0021-9258.

8874.

8956.

28, No. 4, pp. 931-938, ISSN 1019-6439.

by transcriptionally regulating *MYC. J Biol Chem,* Vol. 284, No. 31, pp. 20593–20601,

resistant to TGF-1-mediated growth inhibition despite expression of functional

Ishikawa, M. (2006) Suppression of invasive characteristics by antisense introduction of overexpressed *HOX* genes in ovarian cancer cells. *Int J Oncol,* Vol.

Expression of angiogenesis-related genes and progression of human ovarian carcinomas in nude mice*. J Natl Cancer Inst,* Vol. 90, No. 6, pp. 447-454, ISSN 0027-

homeobox gene in endometrial carcinoma: Role in epithelial-mesenchymal

identifies the cytoskeletal organizer ezrin and the developmental homeoprotein Six-1 as key metastatic regulators. *Nat Med,* Vol. 10, No. 2, pp. 175–181, ISSN 1078-

PAX-2 expression in non-neoplastic, primary neoplastic, and metastatic neoplastic tissue: A comprehensive immunohistochemical study. *Appl Immunohistochem Mol* 

*MSX2* is an oncogenic downstream target of activated WNT signaling in ovarian endometrioid adenocarcinoma. *Oncogene,* [Epub ahead of print], ISSN 0950-9232. Zhang L., Yang N., Garcia J.R., Mohamed A., Benencia F., Rubin S.C., Allman D., Coukos G.

(2002) Generation of a syngeneic mouse model to study the effects of vascular endothelial growth factor in ovarian carcinoma. *Am J Pathol*, Vol. 161, No. 6, pp.

Nephew, K.P. (2008) Identification and characterization of ovarian cancer-initiating cells from primary human tumors. *Cancer Res,* Vol. 68, No. 11, pp. 4311-4320, ISSN According to the American Cancer Society (American Cancer Society, 2011), there were approximately 21,880 new cases of ovarian cancer in the US in 2010, representing ~3% of all cases of newly diagnosed cancer. It is estimated that 13,850 women died from the disease in the same year, thus ranking ovarian cancer as the fifth leading cause of cancer death in the US, only behind lung, breast, colorectal, and pancreatic. These five cancers account for over 60% of cancer deaths in the US. The high mortality rate associated with ovarian cancer is attributable largely to its diagnosis at later stages of progression (Choi et al., 2007) when treatment options are limited and often ineffectual. In the early stages of the disease, most patients are asymptomatic or exhibit rather non-specific symptoms or discomfort.

Epidemiological evidence has established that risk factors for ovarian cancer include a family history of ovarian or breast cancer, often arising from mutations in the BRCA1 or the BRCA2 gene, occurrence of breast cancer, again often due to the same mutations, high body weight, and the use of just estrogen without added progesterone for postmenopausal hormone therapy. Some protection appears to arise from oral contraceptives, pregnancy, tubal ligation, and perhaps hysterectomy (American Cancer Society, 2011).

The etiology of ovarian cancer is overall poorly understood. There is, however, a prevailing theory that the pituitary gonadotropins, luteinizing hormone (LH) and follicle-stimulating hormone (FSH), may be contributory to the development or progression of the disease. The gonadotropins are heterodimeric glycoprotein hormones characterized by a common αsubunit and a hormone specific β-subunit. Both LH and FSH have been shown to have numerous effects on cultured ovarian carcinoma cells (Choi et al., 2007; Leung & Choi, 2007; Mandai et al., 2007; Lau et al., 2010). Moreover, the G protein-coupled receptors for LH (LHR) and FSH (FSHR) are expressed in the epithelial cells of the ovary (Choi et al., 2007). In the postmenopausal years, serum concentrations of LH and FSH are high due to the lack of negative feedback to the hypothalamus and pituitary concomitant with cessation of ovarian

<sup>\*</sup> Corresponding Author

Transcriptomic Analysis of Human Ovarian Cancer Cells:

2008).

second messengers.

**and activation** 

(Tatusov et al., 2000).

**LHR expression and activation** 

Changes Mediated by Luteinizing Hormone Receptor Activation 199

control. Some lines do, however, appear to express LHR and respond to LH (Lau et al., 2010). Following transfection with a full-length human LHR-pcDNA3 or an empty vector, two sub-lines were generated, one expressing about 12,000 receptors per cell (LHR+) and the other, i.e. the mock-transfected cells, that does not express LHR (LHR-) (Warrenfeltz et al.,

The LHR+ cells bound radio-labeled human chorionic gonadotropin with a Kd of 0.3 nM in saturation binding assays and an IC50 of 0.8 nM in competition binding assays. The expressed LHR was functional as determined by increased production of cyclic AMP and inositol phosphates in response to LH. The LHR- cells, in contrast, exhibited no specific binding of human chorionic gonadotropin and showed no response to LH in terms of

Expression of LHR in the SKOV-3 cells, in the absence of LH, had no effect on cell migration or proliferation. It did, however, reduce the invasive index of the cells by a small margin. LH was found to reduce migration and proliferation of LHR+ cells but not of LHR- cells,

**3. The transcriptomic profile of SKOV-3 Cells: Alterations associated with** 

**4. Altered gene expression and pathways associated with LHR expression** 

Among the ~100,000 transcripts profiled in this study, 2,210 and 4,297 were found to show up-regulation and down-regulation with at least 2-fold changes between the LHR+ SKOV3 cells and the control cells, respectively. Most of these differentially expressed transcripts are involved in cell division and in DNA replication and transcription, while genes primarily involved in carbohydrate transport/metabolism and lipid metabolism, cell communication,

When the cells were exposed to LH, 14,903 transcripts exhibited elevated expression, which extend the above functions to include posttranslational modification, RNA processing and modification, intracellular trafficking and secretion, signal transduction mechanisms, and coenzyme metabolism, while 10,389 transcripts found to be down-regulated were associated with cellular defense mechanisms based on our enrichment analyses against COG functions

RNA was extracted from SKOV-3 cells (Warrenfeltz et al., 2008), and the resulting cDNAs were analyzed by Almac Diagnostics (Durham, NC, USA) using the Affymetrix Human U133 Plus2 Arrays (Cui et al., 2011c). In a parallel study on microRNA expression, the ovarian cancer DSATM array (Almac Diagnostics) was used (Cui et al., 2011b). The advantages in using ovarian cancer-specific arrays include the gathering of extensive amounts of novel mRNA data that are not covered by other platforms and putting both microRNA and mRNA probes on the same chips, hence avoiding potential noise introduced during data collection on separate chips. Gene expression profiling was done on both the LHR- and the LHR+ cells, and gene-expression data were also collected on the latter at multiple time points, specifically at 1, 4, 8, and 20 h after incubation with human LH. qRT-PCR was carried out to validate a few significantly altered gene expression patterns

while the invasiveness was not altered (Warrenfeltz et al., 2008).

detected through microarray data analyses (Cui et al., 2011c).

and ECM interaction were only down-regulated.

function. The structural and functional aspects of gonadotropins and their receptors have been recently reviewed (Ascoli & Puett, 2009).

In contrast to the above suggestion that gonadotropins are involved in the initiation or progression of the disease, there are clinical reports showing very little evidence that the use of gonadotropins to treat infertility increases the risk of ovarian cancer (Mosgaard et al., 1997; Sanner et al., 2009). Considering the available data, including gonadotropin ablation with gonadotropin-releasing hormone (GnRH) analogs, the conclusion was reached that if gonadotropins are involved in ovarian cancer, their role is probably more important in tumorigenesis and early growth, not in later stages (Huhtaniemi, 2010).

In this controversial area surrounding gonadotropins and ovarian cancer, there are a number of mixed, often conflicting, reports on established ovarian cancer cell lines regarding the actions of gonadotropins on cell proliferation, invasion, and migration (Choi et al., 2007). Consequently, a thorough examination of LH action on gene expression may aid in determining if LH contributes to any of the essential components of cancer such as self-sufficiency in growth signals, evasion of apoptosis, bypassing growth inhibitors, sustained angiogenesis, activation of metastasis and invasion, indefinite replication, evasion of immune destruction, and altered metabolism (Hanahan & Weinberg, 2011).

There is also considerable interest in developing diagnostic biomarkers for ovarian and other cancers at early stages. We have explored a novel biomarker-search paradigm through effective combination of computational and experimental techniques to enhance biomarker discovery from the rather low-yield trial-and-error methods in current use. Our paradigm involves analysis of gene expression data for identification of differentially expressed genes in cancer *versus* controls in conjunction with prediction of proteins that can be secreted into blood and then excreted into urine. The results obtained with primary tumor tissues from patients with gastric cancer, the number two cancer killer in the world, have demonstrated the success of this approach (Cui et al., 2008, 2011a; Hong et al., 2011). In addition, considerable effort has also been invested into the use of gene expression profiling techniques by others to elucidate their utility in predicting metastasis and survivability in ovarian cancer (Lancaster et al., 2006; Sabatier et al., 2009).

Herein we focus our discussion on the *in vitro* use of an ovarian carcinoma cell line, SKOV-3 cells (Warrenfeltz et al., 2008; Puett et al., 2010; Cui et al., 2011b,c), rather than tumor tissues, in part because of the desire to work with an established cell line in which continuing studies can be performed in a controlled and reproducible manner. Such cell lines have provided an enormous wealth of information on the characteristics of ovarian cells and their responses to various inhibitors or growth factors (Choi et al., 2007; Leung & Choi, 2007; Mandai et al., 2007). The question being addressed is whether transcriptomic profiling can be used to determine if LH, acting on LHR+ cells, is stimulatory, inhibitory, or has no effect on ovarian carcinoma cells. Hence, the experimental design has been focused on LH-mediated effects on cancer cells, not if LH is involved in the transformation process of an epithelial-to-carcinoma cell. Due to a considerable interest in the development of reliable serum or urine biomarkers for early detection of ovarian cancer, the results of earlier work are also mentioned. This chapter provides a summary of those studies and links them to other related findings.

#### **2. Properties of the SKOV-3 ovarian carcinoma cell line**

The SKOV-3 human ovarian cancer cell line was selected since it does not express LHR (Parrott et al., 2001; Mandai et al., 2007; Warrenfeltz et al., 2008) and can serve as a negative

function. The structural and functional aspects of gonadotropins and their receptors have

In contrast to the above suggestion that gonadotropins are involved in the initiation or progression of the disease, there are clinical reports showing very little evidence that the use of gonadotropins to treat infertility increases the risk of ovarian cancer (Mosgaard et al., 1997; Sanner et al., 2009). Considering the available data, including gonadotropin ablation with gonadotropin-releasing hormone (GnRH) analogs, the conclusion was reached that if gonadotropins are involved in ovarian cancer, their role is probably more important in

In this controversial area surrounding gonadotropins and ovarian cancer, there are a number of mixed, often conflicting, reports on established ovarian cancer cell lines regarding the actions of gonadotropins on cell proliferation, invasion, and migration (Choi et al., 2007). Consequently, a thorough examination of LH action on gene expression may aid in determining if LH contributes to any of the essential components of cancer such as self-sufficiency in growth signals, evasion of apoptosis, bypassing growth inhibitors, sustained angiogenesis, activation of metastasis and invasion, indefinite replication, evasion

There is also considerable interest in developing diagnostic biomarkers for ovarian and other cancers at early stages. We have explored a novel biomarker-search paradigm through effective combination of computational and experimental techniques to enhance biomarker discovery from the rather low-yield trial-and-error methods in current use. Our paradigm involves analysis of gene expression data for identification of differentially expressed genes in cancer *versus* controls in conjunction with prediction of proteins that can be secreted into blood and then excreted into urine. The results obtained with primary tumor tissues from patients with gastric cancer, the number two cancer killer in the world, have demonstrated the success of this approach (Cui et al., 2008, 2011a; Hong et al., 2011). In addition, considerable effort has also been invested into the use of gene expression profiling techniques by others to elucidate their utility in predicting metastasis and survivability in

Herein we focus our discussion on the *in vitro* use of an ovarian carcinoma cell line, SKOV-3 cells (Warrenfeltz et al., 2008; Puett et al., 2010; Cui et al., 2011b,c), rather than tumor tissues, in part because of the desire to work with an established cell line in which continuing studies can be performed in a controlled and reproducible manner. Such cell lines have provided an enormous wealth of information on the characteristics of ovarian cells and their responses to various inhibitors or growth factors (Choi et al., 2007; Leung & Choi, 2007; Mandai et al., 2007). The question being addressed is whether transcriptomic profiling can be used to determine if LH, acting on LHR+ cells, is stimulatory, inhibitory, or has no effect on ovarian carcinoma cells. Hence, the experimental design has been focused on LH-mediated effects on cancer cells, not if LH is involved in the transformation process of an epithelial-to-carcinoma cell. Due to a considerable interest in the development of reliable serum or urine biomarkers for early detection of ovarian cancer, the results of earlier work are also mentioned. This chapter

The SKOV-3 human ovarian cancer cell line was selected since it does not express LHR (Parrott et al., 2001; Mandai et al., 2007; Warrenfeltz et al., 2008) and can serve as a negative

provides a summary of those studies and links them to other related findings.

**2. Properties of the SKOV-3 ovarian carcinoma cell line** 

tumorigenesis and early growth, not in later stages (Huhtaniemi, 2010).

of immune destruction, and altered metabolism (Hanahan & Weinberg, 2011).

ovarian cancer (Lancaster et al., 2006; Sabatier et al., 2009).

been recently reviewed (Ascoli & Puett, 2009).

control. Some lines do, however, appear to express LHR and respond to LH (Lau et al., 2010). Following transfection with a full-length human LHR-pcDNA3 or an empty vector, two sub-lines were generated, one expressing about 12,000 receptors per cell (LHR+) and the other, i.e. the mock-transfected cells, that does not express LHR (LHR-) (Warrenfeltz et al., 2008).

The LHR+ cells bound radio-labeled human chorionic gonadotropin with a Kd of 0.3 nM in saturation binding assays and an IC50 of 0.8 nM in competition binding assays. The expressed LHR was functional as determined by increased production of cyclic AMP and inositol phosphates in response to LH. The LHR- cells, in contrast, exhibited no specific binding of human chorionic gonadotropin and showed no response to LH in terms of second messengers.

Expression of LHR in the SKOV-3 cells, in the absence of LH, had no effect on cell migration or proliferation. It did, however, reduce the invasive index of the cells by a small margin. LH was found to reduce migration and proliferation of LHR+ cells but not of LHR- cells, while the invasiveness was not altered (Warrenfeltz et al., 2008).

#### **3. The transcriptomic profile of SKOV-3 Cells: Alterations associated with LHR expression and activation**

RNA was extracted from SKOV-3 cells (Warrenfeltz et al., 2008), and the resulting cDNAs were analyzed by Almac Diagnostics (Durham, NC, USA) using the Affymetrix Human U133 Plus2 Arrays (Cui et al., 2011c). In a parallel study on microRNA expression, the ovarian cancer DSATM array (Almac Diagnostics) was used (Cui et al., 2011b). The advantages in using ovarian cancer-specific arrays include the gathering of extensive amounts of novel mRNA data that are not covered by other platforms and putting both microRNA and mRNA probes on the same chips, hence avoiding potential noise introduced during data collection on separate chips. Gene expression profiling was done on both the LHR- and the LHR+ cells, and gene-expression data were also collected on the latter at multiple time points, specifically at 1, 4, 8, and 20 h after incubation with human LH. qRT-PCR was carried out to validate a few significantly altered gene expression patterns detected through microarray data analyses (Cui et al., 2011c).

#### **4. Altered gene expression and pathways associated with LHR expression and activation**

Among the ~100,000 transcripts profiled in this study, 2,210 and 4,297 were found to show up-regulation and down-regulation with at least 2-fold changes between the LHR+ SKOV3 cells and the control cells, respectively. Most of these differentially expressed transcripts are involved in cell division and in DNA replication and transcription, while genes primarily involved in carbohydrate transport/metabolism and lipid metabolism, cell communication, and ECM interaction were only down-regulated.

When the cells were exposed to LH, 14,903 transcripts exhibited elevated expression, which extend the above functions to include posttranslational modification, RNA processing and modification, intracellular trafficking and secretion, signal transduction mechanisms, and coenzyme metabolism, while 10,389 transcripts found to be down-regulated were associated with cellular defense mechanisms based on our enrichment analyses against COG functions (Tatusov et al., 2000).

Transcriptomic Analysis of Human Ovarian Cancer Cells:

**Clusters**  LHR-,LHR+,LH 1-20h (# of genes)

C1

C2

157

C3 152

C4

205

 144

2011c).

LH(↑)

LH(-)

Changes Mediated by Luteinizing Hormone Receptor Activation 201

Earlier *in vitro* studies demonstrated that LHR expression, in the absence of LH, slightly inhibited invasiveness, but had no effect on cell proliferation or migration. The addition of LH reduced the growth rate and migratory properties, but was without effect on invasiveness (Warrenfeltz et al., 2008). The current transcriptomic analysis shows that the observed expression changes in the above-mentioned pathways support the previous observations about the measured cellular properties (Warrenfeltz et al., 2008; Cui et al.,

**LHR activated genes** 

regulation of vesicle fusion 4.38E-03

TCR 9.66E-0.2; EGFR1 1.68E-01; IL4 1.79E-01

negative regulation of apoptosis 3.57E-04

carboxylic acid metabolic process 7.47E-04

multicellular organismal development 3.80E-06

cyclic-nucleotide phosphodiesterase activity 2.86E-04 regulation of transcription, DNA-dependent 3.44E-04

leukocyte differentiation 5.75E-04

EGFR1 2.09E-03; TGFBR 4.94E-03; ID 9.55E-03; Kit Receptor 4.23E-02

cell proliferation 2.31E-05

cell-cell signaling 4.49E-04

neurogenesis 7.84E-04 notch binding 1.71E-03 calcium ion binding 2.16E-03 cell morphogenesis 4.22E-03

Hematopoietic cell lineage 1.40E-02

nervous system development 7.37E-05

4.38E-03

pathways:

pathways:

pathways:

pathways:

NOTCH 8.29E-02

**Enriched GO functions/pathways (P-value)** 

platelet-derived growth factor alpha-receptor activity

hydroxyacid-oxoacid transhydrogenase activity 4.38E-03

extracellular matrix structural constituent 6.54E-04

In total, 2,373 genes were differentially expressed in LHR+ cells (in the absence of LH) *versus* control (LHR- cells) or LH-treated LHR+ cells. Of these, 689 genes are cancer relevant and 265 are highly expressed in the ovary (GeneGo, 2000). These genes participate in pathways involved in the cell cycle, focal adhesion, cytokine-cytokine receptor interaction, regulation of the actin cytoskeleton, purine metabolism, and the key signaling pathways involved in cell growth, e.g. MAPK, TGF-β, p53, and Jak-STAT. Functional analysis revealed that five major families, namely growth factors, translation regulators, transporters, GPCRs, and ligand-dependent nuclear receptors, were significantly enriched (Fig. 1).

Fig. 1. Distribution of the 2,373 differentially expressed genes in SKOV-3 cells across 13 major functional families. Each red bar represents the percentage of differentially expressed genes associated with LHR expression; each green bar represents the percentage of differentially expressed genes upon incubation with LH; each blue bar is the percentage of all human genes. The y-axis represents the percentage; the x-axis denotes functional families.

Twelve gene clusters, each containing a highly co-expressed pattern, were identified among the 2,373 genes through a clustering analysis, termed a self-organizing map (SOM) (Kohonen 1982) (Table 1). On each gene cluster, Gene Ontology (GO) and pathway enrichment analyses were conducted to identify functional groups and cellular processes that are possibly affected by LHR expression and activation. The details of the findings are discussed elsewhere (Cui et al., 2011c), but the major observations are as follows: LHR expression in control SKOV-3 cells seems to have a positive impact on the activation of gap junctions and the associated growth signaling pathways, and to have moderately suppressed apoptosis, DNA mismatch repair, and the Ras-independent pathways in NK cell-mediated cytotoxicity, which are overall advantageous to cell growth; LH, subsequently, regulated expression of genes involved in the cell cycle, p53 and VEGF signaling, gap junction pathways, immune responses, and the complement and coagulation cascades, as well as a few metabolic pathways (Cui et al., 2011c).

In total, 2,373 genes were differentially expressed in LHR+ cells (in the absence of LH) *versus* control (LHR- cells) or LH-treated LHR+ cells. Of these, 689 genes are cancer relevant and 265 are highly expressed in the ovary (GeneGo, 2000). These genes participate in pathways involved in the cell cycle, focal adhesion, cytokine-cytokine receptor interaction, regulation of the actin cytoskeleton, purine metabolism, and the key signaling pathways involved in cell growth, e.g. MAPK, TGF-β, p53, and Jak-STAT. Functional analysis revealed that five major families, namely growth factors, translation regulators, transporters, GPCRs, and ligand-dependent nuclear receptors, were

Fig. 1. Distribution of the 2,373 differentially expressed genes in SKOV-3 cells across 13 major functional families. Each red bar represents the percentage of differentially expressed

**Families**

LHR expression LH treatment all human gene

differentially expressed genes upon incubation with LH; each blue bar is the percentage of all human genes. The y-axis represents the percentage; the x-axis denotes functional

Twelve gene clusters, each containing a highly co-expressed pattern, were identified among the 2,373 genes through a clustering analysis, termed a self-organizing map (SOM) (Kohonen 1982) (Table 1). On each gene cluster, Gene Ontology (GO) and pathway enrichment analyses were conducted to identify functional groups and cellular processes that are possibly affected by LHR expression and activation. The details of the findings are discussed elsewhere (Cui et al., 2011c), but the major observations are as follows: LHR expression in control SKOV-3 cells seems to have a positive impact on the activation of gap junctions and the associated growth signaling pathways, and to have moderately suppressed apoptosis, DNA mismatch repair, and the Ras-independent pathways in NK cell-mediated cytotoxicity, which are overall advantageous to cell growth; LH, subsequently, regulated expression of genes involved in the cell cycle, p53 and VEGF signaling, gap junction pathways, immune responses, and the complement and coagulation

genes associated with LHR expression; each green bar represents the percentage of

cascades, as well as a few metabolic pathways (Cui et al., 2011c).

significantly enriched (Fig. 1).

families.

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0%

**Percentage**

Earlier *in vitro* studies demonstrated that LHR expression, in the absence of LH, slightly inhibited invasiveness, but had no effect on cell proliferation or migration. The addition of LH reduced the growth rate and migratory properties, but was without effect on invasiveness (Warrenfeltz et al., 2008). The current transcriptomic analysis shows that the observed expression changes in the above-mentioned pathways support the previous observations about the measured cellular properties (Warrenfeltz et al., 2008; Cui et al., 2011c).


Transcriptomic Analysis of Human Ovarian Cancer Cells:

05

pathways:

pathways:

pathways: BCR 5.92E-02

respectively and "-" denotes no alteration of gene expression.

mitosis 9.04E43

LH(-)

LH(↓)

C10

288

C11

71

C12

191

Changes Mediated by Luteinizing Hormone Receptor Activation 203

**LHR suppressed genes Clusters Enriched GO functions/pathways (P-value)** 

> polysaccharide binding 3.04e-08 glycosaminoglycan binding 2.22E-06 regulation of defense response 6.56E-06

enzyme inhibitor activity 3.59E-05

Wnt 5.23E-02; EGFR1 1.81E-01

epidermis development 1.54E-03 growth factor activity 2.44E-03

NOTCH 1.58E-02; TGFBR 4.74E-02

microtubule cytoskeleton 4.80E-19

Table 1. Enriched GO functions and pathways in each of the 12 gene clusters identified from the differentially expressed genes (modified and extended from Cui et al., 2011c). Plots represent the expression pattern across six groups (LHR- SKOV3 control, LHR expression but with no added LH, and incubation of the LHR+ cells with LH for 1, 4, 8, and 20 h.) "↑" and "↓" denote responses of up-regulation and down-regulation of gene expression,

Some of the affected major processes related to cell growth and death (Cui et al., 2011c) have been further investigated. It was found that LHR expression in the control cells led to the up-regulation of genes involved in gap junction, purine metabolism, calcium signaling, and actin cytoskeleton regulation, indicating a possible activation of these processes at a moderate level. In particular, the up-regulation of genes involved in gap junction formation and function may indicate reduced tumor progression and metastasis (Holder et al., 1993). LH-activated genes are involved in VEGF signaling, the Toll-like receptor signaling, and the B-cell receptor signaling pathway, as well as those involved in gap junction and Notch signaling, which may accelerate cell-cell communication and influence several key aspects of normal cell development by regulating differentiation, proliferation, and apoptosis (Sjolund et al., 2005). One particularly interesting observation was that the substantially increased expression of the tumor necrosis factor member 10 gene (*TNFSF10*), involved in natural killer cell-mediated cytotoxicity, may induce apoptosis (Pan et al., 1997). LH also led to

cell cycle phase 1.56E-43

proteinaceous extracellular matrix 6.00E-09

G-protein signaling, coupled to IP3 second messenger 1.15E-

regulation of aldosterone metabolic process 9.16E-06 regulation of hormone metabolic process 5.93E-04 auditory receptor cell differentiation 1.36E-03


**LHR activated genes** 

cadmium ion binding 1.70E-06

transcription 2.32E-04

pathways:

pathways:

pathways:

pathways

pathway:

response to external stimulus 1.08E-04

Androgen Receptor 8.42E-02; EGFR1 2.32E-01

spermidine biosynthetic process 5.52E-04 regulation of RNA metabolic process 7.51E-04

6.76E-02; TNF alpha/NF-kB 9.59E-02

positive regulation of heart rate 2.03E-05 calcium-mediated signaling 6.13E-05 leukocyte chemotaxis 9.57E-05 regulation of cell migration 2.02E-04

complement component C3b binding 3.63e-05

IL-7 2.84e-03; IL3 3.00E-01; Wnt 3.98e-01

IL-7 1.16E-01; ID 2.00-01; EGFR1 3.90E-01

neutrophil chemotaxis 5.52E-06

IL-7 9.19E-02; ID 1.60E-01 **LHR suppressed genes Clusters Enriched GO functions/pathways (P-value)** 

> extracellular region 6.34E-11 collagen fibril organization 2.68E-05

fibrillar collagen 4.56E-05 inflammatory response 9.80E-05 response to external stimulus 3.44E-04

protein digestion 3.98E-04

synaptogenesis 3.67E-06

alpha-amylase activity 4.37E-11 amylase activity 2.61E-10 calcium ion binding 5.02E-09 homophilic cell adhesion 3.57E-07

**Enriched GO functions/pathways (P-value)** 

MT-Heavy Metal-Pathway 1.96E-04; TCR 2.71E-02; IL4

positive regulation of cellular metabolic process 1.54E-05

**Clusters**  LHR-,LHR+,LH 1-20h (# of genes)

C5

157

C6

270

C7

167

C8

145

C9

261

LH(↓)

LH(↑)

Table 1. Enriched GO functions and pathways in each of the 12 gene clusters identified from the differentially expressed genes (modified and extended from Cui et al., 2011c). Plots represent the expression pattern across six groups (LHR- SKOV3 control, LHR expression but with no added LH, and incubation of the LHR+ cells with LH for 1, 4, 8, and 20 h.) "↑" and "↓" denote responses of up-regulation and down-regulation of gene expression, respectively and "-" denotes no alteration of gene expression.

Some of the affected major processes related to cell growth and death (Cui et al., 2011c) have been further investigated. It was found that LHR expression in the control cells led to the up-regulation of genes involved in gap junction, purine metabolism, calcium signaling, and actin cytoskeleton regulation, indicating a possible activation of these processes at a moderate level. In particular, the up-regulation of genes involved in gap junction formation and function may indicate reduced tumor progression and metastasis (Holder et al., 1993). LH-activated genes are involved in VEGF signaling, the Toll-like receptor signaling, and the B-cell receptor signaling pathway, as well as those involved in gap junction and Notch signaling, which may accelerate cell-cell communication and influence several key aspects of normal cell development by regulating differentiation, proliferation, and apoptosis (Sjolund et al., 2005). One particularly interesting observation was that the substantially increased expression of the tumor necrosis factor member 10 gene (*TNFSF10*), involved in natural killer cell-mediated cytotoxicity, may induce apoptosis (Pan et al., 1997). LH also led to

Transcriptomic Analysis of Human Ovarian Cancer Cells:

in relating gene expression and tumor cell properties.

et al., 2008; Croce, 2009; Wyman et al., 2009).

proliferation in response to LH.

Changes Mediated by Luteinizing Hormone Receptor Activation 205

(Cui et al., 2011c). This observation, along with findings that ETAR shows a moderate elevation in expression, while endothelin-converting enzyme-1 and the endothelin B receptor show no changes in their expression, may indicate a possible enhancement of cell

SCD-1 has been reported to increase the invasiveness and migration of breast cancer cells (Kang et al., 2005), and IGF2 is known to be a fetal promoter of cell proliferation in various cancers (Zaina et al., 2002). The up-regulation of these genes may suggest that LH exerts positive effects on tumor growth and metastasis. However, reduced cell growth is manifested in LH-treated cells (Warrenfeltz et al., 2008), and thus expression of the negative regulators, e.g. *c-JUN, TNFSF10,* and *MMPs* (Cui et al., 2011c), must assume a dominant role

**7. MicroRNA regulation involved in LH treatment of LHR+ SKOV3 cells** 

In addition to the aforementioned ~100,000 protein-encoding transcripts, 132 microRNAs were selectively profiled on the DSA array. It is known that small non-coding RNAs serve in various regulatory roles in degradation of mRNAs and inhibition of translation (Bartel, 2004) in all major cellular processes, such as differentiation, apoptosis, and proliferation (Ambros, 2004; Bartel, 2004). Many microRNAs are androgen related, and their deregulation is highly correlated with initiation, progression, and prognosis of cancer (Calin et al., 2005; Yanaihara et al., 2006; Bloomston et al., 2007; Ambs et al., 2008; Garzon et al., 2008; Schetter

Recently, 17 microRNAs were found to be differentially expressed in LHR+ SKOV-3 cells *versus* control cells (Cui et al., 2011b), specifically: six up-regulated (miR-101-1, -101-2, -199b, -559, -573, and -7-3) and 11 down-regulated (miR-103-2, -200c, 151, 29c, 301b, 548a2, 552, 561, 566, 613, and 642). After incubation with LH, 57 microRNAs were found to be differentially expressed, including the most highly-expressed microRNAs, such as miR-21, -200c, -593, - 103-1, and -124-3. Some of these microRNAs are located in the fragile sites (also called *hot spots*) in the human genome (Calin et al., 2004; Bignell et al., 2010), where genomic alternations in these regions were found to be associated with certain types of cancer. For example, the loss of 11p15 (covering miR-210) is found in ovarian cancer (Peng et al., 2000), and amplification of 17q23 (covering miR-301a and miR-21) is found in breast cancer

The present focus is to examine LH-mediated transcriptional changes of the microRNAs, but it should be mentioned that the SKOV-3 cancer cells have probably undergone some genomic alternations, resulting in altered gene expression levels in the control cancer cells that cannot be determined by our current expression data. Collectively, 65 microRNAs have been identified to be differentially expressed in LHR+ SKOV-3 cells *versus* control. The mRNAs with which the microRNA may interact have been studied in order to infer

Specifically, Spearman correlation analysis was performed between the expression of the 65 differentially-expressed microRNAs and the expression of 60,860 well-annotated mRNAs across all sample groups under consideration, resulting in 62,150 and 931,009 microRNAmRNA pairs with significantly correlated expressions, positive or negative, using cutoffs |rho|>0.8 and *P*-value <0.05, where rho represents the Spearman correlation coefficient. More positively correlated microRNA-mRNA pairs than negatively correlated pairs were found for the vast majority of the microRNAs, except for nine microRNAs: miR-181B2, miR-

(Barlund et al., 2000), as well as those reported (Calin et al., 2002; He et al., 2005).

potentially regulated processes involving the microRNAs (Cui et al., 2011b).

considerable activation of the genes for interleukin-6 (*IL-6*) and IL-8, pleiotropic cytokines, which are believed to be involved in ovarian carcinogenesis and angiogenesis (Asschert et al., 1999; Schwartz et al., 2001; Chou et al., 2005).

#### **5. Comparative profiling between SKOV-3 cells and normal Human Ovarian Surface Epithelium (HOSE) cells, and molecular markers**

The genes most highly expressed (top 5%) in SKOV-3 cells are largely different from those in the normal HOSE cells. It was found that 1,056 such genes were specific to the LHR- SKOV-3 cells, involved in regulation of translation, cell division, chromosome partitioning, posttranslational modifications, protein turnover, chaperones, and signal transduction mechanisms, suggesting increased cell growth and proliferation. Another 689 genes were specific to the LH-treated cells and found to be associated with coenzyme metabolism, posttranslational modifications, nucleotide transport, DNA replication and repair, intracellular trafficking, and secretion.

Two lists of genes, one with 185 up-regulated genes and the other with 248 downregulated genes, were identified to show differential expressions between normal and cancer cells regardless of LHR expression or activation (Cui et al., 2011c). Functional analyses revealed that the up-regulated genes are involved in cell-cell communication, ECM-receptor interaction, focal adhesion, cell division and chromosome partitioning, as well as carbohydrate transport and metabolism, which are essential to cancer growth. Of interest, 106 of these genes were found to be specific to ovarian cancer, based on our analyses of their differential expression patterns in ovarian cancer *versus* those of other human diseases with available genome-scale expression data in the public database (http://bioinfosrv1.bmb.uga.edu/DMarker/).

These results engender confidence in proposing some of them as potential molecular markers for ovarian epithelial carcinoma cells *versus* normal HOSE cells. Using a prediction method that we recently developed and validated (Cui et al., 2008), 103 of these genes were predicted to have their protein products secreted into circulation, thus providing another important pool of potential serum markers for ovarian cancer (Cui et al., 2011c).

#### **6. Known therapeutic targets involved in the cellular response to LH action**

As of now, 39 genes have been proposed as therapeutic targets for ovarian cancer based on our database search against the Therapeutic Target Database (TTD) (Mandai et al., 2007). Among them, endothelin-1 (ET-1), stromal cell-derived factor 1 (SCD-1), and insulin-like growth factor II (IGF2) show the most significant expression changes associated with LH.

ET-1, acting through its receptor, ETAR, has been extensively studied in its physiological roles in vasoconstriction and proliferation of smooth muscle cells, as well as its pathophysiological role in hypertension, heart failure, and coronary vasospasms. Recently it was also identified as important in ovarian cancer initiation and progression (Bagnato & Rosano, 2008; Bhalla et al., 2009; Rosano et al., 2010). These findings have led to the development of endothelin-converting enzyme-1 inhibitors and small interfering RNAs as new therapeutic agents for ovarian cancer (Rayhman et al., 2008). LH increases gene expression of ET-1 by some 10-fold, peaking at 1 h, an observation documented by qRT-PCR

considerable activation of the genes for interleukin-6 (*IL-6*) and IL-8, pleiotropic cytokines, which are believed to be involved in ovarian carcinogenesis and angiogenesis (Asschert et

**5. Comparative profiling between SKOV-3 cells and normal Human Ovarian** 

The genes most highly expressed (top 5%) in SKOV-3 cells are largely different from those in the normal HOSE cells. It was found that 1,056 such genes were specific to the LHR- SKOV-3 cells, involved in regulation of translation, cell division, chromosome partitioning, posttranslational modifications, protein turnover, chaperones, and signal transduction mechanisms, suggesting increased cell growth and proliferation. Another 689 genes were specific to the LH-treated cells and found to be associated with coenzyme metabolism, posttranslational modifications, nucleotide transport, DNA replication and repair, intracellular

Two lists of genes, one with 185 up-regulated genes and the other with 248 downregulated genes, were identified to show differential expressions between normal and cancer cells regardless of LHR expression or activation (Cui et al., 2011c). Functional analyses revealed that the up-regulated genes are involved in cell-cell communication, ECM-receptor interaction, focal adhesion, cell division and chromosome partitioning, as well as carbohydrate transport and metabolism, which are essential to cancer growth. Of interest, 106 of these genes were found to be specific to ovarian cancer, based on our analyses of their differential expression patterns in ovarian cancer *versus* those of other human diseases with available genome-scale expression data in the public database

These results engender confidence in proposing some of them as potential molecular markers for ovarian epithelial carcinoma cells *versus* normal HOSE cells. Using a prediction method that we recently developed and validated (Cui et al., 2008), 103 of these genes were predicted to have their protein products secreted into circulation, thus providing another

**6. Known therapeutic targets involved in the cellular response to LH action** As of now, 39 genes have been proposed as therapeutic targets for ovarian cancer based on our database search against the Therapeutic Target Database (TTD) (Mandai et al., 2007). Among them, endothelin-1 (ET-1), stromal cell-derived factor 1 (SCD-1), and insulin-like growth factor II (IGF2) show the most significant expression changes

ET-1, acting through its receptor, ETAR, has been extensively studied in its physiological roles in vasoconstriction and proliferation of smooth muscle cells, as well as its pathophysiological role in hypertension, heart failure, and coronary vasospasms. Recently it was also identified as important in ovarian cancer initiation and progression (Bagnato & Rosano, 2008; Bhalla et al., 2009; Rosano et al., 2010). These findings have led to the development of endothelin-converting enzyme-1 inhibitors and small interfering RNAs as new therapeutic agents for ovarian cancer (Rayhman et al., 2008). LH increases gene expression of ET-1 by some 10-fold, peaking at 1 h, an observation documented by qRT-PCR

important pool of potential serum markers for ovarian cancer (Cui et al., 2011c).

**Surface Epithelium (HOSE) cells, and molecular markers** 

al., 1999; Schwartz et al., 2001; Chou et al., 2005).

(http://bioinfosrv1.bmb.uga.edu/DMarker/).

trafficking, and secretion.

associated with LH.

(Cui et al., 2011c). This observation, along with findings that ETAR shows a moderate elevation in expression, while endothelin-converting enzyme-1 and the endothelin B receptor show no changes in their expression, may indicate a possible enhancement of cell proliferation in response to LH.

SCD-1 has been reported to increase the invasiveness and migration of breast cancer cells (Kang et al., 2005), and IGF2 is known to be a fetal promoter of cell proliferation in various cancers (Zaina et al., 2002). The up-regulation of these genes may suggest that LH exerts positive effects on tumor growth and metastasis. However, reduced cell growth is manifested in LH-treated cells (Warrenfeltz et al., 2008), and thus expression of the negative regulators, e.g. *c-JUN, TNFSF10,* and *MMPs* (Cui et al., 2011c), must assume a dominant role in relating gene expression and tumor cell properties.

### **7. MicroRNA regulation involved in LH treatment of LHR+ SKOV3 cells**

In addition to the aforementioned ~100,000 protein-encoding transcripts, 132 microRNAs were selectively profiled on the DSA array. It is known that small non-coding RNAs serve in various regulatory roles in degradation of mRNAs and inhibition of translation (Bartel, 2004) in all major cellular processes, such as differentiation, apoptosis, and proliferation (Ambros, 2004; Bartel, 2004). Many microRNAs are androgen related, and their deregulation is highly correlated with initiation, progression, and prognosis of cancer (Calin et al., 2005; Yanaihara et al., 2006; Bloomston et al., 2007; Ambs et al., 2008; Garzon et al., 2008; Schetter et al., 2008; Croce, 2009; Wyman et al., 2009).

Recently, 17 microRNAs were found to be differentially expressed in LHR+ SKOV-3 cells *versus* control cells (Cui et al., 2011b), specifically: six up-regulated (miR-101-1, -101-2, -199b, -559, -573, and -7-3) and 11 down-regulated (miR-103-2, -200c, 151, 29c, 301b, 548a2, 552, 561, 566, 613, and 642). After incubation with LH, 57 microRNAs were found to be differentially expressed, including the most highly-expressed microRNAs, such as miR-21, -200c, -593, - 103-1, and -124-3. Some of these microRNAs are located in the fragile sites (also called *hot spots*) in the human genome (Calin et al., 2004; Bignell et al., 2010), where genomic alternations in these regions were found to be associated with certain types of cancer. For example, the loss of 11p15 (covering miR-210) is found in ovarian cancer (Peng et al., 2000), and amplification of 17q23 (covering miR-301a and miR-21) is found in breast cancer (Barlund et al., 2000), as well as those reported (Calin et al., 2002; He et al., 2005).

The present focus is to examine LH-mediated transcriptional changes of the microRNAs, but it should be mentioned that the SKOV-3 cancer cells have probably undergone some genomic alternations, resulting in altered gene expression levels in the control cancer cells that cannot be determined by our current expression data. Collectively, 65 microRNAs have been identified to be differentially expressed in LHR+ SKOV-3 cells *versus* control. The mRNAs with which the microRNA may interact have been studied in order to infer potentially regulated processes involving the microRNAs (Cui et al., 2011b).

Specifically, Spearman correlation analysis was performed between the expression of the 65 differentially-expressed microRNAs and the expression of 60,860 well-annotated mRNAs across all sample groups under consideration, resulting in 62,150 and 931,009 microRNAmRNA pairs with significantly correlated expressions, positive or negative, using cutoffs |rho|>0.8 and *P*-value <0.05, where rho represents the Spearman correlation coefficient. More positively correlated microRNA-mRNA pairs than negatively correlated pairs were found for the vast majority of the microRNAs, except for nine microRNAs: miR-181B2, miR-

Transcriptomic Analysis of Human Ovarian Cancer Cells:

consistent with our previous report (Warrenfeltz et al., 2008).

AGO proteins may efficiently improve the prediction performance.

**8. Conclusions** 

Changes Mediated by Luteinizing Hormone Receptor Activation 207

impact of LH on cancer development. However, our other observations such as upregulation of *NF1*, *RB1*, and *SUFU* may indicate a negative effect on cancer growth,

As important gene regulators, microRNAs exhibit characteristics that allow speculation on some of the key roles they may have in regulating the downstream LHR signaling in ovarian cancer. The indicative clues of regulating apoptosis and cell proliferation may provide useful guidance for further research on the causes and treatment of ovarian cancer. It should be noted from the above analysis that detection of microRNA-mRNA pairs is a key step to understand the functions of microRNAs. All current computational programs used for predicting microRNA/mRNA interactions are mainly based on information embodied in the sequence and structure (Dai & Zhou, 2010). Yet the Argonaute (Ago) proteins have a central role in recognizing and binding to target mRNAs (Wang et al., 2010), and we thus anticipate that a method taking into consideration the sequence or structure information of

Numerous studies have appeared on the physiological roles and biochemical mechanisms of the pituitary gonadotropins, LH and FSH, in developmental and reproductive processes, as well as the pathophysiology associated with aberrant expression and mutations in the genes encoding the three gonadotropin subunits and the two gonadotropin receptors. Recent experimental findings and epidemiological evidence has arisen suggesting that the hormones and receptors are also involved in the development and/or progression of ovarian cancer. There is, however, much controversy associated with the role(s), if any, of the gonadotropins in ovarian cancer. Research undertaken in our laboratory has focused on experimental measurements of altered cellular properties and transcriptomic profiling of SKOV-3 cells in response to LH, in an effort to clarify aspects of this important area (Warrenfeltz et al., 2008; Puett et al., 2010; Cui et al., 2011a,b,c). This work has established that the expression of many genes and microRNAs is altered by LHR expression in SKOV-3 cells and by activation of the receptor with its natural ligand, LH. Some of the changes involve genes and pathways associated with cell growth, apoptosis, and many more cellular processes. Of interest was the observation that many genes had altered expression patterns upon expression of LHR in the absence of ligand. Such changes presumably arise from ligand-independent actions of LHR and from the unoccupied receptor adopting an active, or active-like, conformation periodically. Incubation of the cells with LH resulted in the expression of myriad genes (over 2,000), many of which are important in biological processes such as proliferation, apoptosis, and others. Further studies on microRNAs identified 65 that exhibited altered expression in LHR+ cells and LHR+ cells incubated with LH. Some of these microRNAs may aid in diminishing cell proliferation and possibly enhancing apoptosis. The conflicting results often obtained with transcriptomic profiling, including evidence for both positive and negative enhancement of important processes such as proliferation, apoptosis, etc., documents the need to always have experimental studies on the cellular phenotype with regard, in the present case, to LHR expression and LHmediated LHR activation. The net effect, as determined from cell studies is a slight inhibition of proliferation, invasiveness, and migration upon LHR expression and activation. Of potential importance was the observation that some 100 genes were identified that may lead to secreted proteins, thus offering an array of possible serum

582, miR-497, miR-559, miR-561, miR-101-1, miR-187, miR-572, and miR-301A. The prevalence in positively correlated microRNA-mRNA pairs suggests that such microRNAs are probably located in introns or 5' untranslated regions of the corresponding mRNAs and hence are regulated by the same transcription regulators of their host genes. The negatively correlated pairs may indicate possible biochemically important interactions.

MiRanda (Miranda et al., 2006) and TargetScan (Lewis et al., 2005) were applied for microRNA target prediction. A total of 584 genes were predicted to interact with the 65 differentially expressed microRNAs. Although not all predicted pairs possess high correlations, it does show a trend that the percentage of predicted pairs decreased as the coefficients increase along its distribution. With the above cutoffs, only 155 genes were retained as highly-confident microRNA targets for further analysis. For each of the 65 microRNAs under consideration, its function was predicted through identification of the functions and pathways enriched by their target genes; the functions of 16 differentiallyexpressed microRNAs were so predicted (Cui et al., 2011b).

For example, miR-199b-5p is predicted to participate in angiogenesis, nucleotide excision repair, the PDGF signaling pathway, the cadherin/Wnt/integrin signaling pathway, apoptosis, and the MAPK signaling pathway. MiR-101 is predicted to be involved in the Wnt/MAPK/cadherin signaling pathway, as well as in hypertrophic cardiomyopathy, melanogenesis, the metabotropic glutamate receptor group III pathway, and ubiquitinmediated proteolysis. In addition, it may also be involved in the regulation of the mRNAs involved in cyclic AMP regulation; cyclic AMP-specific phosphodiesterase 4D (*PDE4D*) was highly up-regulated by LH in LHR+ cells (Cui et al., 2011c). MiR-29c is predicted to regulate ECM-receptor interaction, focal adhesion, collagen α chains, and the integrin signaling pathway. It is noteworthy that several of the microRNAs are predicted to be potentially involved in regulation of various tyrosine and serine/threonine kinases (Cui et al., 2011b). The main regulation of miR-129 is that of angiogenesis, the Wnt signaling pathway, transcription regulation, and cell junction. The predicted involvement of miR-199b-5p, miR-101, and miR-129 in the Wnt pathway may suggest its possible role in ovarian tumorigenesis (Gatcliffe et al., 2008).

To affirm that some microRNAs participate in the LH regulation of cancer cells, the experimentally validated targets of the 65 differentially-expressed microRNAs were examined, and 70 such genes were extracted from miRecords (Xiao et al., 2009). Of these, 20 genes are differentially expressed in the LH-treated cells, and some are known to be involved in the regulation of cell migration and proliferation (IRS1, IRS2, IL6R, TPM1, GLI1, BMPR2, GRN), cell surface receptor-linked signal transduction (SOCS5, RAF1), antiapoptosis (FAS, MCL1, SGK3), and transcription regulation (DNMT3B, GLI1, EZH2) (Cui et al., 2011b). Only six of the 20 genes exhibited highly correlated expression patterns with some of the 65 microRNAs (Cui et al., 2011c), namely IRS1, IRS2, and RAF1 with miR-7-1, SGK3 and MTAP with miR-21, and GRN with miR-659. The expression changes of these genes indicate that LH may impose a positive regulation of cell proliferation, nucleotide metabolic processes, and cell surface receptor-linked signal transduction, and a negative regulation of apoptosis on ovarian cancer cells through these microRNAs.

Additionally, 54 oncogene and tumor-suppressor genes (Jiang et al., 2009; Bignell et al., 2010) were examined to determine if some of the microRNAs may participate in transcriptional regulation (Cui et al., 2011b). It was found that miR-21 was up-regulated while its target, TPM1, a tumor suppressor gene, was down-regulated in response to LH, suggesting a role of miR-21 in inhibiting apoptosis and subsequently having a positive impact of LH on cancer development. However, our other observations such as upregulation of *NF1*, *RB1*, and *SUFU* may indicate a negative effect on cancer growth, consistent with our previous report (Warrenfeltz et al., 2008).

As important gene regulators, microRNAs exhibit characteristics that allow speculation on some of the key roles they may have in regulating the downstream LHR signaling in ovarian cancer. The indicative clues of regulating apoptosis and cell proliferation may provide useful guidance for further research on the causes and treatment of ovarian cancer. It should be noted from the above analysis that detection of microRNA-mRNA pairs is a key step to understand the functions of microRNAs. All current computational programs used for predicting microRNA/mRNA interactions are mainly based on information embodied in the sequence and structure (Dai & Zhou, 2010). Yet the Argonaute (Ago) proteins have a central role in recognizing and binding to target mRNAs (Wang et al., 2010), and we thus anticipate that a method taking into consideration the sequence or structure information of AGO proteins may efficiently improve the prediction performance.

### **8. Conclusions**

206 Ovarian Cancer – Basic Science Perspective

582, miR-497, miR-559, miR-561, miR-101-1, miR-187, miR-572, and miR-301A. The prevalence in positively correlated microRNA-mRNA pairs suggests that such microRNAs are probably located in introns or 5' untranslated regions of the corresponding mRNAs and hence are regulated by the same transcription regulators of their host genes. The negatively

MiRanda (Miranda et al., 2006) and TargetScan (Lewis et al., 2005) were applied for microRNA target prediction. A total of 584 genes were predicted to interact with the 65 differentially expressed microRNAs. Although not all predicted pairs possess high correlations, it does show a trend that the percentage of predicted pairs decreased as the coefficients increase along its distribution. With the above cutoffs, only 155 genes were retained as highly-confident microRNA targets for further analysis. For each of the 65 microRNAs under consideration, its function was predicted through identification of the functions and pathways enriched by their target genes; the functions of 16 differentially-

For example, miR-199b-5p is predicted to participate in angiogenesis, nucleotide excision repair, the PDGF signaling pathway, the cadherin/Wnt/integrin signaling pathway, apoptosis, and the MAPK signaling pathway. MiR-101 is predicted to be involved in the Wnt/MAPK/cadherin signaling pathway, as well as in hypertrophic cardiomyopathy, melanogenesis, the metabotropic glutamate receptor group III pathway, and ubiquitinmediated proteolysis. In addition, it may also be involved in the regulation of the mRNAs involved in cyclic AMP regulation; cyclic AMP-specific phosphodiesterase 4D (*PDE4D*) was highly up-regulated by LH in LHR+ cells (Cui et al., 2011c). MiR-29c is predicted to regulate ECM-receptor interaction, focal adhesion, collagen α chains, and the integrin signaling pathway. It is noteworthy that several of the microRNAs are predicted to be potentially involved in regulation of various tyrosine and serine/threonine kinases (Cui et al., 2011b). The main regulation of miR-129 is that of angiogenesis, the Wnt signaling pathway, transcription regulation, and cell junction. The predicted involvement of miR-199b-5p, miR-101, and miR-129 in the Wnt pathway may suggest its possible role in ovarian tumorigenesis

To affirm that some microRNAs participate in the LH regulation of cancer cells, the experimentally validated targets of the 65 differentially-expressed microRNAs were examined, and 70 such genes were extracted from miRecords (Xiao et al., 2009). Of these, 20 genes are differentially expressed in the LH-treated cells, and some are known to be involved in the regulation of cell migration and proliferation (IRS1, IRS2, IL6R, TPM1, GLI1, BMPR2, GRN), cell surface receptor-linked signal transduction (SOCS5, RAF1), antiapoptosis (FAS, MCL1, SGK3), and transcription regulation (DNMT3B, GLI1, EZH2) (Cui et al., 2011b). Only six of the 20 genes exhibited highly correlated expression patterns with some of the 65 microRNAs (Cui et al., 2011c), namely IRS1, IRS2, and RAF1 with miR-7-1, SGK3 and MTAP with miR-21, and GRN with miR-659. The expression changes of these genes indicate that LH may impose a positive regulation of cell proliferation, nucleotide metabolic processes, and cell surface receptor-linked signal transduction, and a negative

Additionally, 54 oncogene and tumor-suppressor genes (Jiang et al., 2009; Bignell et al., 2010) were examined to determine if some of the microRNAs may participate in transcriptional regulation (Cui et al., 2011b). It was found that miR-21 was up-regulated while its target, TPM1, a tumor suppressor gene, was down-regulated in response to LH, suggesting a role of miR-21 in inhibiting apoptosis and subsequently having a positive

regulation of apoptosis on ovarian cancer cells through these microRNAs.

correlated pairs may indicate possible biochemically important interactions.

expressed microRNAs were so predicted (Cui et al., 2011b).

(Gatcliffe et al., 2008).

Numerous studies have appeared on the physiological roles and biochemical mechanisms of the pituitary gonadotropins, LH and FSH, in developmental and reproductive processes, as well as the pathophysiology associated with aberrant expression and mutations in the genes encoding the three gonadotropin subunits and the two gonadotropin receptors. Recent experimental findings and epidemiological evidence has arisen suggesting that the hormones and receptors are also involved in the development and/or progression of ovarian cancer. There is, however, much controversy associated with the role(s), if any, of the gonadotropins in ovarian cancer. Research undertaken in our laboratory has focused on experimental measurements of altered cellular properties and transcriptomic profiling of SKOV-3 cells in response to LH, in an effort to clarify aspects of this important area (Warrenfeltz et al., 2008; Puett et al., 2010; Cui et al., 2011a,b,c). This work has established that the expression of many genes and microRNAs is altered by LHR expression in SKOV-3 cells and by activation of the receptor with its natural ligand, LH. Some of the changes involve genes and pathways associated with cell growth, apoptosis, and many more cellular processes. Of interest was the observation that many genes had altered expression patterns upon expression of LHR in the absence of ligand. Such changes presumably arise from ligand-independent actions of LHR and from the unoccupied receptor adopting an active, or active-like, conformation periodically. Incubation of the cells with LH resulted in the expression of myriad genes (over 2,000), many of which are important in biological processes such as proliferation, apoptosis, and others. Further studies on microRNAs identified 65 that exhibited altered expression in LHR+ cells and LHR+ cells incubated with LH. Some of these microRNAs may aid in diminishing cell proliferation and possibly enhancing apoptosis. The conflicting results often obtained with transcriptomic profiling, including evidence for both positive and negative enhancement of important processes such as proliferation, apoptosis, etc., documents the need to always have experimental studies on the cellular phenotype with regard, in the present case, to LHR expression and LHmediated LHR activation. The net effect, as determined from cell studies is a slight inhibition of proliferation, invasiveness, and migration upon LHR expression and activation. Of potential importance was the observation that some 100 genes were identified that may lead to secreted proteins, thus offering an array of possible serum

Transcriptomic Analysis of Human Ovarian Cancer Cells:

Changes Mediated by Luteinizing Hormone Receptor Activation 209

Calin, G.A., Sevignani, C., Dumitru, C.D., Hyslop, T., Noch, E., Yendamuri, S., Shimizu, M.,

Choi, J.H., Wong, A.S., Huang, H.F. & Leung, P.C. (2007). Gonadotropins and Ovarian

Chou, C.H., Wei, L.H., Kuo, M.L., Huang, Y.J., Lai, K.P., Chen, C.A. & Hsieh, C.Y. (2005).

Tatusov, R.L., Galperin, M.Y., Natale, D.A. & Koonin, E.V. (2000). The COG Database: A

Croce, C.M. (2009). Causes and Consequences of microRNA Dysregulation in Cancer. *Nature* 

Cui, J., Liu, Q., Puett, D. & Xu, Y. (2008). Computational Prediction of Human Proteins that

Cui, J., Chen, Y., Chou, W.C., Sun, L., Chen, L., Suo, J., Ni, Z., Zhang, M., Kong, X., Hoffman,

Cui, J., Eldredge, J.B., Xu, Y. & Puett, D. (2011b). microRNA Expression and Regulation in

Cui, J., Miner, B., Eldredge, J.B., Warrenfeltz, S.W., Xu, Y. & Puett, D. (2011c). Gene

Dai, Y. & Zhou, X. (2010). Computational Methods for the Identificaiton of microRNA

Garzon, R., Volinia, S., Liu, C.G., Fernandez-Cymering, C., Palumbo, T., Pichiorri, F., Fabbri,

Receptor Activation. *BMC Cancer*, Vol. 11: 280, pp. 1-16.

Myeloid Leukemia. *Blood*, Vol. 111, No. 6, pp. 3183-3189.

Targets. *Open Access Bioinformatics*, No. 2, pp. 29-39.

Up-Regulation of Interleukin-6 in Human Ovarian Cancer Cell via a Gi/PI3K-Akt/NF-kappaB Pathway by Lysophosphatidic Acid, an Ovarian Cancer-

Tool for Genome-Scale Analysis of Protein Functions and Evolution. *Nucleic Acids* 

can be Secreted into the Bloodstream. *Bioinformatics*, Vol. 24, No. 20, pp. 2370-2375.

L.L., Kang, J., Su, Y., Olman, V., Johnson, D., Tench, D.W., Amster, I.J., Orlando, R., Puett, D., Li, F. & Xu, Y. (2011a). An Integrated Transcriptomic and Computational Analysis for Biomarker Identification in Gastric Cancer. *Nucleic Acids Research*, Vol.

Human Ovarian Carcinoma Cells by Luteinizing Hormone. *PLoS One* **,** Vol. 6, No.

Expression Profiling of Ovarian Cancer Cells: Alterations by Luteinizing Hormone

M., Coombes, K., Alder, H., Nakamura, T., Flomenberg, N., Marcucci, G., Calin, G.A., Kornblau, S.M., Kantarjian, H., Bloomfield, C.D., Andreeff, M. & Croce, C.M. (2008). MicroRNA Signatures Associated with Cytogenetics and Prognosis in Acute

*Academy of Sciences USA*, Vol. 99, No. 24, pp. 15524-15529.

*of Medicine*, Vol. 353, No. 17, pp. 1793-1801.

*Research,* Vol. 288, No. 1, pp. 33-36.

39, No. 4, pp. 1197-1207.

7, e21730.

*Review of Genetics*, Vol. 10, No. 10, pp. 704-714.

Cancer. *Endocrine Reviews*, Vol. 28, No. 4, pp. 440-461.

Activating Factor. *Carcinogenesis*, Vol. 26, No. 1, pp. 45-52.

(2002). Frequent Deletions and Down-Regulation of micro- RNA Genes miR15 and miR16 at 13q14 in Chronic Lymphocytic Leukemia. *Proceedings of the National* 

Rattan, S., Bullrich, F., Negrini, M. & Croce, C.M. (2004). Human microRNA Genes are Frequently Located at Fragile Sites and Genomic Regions Involved in Cancers. *Proceedings of the National Academy of Sciences USA*, Vol. 101, No. 9, pp. 2999-3004. Calin, G.A., Ferracin, M., Cimmino, A., Di Leva, G., Shimizu, M., Wojcik, S.E., Iorio, M.V.,

Visone, R., Sever, N.I., Fabbri, M., Iuliano, R., Palumbo, T., Pichiorri, F., Roldo, C., Garzon, R., Sevignani, C., Rassenti, L., Alder, H., Volinia, S., Liu, C.G., Kipps, T.J., Negrini, M. & Croce, C.M. (2005). A microRNA Signature Associated with Prognosis and Progression in Chronic Lymphocytic Leukemia. *New England Journal* 

biomarkers specific for ovarian cancers expressing LHR in the presence of circulating LH, often the case in post-menopausal women.

#### **9. Acknowledgments**

This research was supported by NIH (DK033973, DK069711, GM075331) and NSF (DEB-0830024, DBI-0542119). This paper is dedicated to the memory of Stacie Rosenblum who lost her valiant battle with ovarian cancer at a young age.

#### **10. References**


biomarkers specific for ovarian cancers expressing LHR in the presence of circulating LH,

This research was supported by NIH (DK033973, DK069711, GM075331) and NSF (DEB-0830024, DBI-0542119). This paper is dedicated to the memory of Stacie Rosenblum who lost

Ambros, V. (2004). The Functions of Animal microRNAs, *Nature,* Vol. 431, No. 7006), pp.

Ambs, S., Prueitt, R.L., Yi, M., Hudson, R.S., Howe, T.M., Petrocca, F., Wallace, T.A., Liu,

Ascoli, M. & Puett, D. (2009). The Gonadotropins and Their Receptors, In *Yen and Jaffee`s* 

Asschert, J.G., Vellenga, E., Ruiters, M.H. & de Vries, E.G. (1999). Regulation of Spontaneous

Bagnato, A. & Rosano, L. (2008). The Endothelin Axis in Cancer, Int*ernational Journal of* 

Barlund, M., Monni, O., Kononen, J., Cornelison, R., Torhorst, J., Sauter, G., Kallioniemi, O.-

Bhalla, A., Haque, S., Taylor, I., Winslet, M. & Loizidou, M. (2009). Endothelin Receptor

Bignell, G.R., Greenman, C.D., Davies, H., Butler, A.P., Edkins, S., Andrews, J.M., Buck, G.,

Bloomston, M., Frankel, W.L., Petrocca, F., Volinia, S., Alder, H., Hagan, J.P., Liu, C.G.,

Calin, G.A., Dumitru, C.D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S.,

Lines, *International Journal of Cancer*, Vol. 82, No. 2, pp. 244-249.

*Biochemistry and Cell Biology*, Vol. 40, No. 8, pp. 1443-1451.

C.G., Volinia, S., Calin, G.A., Yfantis, H.G., Stephens, R.M. & Croce, C.M. (2008). Genomic Profiling of microRNA and Messenger RNA Reveals Deregulated microRNA Expression in Prostate Cancer, *Cancer Research*, Vol. 68, No. 15, pp. 6162-

*Reproductive Endocrinology*. Strauss III, J.L. & Barbieri, R. (Editors). Philadelphia,

and TNF/IFN-Induced IL-6 Expression in Two Human Ovarian-carcinoma Cell

P. & Kallioniemi, A. (2000). Multiple Genes at 17q23 Undergo Amplification and Overexpression in Breast Cancer, *Cancer Research*, Vol. 60, No. 19, pp. 5340-5344. Bartel, D.P. (2004). MicroRNAs: Genomics, Biogenesis, Mechanism, and Function, *Cell*, Vol.

Antagonism and Cancer, *European Journal of Clinical Investigation*, Vol. 39, Suppl 2,

Chen, L., Beare, D., Latimer, C., Widaa, S., Hinton, J., Fahey, C., Fu, B., Swamy, S., Dalgliesh, G.L., Teh, B.T., Deloukas, P., Yang, F., Campbell, P.J., Futreal, P.A. & Stratton, M.R. (2010). Signatures of Mutation and Selection in the Cancer Genome,

Bhatt, D., Taccioli, C. & Croce, C.M. (2007). MicroRNA Expression Patterns to Differentiate Pancreatic Adenocarcinoma from Normal Pancreas and Chronic Pancreatitis. *Journal of the American Medical Association*, Vol. 297, No. 17, pp. 1901-

Keating, M., Rai, K., Rassenti, L., Kipps, T., Negrini, M., Bullrich, F. & Croce, C.M.

often the case in post-menopausal women.

her valiant battle with ovarian cancer at a young age.

American Cancer Society (2011) http://www.cancer.org.

*Nature,* Vol. 463, No. 7283, pp. 893-898.

Elsevier Publ. Co.**,** pp. 35-55.

116, No. 2, pp. 281-297.

pp. 74-77.

1908.

**9. Acknowledgments** 

350-355.

6170.

**10. References** 

(2002). Frequent Deletions and Down-Regulation of micro- RNA Genes miR15 and miR16 at 13q14 in Chronic Lymphocytic Leukemia. *Proceedings of the National Academy of Sciences USA*, Vol. 99, No. 24, pp. 15524-15529.


Transcriptomic Analysis of Human Ovarian Cancer Cells:

*Sterility*, Vol. 67, No. 6, pp. 1005-1012.

1203-1217.

R395-404.

pp. 2620-2629.

No. 5327, pp. 815-818.

Vol. 86, No. 5, pp. 690-694.

*Cancer Research*, Vol. 68, No. 22, pp. 9265-9273.

*Oncology and Hematology*, Vol. 72, No. 2, pp. 98-109.

*Association*, Vol. 299**,** No. 4, pp. 425-436.

Sweden. *Fertility and Sterility*, Vol. 91, No. 4, pp. 1152-1158.

Changes Mediated by Luteinizing Hormone Receptor Activation 211

Mosgaard, B.J., Lidegaard, O., Kjaer, S.K., Schou, G. & Andersen, A.N. (1997). Infertility,

Pan, G., Ni, J., Wei, Y.F., Yu, G., Gentz, R. & Dixit, V.M. (1997). An Antagonist Decoy

Parrott, J.A., Doraiswamy, V., Kim, G., Mosher, R. & Skinner, M.K. (2001). Expression and

Peng, H., Xu, F., Pershad, R., Hunt, K.K., Frazier, M.L., Berchuck, A., Gray, J.W., Hogg, D.,

Puett, D., Angelova, K., da Costa, M.R., Warrenfeltz, S.W. & Fanelli, F. (2010). The

Rosano, L., Spinella, F. & Bagnato, A. (2010). The Importance of Endothelin Axis in

Sabatier, R., Finetti, P., Cervera, N., Birnbaum, D. & Bertucci, F. (2009). Gene Expression

Sanner, K., Conner, P., Bergfeldt, K., Dickman, P., Sundfeldt, K., Bergh, T., Hagenfeldt, K.,

Schetter, A.J., Leung, S.Y., Sohn, J.J., Zanetti, K.A., Bowman, E.D., Yanaihara, N., Yuen, S.T.,

Schwartz, B.M., Hong, G., Morrison, B.H., Wu, W., Baudhuin, L.M., Xiao, Y.J., Mok, S.C. &

Sjolund, J., Manetopoulos, C., Stockhausen, M.T. & Axelson, H. (2005). The Notch Pathway

Cancer Cells. *Gynecological Oncology*, Vol. 81, No. 2, pp. 291-300.

Cells. *Molecular and Cellular Endocrinology*, Vol. 329, Nos. 1-2, pp. 47-55. Rayhman, O., Klipper, E., Muller, L., Davidson, B., Reich, R. & Meidan, R. (2008). Small

*Molecular and Cellular Endocrinology*, Vol. 172, Nos. 1-2, pp. 213-222.

Binding Sites and Their Corresponding Heteroduplexes. *Cell*, Vol. 126, No. 6, pp.

Fertility Drugs, and Invasive Ovarian Cancer: A Case-control Study. *Fertility and* 

Receptor and a Death Domain-Containing Receptor for TRAIL. *Science*, Vol. 277,

Actions of Both the Follicle Stimulating Hormone Receptor and the Luteinizing Hormone Receptor in Normal Ovarian Surface Epithelium and Ovarian Cancer.

Bast, Jr., R.C. & Yu, Y. (2000). ARHI is the Center of Allelic Deletion on Chromosome 1p31 in Ovarian and Breast Cancers. *International Journal of Cancer*,

Luteinizing Hormone Receptor: Insights into Structure-Function Relationships and Hormone-Receptor-Mediated Changes in Gene Expression in Ovarian Cancer

Interfering RNA Molecules Targeting Endothelin-Converting Enzyme-1 Inhibit Endothelin-1 Synthesis and the Invasive Phenotype of Ovarian Carcinoma Cells.

Initiation, Progression, and Therapy of Ovarian Cancer. *American Journal of Physiology: Regulatory, Integrative, and Comparative Physiology*, Vol. 299, No. 2, pp.

Profiling and Prediction of Clinical Outcome in Ovarian Cancer. *Critical Reviews in* 

Janson, P.O., Nilsson, S. & Persson, I. (2009). Ovarian Epithelial Neoplasia after Hormonal Infertility Treatment: Long-Term Follow-Up of a Historical Cohort in

Chan, T.L., Kwong, D.L., Au, G.K., Liu, C.G., Calin, G.A., Croce, C.M. & Harris, C.C. (2008). microRNA Expression Profiles Associated with Prognosis and Therapeutic Outcome in Colon Adenocarcinoma. *Journal of the American Medical* 

Xu, Y. (2001). Lysophospholipids Increase Interleukin-8 Expression in Ovarian

in Cancer: Differentiation Gone Awry. *European Journal of Cancer*, Vol. 41, No. 17,


Gatcliffe, T.A., Monk, B.J., Planutis, K. & Holcombe, R.F. (2008). Wnt Signaling in Ovarian

Hanahan, D. & Weinberg, R.A. (2011). The Hallmarks of Cancer: The Next Generation. *Cell*,

He, L., Thomson, J.M., Hemann, M.T., Hernando-Monge, E., Mu, D., Goodson, S., Powers,

Holder, J.W., Elmore, E. & Barrett, J.C. (1993). Gap Junction Function and Cancer. *Cancer* 

Hong, C.S., Cui, J., Ni, Z., Su, Y., Puett, D., Li, F. & Xu, Y. (2011). A Computational Method

Huhtaniemi, I. (2010). Are Gonadotrophins Tumorigenic--A Critical Review of Clinical and

Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., Li, M., Wang, G. & Liu, Y. (2009).

Human Disease. *Nucleic Acids Research*, Vol. 37 (Database issue): D98-104. Kang, H., Watkins, G., Parr, C., Douglas-Jones, A., Mansel, R.E. & Jiang, W.G. (2005).

Human Breast Cancer. *Breast Cancer Research*, Vol. 7, No. 4, pp. R402-410. Kohonen, T. (1982). Self-organized Formation of Topologically Correct Feature Maps.

Lancaster, J.M., Dressman, H.K., Clarke, J.P., Sayer, R.A., Martino, M.A., Cragun, J.M.,

Lau, M.T., Wong, A.S. & Leung, P.C. (2010). Gonadotropins Induce Tumor Cell Migration

Leung, P.C. & Choi, J.H. (2007). Endocrine Signaling in Ovarian Surface Epithelium and

Lewis, B.P., Burge, C.B. & Bartel, D.P. (2005). Conserved Seed Pairing, Often Flanked by

Mandai, M., Konishi, I., Kuroda, H. & Fujii, S. (2007). LH/hCG Action and Development of

Aspects. *Molecular and Cellular Endocrinology*, Vol. 269, Nos. 1-2, pp. 61-64. Miranda, K.C., Huynh, T., Tay, Y., Ang, Y.S., Tam, W.L., Thomson, A.M., Lim, B. &

Cancer. *Human Reproduction Update*, Vol. 13, No. 2, pp. 143-162.

Cancer Markers in Urine. *PLoS One*, Vol. 6, No. 2, e16875.

962.

pp. 828-833.

61.

2985-2993.

GeneGo (2000). Ingenuity Pathways Analysis(IPA).

*Research*, Vol. 53, No. 15, pp. 3475-3485.

*Biological Cybernetics*, Vol. 43, pp. 59-69.

*Cell*, Vol. 120, No. 1, pp. 15-20.

*Gynecological Cancer*, Vol. 16, No. 5, pp. 1733-1745.

Vol. 144, No. 5, pp. 646-674.

Tumorigenesis. *International Journal of Gynecological Cancer*, Vol. 18, No. 5, pp. 954-

S., Cordon-Cardo, C., Lowe, S.W., Hannon, G.J. & Hammond, S.M. (2005). A microRNA Polycistron as a Potential Human Oncogene. *Nature*, Vol. 435, No. 7043,

for Prediction of Excretory Proteins and Application to Identification of Gastric

Experimental Data. *Molecular and Cellular Endocrinology*, Vol. 329, Nos. 1-2, pp. 56-

miR2Disease: A Manually Curated Database for microRNA Deregulation in

Stromal Cell Derived Factor-1: Its Influence on Invasiveness and Migration of Breast Cancer Cells in vitro, and Its Association with Prognosis and Survival in

Henriott, A.H., Gray, J., Sutphen, R., Elahi, A., Whitaker, R.S., West,M., Marks, J.R., Nevins, J.R. & Berchuck, A. (2006). Identification of Genes Associated with Ovarian Cancer Metastasis Using Microarray Expression Analysis. *International Journal of* 

and Invasion by Increasing Cyclooxygenases Expression and Prostaglandin E(2) Production in Human Ovarian Cancer Cells. *Endocrinology*, Vol. 151, No. 7, pp.

Adenosines, Indicates that Thousands of Human Genes are microRNA Targets.

Ovarian Cancer--A Short Review on Biological and Clinical/Epidemiological

Rigoutsos, I. (2006). A Pattern-Based Method for the Identification of microRNA

Binding Sites and Their Corresponding Heteroduplexes. *Cell*, Vol. 126, No. 6, pp. 1203-1217.


**12** 

*USA* 

Ryan Serio and Blase Billack

*St. John's University, Department of Pharmaceutical Sciences* 

**Potential Tumor Biomarkers for Ovarian Cancer** 

In the United States, invasive ovarian cancer is the 5th most deadly malignancy in females, accounting for an estimated 13,850 deaths in 2010 (Ahmad, 2011; American Cancer Society, 2010). The risk of dying from ovarian cancer depends on staging and varies greatly. Ovarian cancer patients diagnosed at the localized stage exhibit a 5 year survival rate of 94%. This rate is 73% when diagnosed at the regional stage following local dissemination and drops to 28% when a patient is diagnosed at the distant stage with metastasis to organs outside the pelvis. Overall, the combined 5 year survival rate for all ovarian cancer patients is an

Upon histological evaluation, most ovarian cancers are found to be epithelial in nature and are collectively referred to as ovarian epithelial cancers (OEC). The most common OEC subtypes include, in decreasing order of frequency, serous adenocarcinomas, followed by endometrioid, and smaller subsets of mucinous, clear cell, transitional, and undifferentiated

The typical progression of invasive ovarian cancer is dissemination from the primary site into the peritoneal mesothelium. The close proximity of the ovary to the mesothelium explains the high incidence of peritoneal dissemination observed in nearly all cases of ovarian cancer. Tumors are thought to arise either from implanted cells from the fringe of the fallopian tube (Jarboe et al, 2008) or from dysplastic inclusion cysts which develop out of the mesothelial-like ovarian surface epithelium (OSE). As the tumor progresses, cells shed into the peritoneal fluid, escape apoptotic mechanisms, and begin to attach to their surrounding mesothelium via integrin-mediated interactions with extracellular matrix components (Ahmed et al, 2005; Cannistra et al., 1995; Yokoyama et al., 2007). Unlike most malignancies, ovarian cancers rarely metastasize through the hematogenous route until the advanced stages (Rose et al., 1989). Approximately 62% of cases of ovarian cancer are diagnosed at the distant stage (American Cancer Society, 2010) and the clinical prognosis for

The high mortality associated with ovarian cancer stems, in part, from late detection and underpins the exigent need to identify predictive and early stage diagnostic biomarkers. The task is not an easy one. Difficulty in the validation of current screening tests is mainly attributed to the lack of uniformity in clinical presentation of the disease, which varies with epithelial cell morphology, depending on whether the carcinoma is of a serous, clear cell, mucinous, or endometrioid type. To the present date, blood concentration measurements of CA125 (mucin-16), in conjunction with ultrasonography, have been used to screen for ovarian cancer. However, it has been found that detection of serum CA125 alone is

**1. Introduction** 

such patients is poor.

unmanageable 46% (American Cancer Society, 2010).

carcinomas (Tavassoli and Devilee, 2003).


### **Potential Tumor Biomarkers for Ovarian Cancer**

Ryan Serio and Blase Billack

*St. John's University, Department of Pharmaceutical Sciences USA* 

#### **1. Introduction**

212 Ovarian Cancer – Basic Science Perspective

Wang, Y., Li, Y., Ma, Z., Yang, W. & Ai, C. (2010). Mechanism of microRNA-Target

Warrenfeltz, S.W., Lott, S.A., Palmer, T.M., Gray, J.C. & Puett, D. (2008). Luteinizing

Wyman, S.K., Parkin, R.K., Mitchell, P.S., Fritz, B.R., O'Briant, K., Godwin, A.K., Urban, N.,

Xiao, F., Zuo, Z., Cai, G., Kang, S., Gao, X. & Li, T. (2009). miRecords: An Integrated

Yanaihara, N., Caplen, N., Bowman, E., Seike, M., Kumamoto, K., Yi, M., Stephens, R.M.,

Zaina, S., Pettersson, L., Ahren, B., Branen, L., Hassan, A.B., Lindholm, M., Mattsson, R.,

*Computational Biology*, Vol. 6, No. 7: e1000866.

RNA cDNA Libraries. *PLoS One*, Vol. 4, No. 4: e5311.

Prognosis. *Cancer Cell*, Vol. 9, No. 3, pp. 189-198.

(Database issue), pp. D105-110.

1775-1785.

pp. 4505-4511.

Interaction: Molecular Dynamics Simulations and Thermodynamics Analysis. *PLoS* 

Hormone-Induced Up-Regulation of ErbB-2 is Insufficient Stimulant of Growth and Invasion in Ovarian Cancer Cells. *Molecular Cancer Research*, Vol. 6, No. 11, pp.

Drescher, C.W., Knudsen, B.S. & Tewari, M. (2009). Repertoire of microRNAs in Epithelial Ovarian Cancer as Determined by Next Generation Sequencing of Small

Resource for microRNA-Target Interactions. *Nucleic Acids Research*, Vol. 37

Okamoto, A., Yokota, J., Tanaka, T., Calin, G.A., Liu, C.G., Croce, C.M. & Harris, C.C. (2006). Unique microRNA Molecular Profiles in Lung Cancer Diagnosis and

Thyberg, J. & Nilsson, J. (2002). Insulin-Like Growth Factor II Plays a Central Role in Atherosclerosis in a Mouse Model. *Journal of Biological Chemistry*, Vol. 277, No. 6, In the United States, invasive ovarian cancer is the 5th most deadly malignancy in females, accounting for an estimated 13,850 deaths in 2010 (Ahmad, 2011; American Cancer Society, 2010). The risk of dying from ovarian cancer depends on staging and varies greatly. Ovarian cancer patients diagnosed at the localized stage exhibit a 5 year survival rate of 94%. This rate is 73% when diagnosed at the regional stage following local dissemination and drops to 28% when a patient is diagnosed at the distant stage with metastasis to organs outside the pelvis. Overall, the combined 5 year survival rate for all ovarian cancer patients is an unmanageable 46% (American Cancer Society, 2010).

Upon histological evaluation, most ovarian cancers are found to be epithelial in nature and are collectively referred to as ovarian epithelial cancers (OEC). The most common OEC subtypes include, in decreasing order of frequency, serous adenocarcinomas, followed by endometrioid, and smaller subsets of mucinous, clear cell, transitional, and undifferentiated carcinomas (Tavassoli and Devilee, 2003).

The typical progression of invasive ovarian cancer is dissemination from the primary site into the peritoneal mesothelium. The close proximity of the ovary to the mesothelium explains the high incidence of peritoneal dissemination observed in nearly all cases of ovarian cancer. Tumors are thought to arise either from implanted cells from the fringe of the fallopian tube (Jarboe et al, 2008) or from dysplastic inclusion cysts which develop out of the mesothelial-like ovarian surface epithelium (OSE). As the tumor progresses, cells shed into the peritoneal fluid, escape apoptotic mechanisms, and begin to attach to their surrounding mesothelium via integrin-mediated interactions with extracellular matrix components (Ahmed et al, 2005; Cannistra et al., 1995; Yokoyama et al., 2007). Unlike most malignancies, ovarian cancers rarely metastasize through the hematogenous route until the advanced stages (Rose et al., 1989). Approximately 62% of cases of ovarian cancer are diagnosed at the distant stage (American Cancer Society, 2010) and the clinical prognosis for such patients is poor.

The high mortality associated with ovarian cancer stems, in part, from late detection and underpins the exigent need to identify predictive and early stage diagnostic biomarkers. The task is not an easy one. Difficulty in the validation of current screening tests is mainly attributed to the lack of uniformity in clinical presentation of the disease, which varies with epithelial cell morphology, depending on whether the carcinoma is of a serous, clear cell, mucinous, or endometrioid type. To the present date, blood concentration measurements of CA125 (mucin-16), in conjunction with ultrasonography, have been used to screen for ovarian cancer. However, it has been found that detection of serum CA125 alone is

Potential Tumor Biomarkers for Ovarian Cancer 215

molecular stability of the glycoconjugate. Unlike most other carbohydrate moieties, fucose contains only one free hydroxyl group available for hydrogen bonding. This feature restricts rotational freedom and enhances stability. The presence of bulky terminal fucose groups in a glycoconjugate restricts access to galactose residues. These moieties are normally recognized by asialoglycoproteins that target the molecules for degradation. This inevitably leads to lifespan extension of the modified glycoconjugate. Changes in structure/function mechanics are attributed to core fucosylation as well. Core fucosylation of the innermost residue in the chain greatly affects ligand specificity of glycoproteins by providing an extended

A glycoprotein that has thus far become a legitimate candidate as a potential biomarker is thrombospondin 1 (THBS-1). THBS-1 is released by platelets to negatively regulate angiogenesis by disrupting vascular endothelial growth factor (VEGF) signaling (Zaslavsky et al, 2010). A four-fold increase in expression levels of this protein has been observed in serum of ovarian cancer patients, and shows considerable core fucosylation not seen in healthy patients as determined by reactivity with the Aleuria aurantia lectin (AAL), which preferentially binds most strongly with core fucose-containing glycoconjugates (Abbott et al, 2010, Yamashita et al, 1985). Abbott et al (2010) also identified a second marker, periostin (POSTN), that exhibited altered glycosylation in the form of increased bisecting Nacetylglucosamine (GlcNAc) in serum from ovarian cancer compared to sera from healthy controls (Abbott et al, 2010). These modifications were examined only in endometrioid OEC cases (Abbott et al, 2010). THBS-1 and POSTN are known to be highly expressed in other subtypes (Kodama et al, 2001, Zhu et al, 2010); though POSTN is associated more with late stages (Zhu et al, 2010). Core fucosylation patterns have not yet been described for ovarian cancers displaying non-endometrioid histology. If tumor-specific alterations in fucosylation patterns are replicated in other types of ovarian cancers, particularly in the prevalent serous phenotype, this glycan-modified cancer marker may be a useful diagnostic indicator in the

Fucosylation affects OEC physiology in additional ways. For instance, the difucosylated oligosaccharide, LeY (CD174), is often highly expressed on mucins 1 and 16 (Yin et al, 1996). Mucins are large glycoproteins that are widely expressed in a number of carcinomas, including OEC. Their ability to contribute to disease pathogenesis by a variety of mechanisms is well-documented (Bafna et al, 2010; Thériault et al, 2011). Increased Ley antigens have been correlated with a number of tumorigenic effects, such as enhanced binding to mesothelial CD44, stimulation of α5β1 signaling, increased expression of MMP-2/9, and down-regulation of inhibitory TIMP-1/2 (Gao et al, 2011; Li et al, 2010b; Yan et al, 2010). A positive effect on growth factor activation has additionally been observed, as LeY participates in EGFR signaling and aids in the secretion of the angiogenic factors VEGF and

LeY displays specificity for epithelium-derived cancers, and is present in 70-90% of malignancies with this provenance (Chhieng et al, 2003). This Lewis antigen is most frequently active during embryonic development, and its expression in adults is limited solely to epithelial cells and granulocytes (Li et al, 2010b). Specificity is somewhat diminished by its appearance in certain non-malignant conditions, such as in ovarian cysts (Yang et al, 2009). The LeY antigen is not expressed in normal OSE, however, and is expressed in only 25% of benign tumors compared to 81% of malignant and 52% of borderline tumors (Wang et al, 2011). Based on these data, LeY is a promising potential biomarker for OEC and its

value in the recognition of specific cancer stages awaits further studies.

conformation with altered binding affinity (Stubbs et al., 1996).

future.

basic FGF (Basu et al, 1987; Li et al, 2010b).

inadequate for reliably detecting ovarian cancer for a number of reasons. These include a lack of specificity, questionable prognostic ability, frequent false positive readings, liver clearance of circulating antigen, and elevation associated more often with progression in late rather than early stages (Helzlsouer et al., 1993; Jacobs and Bast, 1989; Maggino et al, 1994). These confounds have hindered the diagnostic potential of CA125 for detecting OEC in stage I or II, when the disease shows much a much higher cure rate. Moreover, annual screening with CA125 and ultrasonography fails to reduce mortality and deleterious complications may arise from surgical interventions in women exhibiting false positive results (Buys et al, 2011). Clearly, efforts to identify better diagnostic markers are warranted.

#### **2. Carbohydrate biomarkers**

A major benefit of using carbohydrates as tumor biomarkers comes as a result of both their abundance and importance in shaping and maintaining the tumor microenvironment (Fukuda, 1996). Cellular communication, adhesion, and trafficking are all major functions of carbohydrate polymers, or glycans, which constitute a significant portion of glycoconjugates (Li et al., 2010a; Ohtsubo and Marth, 2006; Varki et al., 2009). Glycosylation, the formation of a linkage of a glycan with a protein, a lipid, or other organic molecule, greatly increases the complexity of those latter molecules and, resultantly, the potential for information storage. Altered protein glycosylation is believed to be an early event in tumorigenesis which contributes to invasion and metastasis of tumor cells (Chiang et al., 2010; Dall'Olio and Chiricolo, 2001; Hakomori, 1996; Hakomori, 2002; Varki et al, 1969; Kuzmanov et al., 2009; Meezan et al., 1969; Saldova et al, 2008; Yogeeswaran and Salk, 1981). Many prominent proteins in OEC pathophysiology, such as integrins and the receptor for epidermal growth factor (EGFR), are found to be heavily glycosylated (Stroop et al, 2000; Gu and Taniguchi, 2004).

Some important factors in cancer are the percentage of glycosylated proteins, the degree of branching versus linear polymers, the preponderance of specific chemical groups added to glycans, and the type, or "signature," of glycosylation observed. Proteins in cancer are generally highly glycosylated compared to non-malignant phenotypes, particularly on the cell surface and on proteins with a secretory function (Hakomori, 2002), suggesting an active role in establishing the extracellular tumor microenvironment. Varying conformations associated with differential glycan arrangements serve as molecular switches which can alter protein functions. Frequent protein modifications observed in cancer cells include alterations in core and terminal fucosylation, changes in sulfation and sialylation patterns, increased glycan branching, and alterations in Lewis isotopes (Varki et al, 2009). Lewis isotopes are glycoprotein antigens confined to red blood cells and epithelial secretions that belong to the Lewis blood group system.

Growing knowledge of the changing glycosylation patterns of small, soluble glycoproteins specific for certain cancers have made the possibility for developing novel diagnostic serum and even urine tests using protein markers with aberrantly expressed glycosylation patterns an endeavor worthy of pursuit. Some of the changes in glycosylation observed in OEC are described below, with potential tumor markers revealed.

#### **2.1 Fucosylation**

Addition of the five carbon sugar, fucose, to glycans reduces flexibility around glycosidic linkages of branching point antennae to enhance selectivity for ligands and increase

inadequate for reliably detecting ovarian cancer for a number of reasons. These include a lack of specificity, questionable prognostic ability, frequent false positive readings, liver clearance of circulating antigen, and elevation associated more often with progression in late rather than early stages (Helzlsouer et al., 1993; Jacobs and Bast, 1989; Maggino et al, 1994). These confounds have hindered the diagnostic potential of CA125 for detecting OEC in stage I or II, when the disease shows much a much higher cure rate. Moreover, annual screening with CA125 and ultrasonography fails to reduce mortality and deleterious complications may arise from surgical interventions in women exhibiting false positive results (Buys et al, 2011).

A major benefit of using carbohydrates as tumor biomarkers comes as a result of both their abundance and importance in shaping and maintaining the tumor microenvironment (Fukuda, 1996). Cellular communication, adhesion, and trafficking are all major functions of carbohydrate polymers, or glycans, which constitute a significant portion of glycoconjugates (Li et al., 2010a; Ohtsubo and Marth, 2006; Varki et al., 2009). Glycosylation, the formation of a linkage of a glycan with a protein, a lipid, or other organic molecule, greatly increases the complexity of those latter molecules and, resultantly, the potential for information storage. Altered protein glycosylation is believed to be an early event in tumorigenesis which contributes to invasion and metastasis of tumor cells (Chiang et al., 2010; Dall'Olio and Chiricolo, 2001; Hakomori, 1996; Hakomori, 2002; Varki et al, 1969; Kuzmanov et al., 2009; Meezan et al., 1969; Saldova et al, 2008; Yogeeswaran and Salk, 1981). Many prominent proteins in OEC pathophysiology, such as integrins and the receptor for epidermal growth factor (EGFR), are found to be heavily glycosylated (Stroop et al, 2000; Gu and Taniguchi,

Some important factors in cancer are the percentage of glycosylated proteins, the degree of branching versus linear polymers, the preponderance of specific chemical groups added to glycans, and the type, or "signature," of glycosylation observed. Proteins in cancer are generally highly glycosylated compared to non-malignant phenotypes, particularly on the cell surface and on proteins with a secretory function (Hakomori, 2002), suggesting an active role in establishing the extracellular tumor microenvironment. Varying conformations associated with differential glycan arrangements serve as molecular switches which can alter protein functions. Frequent protein modifications observed in cancer cells include alterations in core and terminal fucosylation, changes in sulfation and sialylation patterns, increased glycan branching, and alterations in Lewis isotopes (Varki et al, 2009). Lewis isotopes are glycoprotein antigens confined to red blood cells and epithelial secretions that

Growing knowledge of the changing glycosylation patterns of small, soluble glycoproteins specific for certain cancers have made the possibility for developing novel diagnostic serum and even urine tests using protein markers with aberrantly expressed glycosylation patterns an endeavor worthy of pursuit. Some of the changes in glycosylation observed in OEC are

Addition of the five carbon sugar, fucose, to glycans reduces flexibility around glycosidic linkages of branching point antennae to enhance selectivity for ligands and increase

Clearly, efforts to identify better diagnostic markers are warranted.

**2. Carbohydrate biomarkers** 

belong to the Lewis blood group system.

**2.1 Fucosylation** 

described below, with potential tumor markers revealed.

2004).

molecular stability of the glycoconjugate. Unlike most other carbohydrate moieties, fucose contains only one free hydroxyl group available for hydrogen bonding. This feature restricts rotational freedom and enhances stability. The presence of bulky terminal fucose groups in a glycoconjugate restricts access to galactose residues. These moieties are normally recognized by asialoglycoproteins that target the molecules for degradation. This inevitably leads to lifespan extension of the modified glycoconjugate. Changes in structure/function mechanics are attributed to core fucosylation as well. Core fucosylation of the innermost residue in the chain greatly affects ligand specificity of glycoproteins by providing an extended conformation with altered binding affinity (Stubbs et al., 1996).

A glycoprotein that has thus far become a legitimate candidate as a potential biomarker is thrombospondin 1 (THBS-1). THBS-1 is released by platelets to negatively regulate angiogenesis by disrupting vascular endothelial growth factor (VEGF) signaling (Zaslavsky et al, 2010). A four-fold increase in expression levels of this protein has been observed in serum of ovarian cancer patients, and shows considerable core fucosylation not seen in healthy patients as determined by reactivity with the Aleuria aurantia lectin (AAL), which preferentially binds most strongly with core fucose-containing glycoconjugates (Abbott et al, 2010, Yamashita et al, 1985). Abbott et al (2010) also identified a second marker, periostin (POSTN), that exhibited altered glycosylation in the form of increased bisecting Nacetylglucosamine (GlcNAc) in serum from ovarian cancer compared to sera from healthy controls (Abbott et al, 2010). These modifications were examined only in endometrioid OEC cases (Abbott et al, 2010). THBS-1 and POSTN are known to be highly expressed in other subtypes (Kodama et al, 2001, Zhu et al, 2010); though POSTN is associated more with late stages (Zhu et al, 2010). Core fucosylation patterns have not yet been described for ovarian cancers displaying non-endometrioid histology. If tumor-specific alterations in fucosylation patterns are replicated in other types of ovarian cancers, particularly in the prevalent serous phenotype, this glycan-modified cancer marker may be a useful diagnostic indicator in the future.

Fucosylation affects OEC physiology in additional ways. For instance, the difucosylated oligosaccharide, LeY (CD174), is often highly expressed on mucins 1 and 16 (Yin et al, 1996). Mucins are large glycoproteins that are widely expressed in a number of carcinomas, including OEC. Their ability to contribute to disease pathogenesis by a variety of mechanisms is well-documented (Bafna et al, 2010; Thériault et al, 2011). Increased Ley antigens have been correlated with a number of tumorigenic effects, such as enhanced binding to mesothelial CD44, stimulation of α5β1 signaling, increased expression of MMP-2/9, and down-regulation of inhibitory TIMP-1/2 (Gao et al, 2011; Li et al, 2010b; Yan et al, 2010). A positive effect on growth factor activation has additionally been observed, as LeY participates in EGFR signaling and aids in the secretion of the angiogenic factors VEGF and basic FGF (Basu et al, 1987; Li et al, 2010b).

LeY displays specificity for epithelium-derived cancers, and is present in 70-90% of malignancies with this provenance (Chhieng et al, 2003). This Lewis antigen is most frequently active during embryonic development, and its expression in adults is limited solely to epithelial cells and granulocytes (Li et al, 2010b). Specificity is somewhat diminished by its appearance in certain non-malignant conditions, such as in ovarian cysts (Yang et al, 2009). The LeY antigen is not expressed in normal OSE, however, and is expressed in only 25% of benign tumors compared to 81% of malignant and 52% of borderline tumors (Wang et al, 2011). Based on these data, LeY is a promising potential biomarker for OEC and its value in the recognition of specific cancer stages awaits further studies.

Potential Tumor Biomarkers for Ovarian Cancer 217

molecules and cells from degradation and the modulation of cellular interactions with the microenvironment (Varki and Schauer, 2009). The contribution of sialylation per se to increased tumorigenesis rests in its ability to allow permissive regulation of cellular interactions. The strong negative charge resulting from the low acid-base dissociation constant of sialic acids produces a charge repulsion effect. This, in addition to prominent hydration and conformational instability give heavily sialylated glycans a slippery effect to substantially reduce cell-cell interactions (Dall'Olio and Chiricolo, 2001; Schauer et al, 2011). As a result, adhesion and differentiation effects are not favored, and metastatic characteristics of migration and invasion become exacerbated when sialylation becomes enhanced by constituents of the microenvironment. The presence of sialic acids can also mask antigenic sites and, in this regard, thwart the activity of immune cells (Schauer, 2000). Finally, through their ability to avoid recognition by immune cells, highly sialylated cancer cells can efficiently evade tumor surveillance mechanisms, further promoting the malignant

The most abundant sialic acid in human cells is Neu5Ac, in which the C-5 is substituted with an N-acetyl group. Other mammals mainly produce Neu5Gc, in which the substituted N-acetyl group is hydroxylated to form an N-glycol substituent. This latter modification of neuraminic acid can be acquired by humans through diet and, following absorption, can generate an antigenic inflammatory response (Hedlund et al, 2008; Tangvoranuntakul et al., 2003). While tumor tissue and serum samples have been found to contain secreted Neu5Gc (Higashi et al, 1984; Siskos and Spyridaki, 1999), the potential association between Neu5Gc intake from the diet and ovarian cancer risk requires further study. Evidence supporting a major role for Neu5Gc in OEC was discovered in the ovarian cancer cell line JHOC-5, where secreted sialoglycoproteins, and especially mucin-like proteins, exhibited up to 40% representation of total sialic acid content by this exogenous carbohydrate moiety (Inoue et al, 2010). These data suggest a possible role for Neu5Gc as a predictive biomarker for

Under usual circumstances, sialic acid is attached to substrates such as glycosphingolipids and N- or O-linked glycans as single molecules or short oligomers. The attachment of longer chains of sialic acids, known as polysialic acids, to substrates is less common. Sialic acids are normally removed from substrates through the activity of another class of enzymes known as the sialidases. The neuronal cell adhesion molecule (NCAM, CD56) is, however, a noteworthy exception which is modified post-translationally via polysialylation, particularly during embryonic development and then downregulated shortly thereafter (Rutishauser and Landmesser, 1996). The reappearance of polysialylated NCAM has been observed in some forms of cancer, such as malignant neuroblastomas and rhabdomyosarcomas (Daniel et al., 2001; Fukuda, 1996; Gluer et al., 1998a; Gluer et al, 1998b; Jensen and Berthold, 2007) and correlates with increased metastatic potential and poor clinical outcome (Seidenfaden et al., 2003). Recently, NCAM expression has been studied in serous ovarian tumors and found to correlate with greater peritoneal dissemination and larger tumor volume following surgical debulking (Zueva et al, 2010). Sialylation status of

Glycan branching in cancers inhibits molecular clustering and cell adhesion while increasing the number of available sialylation sites and facilitating migratory potential (André et al, 2009), so it is not surprising that ovarian cancer is associated with increased α2,6 branched sialyl expression and decreased α2,3 linear sialylation (Wang et al, 2005). A major

phenotype (Schauer et al, 2011).

NCAM in ovarian cancer is yet to be deciphered.

ovarian cancer.

#### **2.2 Sulfation**

Enhanced adhesion of heparin-binding epidermal growth factor–like growth factor (HB-EGF) to heparan sulfate proteoglycans (HSPGs), and subsequent activation in OEC, is attributed to changes in sulfation patterns of these cell surface molecules (Shipp and Hsieh-Wilson, 2007). As HSPG sulfation increases, potential for interaction with HB-EGF also rises (Lai et al, 2003). Increased sulfation of glycosaminoglycan chains on HSPGs is a common feature in many epithelial cancers, including breast, kidney, hepatocellular, and ovarian cancers (Bret et al, 2011; Lai et al., 2003). A major mechanism by which this is achieved is through the down-regulation of HSulf-1. This enzyme is an arylsulfatase that degrades heparin preferentially at the C-6 position of glucosamine within specific subregions of the heparin molecule (Morimoto-Tomita et al, 2002). It is expressed ubiquitously in nonlymphoid tissue but significantly reduced in many epithelial cancers, including ovarian cancer.

Loss of HSulf-1 leads to increased sulfation of cell surface HSPGs and an expansion in the number of binding sites with HB-EGF. HB-EGF promotes transcoelomic metastasis in ovarian cancer through its involvement in epithelial-mesenchymal transition (EMT) (Yagi et al., 2008). Re-expression of the enzyme *in vitro* has been shown to diminish downstream signaling of HB-EGF, as demonstrated by reduction of ERK activity and abrogation of EGFR phosphorylation (Lai et al, 2003). Loss of HSulf-1 and the resultant hypersulfated state also modulates angiogenesis via binding of a VEGF isoform through its heparin-binding domain (Narita et al, 2006). Evidence suggests HSulf-1 down-regulation is an early event in tumorigenesis, as total inactivation of this enzyme was observed in fibrocystic breast cells with a normal phenotype while 80% of stage I/II ovarian cancer tumors exhibited barely detectable mRNA levels (Lai et al, 2003). Interestingly, the same study reported that >75% of ovarian tumors lacked HSulf-1 expression (Lai et al., 2003). Taken together, these observations suggest that loss of HSulf-1 could serve as an early biomarker for upcoming EMT events.

Down-regulation of HSulf-1 is a finding consistent with many epithelium-derived malignancies, and thus might not be highly effective as a stand-alone diagnostic marker for ovarian cancer. Its presence in serum may prove to be indicative of a cancerous state when concomitantly evaluated alongside additional markers. Further assessment of effects on specific HSPG substrates, such as the highly expressed proteoglycan, syndecan-1 (SDC1), may raise the value of HSulf-1 in OEC tumor diagnosis. The combination of HSulf-1 inhibition and SDC1 expression may be more specific for OEC than abrogation of HSulf-1 alone. SDC1 is a type of HSPG that is not expressed in normal OSE but quotidian to ovarian tumor tissue (Davies et al, 2004). In contrast, other HSPGs studied are found to be ubiquitously expressed in normal and diseased ovaries (Davies et al, 2004). Furthermore, the presence of SDC1 in serum as circulating CD138 antigen makes it relatively simple to detect in a noninvasive manner.

#### **2.3 Sialylation**

Over 50 types of neuraminic acid-derived monosaccharides have been described and are collectively referred to as sialic acids (Varki and Schauer, 2009). Sialic acids have been found to exhibit numerous cellular functions, examples of which include the stabilization of molecules and cell membranes, the enhancement of mucin viscosity, the protection of

Enhanced adhesion of heparin-binding epidermal growth factor–like growth factor (HB-EGF) to heparan sulfate proteoglycans (HSPGs), and subsequent activation in OEC, is attributed to changes in sulfation patterns of these cell surface molecules (Shipp and Hsieh-Wilson, 2007). As HSPG sulfation increases, potential for interaction with HB-EGF also rises (Lai et al, 2003). Increased sulfation of glycosaminoglycan chains on HSPGs is a common feature in many epithelial cancers, including breast, kidney, hepatocellular, and ovarian cancers (Bret et al, 2011; Lai et al., 2003). A major mechanism by which this is achieved is through the down-regulation of HSulf-1. This enzyme is an arylsulfatase that degrades heparin preferentially at the C-6 position of glucosamine within specific subregions of the heparin molecule (Morimoto-Tomita et al, 2002). It is expressed ubiquitously in nonlymphoid tissue but significantly reduced in many epithelial cancers, including ovarian

Loss of HSulf-1 leads to increased sulfation of cell surface HSPGs and an expansion in the number of binding sites with HB-EGF. HB-EGF promotes transcoelomic metastasis in ovarian cancer through its involvement in epithelial-mesenchymal transition (EMT) (Yagi et al., 2008). Re-expression of the enzyme *in vitro* has been shown to diminish downstream signaling of HB-EGF, as demonstrated by reduction of ERK activity and abrogation of EGFR phosphorylation (Lai et al, 2003). Loss of HSulf-1 and the resultant hypersulfated state also modulates angiogenesis via binding of a VEGF isoform through its heparin-binding domain (Narita et al, 2006). Evidence suggests HSulf-1 down-regulation is an early event in tumorigenesis, as total inactivation of this enzyme was observed in fibrocystic breast cells with a normal phenotype while 80% of stage I/II ovarian cancer tumors exhibited barely detectable mRNA levels (Lai et al, 2003). Interestingly, the same study reported that >75% of ovarian tumors lacked HSulf-1 expression (Lai et al., 2003). Taken together, these observations suggest that loss of HSulf-1 could serve as an early biomarker for upcoming

Down-regulation of HSulf-1 is a finding consistent with many epithelium-derived malignancies, and thus might not be highly effective as a stand-alone diagnostic marker for ovarian cancer. Its presence in serum may prove to be indicative of a cancerous state when concomitantly evaluated alongside additional markers. Further assessment of effects on specific HSPG substrates, such as the highly expressed proteoglycan, syndecan-1 (SDC1), may raise the value of HSulf-1 in OEC tumor diagnosis. The combination of HSulf-1 inhibition and SDC1 expression may be more specific for OEC than abrogation of HSulf-1 alone. SDC1 is a type of HSPG that is not expressed in normal OSE but quotidian to ovarian tumor tissue (Davies et al, 2004). In contrast, other HSPGs studied are found to be ubiquitously expressed in normal and diseased ovaries (Davies et al, 2004). Furthermore, the presence of SDC1 in serum as circulating CD138 antigen makes it relatively simple to detect

Over 50 types of neuraminic acid-derived monosaccharides have been described and are collectively referred to as sialic acids (Varki and Schauer, 2009). Sialic acids have been found to exhibit numerous cellular functions, examples of which include the stabilization of molecules and cell membranes, the enhancement of mucin viscosity, the protection of

**2.2 Sulfation** 

cancer.

EMT events.

in a noninvasive manner.

**2.3 Sialylation** 

molecules and cells from degradation and the modulation of cellular interactions with the microenvironment (Varki and Schauer, 2009). The contribution of sialylation per se to increased tumorigenesis rests in its ability to allow permissive regulation of cellular interactions. The strong negative charge resulting from the low acid-base dissociation constant of sialic acids produces a charge repulsion effect. This, in addition to prominent hydration and conformational instability give heavily sialylated glycans a slippery effect to substantially reduce cell-cell interactions (Dall'Olio and Chiricolo, 2001; Schauer et al, 2011). As a result, adhesion and differentiation effects are not favored, and metastatic characteristics of migration and invasion become exacerbated when sialylation becomes enhanced by constituents of the microenvironment. The presence of sialic acids can also mask antigenic sites and, in this regard, thwart the activity of immune cells (Schauer, 2000). Finally, through their ability to avoid recognition by immune cells, highly sialylated cancer cells can efficiently evade tumor surveillance mechanisms, further promoting the malignant phenotype (Schauer et al, 2011).

The most abundant sialic acid in human cells is Neu5Ac, in which the C-5 is substituted with an N-acetyl group. Other mammals mainly produce Neu5Gc, in which the substituted N-acetyl group is hydroxylated to form an N-glycol substituent. This latter modification of neuraminic acid can be acquired by humans through diet and, following absorption, can generate an antigenic inflammatory response (Hedlund et al, 2008; Tangvoranuntakul et al., 2003). While tumor tissue and serum samples have been found to contain secreted Neu5Gc (Higashi et al, 1984; Siskos and Spyridaki, 1999), the potential association between Neu5Gc intake from the diet and ovarian cancer risk requires further study. Evidence supporting a major role for Neu5Gc in OEC was discovered in the ovarian cancer cell line JHOC-5, where secreted sialoglycoproteins, and especially mucin-like proteins, exhibited up to 40% representation of total sialic acid content by this exogenous carbohydrate moiety (Inoue et al, 2010). These data suggest a possible role for Neu5Gc as a predictive biomarker for ovarian cancer.

Under usual circumstances, sialic acid is attached to substrates such as glycosphingolipids and N- or O-linked glycans as single molecules or short oligomers. The attachment of longer chains of sialic acids, known as polysialic acids, to substrates is less common. Sialic acids are normally removed from substrates through the activity of another class of enzymes known as the sialidases. The neuronal cell adhesion molecule (NCAM, CD56) is, however, a noteworthy exception which is modified post-translationally via polysialylation, particularly during embryonic development and then downregulated shortly thereafter (Rutishauser and Landmesser, 1996). The reappearance of polysialylated NCAM has been observed in some forms of cancer, such as malignant neuroblastomas and rhabdomyosarcomas (Daniel et al., 2001; Fukuda, 1996; Gluer et al., 1998a; Gluer et al, 1998b; Jensen and Berthold, 2007) and correlates with increased metastatic potential and poor clinical outcome (Seidenfaden et al., 2003). Recently, NCAM expression has been studied in serous ovarian tumors and found to correlate with greater peritoneal dissemination and larger tumor volume following surgical debulking (Zueva et al, 2010). Sialylation status of NCAM in ovarian cancer is yet to be deciphered.

Glycan branching in cancers inhibits molecular clustering and cell adhesion while increasing the number of available sialylation sites and facilitating migratory potential (André et al, 2009), so it is not surprising that ovarian cancer is associated with increased α2,6 branched sialyl expression and decreased α2,3 linear sialylation (Wang et al, 2005). A major

Potential Tumor Biomarkers for Ovarian Cancer 219

selectin ligands (Kanoh et al, 2006). 6-sulfo-sLex (CD15su) is readily detectable in serum and

thus may be conducive to analysis as a potential ovarian cancer biomarker.

Table 1. Carbohydrate Modifications as Potential Biomarkers for Ovarian Cancer.

O-glycans which are covalently α-linked via an N-acetylgalactosamine (GalNAc) moiety to the -OH of serine or threonine by an O-glycosidic bond are designated mucin O-glycans or, for short, mucins (Brockhausen et al., 2009). It is common to find the GalNAc further extended with galactose, N-acetylglucosamine, fucose, or sialic acid; alterations which give rise to different core structures (Brockhausen et al., 2009). These core mucin structures can be modified further with carbohydrate substituents, and can also be branched (Brockhausen et al., 2009). Due to the nature and complexity of their respective structures, mucins tend to be high molecular weight glycoproteins that are heterogeneous and heavily glycosylated. Mucins are synthesized by epithelial cells in various tissues, including the genitourinary epithelium. Mucin-1 (MUC-1) was the first mucin gene to have been identified and, to date, there are about 19 others known to exist (Brockhausen et al., 2009; Spurr-Michaud et al.,

Two general categories of mucins include those which are secreted, to protect epithelial surfaces against damage and infection by pathogens, and those which span the plasma membrane and are involved in cell adhesion (van Klinken et al., 1995; Fukuda, 2002) or cell signaling (Hartel-Schenk et al., 2001). Transmembrane mucins are positioned for mediation of communication between the extracellular milieu and the interior of cells. It has recently

**2.4 Altered glycosylation of epithelial mucins** 

2007).

sialyltransferase responsible for branched sialylation of glycans is ST6Gal-I, which is abundantly expressed in OEC (Christie et al, 2008; Wang et al, 2005). Elevated ST3Gal-I and reduced levels of ST3Gal-III, ST3Gal-IV, and ST3Gal-VI have also been observed (Wang et al, 2005). A major function of ST6Gal-I in ovarian cancer is the sialylation of β1 integrins (Wang et al, 2005). Sialylation enhances integrin-mediated signaling in cancers, leading to increased migration and invasiveness in the extracellular matrix (ECM) (Chiang et al, 2010). ST6Gal-I responds to a variety of genetic, inflammatory, and hormonal signals. Some triggers of ST6Gal-I overexpression that may be relevant to OEC are high IL-6 activity, Ras signaling (from either mutations or overexpression), and ER-α mutations (Hanasaki et al, 1994; Lau et al, 1999; Seales et al, 2003). The presence of serum cancer-specific markers synthesized by ST6Gal-I may adumbrate tumorigenic events if detected sufficiently early. Due to the documented high ST6Gal-I activity in OEC, it would be expected that β1 integrins are hypersialylated. Determining alterations in sialylation patterns compared to controls may be useful in the quest for biomarker identification as these abundantly expressed integrins so crucial to early epithelial-to-mesenchymal transition (EMT) events are detectable in serum (Liu et al, 2005).

The presence of only one glycosylation site makes a candidate marker more amenable to testing than glycoconjugates with more convoluted patterns due to ease of identification with less confounding variables. Cancer-specific aberrations in the glycosylation signature of a macromolecule with a lone glycan moiety would improve sensitivity and specificity of a candidate biomarker. A tumor marker that has garnered much attention in ovarian cancer diagnosis is kallikrein-like peptidase-6 (KLK6) (Bast et al, 2005; El Sherbini et al, 2011; White et al, 2009). This protein is a trypsin-like serine protease consisting of a single Nglycosylation site. When juxtaposed against the same protein derived from a non-malignant site, only KLK6 taken from ovarian cancer ascitic fluid displayed α2,6 branched sialylation (Kuzmanov et al, 2009). KLK6 is also a serum marker and these results may translate to this less invasive approach. Recognition of this specific isoform can only improve the status of KLK6 as a marker for ovarian cancer. KLK6 is up-regulated in most ovarian cancer tumors (Shan et al, 2007). Sensitivity of the marker for early detection does not exceed that of CA125 (El Sherbini et al, 2011), although the combination was shown to improve sensitivity by 10- 30% (Diamandis et al, 2003). Screening for the robust sialylated isoform of KLK6 in OEC tumors may possibly improve accuracy of detection.

There are several other abnormally sialylated molecules that may serve as molecular markers for ovarian cancer. Sialylated Lewis x (sLex) is a terminal glycan epitope that is positioned on the surface of cells attached to glycoconjugates and is preferentially recognized by endothelial selectins to promote cell migration. The sLex epitope of the Lewis blood group is composed of Neu5Ac in an α2,3 linkage to a galactose sugar. Following sialylation of Lex, fucosylation occurs via the action of α(1,3/1,4) fucosyltransferases (Aubert et al, 2000). SLex has been identified in ovarian cancer on the surface of the acute phase proteins α1-acid glycoprotein (AGP), α1-antichymotrypsin, and haptoglobin (Hp) β-chain (Saldova et al, 2007) (See Table 1). The acute phase response is initiated during times of trauma, inflammation, and infection, and provides an environment to keep cells alive during these crisis situations. The combination of sialylation and fucosylation on acute phase proteins has been shown to prolong half-life and reduce apoptosis (Saldova et al, 2008). Sialylation is sometimes combined with sulfation as well. Ovarian cancers of mucinous, papillary serous, and clear cell subtypes often present with increased levels of N-acetylglucosamine 6-Osulfotransferase 2 (GlcNAc6ST-2), which catalyzes formation of a 6-sulfo-sLex group on L-

sialyltransferase responsible for branched sialylation of glycans is ST6Gal-I, which is abundantly expressed in OEC (Christie et al, 2008; Wang et al, 2005). Elevated ST3Gal-I and reduced levels of ST3Gal-III, ST3Gal-IV, and ST3Gal-VI have also been observed (Wang et al, 2005). A major function of ST6Gal-I in ovarian cancer is the sialylation of β1 integrins (Wang et al, 2005). Sialylation enhances integrin-mediated signaling in cancers, leading to increased migration and invasiveness in the extracellular matrix (ECM) (Chiang et al, 2010). ST6Gal-I responds to a variety of genetic, inflammatory, and hormonal signals. Some triggers of ST6Gal-I overexpression that may be relevant to OEC are high IL-6 activity, Ras signaling (from either mutations or overexpression), and ER-α mutations (Hanasaki et al, 1994; Lau et al, 1999; Seales et al, 2003). The presence of serum cancer-specific markers synthesized by ST6Gal-I may adumbrate tumorigenic events if detected sufficiently early. Due to the documented high ST6Gal-I activity in OEC, it would be expected that β1 integrins are hypersialylated. Determining alterations in sialylation patterns compared to controls may be useful in the quest for biomarker identification as these abundantly expressed integrins so crucial to early epithelial-to-mesenchymal transition (EMT) events are

The presence of only one glycosylation site makes a candidate marker more amenable to testing than glycoconjugates with more convoluted patterns due to ease of identification with less confounding variables. Cancer-specific aberrations in the glycosylation signature of a macromolecule with a lone glycan moiety would improve sensitivity and specificity of a candidate biomarker. A tumor marker that has garnered much attention in ovarian cancer diagnosis is kallikrein-like peptidase-6 (KLK6) (Bast et al, 2005; El Sherbini et al, 2011; White et al, 2009). This protein is a trypsin-like serine protease consisting of a single Nglycosylation site. When juxtaposed against the same protein derived from a non-malignant site, only KLK6 taken from ovarian cancer ascitic fluid displayed α2,6 branched sialylation (Kuzmanov et al, 2009). KLK6 is also a serum marker and these results may translate to this less invasive approach. Recognition of this specific isoform can only improve the status of KLK6 as a marker for ovarian cancer. KLK6 is up-regulated in most ovarian cancer tumors (Shan et al, 2007). Sensitivity of the marker for early detection does not exceed that of CA125 (El Sherbini et al, 2011), although the combination was shown to improve sensitivity by 10- 30% (Diamandis et al, 2003). Screening for the robust sialylated isoform of KLK6 in OEC

There are several other abnormally sialylated molecules that may serve as molecular markers for ovarian cancer. Sialylated Lewis x (sLex) is a terminal glycan epitope that is positioned on the surface of cells attached to glycoconjugates and is preferentially recognized by endothelial selectins to promote cell migration. The sLex epitope of the Lewis blood group is composed of Neu5Ac in an α2,3 linkage to a galactose sugar. Following sialylation of Lex, fucosylation occurs via the action of α(1,3/1,4) fucosyltransferases (Aubert et al, 2000). SLex has been identified in ovarian cancer on the surface of the acute phase proteins α1-acid glycoprotein (AGP), α1-antichymotrypsin, and haptoglobin (Hp) β-chain (Saldova et al, 2007) (See Table 1). The acute phase response is initiated during times of trauma, inflammation, and infection, and provides an environment to keep cells alive during these crisis situations. The combination of sialylation and fucosylation on acute phase proteins has been shown to prolong half-life and reduce apoptosis (Saldova et al, 2008). Sialylation is sometimes combined with sulfation as well. Ovarian cancers of mucinous, papillary serous, and clear cell subtypes often present with increased levels of N-acetylglucosamine 6-Osulfotransferase 2 (GlcNAc6ST-2), which catalyzes formation of a 6-sulfo-sLex group on L-

detectable in serum (Liu et al, 2005).

tumors may possibly improve accuracy of detection.

selectin ligands (Kanoh et al, 2006). 6-sulfo-sLex (CD15su) is readily detectable in serum and thus may be conducive to analysis as a potential ovarian cancer biomarker.

Table 1. Carbohydrate Modifications as Potential Biomarkers for Ovarian Cancer.

#### **2.4 Altered glycosylation of epithelial mucins**

O-glycans which are covalently α-linked via an N-acetylgalactosamine (GalNAc) moiety to the -OH of serine or threonine by an O-glycosidic bond are designated mucin O-glycans or, for short, mucins (Brockhausen et al., 2009). It is common to find the GalNAc further extended with galactose, N-acetylglucosamine, fucose, or sialic acid; alterations which give rise to different core structures (Brockhausen et al., 2009). These core mucin structures can be modified further with carbohydrate substituents, and can also be branched (Brockhausen et al., 2009). Due to the nature and complexity of their respective structures, mucins tend to be high molecular weight glycoproteins that are heterogeneous and heavily glycosylated. Mucins are synthesized by epithelial cells in various tissues, including the genitourinary epithelium. Mucin-1 (MUC-1) was the first mucin gene to have been identified and, to date, there are about 19 others known to exist (Brockhausen et al., 2009; Spurr-Michaud et al., 2007).

Two general categories of mucins include those which are secreted, to protect epithelial surfaces against damage and infection by pathogens, and those which span the plasma membrane and are involved in cell adhesion (van Klinken et al., 1995; Fukuda, 2002) or cell signaling (Hartel-Schenk et al., 2001). Transmembrane mucins are positioned for mediation of communication between the extracellular milieu and the interior of cells. It has recently

Potential Tumor Biomarkers for Ovarian Cancer 221

Mucin-16 (muc-16, CA125) is another type of transmembrane mucin that mediates adhesive interactions in ovarian cancer. Adhesion of ovarian cancer cells to the peritoneum is in part facilitated by the binding of cleaved cell surface MUC16 to mesothelin (Rump et al, 2004). Muc-16 additionally dampens immune response by binding to the inhibitory siglec-9 receptor on a wide range of cells involved in both innate and adaptive immunity, allowing

The CA125 assay displays a superior sensitivity of 95% in tumors positive for the cell surface antigen in human serum (Cramer et al, 2011). Muc-16 becomes increasingly elevated with progression of OEC, and is expressed in approximately 80% of patients. Stage I tumors have much lower concentrations of muc-16, expressing the mucin at only a 58% rate (Jacobs and Bast, 1989). In addition, sonography is inefficient for detecting tumors that have not yet developed into a large mass. The CA125 assay has very low specificity, as this mucin is often expressed in additional cancers or inflammatory diseases. As a result, the current diagnosis

Despite inefficiency in early detection, the mainstay of ovarian cancer diagnosis continues to be muc-16 detection combined with ultrasonography. Recently, this has been challenged as a prospective study monitoring over 78,000 women showed that mortality was not decreased in women annually screened via this combination (Buys et al, 2011). In addition, surgical follow-up for false positive readings occurred unnecessarily in 1080 women, with

Because CA125 alone is insufficient for early tumor detection, the focus of much research is the improvement of assay sensitivity by combining this mucin marker with one or more additional indicators. This practice has not yet led to the validation of a composite assay for ovarian cancer diagnosis, mainly because of the tradeoff in specificity encountered when increasing sensitivity via use of multiple agents (Florkowski, 2008). Ideally, a powerful diagnostic assay should consist of the minimum number of test agents possible for this reason. Combining CA125 with a marker that is not only highly expressed in OEC but is replete with a unique glycosylation signature specific for the disease is one viable option for optimizing sensitivity and specificity. The combination of CA125 with other protein markers occasionally yields productive data as well. Improved sensitivity in detecting early ovarian cancer has been observed when CA125 measurement was combined with mesothelin detection (McIntosh et al, 2004). In addition, combination of CA125 with the T-cell expressed B7-H4 protein was demonstrated to improve early detection by 13% over CA125 alone (Simon et al, 2006). Although current guidelines recommend CA125 measurement as the sole biomarker criterion for ovarian cancer diagnosis, it is likely that more powerful assays will develop from its use in combination with one or more highly specific agents. Novel discoveries from the increased use of proteomic and glycomic approaches will assuredly

Epigenetics is a branch of science which has for its purpose the study of heritable changes in gene function that do not occur as the result of changes in DNA sequence (Wu and Morris, 2001). In addition, chromatin architecture is affected by epigenetic mechanisms (Zaina et al., 2010). An "epigenetic pathway" involving three components has recently been proposed. In this pathway, a signal is received from the external environment, after which an epigenetic initiator determines the precise chromatin location to be affected, called the mark, and an

**2.6 Mucin-16 as a biomarker: Strengths and weaknesses**

strategy is highly inadequate for early tumor detection.

growing tumors to evade immune system surveillance (Belisle et al, 2010).

15% experiencing one or more serious complications (Buys et al, 2011).

allow for the search for quality biomarkers to continue unabated.

**3. Epigenetic modifications as tumor markers** 

been proposed that the mucin covering of epithelia may be compromised when exposed to certain triggers, such as during processes involving elevated stress or remodeling (Kufe, 2009; Zhao et al, 2009), providing greater invasive potential. In this manner, a chronic inflammatory condition can theoretically turn the effects of transmembrane mucins against the cells they normally protect. Evidence supporting this assertion can be observed in a number of adenocarcinomas, where specific transmembrane mucins are often overexpressed (Jonckheere and Van Seuningen, 2010). The usual protective effects of mucins in epithelial cells with normal physiologic adhesion patterns become reversed in cancers by a perturbed glycosylation signature.

#### **2.5 The role of hypoglycosylated mucins in cancer**

The serum test for MUC1, also known as carcinoma antigen 15-3 (CA 15-3), has been validated for breast cancer diagnosis. High levels of the splice variant muc-1C have been associated with enhanced growth receptor signaling and activation of NFκβ in breast carcinoma (Ahmad et al, 2009). MUC1 is a potential indicator for OEC as well, as its expression soared from 5% to 90% in a comparison between paraffin-embedded sections of tissue from normal ovarian epithelia and cancerous lesions (Wang et al, 2007). In contrast, muc-16 (CA125), the only antigen FDA approved for diagnosis of ovarian cancer, is expressed in 80% of OEC tissue (Bast et al, 1981). MUC1 is detectable in ascitic fluid and serum in addition to tissue (Tuzun et al, 2009). In a study evaluating 49 biomarkers for ovarian cancer, MUC1 was ranked among the top five best candidates in terms of specificity and sensitivity (Cramer et al, 2011). It has been assessed as an early biomarker for stage I ovarian cancer and has demonstrated improved accuracy in tumor diagnosis as part of a four-marker composite test (Zhang et al, 2007).

A prime feature of mucins is the presence of 10-81 amino acid-comprised tandem repeats of proline-threonine-serine (PTS) in which O-glycosylation occurs at a high rate (Fontenot et al, 1993). In ovarian cancer, and possibly other cancers derived from a glandular origin, there is aberrant hypoglycosylation of mucins evinced by high levels of splice variants lacking the tandem repeat sites. This loss of structural integrity of these towering glycoproteins leading to exposure of the protein core is likely a reason for compromised protection. Smaller hypoglycosylated variants appear to provide better access to epithelium for a number of diverse molecules that would otherwise be thwarted from engaging in cell surface interactions (Zhao et al, 2009). MUC1 additionally promotes EGFR activation by inhibiting its degradation (Pochampalli et al, 2007). High expression of MUC1/Y, MUC1/Z, and, to a lesser extent, MUC1/X, have been demonstrated in ovarian cancer (Obermair et al, 2002). These three variants all lack the signature tandem repeat domain. Elevation of the former two variants has also been shown in prostate cancer, another glandular carcinoma (Schut et al, 2003). Aberrantly glycosylated muc-1 variants identified by glycoforms exhibiting short Tn/sTn oligosaccharides in place of the complex O-glycans that form on PTS repeat sites showed a strong correlation with all forms of ovarian cancer, being exhibited in 84% and 85% of primary tumors and metastatic lesions, respectively (Van Elssen et al, 2010).

The Thomsen-Friedenreich (TF) antigen is an additional short oligosaccharide presenting on a large number of hypoglycosylated epithelial cells. Sialylated TF is commonly found in hematopoietic and somatic cells, but the oligosaccharide is rarely observed in normal cells lacking sialyl groups (Schauer et al., 2011). In its desialylated form, this antigen is thought to be involved in triggering metastasis by stimulating interaction with galactoside-binding galectin-3 and exposing endothelial binding sites to cancer cells (Zhao et al, 2009).

been proposed that the mucin covering of epithelia may be compromised when exposed to certain triggers, such as during processes involving elevated stress or remodeling (Kufe, 2009; Zhao et al, 2009), providing greater invasive potential. In this manner, a chronic inflammatory condition can theoretically turn the effects of transmembrane mucins against the cells they normally protect. Evidence supporting this assertion can be observed in a number of adenocarcinomas, where specific transmembrane mucins are often overexpressed (Jonckheere and Van Seuningen, 2010). The usual protective effects of mucins in epithelial cells with normal physiologic adhesion patterns become reversed in cancers by a perturbed

The serum test for MUC1, also known as carcinoma antigen 15-3 (CA 15-3), has been validated for breast cancer diagnosis. High levels of the splice variant muc-1C have been associated with enhanced growth receptor signaling and activation of NFκβ in breast carcinoma (Ahmad et al, 2009). MUC1 is a potential indicator for OEC as well, as its expression soared from 5% to 90% in a comparison between paraffin-embedded sections of tissue from normal ovarian epithelia and cancerous lesions (Wang et al, 2007). In contrast, muc-16 (CA125), the only antigen FDA approved for diagnosis of ovarian cancer, is expressed in 80% of OEC tissue (Bast et al, 1981). MUC1 is detectable in ascitic fluid and serum in addition to tissue (Tuzun et al, 2009). In a study evaluating 49 biomarkers for ovarian cancer, MUC1 was ranked among the top five best candidates in terms of specificity and sensitivity (Cramer et al, 2011). It has been assessed as an early biomarker for stage I ovarian cancer and has demonstrated improved accuracy in tumor diagnosis as part of a

A prime feature of mucins is the presence of 10-81 amino acid-comprised tandem repeats of proline-threonine-serine (PTS) in which O-glycosylation occurs at a high rate (Fontenot et al, 1993). In ovarian cancer, and possibly other cancers derived from a glandular origin, there is aberrant hypoglycosylation of mucins evinced by high levels of splice variants lacking the tandem repeat sites. This loss of structural integrity of these towering glycoproteins leading to exposure of the protein core is likely a reason for compromised protection. Smaller hypoglycosylated variants appear to provide better access to epithelium for a number of diverse molecules that would otherwise be thwarted from engaging in cell surface interactions (Zhao et al, 2009). MUC1 additionally promotes EGFR activation by inhibiting its degradation (Pochampalli et al, 2007). High expression of MUC1/Y, MUC1/Z, and, to a lesser extent, MUC1/X, have been demonstrated in ovarian cancer (Obermair et al, 2002). These three variants all lack the signature tandem repeat domain. Elevation of the former two variants has also been shown in prostate cancer, another glandular carcinoma (Schut et al, 2003). Aberrantly glycosylated muc-1 variants identified by glycoforms exhibiting short Tn/sTn oligosaccharides in place of the complex O-glycans that form on PTS repeat sites showed a strong correlation with all forms of ovarian cancer, being exhibited in 84% and

85% of primary tumors and metastatic lesions, respectively (Van Elssen et al, 2010).

galectin-3 and exposing endothelial binding sites to cancer cells (Zhao et al, 2009).

The Thomsen-Friedenreich (TF) antigen is an additional short oligosaccharide presenting on a large number of hypoglycosylated epithelial cells. Sialylated TF is commonly found in hematopoietic and somatic cells, but the oligosaccharide is rarely observed in normal cells lacking sialyl groups (Schauer et al., 2011). In its desialylated form, this antigen is thought to be involved in triggering metastasis by stimulating interaction with galactoside-binding

glycosylation signature.

**2.5 The role of hypoglycosylated mucins in cancer** 

four-marker composite test (Zhang et al, 2007).

#### **2.6 Mucin-16 as a biomarker: Strengths and weaknesses**

Mucin-16 (muc-16, CA125) is another type of transmembrane mucin that mediates adhesive interactions in ovarian cancer. Adhesion of ovarian cancer cells to the peritoneum is in part facilitated by the binding of cleaved cell surface MUC16 to mesothelin (Rump et al, 2004). Muc-16 additionally dampens immune response by binding to the inhibitory siglec-9 receptor on a wide range of cells involved in both innate and adaptive immunity, allowing growing tumors to evade immune system surveillance (Belisle et al, 2010).

The CA125 assay displays a superior sensitivity of 95% in tumors positive for the cell surface antigen in human serum (Cramer et al, 2011). Muc-16 becomes increasingly elevated with progression of OEC, and is expressed in approximately 80% of patients. Stage I tumors have much lower concentrations of muc-16, expressing the mucin at only a 58% rate (Jacobs and Bast, 1989). In addition, sonography is inefficient for detecting tumors that have not yet developed into a large mass. The CA125 assay has very low specificity, as this mucin is often expressed in additional cancers or inflammatory diseases. As a result, the current diagnosis strategy is highly inadequate for early tumor detection.

Despite inefficiency in early detection, the mainstay of ovarian cancer diagnosis continues to be muc-16 detection combined with ultrasonography. Recently, this has been challenged as a prospective study monitoring over 78,000 women showed that mortality was not decreased in women annually screened via this combination (Buys et al, 2011). In addition, surgical follow-up for false positive readings occurred unnecessarily in 1080 women, with 15% experiencing one or more serious complications (Buys et al, 2011).

Because CA125 alone is insufficient for early tumor detection, the focus of much research is the improvement of assay sensitivity by combining this mucin marker with one or more additional indicators. This practice has not yet led to the validation of a composite assay for ovarian cancer diagnosis, mainly because of the tradeoff in specificity encountered when increasing sensitivity via use of multiple agents (Florkowski, 2008). Ideally, a powerful diagnostic assay should consist of the minimum number of test agents possible for this reason. Combining CA125 with a marker that is not only highly expressed in OEC but is replete with a unique glycosylation signature specific for the disease is one viable option for optimizing sensitivity and specificity. The combination of CA125 with other protein markers occasionally yields productive data as well. Improved sensitivity in detecting early ovarian cancer has been observed when CA125 measurement was combined with mesothelin detection (McIntosh et al, 2004). In addition, combination of CA125 with the T-cell expressed B7-H4 protein was demonstrated to improve early detection by 13% over CA125 alone (Simon et al, 2006). Although current guidelines recommend CA125 measurement as the sole biomarker criterion for ovarian cancer diagnosis, it is likely that more powerful assays will develop from its use in combination with one or more highly specific agents. Novel discoveries from the increased use of proteomic and glycomic approaches will assuredly allow for the search for quality biomarkers to continue unabated.

#### **3. Epigenetic modifications as tumor markers**

Epigenetics is a branch of science which has for its purpose the study of heritable changes in gene function that do not occur as the result of changes in DNA sequence (Wu and Morris, 2001). In addition, chromatin architecture is affected by epigenetic mechanisms (Zaina et al., 2010). An "epigenetic pathway" involving three components has recently been proposed. In this pathway, a signal is received from the external environment, after which an epigenetic initiator determines the precise chromatin location to be affected, called the mark, and an

Potential Tumor Biomarkers for Ovarian Cancer 223

associated with activation of DNMT1 via PI3K/Akt signaling (Cheng et al, 2011). Upregulated expression of DNMTs occurs frequently in ovarian cancer (Ahluwalia et al, 2001). Complete inactivation of *TP53* by mutation is the most common mutational event in aggressive high grade OEC (Singer et al, 2005, Bell et al, 2011). In contrast, low-grade OEC is characterized instead by mutations in *KRAS*/*BRAF*/*ERBB2.* It is not associated with the sudden, highly invasive phenotype observed in high-grade disease but rather via a slow, indolent progression (Singer et al, 2005). Methylation patterns in low-grade tumors are closer to those of the benign cystadenomas that may develop into them, although more

The inhibitory effects of p53 on the cell adhesion protein, E-cadherin, are multifaceted. Ecadherin can either be transcriptionally repressed by the absence of p53 through Twist activation (Yang et al, 2004), or be silenced by promoter methylation by DNMT1 (Cheng et al, 2011). Benign adenomas exhibit promoter hypermethylation at a 13% rate. The percentage is increased to 17% in low malignancy tumors and 26% in invasive tumors, showing an increase with increasing malignancy potential (Makarla et al, 2005). The steady increase from benign to low-malignancy-potential adenomas appear to reflect the step-bystep progression observed in low grade ovarian tumors unrelated to *TP53* mutation, while widespread CIN coupled with *TP53* inactivation are believed to account for the higher percentage of methylation in high grade tumors. Because loss of E-cadherin is essential in precipitating EMT in certain subsets of OEC (Patel et al, 2003) , it may be speculated that p53 down-regulation as a result of CIN caused by aneuploidy from extensive remodeling of the ECM may have major effects on hypermethylation of tumor suppressor promoters from a fairly early stage (Cheng et al, 2011). In contrast, despite the hereditary involvement of BRCA1/2, aneuploidy and CIN are involved in all cases of serous OEC studied, regardless of BRCA status (Pradhan et al, 2010). In sporadic but not hereditary OEC*, BRCA1* is highly methylated (Bol et al, 2010). Therefore, inactivation of the *BRCA1* gene through either mutation, loss of heterozygosity or promoter hypermethylation may be implicated in maintaining the tumor promoting environment, while *TP53* inactivation may affect gene expression in a more direct manner through its effect on DNMT1 as well as its other effects

While thousands of genes may have their methylation patterns altered, several common genes repressed by promoter methylation in ovarian cancer that may be useful as part of a methylation biomarker panel are listed in Table 2. These include a number of genes involved in tumor suppression, apoptosis, and cell adhesion. Although these genes are frequently silenced by epigenetic dysregulation, a number of them can also be inactivated through other mechanisms, such as loss of heterozygosity, imprinting, mutation, or transcriptional downregulation. Most hypermethylated genes observed in ovarian tumor tissue are detectable in blood via methylation-specific PCR analysis, and various combinations can be tested for utility as composite serum markers for diagnostic screening

Global hypomethylation of genes and repetitive elements is also a frequent finding in OEC, with extent correlating with increasing invasiveness (Shih et al, 2010). Repetitive elements have lost function over the course of evolution, so sudden loss of methylation on DNA components silenced for thousands or millions of years may be a critical factor in the disruption of chromosomal integrity observed in invasive carcinomas (Eden et al, 2003). Hypomethylation of LINE1 transposons and Sat2/Satα repeats commonly occurs in ovarian cancer (Widschwendter et al, 2004). LINE1 elements contain many splice sites that, when

pronounced, reflecting their gradual progression (Shih et al, 2010).

on cell cycle regulation and DNA damage repair.

with high sensitivity (Melnikov et al, 2009).

epigenetic maintainer works to sustain the changed chromatin environment (Berger et al., 2009). Whereas epigenetic initiators include DNA binding factors and non-coding RNAs, epigenetic maintainers include modifiers of histone proteins and histone variants and DNA modifiers, such as DNA methyltransferases (DNMTs) (Berger et al., 2009). The role of RNAs in epigenetic initiation, particularly with respect to marking targeted regions and silencing them via RNA-associated silencing, is also an area of intense study (Malecova and Morris, 2010; Zhou et al., 2010).

There are many examples of epigenetic deregulation in ovarian cancer, which include alterations in patterns of DNA methylation (Makarla et al, 2005; Rathi et al, 2002), histone modifications (Caslini et al, 2006), and microRNA (miRNA) expression (Li et al, 2010c; Wyman et al, 2009). Changes in histone modifications currently have little diagnostic value, due to low sensitivity and the need for obtaining tissue samples. Detection of hyper- and hypomethylation patterns of DNA proffers several advantages in the quest for quality biomarkers for early OEC diagnosis. Testing would be minimally invasive since DNA is easily accessible from the bloodstream and peritoneal fluid that is not quantitatively different from DNA in cells directly extracted from tumors (Asadollahi et al, 2010; Maradeo and Cairns, 2011). The regions of the genomes of cells in serum analyzed are often confined to specific locations, such as the CpG islands of promoter regions of specific genes. Once isolated, the DNA can then be amplified readily using methylation-specific PCR, ensuring high sensitivity (Cairns, 2007). Other advantages include stability of the portent indicators, via their resistance to degradation, and cost-effectiveness. A major limitation, however, is that different phenotypes lead to disparate methylation profiles because of the heterogeneous presentation of ovarian cancer. There is hope that, in time, selection of a combination of aberrantly methylated genes may serve as a composite marker specific for a general OEC phenotype, with certain markers serving as red flags for aggressive forms of cancer. As promoter methylation is a frequent early event in cancers, the ability to detect and analyze patterns consistent with malignancy in ovarian tumors may provide an opportunity for more accurate early detection.

#### **3.1 Altered DNA methylation profiles**

Cells from invasive tumors have widespread hypomethylation of repetitive elements with frequent hypermethylation of CpG dinucleotide-containing promoter regions of genes with tumor suppressive function (Balch et al, 2009). DNA methylation patterns reflect the stage and degree of tumor progression in ovarian cancer (Shih et al, 2010; Yang et al, 2006). Invasive tumors display a much larger set of genes whose methylation patterns are affected, with mean methylation index increasing threefold or higher compared with low malignancy tumors (Makarla et al, 2005). These differences reflect the category of tumor; whether the disease results from an accruement of gradual changes (low grade) or a sudden and more invasive phenotype from widespread chromosomal instability (CIN) (high grade). The latter is more prevalent, occurring in the majority of cases, including approximately 75% of serous carcinoma cases (Shih and Kurman, 2004). These tumors have recently been classified as CpG island methylator phenotype (CIMP) cancers, and are characterized by a rapid inactivation of a large number of genes, often by hypermethylation due to alterations in expression of DNMTs. Inactivation of *TP53* by mutation is a frequent result of CIN and accounted for in 96% of high grade serous ovarian carcinomas (Bell et al, 2011). In addition to the critical effects p53 maintains in cell cycle regulation, its inhibition is thought to play a role in the large scale hypermethylation of tumor suppressor genes, as its abrogation is

epigenetic maintainer works to sustain the changed chromatin environment (Berger et al., 2009). Whereas epigenetic initiators include DNA binding factors and non-coding RNAs, epigenetic maintainers include modifiers of histone proteins and histone variants and DNA modifiers, such as DNA methyltransferases (DNMTs) (Berger et al., 2009). The role of RNAs in epigenetic initiation, particularly with respect to marking targeted regions and silencing them via RNA-associated silencing, is also an area of intense study (Malecova and Morris,

There are many examples of epigenetic deregulation in ovarian cancer, which include alterations in patterns of DNA methylation (Makarla et al, 2005; Rathi et al, 2002), histone modifications (Caslini et al, 2006), and microRNA (miRNA) expression (Li et al, 2010c; Wyman et al, 2009). Changes in histone modifications currently have little diagnostic value, due to low sensitivity and the need for obtaining tissue samples. Detection of hyper- and hypomethylation patterns of DNA proffers several advantages in the quest for quality biomarkers for early OEC diagnosis. Testing would be minimally invasive since DNA is easily accessible from the bloodstream and peritoneal fluid that is not quantitatively different from DNA in cells directly extracted from tumors (Asadollahi et al, 2010; Maradeo and Cairns, 2011). The regions of the genomes of cells in serum analyzed are often confined to specific locations, such as the CpG islands of promoter regions of specific genes. Once isolated, the DNA can then be amplified readily using methylation-specific PCR, ensuring high sensitivity (Cairns, 2007). Other advantages include stability of the portent indicators, via their resistance to degradation, and cost-effectiveness. A major limitation, however, is that different phenotypes lead to disparate methylation profiles because of the heterogeneous presentation of ovarian cancer. There is hope that, in time, selection of a combination of aberrantly methylated genes may serve as a composite marker specific for a general OEC phenotype, with certain markers serving as red flags for aggressive forms of cancer. As promoter methylation is a frequent early event in cancers, the ability to detect and analyze patterns consistent with malignancy in ovarian tumors may provide an

Cells from invasive tumors have widespread hypomethylation of repetitive elements with frequent hypermethylation of CpG dinucleotide-containing promoter regions of genes with tumor suppressive function (Balch et al, 2009). DNA methylation patterns reflect the stage and degree of tumor progression in ovarian cancer (Shih et al, 2010; Yang et al, 2006). Invasive tumors display a much larger set of genes whose methylation patterns are affected, with mean methylation index increasing threefold or higher compared with low malignancy tumors (Makarla et al, 2005). These differences reflect the category of tumor; whether the disease results from an accruement of gradual changes (low grade) or a sudden and more invasive phenotype from widespread chromosomal instability (CIN) (high grade). The latter is more prevalent, occurring in the majority of cases, including approximately 75% of serous carcinoma cases (Shih and Kurman, 2004). These tumors have recently been classified as CpG island methylator phenotype (CIMP) cancers, and are characterized by a rapid inactivation of a large number of genes, often by hypermethylation due to alterations in expression of DNMTs. Inactivation of *TP53* by mutation is a frequent result of CIN and accounted for in 96% of high grade serous ovarian carcinomas (Bell et al, 2011). In addition to the critical effects p53 maintains in cell cycle regulation, its inhibition is thought to play a role in the large scale hypermethylation of tumor suppressor genes, as its abrogation is

2010; Zhou et al., 2010).

opportunity for more accurate early detection.

**3.1 Altered DNA methylation profiles** 

associated with activation of DNMT1 via PI3K/Akt signaling (Cheng et al, 2011). Upregulated expression of DNMTs occurs frequently in ovarian cancer (Ahluwalia et al, 2001). Complete inactivation of *TP53* by mutation is the most common mutational event in aggressive high grade OEC (Singer et al, 2005, Bell et al, 2011). In contrast, low-grade OEC is characterized instead by mutations in *KRAS*/*BRAF*/*ERBB2.* It is not associated with the sudden, highly invasive phenotype observed in high-grade disease but rather via a slow, indolent progression (Singer et al, 2005). Methylation patterns in low-grade tumors are closer to those of the benign cystadenomas that may develop into them, although more pronounced, reflecting their gradual progression (Shih et al, 2010).

The inhibitory effects of p53 on the cell adhesion protein, E-cadherin, are multifaceted. Ecadherin can either be transcriptionally repressed by the absence of p53 through Twist activation (Yang et al, 2004), or be silenced by promoter methylation by DNMT1 (Cheng et al, 2011). Benign adenomas exhibit promoter hypermethylation at a 13% rate. The percentage is increased to 17% in low malignancy tumors and 26% in invasive tumors, showing an increase with increasing malignancy potential (Makarla et al, 2005). The steady increase from benign to low-malignancy-potential adenomas appear to reflect the step-bystep progression observed in low grade ovarian tumors unrelated to *TP53* mutation, while widespread CIN coupled with *TP53* inactivation are believed to account for the higher percentage of methylation in high grade tumors. Because loss of E-cadherin is essential in precipitating EMT in certain subsets of OEC (Patel et al, 2003) , it may be speculated that p53 down-regulation as a result of CIN caused by aneuploidy from extensive remodeling of the ECM may have major effects on hypermethylation of tumor suppressor promoters from a fairly early stage (Cheng et al, 2011). In contrast, despite the hereditary involvement of BRCA1/2, aneuploidy and CIN are involved in all cases of serous OEC studied, regardless of BRCA status (Pradhan et al, 2010). In sporadic but not hereditary OEC*, BRCA1* is highly methylated (Bol et al, 2010). Therefore, inactivation of the *BRCA1* gene through either mutation, loss of heterozygosity or promoter hypermethylation may be implicated in maintaining the tumor promoting environment, while *TP53* inactivation may affect gene expression in a more direct manner through its effect on DNMT1 as well as its other effects on cell cycle regulation and DNA damage repair.

While thousands of genes may have their methylation patterns altered, several common genes repressed by promoter methylation in ovarian cancer that may be useful as part of a methylation biomarker panel are listed in Table 2. These include a number of genes involved in tumor suppression, apoptosis, and cell adhesion. Although these genes are frequently silenced by epigenetic dysregulation, a number of them can also be inactivated through other mechanisms, such as loss of heterozygosity, imprinting, mutation, or transcriptional downregulation. Most hypermethylated genes observed in ovarian tumor tissue are detectable in blood via methylation-specific PCR analysis, and various combinations can be tested for utility as composite serum markers for diagnostic screening with high sensitivity (Melnikov et al, 2009).

Global hypomethylation of genes and repetitive elements is also a frequent finding in OEC, with extent correlating with increasing invasiveness (Shih et al, 2010). Repetitive elements have lost function over the course of evolution, so sudden loss of methylation on DNA components silenced for thousands or millions of years may be a critical factor in the disruption of chromosomal integrity observed in invasive carcinomas (Eden et al, 2003). Hypomethylation of LINE1 transposons and Sat2/Satα repeats commonly occurs in ovarian cancer (Widschwendter et al, 2004). LINE1 elements contain many splice sites that, when

Potential Tumor Biomarkers for Ovarian Cancer 225

tumor grade, and may help to differentiate between ovarian cancers of varying malignant potential as a result (Qu et al, 1999). Several oncogene promoters are hypomethylated as well in OEC, including synuclein-γ(*SNCG*), claudin-4 (*CLDN4*) and insulin-like growth factor-2 (*IGF2*), further contributing to the tumorigenic phenotype (Balch et al, 2009). These compounds have all been investigated as ovarian cancer biomarkers (Hibbs et al, 2004; Palmer et al, 2008), so identification of those genes displaying diminished methylation status

miRNA signatures are 22-23 nucleotides in length once processed from precursor transcripts, and are being actively pursued as composite diagnostic markers for OEC. They can be analyzed in body fluids and show greater stability than mRNAs due to their greater resistance to RNase (Mitchell et al, 2008). Several miRNAs have been shown to be upregulated in repeated experiments, and many have oncogenic potential by either inhibiting translation of tumor suppressors when up-regulated or facilitating unimpeded expression of

Common miRNAs frequently overexpressed in ovarian cancer include miR-93, miR-106b, miR-155, miR-200a/b/c, miR-221/222, and miR-372/373; underexpressed miRNAs include miR-15/16, miR-34b\*/c, miR-125b1, miR-140, miR-145, and let-7i (Balch et al, 2009; Maradeo and Cairns, 2011). Increased neovascularization has been associated with high expression of miR-93, which may serve as an early indicator of tumor growth and angiogenesis (Fang et al, 2011). Other miRNAs, such as miR-106b and miR-221, target cell cycle inhibitors p21 and p27, respectively (le Sage et al, 2007; Li et al, 2011). Down-regulation of miR-34b\*/c has been

Some miRNAs may be up- or downregulated in the same tumor based on differentiation status of cells constituting the mass. Under the regulatory command of Twist, decreased miR-214 and miR-199a were observed in CD44+ OEC cells that were greatly dedifferentiated, while their normally differentiated CD44- counterparts exhibited higher concentrations of these non-coding RNAs (Yin et al, 2010). Low expression levels of these miRNAs, which silence PTEN and IKKβ/NF-κβ pathways, respectively, may have prognostic value, as the CD44+ cells studied displayed stem-like qualities and constitutively active inflammatory signaling (Chen et al, 2007). Additional clues for OEC characterization and prognosis will be provided as more miRNA markers are revealed and their functions elucidated. Along with evaluation of methylation signatures, miRNA signature analysis offers a promising non-invasive technique in the diagnosis and characterization of ovarian

To recapitulate, epigenetic markers are gaining favor as diagnostic biomarkers for ovarian cancer because of their expression early in disease pathogenesis and the fact that most are amenable to the use of serum as a source. The types of methylation profiles vary based on malignancy potential and tumor source, so a panel of commonly expressed methylation markers could essentially help to differentiate between the multitudes of forms characterized by this heterogeneous cancer. Although it is far from an exhaustive list, Table 2 lists some of the more frequently hypermethylated genes frequently observed in ovarian cancer after over a decade of detailed analysis. Concomitant ongoing studies on miRNA profiles in ovarian cancer provide an alternate epigenetic approach for early detection. As patterns of epigenetic alterations are better clarified, panels consisting of the most sensitive

may enhance their specificity for the disease.

oncogenes when down-regulated (Calin and Croce, 2006).

correlated with progression to advanced disease (Corney et al, 2010).

**3.2 miRNAs in ovarian cancer** 

cancer adjuvant to traditional methods.


activated, could cause hybrid splicing events with closely positioned genes to alter their translational products in cancer (Belancio et al, 2006). Satellite repeats in heterochromatic regions of chromosome 1 have been observed to lose methylation status in proportion to

Table 2. Common Genes Repressed by Promoter Methylation in Ovarian Cancer.

activated, could cause hybrid splicing events with closely positioned genes to alter their translational products in cancer (Belancio et al, 2006). Satellite repeats in heterochromatic regions of chromosome 1 have been observed to lose methylation status in proportion to

Table 2. Common Genes Repressed by Promoter Methylation in Ovarian Cancer.

tumor grade, and may help to differentiate between ovarian cancers of varying malignant potential as a result (Qu et al, 1999). Several oncogene promoters are hypomethylated as well in OEC, including synuclein-γ(*SNCG*), claudin-4 (*CLDN4*) and insulin-like growth factor-2 (*IGF2*), further contributing to the tumorigenic phenotype (Balch et al, 2009). These compounds have all been investigated as ovarian cancer biomarkers (Hibbs et al, 2004; Palmer et al, 2008), so identification of those genes displaying diminished methylation status may enhance their specificity for the disease.

#### **3.2 miRNAs in ovarian cancer**

miRNA signatures are 22-23 nucleotides in length once processed from precursor transcripts, and are being actively pursued as composite diagnostic markers for OEC. They can be analyzed in body fluids and show greater stability than mRNAs due to their greater resistance to RNase (Mitchell et al, 2008). Several miRNAs have been shown to be upregulated in repeated experiments, and many have oncogenic potential by either inhibiting translation of tumor suppressors when up-regulated or facilitating unimpeded expression of oncogenes when down-regulated (Calin and Croce, 2006).

Common miRNAs frequently overexpressed in ovarian cancer include miR-93, miR-106b, miR-155, miR-200a/b/c, miR-221/222, and miR-372/373; underexpressed miRNAs include miR-15/16, miR-34b\*/c, miR-125b1, miR-140, miR-145, and let-7i (Balch et al, 2009; Maradeo and Cairns, 2011). Increased neovascularization has been associated with high expression of miR-93, which may serve as an early indicator of tumor growth and angiogenesis (Fang et al, 2011). Other miRNAs, such as miR-106b and miR-221, target cell cycle inhibitors p21 and p27, respectively (le Sage et al, 2007; Li et al, 2011). Down-regulation of miR-34b\*/c has been correlated with progression to advanced disease (Corney et al, 2010).

Some miRNAs may be up- or downregulated in the same tumor based on differentiation status of cells constituting the mass. Under the regulatory command of Twist, decreased miR-214 and miR-199a were observed in CD44+ OEC cells that were greatly dedifferentiated, while their normally differentiated CD44- counterparts exhibited higher concentrations of these non-coding RNAs (Yin et al, 2010). Low expression levels of these miRNAs, which silence PTEN and IKKβ/NF-κβ pathways, respectively, may have prognostic value, as the CD44+ cells studied displayed stem-like qualities and constitutively active inflammatory signaling (Chen et al, 2007). Additional clues for OEC characterization and prognosis will be provided as more miRNA markers are revealed and their functions elucidated. Along with evaluation of methylation signatures, miRNA signature analysis offers a promising non-invasive technique in the diagnosis and characterization of ovarian cancer adjuvant to traditional methods.

To recapitulate, epigenetic markers are gaining favor as diagnostic biomarkers for ovarian cancer because of their expression early in disease pathogenesis and the fact that most are amenable to the use of serum as a source. The types of methylation profiles vary based on malignancy potential and tumor source, so a panel of commonly expressed methylation markers could essentially help to differentiate between the multitudes of forms characterized by this heterogeneous cancer. Although it is far from an exhaustive list, Table 2 lists some of the more frequently hypermethylated genes frequently observed in ovarian cancer after over a decade of detailed analysis. Concomitant ongoing studies on miRNA profiles in ovarian cancer provide an alternate epigenetic approach for early detection. As patterns of epigenetic alterations are better clarified, panels consisting of the most sensitive

Potential Tumor Biomarkers for Ovarian Cancer 227

predicting early recurrence of ovarian cancer, as expression of HE4 increases an average of

Whereas CA125 is a better biomarker for overall ovarian cancer detection than HE4 based on multiple comparative studies (Cramer et al, 2011; Medeiros et al, 2009; Van Gorp et al, 2011), a composite assay measuring concentrations of both proteins may be ideal for enabling early detection. This could potentially translate into higher survival rates as the differences in mortality between early and late stage ovarian cancer are considerable. For this reason, combinatory testing has been explored in several prospective and retrospective studies (Andersen et al, 2010; Jacob et al, 2011; Shah et al, 2009; Van Gorp et al, 2011). The results thus far have been mixed, with naysayers arguing that the benefit of testing for HE4

In a prospective study of 389 patients with a pelvic mass of ovarian origin, ROC-AUC values showed only a slight advantage for HE4 testing in premenopausal patients compared to CA125 (Van Gorp et al, 2011). The CA125 assay was superior for postmenopausal patients, although a Risk of Ovarian Malignancy Algorithm (ROMA) based on a logarithmic formula of HE4 concentrations with menopausal status did improve detection ability in post-menopausal women. Unlike previous studies, sensitivity and specificity for HE4 were poor. Sensitivity was 74.5% at a specificity of 83.3%. In contrast, a case control study that included a large number of early stage patients demonstrated 77% sensitivity for HE4 detection at 94.9% specificity (Andersen et al, 2010). Overall sensitivity was slightly higher for CA125 (81%), but combining the two markers led to a significant increase in sensitivity without a major tradeoff in specificity. HE4 better detected early disease, and high risk patients were identified at 100% sensitivity compared to only 78.6% for CA125 at 95% specificity (Andersen et al, 2010). Shah and colleagues (2009) showed a benefit of HE4 over CA125 in discriminating between risk-matched healthy controls and cases in high risk groups. At a specificity of 95%, sensitivity in these cases was 87.8% for HE4 versus 82.9% for CA125. A cohort study of 160 subjects with mixed phenotypes (18% OEC) reproduced beneficial results for HE4 in early stage cancer detection, as well as a greater propensity for discriminating between borderline and malignant tumors (Jacob et al, 2011). High cost of HE4 screening caused the authors to caution against using the combination, however, as the overall benefits were minimal. Finally, a four marker panel consisting of HE4 and CA125 along with two additional markers (VCAM-1 and CEA) observed a 86% sensitivity at a high

Although the advantages of combination testing with CA125 and HE4 biomarkers have been below expectations, the ability of HE4 to effectively diagnose early disease, identify disease in high risk patients for which screening is essential, and differentiate between borderline and malignant disease have increased its value as a diagnostic indicator. While data from older, post-menopausal women are subpar (Van Gorp et al, 2011), composite testing of CA125 and HE4 may be valuable for certain groups with further investigation,

There are several hereditary syndromes which increase the likelihood of ovarian cancer in a patient. Examples of such include hereditary breast and ovarian cancer (HBOC), hereditary nonpolyposis colorectal cancer (HNPCC), site-specific ovarian cancer (SSOC), Gorlin's syndrome, and Peutz-Jeghers syndrome (Russo et al., 2009). Of these, HBOC, HNPCC and

5-8 months prior to a rise in CA125 in relapsing tumors (Anastasi et al, 2010).

in addition to CA125 is not sufficient to warrant clinical use.

specificity of 98% (Yurkovetsky et al, 2010).

such as premenopausal women at high risk for disease.

**5. Inherited mutations as biomarkers for ovarian cancer** 

and specific of these markers identified will likely be developed for further testing and possible validation.

#### **4. HE4 as a potential early marker**

A promising protein marker receiving much attention for its potential role in the early diagnosis of ovarian cancer is human epididymis secretory protein 4 (HE4). This protein is a member of the whey acidic four-disulfide core (WFDC) family, which includes secretory leukocyte protease inhibitor (SLPI) and elafin. Its function has not yet been elucidated, although it does not appear to exhibit protease inhibitor activity like most other members of the WFDC family. HE4 was first identified in human epididymis epithelium (Kirchhoff et al, 1991). Since its discovery, HE4 has been found in some other tissues as well, including the respiratory tract and nasopharynx. It is a frequently expressed selective early marker for this disease. While normal OSE does not express HE4, the protein can be detected in sera of patients diagnosed with the most prevalent forms of OEC, and is detectable even in inclusion cysts that may precede tumor formation (Drapkin et al, 2005).

Finding a protein biomarker to rival CA125 in sensitivity and specificity has posed a major challenge. Despite the failure of CA125 to accurately predict early disease, this marker has alone displayed the greatest overall diagnostic ability in repeated studies (Canney et al, 1984; Cramer et al, 2011; Medeiros et al, 2009). However, detection of HE4 holds some advantages over CA125, and its use in combination with the mucin marker is currently being evaluated. Overall specificity for HE4 is comparable to CA125 with greater discriminatory ability for the detection of early disease in patients with a pelvic mass (Hellstrom and Hellstrom, 2011; Montagnana et al, 2009; Nolen et al, 2010). Detection of HE4 has displayed a better ability to differentiate between benign and malignant disease, as the sensitivity was 56.7% for HE4 compared to 10.8% with CA125 at high specificity (Hellstrom et al, 2003). Receiver operator characteristic (ROC) curves, which plot changes in sensitivity in relation to specificity, were used to ascertain information on the usefulness of both markers in a head-to-head comparison. The AUC values of ROC curves generated for both HE4 and CA125 showed comparable rates for early detection, with HE4 exhibiting slightly higher values. Comparison of ROC curves for all cases yielded superior detection rates for CA125. Similar results from ROC-AUC analyses were reproduced elsewhere (Anastasi et al, 2010; Montagnana et al, 2009). However, ROC curves are not used as diagnostic criteria for ovarian cancer detection. The major benefit of serum HE4 testing observed in the study by Hellstrom et al (2003) was that there were significantly less false positive readings than with CA125.

In a retrospective study comparing CA125 and two different HE4 assays, the HE4 assays showed better sensitivity (Ruggeri et al, 2011). At 95% specificity, sensitivity was 83.3% and 84.4% for HE4 compared to 76% for CA125, and as the specificity increased to 99%, the difference increased further, with a 79.2% sensitivity for both HE4 assays and a 59.4% sensitivity for the CA125 assay.

An additional benefit of HE4 lies in its ability to be quantified in not only serum and ascitic fluid but urine as well. Specificity and sensitivity rates for urine samples were demonstrated to be comparable to serum concentrations, displaying results of 94.4% and 86.6%, respectively, for stage I/II cancers (Hellstrom et al, 2010). These data allow for the possibility of a noninvasive urine test adjuvant to other diagnostic criteria for ovarian cancer if this can be reproduced in larger studies. Measurement of serum HE4 is also effective for

and specific of these markers identified will likely be developed for further testing and

A promising protein marker receiving much attention for its potential role in the early diagnosis of ovarian cancer is human epididymis secretory protein 4 (HE4). This protein is a member of the whey acidic four-disulfide core (WFDC) family, which includes secretory leukocyte protease inhibitor (SLPI) and elafin. Its function has not yet been elucidated, although it does not appear to exhibit protease inhibitor activity like most other members of the WFDC family. HE4 was first identified in human epididymis epithelium (Kirchhoff et al, 1991). Since its discovery, HE4 has been found in some other tissues as well, including the respiratory tract and nasopharynx. It is a frequently expressed selective early marker for this disease. While normal OSE does not express HE4, the protein can be detected in sera of patients diagnosed with the most prevalent forms of OEC, and is detectable even in

Finding a protein biomarker to rival CA125 in sensitivity and specificity has posed a major challenge. Despite the failure of CA125 to accurately predict early disease, this marker has alone displayed the greatest overall diagnostic ability in repeated studies (Canney et al, 1984; Cramer et al, 2011; Medeiros et al, 2009). However, detection of HE4 holds some advantages over CA125, and its use in combination with the mucin marker is currently being evaluated. Overall specificity for HE4 is comparable to CA125 with greater discriminatory ability for the detection of early disease in patients with a pelvic mass (Hellstrom and Hellstrom, 2011; Montagnana et al, 2009; Nolen et al, 2010). Detection of HE4 has displayed a better ability to differentiate between benign and malignant disease, as the sensitivity was 56.7% for HE4 compared to 10.8% with CA125 at high specificity (Hellstrom et al, 2003). Receiver operator characteristic (ROC) curves, which plot changes in sensitivity in relation to specificity, were used to ascertain information on the usefulness of both markers in a head-to-head comparison. The AUC values of ROC curves generated for both HE4 and CA125 showed comparable rates for early detection, with HE4 exhibiting slightly higher values. Comparison of ROC curves for all cases yielded superior detection rates for CA125. Similar results from ROC-AUC analyses were reproduced elsewhere (Anastasi et al, 2010; Montagnana et al, 2009). However, ROC curves are not used as diagnostic criteria for ovarian cancer detection. The major benefit of serum HE4 testing observed in the study by Hellstrom et al (2003) was that there were significantly less false positive readings than with

In a retrospective study comparing CA125 and two different HE4 assays, the HE4 assays showed better sensitivity (Ruggeri et al, 2011). At 95% specificity, sensitivity was 83.3% and 84.4% for HE4 compared to 76% for CA125, and as the specificity increased to 99%, the difference increased further, with a 79.2% sensitivity for both HE4 assays and a 59.4%

An additional benefit of HE4 lies in its ability to be quantified in not only serum and ascitic fluid but urine as well. Specificity and sensitivity rates for urine samples were demonstrated to be comparable to serum concentrations, displaying results of 94.4% and 86.6%, respectively, for stage I/II cancers (Hellstrom et al, 2010). These data allow for the possibility of a noninvasive urine test adjuvant to other diagnostic criteria for ovarian cancer if this can be reproduced in larger studies. Measurement of serum HE4 is also effective for

inclusion cysts that may precede tumor formation (Drapkin et al, 2005).

possible validation.

CA125.

sensitivity for the CA125 assay.

**4. HE4 as a potential early marker** 

predicting early recurrence of ovarian cancer, as expression of HE4 increases an average of 5-8 months prior to a rise in CA125 in relapsing tumors (Anastasi et al, 2010).

Whereas CA125 is a better biomarker for overall ovarian cancer detection than HE4 based on multiple comparative studies (Cramer et al, 2011; Medeiros et al, 2009; Van Gorp et al, 2011), a composite assay measuring concentrations of both proteins may be ideal for enabling early detection. This could potentially translate into higher survival rates as the differences in mortality between early and late stage ovarian cancer are considerable. For this reason, combinatory testing has been explored in several prospective and retrospective studies (Andersen et al, 2010; Jacob et al, 2011; Shah et al, 2009; Van Gorp et al, 2011). The results thus far have been mixed, with naysayers arguing that the benefit of testing for HE4 in addition to CA125 is not sufficient to warrant clinical use.

In a prospective study of 389 patients with a pelvic mass of ovarian origin, ROC-AUC values showed only a slight advantage for HE4 testing in premenopausal patients compared to CA125 (Van Gorp et al, 2011). The CA125 assay was superior for postmenopausal patients, although a Risk of Ovarian Malignancy Algorithm (ROMA) based on a logarithmic formula of HE4 concentrations with menopausal status did improve detection ability in post-menopausal women. Unlike previous studies, sensitivity and specificity for HE4 were poor. Sensitivity was 74.5% at a specificity of 83.3%. In contrast, a case control study that included a large number of early stage patients demonstrated 77% sensitivity for HE4 detection at 94.9% specificity (Andersen et al, 2010). Overall sensitivity was slightly higher for CA125 (81%), but combining the two markers led to a significant increase in sensitivity without a major tradeoff in specificity. HE4 better detected early disease, and high risk patients were identified at 100% sensitivity compared to only 78.6% for CA125 at 95% specificity (Andersen et al, 2010). Shah and colleagues (2009) showed a benefit of HE4 over CA125 in discriminating between risk-matched healthy controls and cases in high risk groups. At a specificity of 95%, sensitivity in these cases was 87.8% for HE4 versus 82.9% for CA125. A cohort study of 160 subjects with mixed phenotypes (18% OEC) reproduced beneficial results for HE4 in early stage cancer detection, as well as a greater propensity for discriminating between borderline and malignant tumors (Jacob et al, 2011). High cost of HE4 screening caused the authors to caution against using the combination, however, as the overall benefits were minimal. Finally, a four marker panel consisting of HE4 and CA125 along with two additional markers (VCAM-1 and CEA) observed a 86% sensitivity at a high specificity of 98% (Yurkovetsky et al, 2010).

Although the advantages of combination testing with CA125 and HE4 biomarkers have been below expectations, the ability of HE4 to effectively diagnose early disease, identify disease in high risk patients for which screening is essential, and differentiate between borderline and malignant disease have increased its value as a diagnostic indicator. While data from older, post-menopausal women are subpar (Van Gorp et al, 2011), composite testing of CA125 and HE4 may be valuable for certain groups with further investigation, such as premenopausal women at high risk for disease.

#### **5. Inherited mutations as biomarkers for ovarian cancer**

There are several hereditary syndromes which increase the likelihood of ovarian cancer in a patient. Examples of such include hereditary breast and ovarian cancer (HBOC), hereditary nonpolyposis colorectal cancer (HNPCC), site-specific ovarian cancer (SSOC), Gorlin's syndrome, and Peutz-Jeghers syndrome (Russo et al., 2009). Of these, HBOC, HNPCC and

Potential Tumor Biomarkers for Ovarian Cancer 229

Approximately half of high grade serous carcinomas exhibit defects in HR, solidifying the importance of this process in its implications for disease pathology extending beyond the

The risk of ovarian cancer is about 40% in carriers with BRCA1 mutations (Antoniou et al., 2003; Ford et al., 1994). BRCA1 is composed of 1863 amino acids and possesses a N-terminal RING domain and and two C-terminal BRCT domains, present in tandem, at its C-terminus. The RING domain is protein-protein interaction motif which mediates the binding of BRCA1 to its obligate partner BARD1 (Meza et al., 1999; Wu et al., 1996). The BRCA1:BARD1 complex possesses ubiquitin ligase activity (Starita et al., 2004) while the BRCT domains of BRCA1 serve as sites of numerous protein-protein interactions, regulate transcription, and possess the ability to bind to phosphopeptides (reviewed in Narod and Foulkes, 2004; Starita and Parvin, 2003; Manke et al., 2003). Numerous cancer-associated missense mutations which disrupt interactions with putative binding partners have been described in the RING and BRCT domains of BRCA1 (reviewed in Carvalho et al., 2007;

The risk of ovarian cancer is about 25% in patients with *BRCA2* mutations (Ford et al., 1998). BRCA2 is composed of 3418 amino acids and possesses two distinct classes of BRC repeats which interact with the RAD51 protein, the mammalian homolog of *Escherichia coli* RecA (Carreira and Kowalczykowski, 2011). In addition, the C-terminal region of BRCA2, TR2, interacts with RAD51 (van der Groep et al., 2011). A major mechanism by which RAD51 is recruited to damaged DNA is via its interaction with BRCA2 and, along with the latter, plays a critical role in homologous recombination (Badie et al., 2010; Davies et al., 2001; Jensen et al., 2010). Cancer associated point mutations on BRC repeats which disrupt interaction of BRCA2 with RAD51 have been reported (Venkitaraman, 2009). Based on the observation that BRC repeats bind distinct regions of RAD51 and are not equal in their mode of interaction, it was hypothesized that a mutation within even one of the eight BRC repeats in this region could be sufficient to affect the way that BRCA2 interacts with RAD51, and lead to an increased risk of cancer (Galkin et al., 2005). Interestingly, certain families exhibit *BRCA2* mutations which appear to predispose carriers to ovarian cancer and which are located within exon 11 (Gayther et al., 1997; Lubinski et al., 2004; Petrucelli et al., 2002; Thompson et al., 2001). While this area is generally referred to as the ovarian cancer cluster region, Al-Saffar and Foulkes (2002) proposed that this region of exon 11 be known as the diminished breast cancer risk region. Ovarian tumors in women carrying mutations in *BRCA1* or *BRCA2* are generally serous carcinomas and tend to be of high grade when diagnosed (Sowter and Ashworth, 2005). High grade serous carcinomas associated with *BRCA* mutations are believed to arise from the distal fallopian tube (Crum, 2009; Piek et al., 2003) and are frequently accompanied by mutations in *TP53* (Ahmed et al., 2010; Milner et al, 1993; reviewed in Hall et al., 2004). A comprehensive model for the development of high grade serous ovarian cancer has been put forth by Bowtell (2010) in which the loss of p53 and BRCA disrupts the HR repair of damaged DNA and, in turn, leads to CIN and carcinogenesis. A link between ovarian inclusion cysts and serous carcinomas has been proposed (Sowter and Ashworth, 2005) and may be explained by a mechanism in which cells from the fimbria travel to inclusion cysts and there become transformed and malignant via endometriosis or a series of mitogenic events and malignant (Crum, 2009). Alternatively, high grade serous carcinomas may be derived from stem-like ovarian cancer cells which have been dysregulated due, at least in part, to *BRCA* inactivation (Foulkes, 2004; Yin et al., 2010). Other hypotheses to explain the tissue specific cancers observed in mutant *BRCA* carriers have also been reviewed elsewhere

presence of germline mutations (Bell et al., 2011).

Morris and Solomon, 2004; Szabo et al., 2004).

SSOC comprise about 99% of hereditary ovarian cancers. However, it is important to note that 10-13% of all ovarian cancer cases can be classified as hereditary and linked to the inherited mutations described below (Pal et al., 2005; Risch, 2001; Stratton JF, 1999; Sowter and Ashworth, 2005). In sporadic cancers, the mutational activation of oncogenes, coupled with non-mutational inactivation of tumor suppressor genes, is often observed (Kenemans et al., 2004). In hereditary cancers, germline mutations in a single allele confer an elevated risk for cancer development (Radice, 2002). Therefore, while genetic screening to identify at risk individuals is highly desirable in patients with a family history of breast, ovarian or colon cancer, the potential biomarkers described below may or may not be applicable for the detection of sporadic ovarian cancers.

#### **5.1 Human MutS homolog 2 (***hMSH2***) and Human MutL homolog 1 (***hMLH1***)**

Ovarian carcinomas in patients from HNPCC families typically present as early-onset, nonserous epithelial tumors (Ketabi et al., 2011). *hMSH2* and *hMLH1* are the two most frequently mutated genes in this syndrome and confer a 9-12% lifetime risk of ovarian cancer (Aarnio et al., 1995; Brown et al., 2001; Kasprzak et al., 1999; Russo et al., 2009). The hMSH2 and hMLH1 proteins are the fundamental components of DNA mismatch repair (MMR) (Kolodner et al., 1994) and defects in these genes significantly increase the rate of mutation, which is believed to contribute to cancer development (Loeb, 2011; Valeri et al., 2010). In particular, microsatellite instability (MSI) has been observed in tumors from HNPCC patients (Dietmaier et al., 1997) and stems, at least in part, from a mutation or inherited epigenetic inactivation of hMLH1 (Gazzoli et al., 2002; Goecke et al., 2006; Hitchins et al, 2007; Kane et al., 1997). Interestingly, Valeri and colleagues (2010) reported that a noncoding miRNA designated as miR-155 is significantly overexpressed in human colorectal cancers and that an inverse correlation exists between the expression of miR-155 and the expression of hMLH1 or hMSH2 proteins in these tissues. miR-155 has been detected in blood samples derived from patients with ovarian cancer, though the sensitivity is still too low to be used as a reliable and predictive indicator of disease progression (Hausler et al., 2010). miR-155 has been put forth as a potential biomarker for the detection of early pancreatic neoplasia (Habbe et al., 2009).

Screening for mutations in genes important to MMR, such as *hMSH2* and *hMLH1*, and for epigenetic changes relevant to MMR such as hMLH1 promoter methylation, should prove to be an effective strategy for identifying patients in HNPCC families who may also be at risk for developing ovarian cancer. Moreover, screening for the upregulation of the noncoding RNA miR-155 may also prove to be effective in this regard. Important questions concerning the latter remain to be answered; including whether miR-155 upregulation is involved with sporadic ovarian cancers and if this noncoding RNA can be used as an early diagnostic marker.

#### **5.2 Breast Cancer Susceptibility Genes (***BRCA1* **and** *BRCA2***)**

BRCA1 and BRCA2 are large nuclear proteins which act as tumor suppressors and contribute to genetic stability and DNA damage repair (Arai et al., 2004; Meindl et al., 2011; van der Groep et al., 2011). Whereas numerous biochemical and molecular functions have been described for both proteins (reviewed in Narod & Foulkes, 2004; Venkitaraman, 2002), they have both been implicated in the repair of double-strand breaks (DSBs) by homologous recombination (HR) (Badie et al., 2010; Boulton, 2006; Moynahan et al., 1999; Moynahan et al., 2001; Murphy and Moynahan, 2010; Shrivastav et al., 2008; Venkitaraman, 2003).

SSOC comprise about 99% of hereditary ovarian cancers. However, it is important to note that 10-13% of all ovarian cancer cases can be classified as hereditary and linked to the inherited mutations described below (Pal et al., 2005; Risch, 2001; Stratton JF, 1999; Sowter and Ashworth, 2005). In sporadic cancers, the mutational activation of oncogenes, coupled with non-mutational inactivation of tumor suppressor genes, is often observed (Kenemans et al., 2004). In hereditary cancers, germline mutations in a single allele confer an elevated risk for cancer development (Radice, 2002). Therefore, while genetic screening to identify at risk individuals is highly desirable in patients with a family history of breast, ovarian or colon cancer, the potential biomarkers described below may or may not be applicable for the

**5.1 Human MutS homolog 2 (***hMSH2***) and Human MutL homolog 1 (***hMLH1***)** 

Ovarian carcinomas in patients from HNPCC families typically present as early-onset, nonserous epithelial tumors (Ketabi et al., 2011). *hMSH2* and *hMLH1* are the two most frequently mutated genes in this syndrome and confer a 9-12% lifetime risk of ovarian cancer (Aarnio et al., 1995; Brown et al., 2001; Kasprzak et al., 1999; Russo et al., 2009). The hMSH2 and hMLH1 proteins are the fundamental components of DNA mismatch repair (MMR) (Kolodner et al., 1994) and defects in these genes significantly increase the rate of mutation, which is believed to contribute to cancer development (Loeb, 2011; Valeri et al., 2010). In particular, microsatellite instability (MSI) has been observed in tumors from HNPCC patients (Dietmaier et al., 1997) and stems, at least in part, from a mutation or inherited epigenetic inactivation of hMLH1 (Gazzoli et al., 2002; Goecke et al., 2006; Hitchins et al, 2007; Kane et al., 1997). Interestingly, Valeri and colleagues (2010) reported that a noncoding miRNA designated as miR-155 is significantly overexpressed in human colorectal cancers and that an inverse correlation exists between the expression of miR-155 and the expression of hMLH1 or hMSH2 proteins in these tissues. miR-155 has been detected in blood samples derived from patients with ovarian cancer, though the sensitivity is still too low to be used as a reliable and predictive indicator of disease progression (Hausler et al., 2010). miR-155 has been put forth as a potential biomarker for the detection of early

Screening for mutations in genes important to MMR, such as *hMSH2* and *hMLH1*, and for epigenetic changes relevant to MMR such as hMLH1 promoter methylation, should prove to be an effective strategy for identifying patients in HNPCC families who may also be at risk for developing ovarian cancer. Moreover, screening for the upregulation of the noncoding RNA miR-155 may also prove to be effective in this regard. Important questions concerning the latter remain to be answered; including whether miR-155 upregulation is involved with sporadic ovarian cancers and if this noncoding RNA can be used as an early diagnostic

BRCA1 and BRCA2 are large nuclear proteins which act as tumor suppressors and contribute to genetic stability and DNA damage repair (Arai et al., 2004; Meindl et al., 2011; van der Groep et al., 2011). Whereas numerous biochemical and molecular functions have been described for both proteins (reviewed in Narod & Foulkes, 2004; Venkitaraman, 2002), they have both been implicated in the repair of double-strand breaks (DSBs) by homologous recombination (HR) (Badie et al., 2010; Boulton, 2006; Moynahan et al., 1999; Moynahan et al., 2001; Murphy and Moynahan, 2010; Shrivastav et al., 2008; Venkitaraman, 2003).

detection of sporadic ovarian cancers.

pancreatic neoplasia (Habbe et al., 2009).

**5.2 Breast Cancer Susceptibility Genes (***BRCA1* **and** *BRCA2***)**

marker.

Approximately half of high grade serous carcinomas exhibit defects in HR, solidifying the importance of this process in its implications for disease pathology extending beyond the presence of germline mutations (Bell et al., 2011).

The risk of ovarian cancer is about 40% in carriers with BRCA1 mutations (Antoniou et al., 2003; Ford et al., 1994). BRCA1 is composed of 1863 amino acids and possesses a N-terminal RING domain and and two C-terminal BRCT domains, present in tandem, at its C-terminus. The RING domain is protein-protein interaction motif which mediates the binding of BRCA1 to its obligate partner BARD1 (Meza et al., 1999; Wu et al., 1996). The BRCA1:BARD1 complex possesses ubiquitin ligase activity (Starita et al., 2004) while the BRCT domains of BRCA1 serve as sites of numerous protein-protein interactions, regulate transcription, and possess the ability to bind to phosphopeptides (reviewed in Narod and Foulkes, 2004; Starita and Parvin, 2003; Manke et al., 2003). Numerous cancer-associated missense mutations which disrupt interactions with putative binding partners have been described in the RING and BRCT domains of BRCA1 (reviewed in Carvalho et al., 2007; Morris and Solomon, 2004; Szabo et al., 2004).

The risk of ovarian cancer is about 25% in patients with *BRCA2* mutations (Ford et al., 1998). BRCA2 is composed of 3418 amino acids and possesses two distinct classes of BRC repeats which interact with the RAD51 protein, the mammalian homolog of *Escherichia coli* RecA (Carreira and Kowalczykowski, 2011). In addition, the C-terminal region of BRCA2, TR2, interacts with RAD51 (van der Groep et al., 2011). A major mechanism by which RAD51 is recruited to damaged DNA is via its interaction with BRCA2 and, along with the latter, plays a critical role in homologous recombination (Badie et al., 2010; Davies et al., 2001; Jensen et al., 2010). Cancer associated point mutations on BRC repeats which disrupt interaction of BRCA2 with RAD51 have been reported (Venkitaraman, 2009). Based on the observation that BRC repeats bind distinct regions of RAD51 and are not equal in their mode of interaction, it was hypothesized that a mutation within even one of the eight BRC repeats in this region could be sufficient to affect the way that BRCA2 interacts with RAD51, and lead to an increased risk of cancer (Galkin et al., 2005). Interestingly, certain families exhibit *BRCA2* mutations which appear to predispose carriers to ovarian cancer and which are located within exon 11 (Gayther et al., 1997; Lubinski et al., 2004; Petrucelli et al., 2002; Thompson et al., 2001). While this area is generally referred to as the ovarian cancer cluster region, Al-Saffar and Foulkes (2002) proposed that this region of exon 11 be known as the diminished breast cancer risk region.

Ovarian tumors in women carrying mutations in *BRCA1* or *BRCA2* are generally serous carcinomas and tend to be of high grade when diagnosed (Sowter and Ashworth, 2005). High grade serous carcinomas associated with *BRCA* mutations are believed to arise from the distal fallopian tube (Crum, 2009; Piek et al., 2003) and are frequently accompanied by mutations in *TP53* (Ahmed et al., 2010; Milner et al, 1993; reviewed in Hall et al., 2004). A comprehensive model for the development of high grade serous ovarian cancer has been put forth by Bowtell (2010) in which the loss of p53 and BRCA disrupts the HR repair of damaged DNA and, in turn, leads to CIN and carcinogenesis. A link between ovarian inclusion cysts and serous carcinomas has been proposed (Sowter and Ashworth, 2005) and may be explained by a mechanism in which cells from the fimbria travel to inclusion cysts and there become transformed and malignant via endometriosis or a series of mitogenic events and malignant (Crum, 2009). Alternatively, high grade serous carcinomas may be derived from stem-like ovarian cancer cells which have been dysregulated due, at least in part, to *BRCA* inactivation (Foulkes, 2004; Yin et al., 2010). Other hypotheses to explain the tissue specific cancers observed in mutant *BRCA* carriers have also been reviewed elsewhere

Potential Tumor Biomarkers for Ovarian Cancer 231

There are medical options for a woman with a strong family history of cancer and altered *BRCA* status. In particular, a woman with a highly penetrant cancer-associated *BRCA* mutation who undergoes a prophylactic bilateral salpingo-oophorectomy decreases her risk of ovarian cancer by 80% (Brown and Parker, 2011; Finch et al., 2006). A decreased risk of ovarian cancer has been observed in carriers of *BRCA* mutations who undergo tubal ligation, though it should be noted that this procedure is not as effective as removal of the ovaries (Brown and Parker, 2011). About 2-5% of patients who undergo the prophylactic oophorectomy procedure exhibit an occult cancer of the ovaries upon histological examination (Lu et al., 2000; Schrag et al., 1997). Based on these observations, prophylactic oophorectomy for BRCA1 or BRCA2 mutation carriers appears to be the more effective method of reducing cancer risk, particularly if reproduction and child rearing has occurred (Olopade and Artioli, 2004; Salhab et al., 2010). It is also worthy to note that ovarian cancer patients with traditional *BRCA* mutations have been found to show better survival rates than those with hypermethylation silencing (Bell et al., 2011). It is therefore imperative to identify at risk patients harboring cancer predisposing and inherited mutations in *BRCA1*

PCR-based technologies have the potential to allow for the rapid identification of patients who exhibit genetic variations within gene sequences, introns, promoters and other important regions of DNA, such as cancer susceptibility loci. Genetic variations associated with the androgen receptor have been observed to increase the risk of sporadic ovarian cancer in both Caucasian (Ludwig, 2009) and African-American (Schilkdkraut, 2007) populations. Furthermore, single nucleotide polymorphisms (SNPs) have been identified in several genes which are likely or very likely to associate with ovarian cancer including *CCND1* (Quaye et al., 2009), *MRPL23* (Quaye et al., 2009), *CDKN1B* (Goode et al., 2009), *CDKN2A/2B* (Goode et al., 2009) and *RB1* (Song et al., 2006; Braem et al., 2011). Aside from these SNPs in specific genes, several ovarian cancer susceptibility loci have been identified and analyzed using genome wide association studies. These studies have been reviewed by Braem and colleagues (2011), who conclude that there is strong evidence to establish a correlation between ovarian cancer and SNPs on chromosomes 9p22.2, 2q31, 8q24, and 3q25. Taken together, these studies point to several genes and susceptibility loci which may be amenable to high throughput screening and may help to identify ovarian cancer before it

Understanding of the landscape of ovarian cancer pathogenesis has evolved over recent years, and with it, strategies for patient care. Early detection continues to be a top priority to diagnose this pernicious disease when it is still highly responsive to treatment. Novel discoveries in genomics, epigenetics, proteomics, and functional glycomics have rapidly expanded the number of potential tumor markers available. To make better sense of which candidate markers have the greatest significance, several strategies have been employed. Identification of cancer-specific alterations in glycosylation signatures and development of composite epigenetic serum panels are two minimally invasive approaches that may, in

**6. Sporadic ovarian cancer and new genetic markers** 

begins or in early stages, when survival is highest.

time, allow for more accurate early detection of ovarian cancer.

**7. Summary and future directions** 

and *BRCA2.*

(Billack and Monteiro, 2005). It is interesting to note that while epigenetic silencing of *BRCA1* in high grade tumors has been reported (Wilson et al., 1999), somatic mutations in *BRCA1* and *BRCA2* are rare in sporadic breast and ovarian cancers (Futreal et al., 1994; Lancaster et al., 1996).

While DNA testing for *BRCA* mutations is becoming more common, not all women will obtain a clear cut result. One possible outcome of *BRCA* genetic testing is the finding that the patient possesses a *BRCA* variant of uncertain significance for which there is no clinical information regarding its cancer association. Methods have been developed to assess the cancer risk of unclassified *BRCA* variants which involve the use of functional assays (Carvalho et al., 2007; Lee et al., 2010) and structure-based supervised learning computation models (Karchin et al., 2007). One example of how functional assays and computational models can be used to characterize rare *BRCA* alleles was recently described in a collaborative study involving our lab (Carvalho et al., 2009). In that study, a Swedish kindred L1383 revealed a proband with ovarian cancer at age 59 (Figure 1A, arrow). The proband's mother also had ovarian cancer while the proband's grandmother died from rectal cancer. Upon analysis it was found that this patient had a rare variant of *BRCA1* denoted as 5673insC which codes for an insertion of a cytosine at nt5673 in exon 24. The cytosine insertion produces a frameshift in which the last 12 amino acids of the protein are changed to a modified 15-amino acid segment. Functional growth assays utilizing a reporter gene driven by LexA were carried out to examine the effect of this insertion. Yeast transformed with fusion constructs coding for either wildtype (W) or mutated BRCA1 (5673insC) fused to a LexA DNA binding domain revealed that the mutant failed to activate the reporter gene, resulting in a significantly reduced growth compared to yeast expressing the wildtype construct (Figure 1B). Use of computational structural modeling suggested that the insertion could generate a novel 13-residue α-helix that might modify the binding of phosphopeptide to the BRCT binding pocket (Figure 1C, golden helix). Taken together, the functional data and the structure prediction suggest that the insertion leads to an impact on protein function. Despite the wealth of information generated via these functional and computational approaches, clinical validation is difficult to obtain due to the rarity of most uncharacterized *BRCA* variants. Moreover, the complexity of this approach makes high throughput analyses cumbersome. Nevertheless, the more information available for genetic counseling purposes the better.

Fig. 1. Use of family history (Panel A), functional analysis (Panel B) and structure-based supervised learning computation models (Panel C) to assess uncharacterized variants of BRCA1. Reprinted from Carvalho et al., 2009, with permission from Elsevier.

(Billack and Monteiro, 2005). It is interesting to note that while epigenetic silencing of *BRCA1* in high grade tumors has been reported (Wilson et al., 1999), somatic mutations in *BRCA1* and *BRCA2* are rare in sporadic breast and ovarian cancers (Futreal et al., 1994;

While DNA testing for *BRCA* mutations is becoming more common, not all women will obtain a clear cut result. One possible outcome of *BRCA* genetic testing is the finding that the patient possesses a *BRCA* variant of uncertain significance for which there is no clinical information regarding its cancer association. Methods have been developed to assess the cancer risk of unclassified *BRCA* variants which involve the use of functional assays (Carvalho et al., 2007; Lee et al., 2010) and structure-based supervised learning computation models (Karchin et al., 2007). One example of how functional assays and computational models can be used to characterize rare *BRCA* alleles was recently described in a collaborative study involving our lab (Carvalho et al., 2009). In that study, a Swedish kindred L1383 revealed a proband with ovarian cancer at age 59 (Figure 1A, arrow). The proband's mother also had ovarian cancer while the proband's grandmother died from rectal cancer. Upon analysis it was found that this patient had a rare variant of *BRCA1* denoted as 5673insC which codes for an insertion of a cytosine at nt5673 in exon 24. The cytosine insertion produces a frameshift in which the last 12 amino acids of the protein are changed to a modified 15-amino acid segment. Functional growth assays utilizing a reporter gene driven by LexA were carried out to examine the effect of this insertion. Yeast transformed with fusion constructs coding for either wildtype (W) or mutated BRCA1 (5673insC) fused to a LexA DNA binding domain revealed that the mutant failed to activate the reporter gene, resulting in a significantly reduced growth compared to yeast expressing the wildtype construct (Figure 1B). Use of computational structural modeling suggested that the insertion could generate a novel 13-residue α-helix that might modify the binding of phosphopeptide to the BRCT binding pocket (Figure 1C, golden helix). Taken together, the functional data and the structure prediction suggest that the insertion leads to an impact on protein function. Despite the wealth of information generated via these functional and computational approaches, clinical validation is difficult to obtain due to the rarity of most uncharacterized *BRCA* variants. Moreover, the complexity of this approach makes high throughput analyses cumbersome. Nevertheless, the more information available for genetic

Fig. 1. Use of family history (Panel A), functional analysis (Panel B) and structure-based supervised learning computation models (Panel C) to assess uncharacterized variants of

BRCA1. Reprinted from Carvalho et al., 2009, with permission from Elsevier.

Lancaster et al., 1996).

counseling purposes the better.

There are medical options for a woman with a strong family history of cancer and altered *BRCA* status. In particular, a woman with a highly penetrant cancer-associated *BRCA* mutation who undergoes a prophylactic bilateral salpingo-oophorectomy decreases her risk of ovarian cancer by 80% (Brown and Parker, 2011; Finch et al., 2006). A decreased risk of ovarian cancer has been observed in carriers of *BRCA* mutations who undergo tubal ligation, though it should be noted that this procedure is not as effective as removal of the ovaries (Brown and Parker, 2011). About 2-5% of patients who undergo the prophylactic oophorectomy procedure exhibit an occult cancer of the ovaries upon histological examination (Lu et al., 2000; Schrag et al., 1997). Based on these observations, prophylactic oophorectomy for BRCA1 or BRCA2 mutation carriers appears to be the more effective method of reducing cancer risk, particularly if reproduction and child rearing has occurred (Olopade and Artioli, 2004; Salhab et al., 2010). It is also worthy to note that ovarian cancer patients with traditional *BRCA* mutations have been found to show better survival rates than those with hypermethylation silencing (Bell et al., 2011). It is therefore imperative to identify at risk patients harboring cancer predisposing and inherited mutations in *BRCA1* and *BRCA2.*

#### **6. Sporadic ovarian cancer and new genetic markers**

PCR-based technologies have the potential to allow for the rapid identification of patients who exhibit genetic variations within gene sequences, introns, promoters and other important regions of DNA, such as cancer susceptibility loci. Genetic variations associated with the androgen receptor have been observed to increase the risk of sporadic ovarian cancer in both Caucasian (Ludwig, 2009) and African-American (Schilkdkraut, 2007) populations. Furthermore, single nucleotide polymorphisms (SNPs) have been identified in several genes which are likely or very likely to associate with ovarian cancer including *CCND1* (Quaye et al., 2009), *MRPL23* (Quaye et al., 2009), *CDKN1B* (Goode et al., 2009), *CDKN2A/2B* (Goode et al., 2009) and *RB1* (Song et al., 2006; Braem et al., 2011). Aside from these SNPs in specific genes, several ovarian cancer susceptibility loci have been identified and analyzed using genome wide association studies. These studies have been reviewed by Braem and colleagues (2011), who conclude that there is strong evidence to establish a correlation between ovarian cancer and SNPs on chromosomes 9p22.2, 2q31, 8q24, and 3q25. Taken together, these studies point to several genes and susceptibility loci which may be amenable to high throughput screening and may help to identify ovarian cancer before it begins or in early stages, when survival is highest.

#### **7. Summary and future directions**

Understanding of the landscape of ovarian cancer pathogenesis has evolved over recent years, and with it, strategies for patient care. Early detection continues to be a top priority to diagnose this pernicious disease when it is still highly responsive to treatment. Novel discoveries in genomics, epigenetics, proteomics, and functional glycomics have rapidly expanded the number of potential tumor markers available. To make better sense of which candidate markers have the greatest significance, several strategies have been employed. Identification of cancer-specific alterations in glycosylation signatures and development of composite epigenetic serum panels are two minimally invasive approaches that may, in time, allow for more accurate early detection of ovarian cancer.

Potential Tumor Biomarkers for Ovarian Cancer 233

Andersen M.R. et al. (2010) Use of a Symptom Index, CA125, and HE4 to predict ovarian

Andre S. et al. (2009) From structural to functional glycomics: core substitutions as

Antoniou A. et al. (2003) Average risks of breast and ovarian cancer associated with BRCA1

Arai M., Utsunomiya J., & Miki Y. (2004) Familial breast and ovarian cancers.

Asadollahi R., Hyde C.A., & Zhong X.Y. (2010) Epigenetics of ovarian cancer: from the lab to

Aubert M. et al. (2000) Peritoneal colonization by human pancreatic cancer cells is inhibited

Badie S. et al. (2010) BRCA2 acts as a RAD51 loader to facilitate telomere replication and

Bafna S., Kaur S., & Batra S.K. (2010) Membrane-bound mucins: the mechanistic basis for alterations in the growth and survival of cancer cells. *Oncogene.* 20;29, 2893-2904. Balch C. et al. (2009) Minireview: epigenetic changes in ovarian cancer. *Endocrinology.* 150,

Bast R.C., Jr. et al. (1981) Reactivity of a monoclonal antibody with human ovarian

Bast R.C., Jr. et al. (2005) New tumor markers: CA125 and beyond. *Int.J.Gynecol.Cancer.* 15

Basu A. et al. (1987) Presence of tumor-associated antigens in epidermal growth factor receptors from different human carcinomas. *Cancer Res.* 47, 2531-2536. Belancio V.P., Hedges D.J., & Deininger P. (2006) LINE-1 RNA splicing and influences on

Belisle J.A. et al. (2010) Identification of Siglec-9 as the receptor for MUC16 on human NK

Bol G.M. et al. (2010) Methylation profiles of hereditary and sporadic ovarian cancer.

Boulton S.J. (2006) Cellular functions of the BRCA tumour-suppressor proteins.

Bowtell D.D. (2010) The genesis and evolution of high-grade serous ovarian cancer.

Braem M.G. et al. (2011). Genetic susceptibility to sporadic ovarian cancer: A systematic

Bret C. et al. (2011) SULFs in human neoplasia: implication as progression and prognosis

Brockhausen I, Schachter H, Stanley P. O-GalNAc Glycans. In: Varki A, Cummings RD,

Esko JD, Freeze HH, Stanley P, Bertozzi CR, Hart GW, Etzler ME, editors.

Bell D. (2011) Integrated genomic analyses of ovarian carcinoma. *Nature.* 474, 609-615. Berger S.L. et al. (2009) An operational definition of epigenetics. *Genes Dev.* 23, 781-783. Billack B. & Monteiro A.N. (2005) BRCA1 in breast and ovarian cancer predisposition.

mammalian gene expression. *Nucleic Acids Res.* 34, 1512-1521.

cells, B cells, and monocytes. *Mol.Cancer.* 9:118., 118.

combined analysis of 22 studies. *Am.J.Hum.Genet.* 72, 1117-1130.

by antisense FUT3 sequence. *Int.J.Cancer.* 88, 558-565.

molecular switches for shape and lectin affinity of N-glycans. *Biol.Chem.* 390, 557-

or BRCA2 mutations detected in case Series unselected for family history: a

cancer. *Gynecol.Oncol.* 116, 378-383.

*Int.J.Clin.Oncol.* 9, 270-282.

the clinic. *Gynecol.Oncol.* 118, 81-87.

capping. *Nat.Struct.Mol.Biol.* 17, 1461-1469.

carcinoma. *J.Clin.Invest.* 68, 1331-1337.

Suppl 3:274-81., 274-281.

*Cancer Lett.* 227, 1-7.

*Histopathology.* 57, 363-370.

*Biochem.Soc.Trans.* 34, 633-645.

review. *Biochem. Biophys. Acta* 1816, 132-146.

*Nat.Rev.Cancer.* 10, 803-808.

factors. *J.Transl.Med.* 9:72., 72.

565.

4003-4011.

Aside from CA125, which currently remains the sole validated ovarian cancer biomarker, other serum markers may be comparable or superior for early detection. Among these, HE4 appears especially promising, and the use of CA125 testing with HE4 or other emerging markers may prove to be clinically useful. In addition, better identification of women with greatest genetic risk may help to isolate a small subset of the population that requires the closest monitoring. By employing strategies such as those described above, it is hopeful that ovarian cancer mortality rates, which have remained intractably high over the past several decades, will finally begin to decline.

#### **8. Acknowledgements**

We gratefully appreciate the efforts of the following colleagues who provided helpful comments during the preparation of this chapter: Drs. C. Lau-Cam and L. Schramm (St. John's University, Jamaica, NY), Dr. Dong-Hua Yang (Fox Chase Cancer Center, Philadelphia, PA), Dr. Alvaro Monteiro (Moffitt Cancer Center, Tampa, FL) and Dr. X.X. Xu (University of Miami Miller School of Medicine, Miami, FL).

#### **9. Dedication**

This chapter is dedicated to all women who are living with and those who have died from ovarian cancer.

#### **10. References**


Aside from CA125, which currently remains the sole validated ovarian cancer biomarker, other serum markers may be comparable or superior for early detection. Among these, HE4 appears especially promising, and the use of CA125 testing with HE4 or other emerging markers may prove to be clinically useful. In addition, better identification of women with greatest genetic risk may help to isolate a small subset of the population that requires the closest monitoring. By employing strategies such as those described above, it is hopeful that ovarian cancer mortality rates, which have remained intractably high over the past several

We gratefully appreciate the efforts of the following colleagues who provided helpful comments during the preparation of this chapter: Drs. C. Lau-Cam and L. Schramm (St. John's University, Jamaica, NY), Dr. Dong-Hua Yang (Fox Chase Cancer Center, Philadelphia, PA), Dr. Alvaro Monteiro (Moffitt Cancer Center, Tampa, FL) and Dr. X.X. Xu

This chapter is dedicated to all women who are living with and those who have died from

Aarnio M. et al. (1995) Life-time risk of different cancers in hereditary non-polyposis

Abbott K.L. et al. (2010) Identification of candidate biomarkers with cancer-specific

Ahluwalia A. et al. (2001) DNA methylation in ovarian cancer. II. Expression of DNA

Ahmad R. et al. (2009) MUC1-C oncoprotein functions as a direct activator of the nuclear

Ahmad S. (2011) Advances in ovarian cancer screening: health and medicine for women: a

Ahmed A.A. et al. (2010) Driver mutations in TP53 are ubiquitous in high grade serous

Ahmed N. et al. (2005) Role of integrin receptors for fibronectin, collagen and laminin in the

Al-Saffar M. & Foulkes W.D. (2002). Hereditary ovarian cancer resulting from a non-ovarian

Anastasi E. et al. (2010) HE4: a new potential early biomarker for the recurrence of ovarian

glycosylation in the tissue and serum of endometrioid ovarian cancer patients by

methyltransferases in ovarian cancer cell lines and normal ovarian epithelial cells.

multidisciplinary, evidence-based review of mid-life health concerns. *Yale* 

regulation of ovarian carcinoma functions in response to a matrix

cancer cluster region (OCCR) BRCA2 mutation: is the OCCR useful clinically? *J.* 

colorectal cancer (HNPCC) syndrome. *Int.J.Cancer.* 64, 430-433.

factor-kappaB p65 transcription factor. *Cancer Res.* 69, 7013-7021.

glycoproteomic analysis. *Proteomics.* 10, 470-481.

carcinoma of the ovary. *J.Pathol.* 221, 49-56.

microenvironment. *Clin.Exp.Metastasis.* 22, 391-402.

*Gynecol.Oncol.* 82, 299-304.

*J.Biol.Med.* 84, 47-49.

*Med. Genet.* 39, e68.

cancer. *Tumour.Biol.* 31, 113-119.

decades, will finally begin to decline.

(University of Miami Miller School of Medicine, Miami, FL).

**8. Acknowledgements** 

**9. Dedication** 

ovarian cancer.

**10. References** 


Potential Tumor Biomarkers for Ovarian Cancer 235

Crum C.P. (2009) Intercepting pelvic cancer in the distal fallopian tube: theories and

Davies A.A. et al. (2001) Role of BRCA2 in control of the RAD51 recombination and DNA

Davies E.J. et al. (2004) Distribution and clinical significance of heparan sulfate

Diamandis E.P. et al. (2003) Human kallikrein 6 (hK6): a new potential serum biomarker for diagnosis and prognosis of ovarian carcinoma. *J.Clin.Oncol.* 21, 1035-1043. Dietmaier W. et al. (1997) Diagnostic microsatellite instability: definition and correlation

Drapkin R. et al. (2005) Human epididymis protein 4 (HE4) is a secreted glycoprotein that is

Eden A. et al. (2003) Chromosomal instability and tumors promoted by DNA

El Sherbini M.A. et al. (2011) Diagnostic value of serum kallikrein-related peptidases 6 and 10 versus CA125 in ovarian cancer. *Int.J.Gynecol.Cancer.* 21, 625-632. Fang L. et al. (2011) MicroRNA miR-93 promotes tumor growth and angiogenesis by

Finch A. et al. (2006) Salpingo-oophorectomy and the risk of ovarian, fallopian tube, and

Florkowski C.M. (2008) Sensitivity, specificity, receiver-operating characteristic (ROC)

Fontenot J.D. et al. (1993) Biophysical characterization of one-, two-, and three-tandem repeats of human mucin (muc-1) protein core. *Cancer Res.* 53, 5386-5394. Ford D. et al. (1994) Risks of cancer in BRCA1-mutation carriers. Breast Cancer Linkage

Ford D. et al. (1998) Genetic heterogeneity and penetrance analysis of the BRCA1 and

Foulkes W.D. (2004) BRCA1 functions as a breast stem cell regulator. *J.Med.Genet.* 41, 1-5. Fukuda M. (1996) Possible roles of tumor-associated carbohydrate antigens. *Cancer Res.* 56,

Fukuda M. (2002) Roles of mucin-type O-glycans in cell adhesion. *Biochim.Biophys.Acta.*

Futreal P.A. et al. (1994) BRCA1 mutations in primary breast and ovarian carcinomas.

Galkin V.E. et al. (2005) BRCA2 BRC motifs bind RAD51-DNA filaments.

peritoneal cancers in women with a BRCA1 or BRCA2 Mutation. *JAMA.* 296, 185-

curves and likelihood ratios: communicating the performance of diagnostic tests.

BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium.

overexpressed by serous and endometrioid ovarian carcinomas. *Cancer Res.* 65,

evaluating the role of polysialic acids in the metastatic process. *Oncogene.* 20, 997-

Dall'Olio F. & Chiricolo M. (2001) Sialyltransferases in cancer. *Glycoconj.J.* 18, 841-850. Daniel L. et al. (2001) A nude mice model of human rhabdomyosarcoma lung metastases for

proteoglycans in ovarian cancer. *Clin.Cancer Res.* 10, 5178-5186.

with mismatch repair protein expression. *Cancer Res.* 57, 4749-4756.

realities. *Mol.Oncol.* 3, 165-170.

repair protein. *Mol.Cell.* 7, 273-282.

hypomethylation. *Science.* 300, 455.

*Clin.Biochem.Rev.* 29 Suppl 1: S83-S87.

Consortium. *Lancet.* 19;343, 692-695.

*Proc.Natl.Acad.Sci.U.S.A.* 102, 8537-8542.

*Am.J.Hum.Genet.* 62, 676-689.

targeting integrin-beta8. *Oncogene.* 30, 806-821.

1004.

2162-2169.

192.

2237-2244.

19;1573, 394-405.

*Science.* 266, 120-122.

Essentials of Glycobiology. 2nd edition. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2009. Chapter 9.


Brown G.J. et al. (2001) Cancer risk in young women at risk of hereditary nonpolyposis

Brown K.R., Parker, L.P. (2011). Hereditary Ovarian Cancer and Other Gynecologic

Cairns P. (2007) Gene methylation and early detection of genitourinary cancer: the road

Calin G.A. & Croce C.M. (2006) MicroRNA-cancer connection: the beginning of a new tale.

Canney P.A. et al. (1984) Ovarian cancer antigen CA125: a prospective clinical assessment of

Cannistra S.A. et al. (1995) Expression and function of beta 1 and alpha v beta 3 integrins in

Carreira A. & Kowalczykowski S.C. (2011) Two classes of BRC repeats in BRCA2 promote

Carvalho M. et al. (2009) Analysis of a set of missense, frameshift, and in-frame deletion

Carvalho M.A., Couch F.J., & Monteiro A.N. (2007) Functional assays for BRCA1 and

Caslini C. et al. (2006) Histone modifications silence the GATA transcription factor genes in

Chen R. et al. (2007) Inflammation, cancer and chemoresistance: taking advantage of the toll-

Cheng J.C., Auersperg N., & Leung P.C. (2011) Inhibition of p53 represses E-cadherin

Chhieng D.C. et al. (2003) Expression of CEA, Tag-72, and Lewis-Y antigen in primary and

Chiang C.H. et al. (2010) A novel sialyltransferase inhibitor AL10 suppresses invasion and

Christie D.R. et al. (2008) ST6Gal-I expression in ovarian cancer cells promotes an invasive phenotype by altering integrin glycosylation and function. *J.Ovarian.Res.* 1, 3. Corney D.C. et al. (2010) Frequent downregulation of miR-34 family in human ovarian

Cramer D.W. et al. (2011) Ovarian cancer biomarker performance in prostate, lung,

expression by increasing DNA methyltransferase-1 and promoter methylation in

metastasis of lung cancer cells by inhibiting integrin-mediated signaling. *J.Cell* 

colorectal, and ovarian cancer screening trial specimens. *Cancer Prev.Res.(Phila).* 4,

like receptor signaling pathway. *Am.J.Reprod.Immunol.* 57, 93-107.

metastatic lesions of ovarian carcinoma. *Hum.Pathol.* 34, 1016-1021.

RAD51 nucleoprotein filament function by distinct mechanisms.

Management.. 2nd edition. Springer Science Press (NY); Chapter 10. Buys S.S. et al. (2011) Effect of screening on ovarian cancer mortality: the Prostate, Lung,

Harbor Laboratory Press; 2009. Chapter 9.

349.

*JAMA.* 305, 2295-2303.

*Cancer Res.* 66, 7390-7394.

*Proc.Natl.Acad.Sci.U.S.A.* 

*Physiol.* 223, 492-499.

365-374.

ahead. *Nat.Rev.Cancer.* 7, 531-543.

its role as a tumour marker. *Br.J.Cancer.* 50, 765-769.

ovarian cancer. *Gynecol.Oncol.* 58, 216-225.

variants of BRCA1. *Mutat.Res.* 660, 1-11.

BRCA2. *Int.J.Biochem.Cell Biol.* 39, 298-310.

ovarian cancer. *Oncogene.* 25, 5446-5461.

cancers. *Clin.Cancer Res.* 16, 1119-1128.

serous borderline ovarian tumor cells. *Oncogene.* 

Essentials of Glycobiology. 2nd edition. Cold Spring Harbor (NY): Cold Spring

colorectal cancer: implications for gynecologic surveillance. *Gynecol.Oncol.* 80, 346-

Malignancies. In: Ellis CN, editor. Inherited Cancer Syndromes: Current Clinical

Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial.


Potential Tumor Biomarkers for Ovarian Cancer 237

Hibbs K. et al. (2004) Differential gene expression in ovarian carcinoma: identification of

Higashi H. et al. (1984) Tumor-associated expression of glycosphingolipid Hanganutziu-

Hitchins M.P. et al. (2007) Inheritance of a cancer-associated MLH1 germ-line epimutation.

Inoue S., Sato C., & Kitajima K. (2010) Extensive enrichment of N-glycolylneuraminic acid in

Jacob F. et al. (2011) No benefit from combining HE4 and CA125 as ovarian tumor markers

Jacobs I. & Bast R.C., Jr. (1989) The CA 125 tumour-associated antigen: a review of the

Jarboe E. et al. (2008) Serous carcinogenesis in the fallopian tube: a descriptive classification.

Jensen M. & Berthold F. (2007) Targeting the neural cell adhesion molecule in cancer. *Cancer* 

Jensen R.B., Carreira A., & Kowalczykowski S.C. (2010) Purified human BRCA2 stimulates

Jonckheere N. & Van Seuningen, I (2010) The membrane-bound mucins: From cell signalling

Kane M.F. et al. (1997) Methylation of the hMLH1 promoter correlates with lack of

Kanoh A. et al. (2006) Ectopic expression of N-acetylglucosamine 6-O-sulfotransferase 2 in chemotherapy-resistant ovarian adenocarcinomas. *Glycoconj.J.* 23, 453-460. Karchin R. et al. (2007) Functional impact of missense variants in BRCA1 predicted by

Kasprzak L., Foulkes W.D., & Shelling A.N. (1999) Forth nightly review: hereditary ovarian

Kenemans P., Verstraeten R.A., & Verheijen R.H. (2004) Oncogenic pathways in hereditary

Ketabi Z. et al. (2011) Ovarian cancer linked to lynch syndrome typically presents as early-

Kirchhoff C. et al. (1991) A major human epididymis-specific cDNA encodes a protein with sequence homology to extracellular proteinase inhibitors. *Biol.Reprod.* 45, 350-357. Kodama J. et al. (2001) Thrombospondin-1 and -2 messenger RNA expression in epithelial

Kolodner R.D. et al. (1994) Structure of the human MSH2 locus and analysis of two Muir-

Kufe D.W. (2009) Mucins in cancer: function, prognosis and therapy. *Nat.Rev.Cancer.* 9, 874-

Kuzmanov U. et al. (2009) Differential N-glycosylation of kallikrein 6 derived from ovarian cancer cells or the central nervous system. *Mol.Cell Proteomics.* 8, 791-798. Lai J. et al. (2003) Loss of HSulf-1 up-regulates heparin-binding growth factor signaling in

onset, non-serous epithelial tumors. *Gynecol.Oncol.* 121, 462-465.

Torre kindreds for msh2 mutations. *Genomics.* 24, 516-526.

to transcriptional regulation and expression in epithelial cancers. *Biochimie.* 92, 1-11.

expression of hMLH1 in sporadic colon tumors and mismatch repair-defective

extracellular sialoglycoproteins abundantly synthesized and secreted by human

potential biomarkers. *Am.J.Pathol.* 165, 397-414.

in a clinical setting. *Gynecol.Oncol.* 121, 487-491.

RAD51-mediated recombination. *Nature.* 467, 678-683.

human tumor cell lines. *Cancer Res.* 57, 808-811.

supervised learning. *PLoS.Comput.Biol.* 3, e26.

and sporadic breast cancer. *Maturitas.* 49, 34-43.

ovarian tumor. *Anticancer Res.* 21, 2983-2987.

cancer. *J.Biol.Chem.* 20;278, 23107-23117.

carcinoma. *BMJ.* 20;318, 786-789.

*N.Engl.J.Med.* 356, 697-705.

cancer cells. *Glycobiology.* 20, 752-762.

literature. *Hum.Reprod.* 4, 1-12.

*Int.J.Gynecol.Pathol.* 27, 1-9.

*Lett.* 258, 9-21.

885.

Deicher antigen in human cancers. *Gann.* 75, 1025-1029.


Gao L. et al. (2011) Enhancive effects of Lewis y antigen on CD44-mediated adhesion and

Gayther S.A. et al. (1997) Variation of risks of breast and ovarian cancer associated with different germline mutations of the BRCA2 gene. *Nat.Genet.* 15, 103-105. Gazzoli I. et al. (2002) A hereditary nonpolyposis colorectal carcinoma case associated with

Gluer S. et al. (1998a) Serum polysialylated neural cell adhesion molecule in childhood

Gluer S. et al. (1998b) Polysialylated neural cell adhesion molecule in childhood

Goecke T. et al. (2006) Genotype-phenotype comparison of German MLH1 and MSH2

Goode E.L. et al. (2009) Candidate gene analysis using imputed genotypes: cell cycle single-

Gu J. & Taniguchi N. (2004) Regulation of integrin functions by N-glycans. *Glycoconj.J.* 21, 9-

Habbe N. et al. (2009) MicroRNA miR-155 is a biomarker of early pancreatic neoplasia.

Hakomori S. (1996) Tumor malignancy defined by aberrant glycosylation and

Hakomori S. (2002) Glycosylation defining cancer malignancy: new wine in an old bottle.

Hall J., Paul J., & Brown R. (2004) Critical evaluation of p53 as a prognostic marker in

Hanasaki K. et al. (1994) Cytokine-induced beta-galactoside alpha-2,6-sialyltransferase in

Hartel-Schenk S. et al. (2001) Novel adapter protein AP162 connects a sialyl-Le(x)-positive mucin with an apoptotic signal transduction pathway. *Glycoconj.J.* 18, 915-923. Hausler S.F. et al. (2010) Whole blood-derived miRNA profiles as potential new tools for

Hedlund M. et al. (2008) Evidence for a human-specific mechanism for diet and antibody-

Hellstrom I. et al. (2003) The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma.

Hellstrom I. et al. (2010) Detection of the HE4 protein in urine as a biomarker for ovarian

Hellstrom I. & Hellstrom K.E. (2011) Two novel biomarkers, mesothelin and HE4, for

Helzlsouer K.J. et al. (1993) Prospective study of serum CA-125 levels as markers of ovarian

diagnosis of ovarian carcinoma. *Expert.Opin.Med.Diagn.* 5, 227-240.

human endothelial cells mediates alpha 2,6-sialylation of adhesion molecules and

mediated inflammation in carcinoma progression. *Proc.Natl.Acad.Sci.U.S.A.* 105,

sphingo(glyco)lipid metabolism. *Cancer Res.* 56, 5309-5318.

*Res.* 62, 3925-3928.

*Prev.* 18, 935-944.

18936-18941.

*Cancer Res.* 63, 3695-3700.

neoplasms. *Cancer Lett.* 296, 43-48.

cancer. *JAMA.* 269, 1123-1126.

*Cancer Biol.Ther.* 8, 340-346.

*Proc.Natl.Acad.Sci.U.S.A.* 99, 10231-10233.

ovarian cancer. *Expert.Rev.Mol.Med.* 6, 1-20.

CD22 ligands. *J.Biol.Chem.* 269, 10637-10643.

ovarian cancer screening. *Br.J.Cancer.* 103, 693-700.

15.

neuroblastoma. *Br.J.Cancer.* 78, 106-110.

rhabdomyosarcoma. *Pediatr.Res.* 43, 145-147.

HNPCC Consortium. *J.Clin.Oncol.* 24, 4285-4292.

spreading of human ovarian cancer cell line RMG-I. *J.Exp.Clin.Cancer Res.* 30:15., 15.

hypermethylation of the MLH1 gene in normal tissue and loss of heterozygosity of the unmethylated allele in the resulting microsatellite instability-high tumor. *Cancer* 

mutation carriers clinically affected with Lynch syndrome: a report by the German

nucleotide polymorphisms and ovarian cancer risk. *Cancer Epidemiol.Biomarkers* 


Potential Tumor Biomarkers for Ovarian Cancer 239

Melnikov A. et al. (2009) Differential methylation profile of ovarian cancer in tissues and

Meza J.E. et al. (1999) Mapping the functional domains of BRCA1. Interaction of the ring

Milner B.J. et al. (1993) p53 mutation is a common genetic event in ovarian carcinoma.

Mitchell P.S. et al. (2008) Circulating microRNAs as stable blood-based markers for cancer

Montagnana M. et al. (2009) The utility of serum human epididymis protein 4 (HE4) in

Morimoto-Tomita M. et al. (2002) Cloning and characterization of two extracellular heparindegrading endosulfatases in mice and humans. *J.Biol.Chem.* 20;277, 49175-49185. Morris J.R. & Solomon E. (2004) BRCA1 : BARD1 induces the formation of conjugated

Moynahan M.E. et al. (1999) Brca1 controls homology-directed DNA repair. *Mol.Cell.* 4, 511-

Moynahan M.E., Pierce A.J., & Jasin M. (2001) BRCA2 is required for homology-directed

Murphy C.G. & Moynahan M.E. (2010) BRCA gene structure and function in tumor

Narita K. et al. (2006) HSulf-1 inhibits angiogenesis and tumorigenesis in vivo. *Cancer Res.*

Narod S.A. & Foulkes W.D. (2004) BRCA1 and BRCA2: 1994 and beyond. *Nat.Rev.Cancer.* 4,

Nolen B. et al. (2010) Serum biomarker panels for the discrimination of benign from malignant cases in patients with an adnexal mass. *Gynecol.Oncol.* 117, 440-445. Obermair A. et al. (2002) Expression of MUC1 splice variants in benign and malignant

Ohtsubo K. & Marth J.D. (2006) Glycosylation in cellular mechanisms of health and disease.

Olopade O.I. & Artioli G. (2004) Efficacy of risk-reducing salpingo-oophorectomy in women with BRCA-1 and BRCA-2 mutations. *Breast J.* 10 Suppl 1:S5-9., S5-S9. Pal T. et al. (2005) BRCA1 and BRCA2 mutations account for a large proportion of ovarian

Palmer C. et al. (2008) Systematic evaluation of candidate blood markers for detecting

Patel I.S. et al. (2003) Cadherin switching in ovarian cancer progression. *Int.J.Cancer.* 20;106,

Petrucelli et al. (2002) Clinical interpretation and recommendations for patients with a

Piek J.M. et al. (2003) Histopathological characteristics of BRCA1- and BRCA2-associated

Pochampalli M.R., el Bejjani R.M., & Schroeder J.A. (2007) MUC1 is a novel regulator of

intraperitoneal cancer: a clinic-based study. *Fam.Cancer.* 2, 73-78.

variant of uncertain significance in BRCA1 or BRCA2: a survey of genetic

ubiquitin structures, dependent on K6 of ubiquitin, in cells during DNA replication

finger domains of BRCA1 and BARD1. *J.Biol.Chem.* 274, 5659-5665.

detection. *Proc.Natl.Acad.Sci.U.S.A.* 105, 10513-10518.

repair of chromosomal breaks. *Mol.Cell.* 7, 263-272.

suppression: a repair-centric perspective. *Cancer J.* 16, 39-47.

and repair. *Hum.Mol.Genet.* 13 , 807-817.

ovarian tumours. *Int.J.Cancer.* 100, 166-171.

carcinoma cases. *Cancer.* 104, 2807-2816.

counseling practice. Genet Test. 2002 6:107-13.

ErbB1 receptor trafficking. *Oncogene.* 26, 1693-1701.

ovarian cancer. *PLoS.One.* 3, e2633.

patients with a pelvic mass. *J.Clin.Lab Anal.* 23, 331-335.

plasma. *J.Mol.Diagn.* 11, 60-65.

*Cancer Res.* 53, 2128-2132.

518.

66, 6025-6032.

*Cell.* 126, 855-867.

665-676.

172-177.


Lancaster J.M. et al. (1996) BRCA2 mutations in primary breast and ovarian cancers.

Lau K.M., Mok S.C., & Ho S.M. (1999) Expression of human estrogen receptor-alpha and -

malignant ovarian epithelial cells. *Proc.Natl.Acad.Sci.U.S.A.* 96, 5722-5727. le Sage C. et al. (2007) Regulation of the p27(Kip1) tumor suppressor by miR-221 and miR-

Lee M.S. et al. (2010) Comprehensive analysis of missense variations in the BRCT domain of BRCA1 by structural and functional assays. *Cancer Res.* 70, 4880-4890. Li B. et al. (2011) Down-regulation of microRNA 106b is involved in p21-mediated cell cycle arrest in response to radiation in prostate cancer cells. *Prostate.* 71, 567-574. Li M., Song L., & Qin X. (2010a) Glycan changes: cancer metastasis and anti-cancer vaccines.

Li Q. et al. (2010b) Expression and correlation of Lewis y antigen and integrins alpha5 and

Li S.D. et al. (2010c) The role of microRNAs in ovarian cancer initiation and progression.

Liu T. et al. (2005) Human plasma N-glycoproteome analysis by immunoaffinity

Loeb L.A. (2011) Human cancers express mutator phenotypes: origin, consequences and

Lu K.H. et al. (2000) Occult ovarian tumors in women with BRCA1 or BRCA2 mutations undergoing prophylactic oophorectomy. *J.Clin.Oncol.* 18, 2728-2732. Lubinski J. et al. (2004) Cancer variation associated with the position of the mutation in the

Ludwig A.H. et al. (2009) Androgen, progesterone, and FSH receptor polymorphisms in ovarian cancer risk and outcome. *Endocr.Relat Cancer.* 16, 1005-1016. Maggino T. et al. (1994) Prospective multicenter study on CA 125 in postmenopausal pelvic

Makarla P.B. et al. (2005) Promoter hypermethylation profile of ovarian epithelial

Malecova B. & Morris K.V. (2010) Transcriptional gene silencing through epigenetic changes

Manke I.A. et al. (2003) BRCT repeats as phosphopeptide-binding modules involved in

Maradeo M.E. & Cairns P. (2011) Translational application of epigenetic alterations: Ovarian

McIntosh M.W. et al. (2004) Combining CA 125 and SMR serum markers for diagnosis and

Medeiros L.R. et al. (2009) Accuracy of CA 125 in the diagnosis of ovarian tumors: a quantitative systematic review. *Eur.J.Obstet.Gynecol.Reprod.Biol.* 142, 99-105. Meindl A. et al. (2011) Hereditary breast and ovarian cancer: new genes, new treatments,

mediated by non-coding RNAs. *Curr.Opin.Mol.Ther.* 12, 214-222.

early detection of ovarian carcinoma. *Gynecol.Oncol.* 95 , 9-15.

beta1 in ovarian serous and mucinous carcinoma. *Int.J.Gynecol.Cancer.* 20, 1482-

subtraction, hydrazide chemistry, and mass spectrometry. *J.Proteome.Res.* 4, 2070-

222 promotes cancer cell proliferation. *EMBO J.* 26, 3699-3708.

beta, progesterone receptor, and androgen receptor mRNA in normal and

*Nat.Genet.* 13, 238-240.

*J.Biosci.* 35, 665-673.

*J.Cell Mol.Med.* 14, 2240-2249.

targeting. *Nat.Rev.Cancer.* 11, 450-457.

BRCA2 gene. *Fam.Cancer.* 3, 1-10.

masses. *Gynecol.Oncol.* 54, 117-123.

neoplasms. *Clin.Cancer Res.* 11, 5365-5369.

protein targeting. *Science.* 302, 636-639.

cancer as a model. *FEBS Lett.* 585, 2112-2120.

new concepts. *Dtsch.Arztebl.Int.* 108, 323-330.

1489.

2080.


Potential Tumor Biomarkers for Ovarian Cancer 241

Shah C.A. et al. (2009) Influence of ovarian cancer risk status on the diagnostic performance

Shan S.J. et al. (2007) Transcriptional upregulation of human tissue kallikrein 6 in ovarian

Shih I. & Kurman R.J. (2004) Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. *Am.J.Pathol.* 164, 1511-1518. Shih I. et al. (2010) Distinct DNA methylation profiles in ovarian serous neoplasms and their implications in ovarian carcinogenesis. *Am.J.Obstet.Gynecol.* 203, 584-22. Shipp E.L. & Hsieh-Wilson L.C. (2007) Profiling the sulfation specificities of

Shrivastav M., De Haro L.P., & Nickoloff J.A. (2008) Regulation of DNA double-strand break

Simon I. et al. (2006) B7-h4 is a novel membrane-bound protein and a candidate serum and

Singer G. et al. (2005) Patterns of p53 mutations separate ovarian serous borderline tumors

Siskos P.A. & Spyridaki M.H. (1999) Determination of sialic acids in biological fluids using

Song H. et al. (2006) Common variants in RB1 gene and risk of invasive ovarian cancer.

Sowter H.M. & Ashworth A. (2005) BRCA1 and BRCA2 as ovarian cancer susceptibility

Spurr-Michaud S., Argueso P., & Gipson I. (2007) Assay of mucins in human tear fluid.

Starita L.M. & Parvin J.D. (2003) The multiple nuclear functions of BRCA1: transcription,

Starita L.M. et al. (2004) BRCA1-dependent ubiquitination of gamma-tubulin regulates

Stratton J.F. et al. (1999) The genetic epidemiology of early-onset epithelial ovarian cancer: a

Stroop C.J. et al. (2000) Characterization of the carbohydrate chains of the secreted form of the human epidermal growth factor receptor. *Glycobiology.* 10, 901-917. Stubbs H.J. et al. (1996) Influence of core fucosylation on the flexibility of a biantennary N-

Szabo C.I., Worley T., & Monteiro A.N. (2004) Understanding germ-line mutations in

Tangvoranuntakul P. et al. (2003) Human uptake and incorporation of an immunogenic nonhuman dietary sialic acid. *Proc.Natl.Acad.Sci.U.S.A.* 100 , 12045-12050. Tavassoli FA, Devilee P (2003) Pathology and genetics of tumors of the breast and female

genital organs*.* In: World Health Organization Classification of Tumors*.* Lyon,

ubiquitination and DNA repair. *Curr.Opin.Cell Biol.* 15, 345-350.

centrosome number. *Mol.Cell Biol.* 24, 8457-8466.

linked oligosaccharide. *Biochemistry.* 35, 937-947.

BRCA1. *Cancer Biol.Ther.* 3, 515-520.

France: IARC,113*–*145.

population-based study. *Am.J.Hum.Genet.* 65, 1725-1732.

tissue biomarker for ovarian cancer. *Cancer Res.* 66, 1570-1575.

cancer: clinical and mechanistic aspects. *Br.J.Cancer.* 96, 362-372.

using microarrays. *Chem.Biol.* 14, 195-208.

repair pathway choice. *Cell Res.* 18, 134-147.

correlation. *Am.J.Surg.Pathol.* 29, 218-224.

*Biomed.Sci.Appl.* 724, 205-212.

*Cancer Res.* 66, 10220-10226.

*Exp.Eye Res.* 84, 939-950.

genes. *Carcinogenesis.* 26, 1651-1656.

*Prev.* 18, 1365-1372.

of the serum biomarkers mesothelin, HE4, and CA125. *Cancer Epidemiol.Biomarkers* 

glycosaminoglycan interactions with growth factors and chemotactic proteins

and low- and high-grade carcinomas and provide support for a new model of ovarian carcinogenesis: a mutational analysis with immunohistochemical

reversed-phase ion-pair high-performance liquid chromatography. *J.Chromatogr.B* 


Pradhan M. et al. (2010) Gross genomic alterations and gene expression profiles of high-

Qu G. et al. (1999) Satellite DNA hypomethylation vs. overall genomic hypomethylation in ovarian epithelial tumors of different malignant potential. *Mutat.Res.* 423, 91-101. Quaye L. et al. (2009) Tagging single-nucleotide polymorphisms in candidate oncogenes and

Radice P. (2002) Mutations of BRCA genes in hereditary breast and ovarian cancer.

Rathi A. et al. (2002) Methylation profiles of sporadic ovarian tumors and nonmalignant

Risch H.A. et al. (2001) Prevalence and penetrance of germline BRCA1 and BRCA2

Rose P.G. et al. (1989) Metastatic patterns in histologic variants of ovarian cancer. An

Ruggeri G. et al. (2011) HE4 and epithelial ovarian cancer: Comparison and clinical

Rump A. et al. (2004) Binding of ovarian cancer antigen CA125/MUC16 to mesothelin

Rutishauser U. & Landmesser L. (1996) Polysialic acid in the vertebrate nervous system: a promoter of plasticity in cell-cell interactions. *Trends Neurosci.* 19, 422-427. Saldova R. et al. (2007) Ovarian cancer is associated with changes in glycosylation in both

Saldova R. et al. (2008) Glycosylation changes on serum glycoproteins in ovarian cancer may

Salhab M., Bismohun S., & Mokbel K. (2010) Risk-reducing strategies for women carrying

Schauer R. (2000) Achievements and challenges of sialic acid research. *Glycoconj.J.* 17, 485-

Schauer R. et al. (2011) O-acetylated sialic acids and their role in immune defense.

Schildkraut J.M. et al. (2007) Trinucleotide repeat polymorphisms in the androgen receptor gene and risk of ovarian cancer. *Cancer Epidemiol.Biomarkers Prev.* 16, 473-480. Schrag D. et al. (1997) Decision analysis--effects of prophylactic mastectomy and

Schut I.C. et al. (2003) MUC1 expression, splice variant and short form transcription (MUC1/Z, MUC1/Y) in prostate cell lines and tissue. *BJU.Int.* 91, 278-283. Seales E.C. et al. (2003) Ras oncogene directs expression of a differentially sialylated,

Seidenfaden R. et al. (2003) Polysialic acid directs tumor cell growth by controlling

BRCA1/2 mutations with a focus on prophylactic surgery. *BMC.Womens Health.*

oophorectomy on life expectancy among women with BRCA1 or BRCA2

heterophilic neural cell adhesion molecule interactions. *Mol.Cell Biol.* 23, 5908-5918.

Russo A. et al. (2009) Hereditary ovarian cancer. *Crit Rev.Oncol.Hematol.* 69, 28-44.

acute-phase proteins and IgG. *Glycobiology.* 17, 1344-1356.

contribute to disease pathogenesis. *Dis.Markers.* 25, 219-232.

functionally altered beta1 integrin. *Oncogene.* 22, 7137-7145.

mutations in a population series of 649 women with ovarian cancer.

evaluation of two immunoassays and a combination algorithm. *Clin.Chim.Acta.* 412,

susceptibility to ovarian cancer. *Br.J.Cancer.* 100, 993-1001.

ovaries from high-risk women. *Clin.Cancer Res.* 8, 3324-3331.

*BMC.Cancer.* 10:493., 493.

*J.Exp.Clin.Cancer Res.* 21, 9-12.

*Am.J.Hum.Genet.* 68, 700-710.

1447-1453.

10:28., 28.

499.

autopsy study. *Cancer.* 64, 1508-1513.

*Adv.Exp.Med.Biol.* 705:525-48., 525-548.

mutations. *N.Engl.J.Med.* 336, 1465-1471.

mediates cell adhesion. *J.Biol.Chem.* 279, 9190-9198.

grade serous carcinoma of the ovary with and without BRCA1 inactivation.


Potential Tumor Biomarkers for Ovarian Cancer 243

White N.M. et al. (2009) Human kallikrein related peptidases 6 and 13 in combination with

Widschwendter M. et al. (2004) DNA hypomethylation and ovarian cancer biology. *Cancer* 

Wilson C.A. et al. (1999) Localization of human BRCA1 and its loss in high-grade, non-

Wu C. & Morris J.R. (2001) Genes, genetics, and epigenetics: a correspondence. *Science.* 293,

Wu L.C. et al. (1996) Identification of a RING protein that can interact in vivo with the

Wyman S.K. et al. (2009) Repertoire of microRNAs in epithelial ovarian cancer as

Yagi H., Yotsumoto F., & Miyamoto S. (2008) Heparin-binding epidermal growth factor-like

Yamashita K. et al. (1985) Fractionation of L-fucose-containing oligosaccharides on

Yan L. et al. (2010) Lewis (y) Antigen Overexpression Increases the Expression of MMP-2

Yang H.J. et al. (2006) Differential DNA methylation profiles in gynecological cancers and

Yang J. et al. (2004) Twist, a master regulator of morphogenesis, plays an essential role in

Yang Z. et al. (2009) Expression of sialyl Lex, sialyl Lea, Lex and Ley glycotopes in secreted

Yin B.W. et al. (1996) Serological and immunochemical analysis of Lewis y (Ley) blood group antigen expression in epithelial ovarian cancer. *Int.J.Cancer.* 65, 406-412. Yin G. et al. (2010) TWISTing stemness, inflammation and proliferation of epithelial ovarian

Yogeeswaran G. & Salk P.L. (1981) Metastatic potential is positively correlated with cell surface sialylation of cultured murine tumor cell lines. *Science.* 212, 1514-1516. Yokoyama Y., Sedgewick G., & Ramakrishnan S. (2007) Endostatin binding to ovarian

Yurkovetsky et al. (2010) Development of a multimarker assay for early detection of ovarian

Zaina S., Perez-Luque E.L., & Lund G. (2010) Genetics talks to epigenetics? The interplay between sequence variants and chromatin structure. *Curr.Genomics.* 11, 359-367. Zaslavsky A. et al. (2010) Platelet-derived thrombospondin-1 is a critical negative regulator

Zhang Z. et al. (2007) Combining multiple serum tumor markers improves detection of stage

cancer cells inhibits peritoneal attachment and dissemination. *Cancer Res.* 67, 10813-

epithelial-mesenchymal transition. *Mol.Cancer Ther.* 7, 3441-3451.

immobilized Aleuria aurantia lectin. *J.Biol.Chem.* 260, 4688-4693.

correlation with clinico-pathological data. *BMC.Cancer.* 6:212., 212.

human ovarian cyst glycoproteins. *Biochimie.* 91 , 423-433.

cancer cells through MIR199A2/214. *Oncogene.* 29, 3545-3553.

and potential biomarker of angiogenesis. *Blood.* 115, 4605-4613.

I epithelial ovarian cancer. *Gynecol.Oncol.* 107, 526-531.

cancer. J Clin Oncol. 2010 May 1;28(13):2159-66

determined by next generation sequencing of small RNA cDNA libraries.

growth factor promotes transcoelomic metastasis in ovarian cancer through

and MMP-9 and Invasion of Human Ovarian Cancer Cells. *Int.J.Mol.Sci.* 11, 4441-

inherited breast carcinomas. *Nat.Genet.* 21, 236-240.

BRCA1 gene product. *Nat.Genet.* 14, 430-440.

tumor metastasis. *Cell.* 117, 927-939.

5, 279-287.

1103-1105.

4452.

10822.

*Res.* 64, 4472-4480.

*PLoS.One.* 4, e5311.

CA125 is a more sensitive test for ovarian cancer than CA125 alone. *Cancer Biomark.*


Theriault C. et al. (2011) MUC16 (CA125) regulates epithelial ovarian cancer cell growth,

Thompson D. & Easton D. (2001) Variation in cancer risks, by mutation position, in BRCA2

Tuzun Y. et al. (2009) Correlation of tumour markers in ascitic fluid and serum: are measurements of ascitic tumour markers a futile attempt? *J.Int.Med.Res.* 37, 79-86. Valeri N. et al. (2010) Modulation of mismatch repair and genomic stability by miR-155.

Van der Groep P., van der W.E., & van Diest P.J. (2011) Pathology of hereditary breast

Van Elssen C.H. et al. (2010) Expression of aberrantly glycosylated Mucin-1 in ovarian

Van Gorp T. et al. (2011) HE4 and CA125 as a diagnostic test in ovarian cancer:

Van Klinken B.J. et al. (1995) Mucin gene structure and expression: protection vs. adhesion.

Varki A. et al. (1969) Glycosylation Changes in Cancer: Comparative studies on the

Varki A, Schauer R. (2009). Sialic acids. In: Varki A, Cummings RD, Esko JD, Freeze HH,

Varki A, Kannagi R, Toole BP. Glycosylation changes in cancer. In: Varki A, Cummings RD,

Venkitaraman A.R. (2002) Cancer susceptibility and the functions of BRCA1 and BRCA2.

Venkitaraman A.R. (2003) A growing network of cancer-susceptibility genes. *N.Engl.J.Med.*

Venkitaraman A.R. (2009) Linking the cellular functions of BRCA genes to cancer

Wang L. et al. (2007) Expression of MUC1 in primary and metastatic human epithelial ovarian cancer and its therapeutic significance. *Gynecol.Oncol.* 105, 695-702. Wang P.H. et al. (2005) Altered mRNA expressions of sialyltransferases in ovarian cancers.

Wang Y. et al. (2011) Study on the Expression and Clinical Significances of Lewis y Antigen

and Integrin alphav, beta3 in Epithelial Ovarian Tumors. *Int.J.Mol.Sci.* 12, 3409-

pathogenesis and treatment. *Annu.Rev.Pathol.* 4:461-87., 461-487.

Varki A. (2001) N-glycolylneuraminic acid deficiency in humans. *Biochimie.* 83, 615-622. Varki A., Schauer R., & Schauer R. (2009) Sialic Acids: Sialic acids as regulators of molecular

and cellular interactions. *Curr.Opin.Struct.Biol.* 19, 507-514.

prospective validation of the Risk of Ovarian Malignancy Algorithm. *Br.J.Cancer.*

carbohydrate-containing membrane components of normal and virus-transformed mouse fibroblasts. II. Separation of glycoproteins and glycopeptides by sephadex

Stanley P, Bertozzi CR, Hart GW, Etzler ME, editors. Essentials of Glycobiology. 2nd edition. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press;

Esko JD, et al., editors (2009) Essentials of Glycobiology. 2nd ed. New York, Cold

tumorigenesis and metastasis. *Gynecol.Oncol.* 121, 434-443.

mutation carriers. *Am.J.Hum.Genet.* 68, 410-419.

*Proc.Natl.Acad.Sci.U.S.A.* 107, 6982-6987.

chromatography. *Biochemistry.* 8, 2518-2524.

Spring Harbor University Press, Ch. 44

cancer. *Cell Oncol.(Dordr.).* 34, 71-88.

cancer. *Histopathology.* 57, 597-606.

*Am.J.Physiol.* 269, G613-G627.

104, 863-870.

Chapter 14.

*Cell.* 108, 171-182.

*Gynecol.Oncol.* 99, 631-639.

348, 1917-1919.

3421.


**13** 

*Italy* 

**Ectoenzymes in Epithelial** 

*Department of Genetics, Biology and Biochemistry, University of Torino Medical School, Torino* 

Erika Ortolan and Ada Funaro

**Ovarian Carcinoma: Potential Diagnostic** 

Nicola Lo Buono, Simona Morone, Rossella Parrotta, Alice Giacomino,

Ovarian cancer is one of the most lethal among the gynaecological malignancies, affecting 1- 2% of women in developed countries (Cannistra, 2004). The lethality of ovarian cancer is primarily attributable to our current inability to detect the disease at an early stage, when it is still limited to the ovary. Therefore, the majority of patients are diagnosed when they have advanced-stage disease. Despite progresses in cytotoxic therapies, only 30% of patients with advanced-stage ovarian cancer survive 5 years after diagnosis. The insidious nature of ovarian cancer stems from its unique biological behaviour: ovarian carcinoma can spread by direct extension to adjacent organs, and exfoliated tumour cells can be transported in peritoneal fluid (Naora et al., 2005). Subsequent implants are characterised by their adhesion to mesothelial cells, migration throughout and invasion of the tumor cells into the omentum and peritoneum. This seeding of the peritoneal cavity is frequently associated with ascites formation. Only secondarily and rather late during the disease progression, are pelvic and para-aortic lymph nodes involved. However, the local peritoneal disease cannot be controlled and remains a factor leading to death (Feki et al., 2009). The cellular processes that lead to local and distant dissemination of ovarian cancer are not fully understood, and the mechanisms of interaction between cancer cells and mesothelium need to be further elucidated to achieve novel information on the biology of this highly aggressive form of cancer and possibly, to identify new potential targets for selective therapeutic strategies. The combined effort of clinicians and researchers has led to the identification of a number of molecules that might facilitate screening, diagnosis, prognosis and monitoring response to treatment or relapse during follow-up. These new molecules might provide specific targets for anti-tumour therapy with antibody-directed treatments, gene therapy or specific inhibitory molecules. An unexpectedly high number of these newly identified molecules have turned out to be cell surface-expressed ectoenzymes. Ectoenzymes are a large, heterogeneous class of membrane proteins whose catalytically active sites face the extracellular environment. The products of their catalytic activities can influence the extracellular environment (for example, several of these products can function as second messengers or regulate the recruitment of cells). Moreover, many ectoenzymes can function

**1. Introduction** 

**Markers and Therapeutic Targets** 


### **Ectoenzymes in Epithelial Ovarian Carcinoma: Potential Diagnostic Markers and Therapeutic Targets**

Nicola Lo Buono, Simona Morone, Rossella Parrotta, Alice Giacomino, Erika Ortolan and Ada Funaro

*Department of Genetics, Biology and Biochemistry, University of Torino Medical School, Torino Italy* 

#### **1. Introduction**

244 Ovarian Cancer – Basic Science Perspective

Zhao Q. et al. (2009) Circulating galectin-3 promotes metastasis by modifying MUC1

Zhou H., Hu H., & Lai M. (2010) Non-coding RNAs and their epigenetic regulatory

Zhu M. et al. (2010) Periostin promotes ovarian cancer angiogenesis and metastasis.

Zueva E.V. et al. (2010) Immunological peculiarities of CD-56-positive serous ovarian

localization on cancer cell surface. *Cancer Res.* 69, 6799-6806.

mechanisms. *Biol.Cell.* 102, 645-655.

adenocarcinoma. *Bull.Exp.Biol.Med.* 149, 604-608.

*Gynecol.Oncol.* 119, 337-344.

Ovarian cancer is one of the most lethal among the gynaecological malignancies, affecting 1- 2% of women in developed countries (Cannistra, 2004). The lethality of ovarian cancer is primarily attributable to our current inability to detect the disease at an early stage, when it is still limited to the ovary. Therefore, the majority of patients are diagnosed when they have advanced-stage disease. Despite progresses in cytotoxic therapies, only 30% of patients with advanced-stage ovarian cancer survive 5 years after diagnosis. The insidious nature of ovarian cancer stems from its unique biological behaviour: ovarian carcinoma can spread by direct extension to adjacent organs, and exfoliated tumour cells can be transported in peritoneal fluid (Naora et al., 2005). Subsequent implants are characterised by their adhesion to mesothelial cells, migration throughout and invasion of the tumor cells into the omentum and peritoneum. This seeding of the peritoneal cavity is frequently associated with ascites formation. Only secondarily and rather late during the disease progression, are pelvic and para-aortic lymph nodes involved. However, the local peritoneal disease cannot be controlled and remains a factor leading to death (Feki et al., 2009). The cellular processes that lead to local and distant dissemination of ovarian cancer are not fully understood, and the mechanisms of interaction between cancer cells and mesothelium need to be further elucidated to achieve novel information on the biology of this highly aggressive form of cancer and possibly, to identify new potential targets for selective therapeutic strategies.

The combined effort of clinicians and researchers has led to the identification of a number of molecules that might facilitate screening, diagnosis, prognosis and monitoring response to treatment or relapse during follow-up. These new molecules might provide specific targets for anti-tumour therapy with antibody-directed treatments, gene therapy or specific inhibitory molecules. An unexpectedly high number of these newly identified molecules have turned out to be cell surface-expressed ectoenzymes. Ectoenzymes are a large, heterogeneous class of membrane proteins whose catalytically active sites face the extracellular environment. The products of their catalytic activities can influence the extracellular environment (for example, several of these products can function as second messengers or regulate the recruitment of cells). Moreover, many ectoenzymes can function

Ectoenzymes in Epithelial Ovarian

**2.2 Functions** 

proximal tubules and the small intestine.

prostate cancers (Dai, et al., 2001).

cancer (Papandreou et al., 1998).

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 247

terminal amino acids form the cytoplasmic tail (Ritz et al., 1980). CD10 is expressed by human lymphoblastic leukaemia cells, by early lymphoid progenitors (Greaves et al., 1983; Hoffmann-Fezer et al., 1982) and by other lymphoid malignancies (Greaves, et al., 1983). It is also expressed in terminally differentiated granulocytes and in non-lymphoid cells, including cultured fibroblasts and bone marrow stromal cells, implying that its biological function is not restricted to lymphoid development (Pesando et al., 1983). CD10 expression has been reported on epithelial cells of various tissues, such as bronchial epithelial cells, renal proximal tubular epithelial cells, small intestinal epithelium, biliary canaliculae (Loke et al., 1990), breast myoepithelium (O'Hare et al., 1991), prostate, endometrium (Suzuki et al., 2001) and placenta (Ino et al., 2000). Several reports have shown that CD10 is also expressed in selected solid tumours of the colon (Fujimoto et al., 2005), lung (Cohen et al., 1996), breast (Burns et al., 1999), prostate (Dai et al., 2001) and ovary (Khin et al., 2003).

CD10 is a cell membrane-associated zinc metalloproteinase that cleaves peptide bonds on the amino-terminal side of hydrophobic amino acids and inactivates a variety of peptides, cytokines and hormones (Shipp et al., 1988). CD10 plays an important role in the maintenance of homeostasis in normal tissues by degrading endothelin-1 (ET-1), enkephalin, oxytocin, neurotensin, bradykinin, bombesin-like peptides, and angiotensin I and II, among others (Erdos et al., 1989). In specific contexts, CD10 works in concert with CD13, BP-1 and CD26 to digest common substrates (Bowes et al., 1987). This enzyme network controls the local concentrations of these substrates, thus regulating their biological

CD10 has been implicated in a variety of processes including stromal cell-dependent B lymphopoiesis (Salles et al., 1992), chemotactic and inflammatory responses (Madara et al., 1993), and T cell activation (Massaia et al., 1988). Apart from the hematopoietic compartment, CD10 participates in the final stage of peptide hydrolysis in the renal

Several reports showed that CD10 plays a role in neoplastic transformation and tumour progression in selected human malignancies by inactivating ET-1 or bombesin, both involved in autocrine/paracrine stimulation of tumour cell proliferation and migration in many epithelial cancers, including breast (Burns, et al., 1999), lung (Cohen, et al., 1996) and

CD10 is expressed in the stroma of malignant ovarian carcinomas, but not in benign adenomas or in normal ovaries, and its expression inversely correlates with histologic tumour grade (Khin, et al., 2003). In ovarian carcinoma, ET-1 promotes cell growth, invasion and angiogenesis by acting as an autocrine/paracrine growth factor (Bagnato et al., 1999; Salani et al., 2000). CD10 may directly influence the local concentration of ET-1 via its enzymatic activity thus contrasting the mitogenic effects of ET-1, suggesting that CD10 plays a role in the biology of neoplastic transformation or in the control of ovarian cancer progression (Kajiyama et al., 2005). It has been suggested that CD10 may function as a tumour suppressor factor in ovarian cancer progression, as well as in lung and prostate

In addition to its role in the control of ovarian carcinoma progression, Kajiyama et al. (Kajiyama, et al., 2005) demonstrated that CD10 enhances susceptibility to paclitaxel in the SKOV3 ovarian carcinoma cell line, resulting in increased apoptosis and reduced tumour formation and invasiveness in *in vivo* models. This evidence suggests that CD10 might serve as

activities and the downstream signal transduction pathways (Shipp & Look, 1993).

both as receptors and signalling molecules through mechanisms that are independent from their catalytic activity. The nomenclature of ectoenzymes is confusing: in addition to several original descriptive names, many of them also have a cluster designation (CD) given by immunologists and an EC number assigned by biochemists.

This chapter presents an overview of the ectoenzymes involved in ovarian cancer biology, development or progression (focusing on CD10, CD13, CD26, CD73, CD157, and Autotaxin/CD203c) and highlights the potential role of these molecules as markers for ovarian cancer outcome or as novel therapeutic targets.

Fig. 1. Schematic representation of ectoenzymes involved in ovarian cancer progression.

### **2. CD10**

#### **2.1 Structure and expression**

Human CD10 (also known as CALLA, NEP, Neprilysin, EC 3.4.24.11) is a 100 kDa cell surface aminopeptidase originally characterised as a T cell differentiation antigen (Common Acute Lymphoblastic Leukaemia Antigen, CALLA) identified for its expression in most acute lymphoblastic leukaemias (Shipp et al., 1989). Subsequently, its identity with neutral endopeptidase 24.11 (NEP) and KII-NA was unequivocally established and a wider distribution attributed to the protein (Shipp et al., 1993).

The CALLA/NEP gene spans more than 80 kilobases (kb) on chromosome 3q21-q27 and is composed of 24 exons (D'Adamio et al., 1989). CD10 is a 749-amino acid type II integral membrane glycoprotein with a single 24-amino acid hydrophobic segment that can function both as a transmembrane region and a signal peptide. The COOH-terminal is composed of 700-amino acids and forms the extracellular protein fragment, whereas the 25 aminoterminal amino acids form the cytoplasmic tail (Ritz et al., 1980). CD10 is expressed by human lymphoblastic leukaemia cells, by early lymphoid progenitors (Greaves et al., 1983; Hoffmann-Fezer et al., 1982) and by other lymphoid malignancies (Greaves, et al., 1983). It is also expressed in terminally differentiated granulocytes and in non-lymphoid cells, including cultured fibroblasts and bone marrow stromal cells, implying that its biological function is not restricted to lymphoid development (Pesando et al., 1983). CD10 expression has been reported on epithelial cells of various tissues, such as bronchial epithelial cells, renal proximal tubular epithelial cells, small intestinal epithelium, biliary canaliculae (Loke et al., 1990), breast myoepithelium (O'Hare et al., 1991), prostate, endometrium (Suzuki et al., 2001) and placenta (Ino et al., 2000). Several reports have shown that CD10 is also expressed in selected solid tumours of the colon (Fujimoto et al., 2005), lung (Cohen et al., 1996), breast (Burns et al., 1999), prostate (Dai et al., 2001) and ovary (Khin et al., 2003).

#### **2.2 Functions**

246 Ovarian Cancer – Basic Science Perspective

both as receptors and signalling molecules through mechanisms that are independent from their catalytic activity. The nomenclature of ectoenzymes is confusing: in addition to several original descriptive names, many of them also have a cluster designation (CD) given by

This chapter presents an overview of the ectoenzymes involved in ovarian cancer biology, development or progression (focusing on CD10, CD13, CD26, CD73, CD157, and Autotaxin/CD203c) and highlights the potential role of these molecules as markers for

**COOH** 

766aa

**NH2**

**E5N** 

858aa

**COOH** 

Fig. 1. Schematic representation of ectoenzymes involved in ovarian cancer progression.

**CD10/NEP CD13/APN CD73/** 

**NH2**

**CD26/ DPPIV**  **COOH** 

**CD157/ BST1** 

523aa

**COOH** 

**NH2**

**CD203c/ Autotaxin** 

**NH2** 318aa

Human CD10 (also known as CALLA, NEP, Neprilysin, EC 3.4.24.11) is a 100 kDa cell surface aminopeptidase originally characterised as a T cell differentiation antigen (Common Acute Lymphoblastic Leukaemia Antigen, CALLA) identified for its expression in most acute lymphoblastic leukaemias (Shipp et al., 1989). Subsequently, its identity with neutral endopeptidase 24.11 (NEP) and KII-NA was unequivocally established and a wider

The CALLA/NEP gene spans more than 80 kilobases (kb) on chromosome 3q21-q27 and is composed of 24 exons (D'Adamio et al., 1989). CD10 is a 749-amino acid type II integral membrane glycoprotein with a single 24-amino acid hydrophobic segment that can function both as a transmembrane region and a signal peptide. The COOH-terminal is composed of 700-amino acids and forms the extracellular protein fragment, whereas the 25 amino-

immunologists and an EC number assigned by biochemists.

ovarian cancer outcome or as novel therapeutic targets.

**COOH** 

967aa

**NH2**

**2. CD10** 

**2.1 Structure and expression** 

**IN** 

**NH2**

**COOH**  750aa

**OUT plasma membrane** 

Cysteine Glycosylation site Disulfide bond GPI molecule

distribution attributed to the protein (Shipp et al., 1993).

CD10 is a cell membrane-associated zinc metalloproteinase that cleaves peptide bonds on the amino-terminal side of hydrophobic amino acids and inactivates a variety of peptides, cytokines and hormones (Shipp et al., 1988). CD10 plays an important role in the maintenance of homeostasis in normal tissues by degrading endothelin-1 (ET-1), enkephalin, oxytocin, neurotensin, bradykinin, bombesin-like peptides, and angiotensin I and II, among others (Erdos et al., 1989). In specific contexts, CD10 works in concert with CD13, BP-1 and CD26 to digest common substrates (Bowes et al., 1987). This enzyme network controls the local concentrations of these substrates, thus regulating their biological activities and the downstream signal transduction pathways (Shipp & Look, 1993).

CD10 has been implicated in a variety of processes including stromal cell-dependent B lymphopoiesis (Salles et al., 1992), chemotactic and inflammatory responses (Madara et al., 1993), and T cell activation (Massaia et al., 1988). Apart from the hematopoietic compartment, CD10 participates in the final stage of peptide hydrolysis in the renal proximal tubules and the small intestine.

Several reports showed that CD10 plays a role in neoplastic transformation and tumour progression in selected human malignancies by inactivating ET-1 or bombesin, both involved in autocrine/paracrine stimulation of tumour cell proliferation and migration in many epithelial cancers, including breast (Burns, et al., 1999), lung (Cohen, et al., 1996) and prostate cancers (Dai, et al., 2001).

CD10 is expressed in the stroma of malignant ovarian carcinomas, but not in benign adenomas or in normal ovaries, and its expression inversely correlates with histologic tumour grade (Khin, et al., 2003). In ovarian carcinoma, ET-1 promotes cell growth, invasion and angiogenesis by acting as an autocrine/paracrine growth factor (Bagnato et al., 1999; Salani et al., 2000). CD10 may directly influence the local concentration of ET-1 via its enzymatic activity thus contrasting the mitogenic effects of ET-1, suggesting that CD10 plays a role in the biology of neoplastic transformation or in the control of ovarian cancer progression (Kajiyama et al., 2005). It has been suggested that CD10 may function as a tumour suppressor factor in ovarian cancer progression, as well as in lung and prostate cancer (Papandreou et al., 1998).

In addition to its role in the control of ovarian carcinoma progression, Kajiyama et al. (Kajiyama, et al., 2005) demonstrated that CD10 enhances susceptibility to paclitaxel in the SKOV3 ovarian carcinoma cell line, resulting in increased apoptosis and reduced tumour formation and invasiveness in *in vivo* models. This evidence suggests that CD10 might serve as

Ectoenzymes in Epithelial Ovarian

(Bhagwat et al., 2001).

chemoresistance of ovarian cancer cells.

**4.1 Structure and expression** 

**4. CD26** 

the tumour-associated antigen L6 (Chang et al., 2005).

(Knopfel et al., 2007) and spermatozoid motility (Carlsson et al., 2006).

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 249

CD13 also acts as a receptor for coronaviruses, which exploit the endocytosis of the molecule to enter into respiratory and intestinal epithelial cells (Nomura et al., 2004). Moreover, CD13 is involved in transduction of intracellular signals, converging on mitogen-activated protein kinases, such as ERK1/2, JNK, and p38, in association with auxiliary proteins such as galectin-3 (Santos et al., 2000), galectin-4 (Danielsen et al., 1997), RECK (Miki et al., 2007) and

CD13 activates or inactivates bioactive peptides on the cell surface, thus regulating their activities on adjacent cells. CD13 has a wide range of functions, including a role in antigen presentation by processing antigenic peptides protruding from MHC class II molecules (Larsen et al., 1996), in phagocytosis (Mina-Osorio et al., 2005), in lymphocyte and monocyte adhesion and aggregation (Mina-Osorio et al., 2006) and intracellular signal transduction (Santos, et al., 2000), in stem cell differentiation (Chen et al., 2007), cholesterol uptake

Several studies have confirmed a correlation between CD13 expression and increased malignant behaviour in melanoma (Carlsson, et al., 2006), prostate (Ishii, et al., 2001a), colon (Hashida, et al., 2002) and lung cancers (Chang, et al., 2005). In these tumours it is implicated in cell motility and in the degradation of and invasion through the extracellular matrix (ECM) (Saiki et al., 1993). By contrast, an inverse correlation has been reported

Several studies have demonstrated that CD13 expression is induced in tumour microvascular endothelial cell by angiogenic cytokines and hypoxia and that it regulates endothelial cell tube formation both in *in vitro* (Hashida, et al., 2002) and in *in vivo* models

Functional studies indicate that CD13 expression is associated with a long spindle fibroblastlike morphology and a migratory phenotype accompanied by enhanced secretion of MMP-2 in various ovarian cancer cell lines (Terauchi et al., 2007). Since CD13 is involved in cell motility, in the invasive potential of tumour cells and in the neoangiogenic processes, it holds promise as a therapeutic tumour target. In ovarian cancer it has been demonstrated that suppression of CD13 activity by specific inhibitors (including blocking antibodies, bestatin or actinonin) reduces the proliferative, migratory and angiogenic potential of tumour cells, as well as the peritoneal dissemination *in vivo* in mouse models, leading to prolonged survival (Terauchi, et al., 2007). It has also been determined that CD13 is involved in the chemosensivity and radiosensitivity of ovarian cancer cells. Indeed, combined treatment of tumour cells with bestatin and paclitaxel showed a significant increase in apoptosis and an improved outcome of ovarian cancer patients (Yamashita, et al., 2007). Taken together, the results from these studies suggest that inhibition of CD13 enzymatic activity may provide a new approach for improving the efficacy of ovarian carcinoma therapy, leading to reduced cell proliferation, motility, invasiveness, angiogenesis and

CD26, also known as dipeptidyl peptidase IV (DPPIV), adenosine deaminase binding protein (ADAbp) or EC 3.4.14.5, is a multifunctional type II cell surface glycoprotein. CD26 is a 110 kDa aminopeptidase that is catalytically active only as a dimer. Each monomer consists of two domains, an α/β-hydrolase domain (residues 39–51 and 501–766) and an

between CD13 expression and tumour progression in renal cancer (Ishii et al., 2001b).

a potential target for gene therapy in metastatic ovarian carcinoma. However, no experimental data in this regard is so far available, and this aspect deserves further investigation.

#### **3. CD13**

#### **3.1 Structure and expression**

Human CD13 was isolated in 1963 from pig kidney (Pfleiderer et al., 1963) and is a transmembrane protein also known as aminopeptidase N (APN), alanine aminopeptidase, microsomal aminopeptidase, amino oligopeptidase, GP150. CD13 cleaves N-terminal neutral amino acids of a number of peptides and proteins. The CD13 gene is located on the long arm of chromosome 15 and the coding sequence spans 20 exons (Lerche et al., 1996). CD13 consists of 967 amino acids constituting a short N-terminal cytoplasmic domain, a single transmembrane fragment and a large ectodomain encompassing the active site (Olsen et al., 1988).

The CD13 protein is predominantly expressed in stem cells and cells of the granulocytic and monocytic lineages at discrete stages of differentiation (Razak et al., 1992). Nonhematopoietic cells, such as renal proximal tubular epithelial cells, small intestinal epithelium, biliary canaliculae, bone marrow stromal cells, fibroblasts and osteoclasts are also CD13-positive (Metzgar et al., 1981; Noren, 1986). Deregulated expression of membrane and/or soluble forms of CD13 has been observed in many diseases. For example, CD13 is overexpressed in acute and chronic myeloid leukaemias (Antczak et al., 2001) and in anaplastic large cell lymphomas (Dunphy et al., 2000). High expression of CD13 has been detected in various solid tumours such as melanoma (Fujii et al., 1995), renal (Kitamura et al., 1990), pancreas (Ikeda et al., 2003), colon (Hashida et al., 2002), prostate (Ishii et al., 2001a), gastric (Carl-McGrath et al., 2004), thyroid (Kehlen et al., 2003) and ovarian cancers (Yamashita et al., 2007). In ovarian cancer CD13 expression is associated with the histological subtype: over 80% of serous and mucinous carcinomas but only 20% of clear cell carcinomas are CD13-positive (van Hensbergen et al., 2004). Moreover, CD13 also exists as a soluble form, likely originating from shedding of the membrane protein, which has a potent enzymatic activity in the plasma and reactive effusions of cancer patients, such as ascites from ovarian cancer patients (van Hensbergen et al., 2002).

#### **3.2 Functions**

CD13 is a multifunctional protein acting as an enzyme, a receptor and a signalling molecule. As an enzyme, CD13 regulates the activity of numerous peptides involved in important biological processes by removing their N-terminal aminoacids, mainly neutral aminoacids (Noren, 1986). CD13 hydrolyses the N-terminal Arg of angiotensin III to generate angiotensin IV (Danziger, 2008) and participates in the metabolism of glutathione, somatostatin, thymopentin, neurokinin A, splenopentin, nociceptin FQ and peptides derived from the thrombin receptor (Noble et al., 1997). In the intestinal brush border, the CD13 enzymatic domain faces the lumen and has been supposed to play an important role in the final stages of the digestion of small peptides (Semenza, 1986). CD13 has been postulated to cooperate with CD10 in the hydrolysis of oligopeptides in the small intestine (Semenza, 1986), and to inactivate opioid peptides and enkephalins in the brain (Matsas et al., 1985) and the chemotactic peptide Met-Leu-Phe during neutrophil-mediated inflammatory responses (Connelly et al., 1985).

CD13 also acts as a receptor for coronaviruses, which exploit the endocytosis of the molecule to enter into respiratory and intestinal epithelial cells (Nomura et al., 2004). Moreover, CD13 is involved in transduction of intracellular signals, converging on mitogen-activated protein kinases, such as ERK1/2, JNK, and p38, in association with auxiliary proteins such as galectin-3 (Santos et al., 2000), galectin-4 (Danielsen et al., 1997), RECK (Miki et al., 2007) and the tumour-associated antigen L6 (Chang et al., 2005).

CD13 activates or inactivates bioactive peptides on the cell surface, thus regulating their activities on adjacent cells. CD13 has a wide range of functions, including a role in antigen presentation by processing antigenic peptides protruding from MHC class II molecules (Larsen et al., 1996), in phagocytosis (Mina-Osorio et al., 2005), in lymphocyte and monocyte adhesion and aggregation (Mina-Osorio et al., 2006) and intracellular signal transduction (Santos, et al., 2000), in stem cell differentiation (Chen et al., 2007), cholesterol uptake (Knopfel et al., 2007) and spermatozoid motility (Carlsson et al., 2006).

Several studies have confirmed a correlation between CD13 expression and increased malignant behaviour in melanoma (Carlsson, et al., 2006), prostate (Ishii, et al., 2001a), colon (Hashida, et al., 2002) and lung cancers (Chang, et al., 2005). In these tumours it is implicated in cell motility and in the degradation of and invasion through the extracellular matrix (ECM) (Saiki et al., 1993). By contrast, an inverse correlation has been reported between CD13 expression and tumour progression in renal cancer (Ishii et al., 2001b).

Several studies have demonstrated that CD13 expression is induced in tumour microvascular endothelial cell by angiogenic cytokines and hypoxia and that it regulates endothelial cell tube formation both in *in vitro* (Hashida, et al., 2002) and in *in vivo* models (Bhagwat et al., 2001).

Functional studies indicate that CD13 expression is associated with a long spindle fibroblastlike morphology and a migratory phenotype accompanied by enhanced secretion of MMP-2 in various ovarian cancer cell lines (Terauchi et al., 2007). Since CD13 is involved in cell motility, in the invasive potential of tumour cells and in the neoangiogenic processes, it holds promise as a therapeutic tumour target. In ovarian cancer it has been demonstrated that suppression of CD13 activity by specific inhibitors (including blocking antibodies, bestatin or actinonin) reduces the proliferative, migratory and angiogenic potential of tumour cells, as well as the peritoneal dissemination *in vivo* in mouse models, leading to prolonged survival (Terauchi, et al., 2007). It has also been determined that CD13 is involved in the chemosensivity and radiosensitivity of ovarian cancer cells. Indeed, combined treatment of tumour cells with bestatin and paclitaxel showed a significant increase in apoptosis and an improved outcome of ovarian cancer patients (Yamashita, et al., 2007).

Taken together, the results from these studies suggest that inhibition of CD13 enzymatic activity may provide a new approach for improving the efficacy of ovarian carcinoma therapy, leading to reduced cell proliferation, motility, invasiveness, angiogenesis and chemoresistance of ovarian cancer cells.

#### **4. CD26**

248 Ovarian Cancer – Basic Science Perspective

a potential target for gene therapy in metastatic ovarian carcinoma. However, no experimental

Human CD13 was isolated in 1963 from pig kidney (Pfleiderer et al., 1963) and is a transmembrane protein also known as aminopeptidase N (APN), alanine aminopeptidase, microsomal aminopeptidase, amino oligopeptidase, GP150. CD13 cleaves N-terminal neutral amino acids of a number of peptides and proteins. The CD13 gene is located on the long arm of chromosome 15 and the coding sequence spans 20 exons (Lerche et al., 1996). CD13 consists of 967 amino acids constituting a short N-terminal cytoplasmic domain, a single transmembrane fragment and a large ectodomain encompassing the active site (Olsen

The CD13 protein is predominantly expressed in stem cells and cells of the granulocytic and monocytic lineages at discrete stages of differentiation (Razak et al., 1992). Nonhematopoietic cells, such as renal proximal tubular epithelial cells, small intestinal epithelium, biliary canaliculae, bone marrow stromal cells, fibroblasts and osteoclasts are also CD13-positive (Metzgar et al., 1981; Noren, 1986). Deregulated expression of membrane and/or soluble forms of CD13 has been observed in many diseases. For example, CD13 is overexpressed in acute and chronic myeloid leukaemias (Antczak et al., 2001) and in anaplastic large cell lymphomas (Dunphy et al., 2000). High expression of CD13 has been detected in various solid tumours such as melanoma (Fujii et al., 1995), renal (Kitamura et al., 1990), pancreas (Ikeda et al., 2003), colon (Hashida et al., 2002), prostate (Ishii et al., 2001a), gastric (Carl-McGrath et al., 2004), thyroid (Kehlen et al., 2003) and ovarian cancers (Yamashita et al., 2007). In ovarian cancer CD13 expression is associated with the histological subtype: over 80% of serous and mucinous carcinomas but only 20% of clear cell carcinomas are CD13-positive (van Hensbergen et al., 2004). Moreover, CD13 also exists as a soluble form, likely originating from shedding of the membrane protein, which has a potent enzymatic activity in the plasma and reactive effusions of cancer patients, such as ascites

CD13 is a multifunctional protein acting as an enzyme, a receptor and a signalling molecule. As an enzyme, CD13 regulates the activity of numerous peptides involved in important biological processes by removing their N-terminal aminoacids, mainly neutral aminoacids (Noren, 1986). CD13 hydrolyses the N-terminal Arg of angiotensin III to generate angiotensin IV (Danziger, 2008) and participates in the metabolism of glutathione, somatostatin, thymopentin, neurokinin A, splenopentin, nociceptin FQ and peptides derived from the thrombin receptor (Noble et al., 1997). In the intestinal brush border, the CD13 enzymatic domain faces the lumen and has been supposed to play an important role in the final stages of the digestion of small peptides (Semenza, 1986). CD13 has been postulated to cooperate with CD10 in the hydrolysis of oligopeptides in the small intestine (Semenza, 1986), and to inactivate opioid peptides and enkephalins in the brain (Matsas et al., 1985) and the chemotactic peptide Met-Leu-Phe during neutrophil-mediated

from ovarian cancer patients (van Hensbergen et al., 2002).

inflammatory responses (Connelly et al., 1985).

data in this regard is so far available, and this aspect deserves further investigation.

**3. CD13** 

et al., 1988).

**3.2 Functions** 

**3.1 Structure and expression** 

#### **4.1 Structure and expression**

CD26, also known as dipeptidyl peptidase IV (DPPIV), adenosine deaminase binding protein (ADAbp) or EC 3.4.14.5, is a multifunctional type II cell surface glycoprotein. CD26 is a 110 kDa aminopeptidase that is catalytically active only as a dimer. Each monomer consists of two domains, an α/β-hydrolase domain (residues 39–51 and 501–766) and an

Ectoenzymes in Epithelial Ovarian

metastasis (Kikkawa et al., 2005).

progression (Zhang et al., 2008).

**5.1 Structure and expression** 

**5. CD73** 

natively CD26-overexpressing cells (Kajiyama et al., 2010).

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 251

CD26 in an ovarian cancer cell line results in marked morphological changes from a fibroblastic/spindle-shaped appearance toward an epithelioid pattern, which is paralleled

Exogenous expression of CD26 leads to a significant reduction in the invasive potential in ovarian carcinoma cell lines *in vitro* and an increased E-cadherin expression. Indeed, CD26 expression in ovarian cancer cell lines positively correlates with E-cadherin expression and induces the upregulation of both E-cadherin and β-catenin, which play a key role in the suppression of invasive and metastatic phenotype of cancer cells (Kajiyama et al., 2003). Moreover, in ovarian carcinoma cell lines CD26 expression negatively correlates with MMP-2 expression, and the expression levels of both MMP-2 and MT1-MMP are significantly reduced in CD26-transfected cells. Overexpression of CD26 also increases expression levels of TIMP-1 and TIMP-2, known to be key inhibitors of tumour invasion, angiogenesis and

Overexpression of CD26 reduced intraperitoneal dissemination of carcinoma cells and prolonged survival time *in vivo* in a mouse orthotopic model. Ovarian carcinoma cell lines with higher CD26 expression has significantly less metastatic potential when injected into the abdominal cavity of nude mice than the CD26-negative control cells (Mizutani et al., 2003). Consistent with this, the intensity of CD26 immunohistochemical staining in tissues proved to be stronger in well-differentiated and non-infiltrating ovarian carcinomas, thus indicating that the decrease of CD26 is related to neoplastic transformation and tumour

A positive correlation between CD26 expression and sensitivity to paclitaxel has been described in several ovarian carcinoma cell lines. Forced expression of CD26 in a CD26 negative ovarian cancer cell line significantly enhanced sensitivity to paclitaxel by increasing the rate of apoptotic cells through the repression of the transcriptional factor Twist, a master regulator of epithelial-mesenchymal transition, linked to paclitaxel resistance. These data were corroborated by the observation that paclitaxel-resistant NOS-PR cells showed reduced expression of CD26. However, no significant alteration in paclitaxel sensitivity was observed in the presence of a specific inhibitor of DPPIV activity in CD26-transfected or

Further understanding of the anti-invasive effect of CD26 may prove useful in devising new strategies in the control of ovarian cancer and other carcinomas. Like other membranebound peptidases, CD26 may soon be destined for use not only as a new diagnostic/prognostic marker, but also as a molecular target in novel therapeutic strategies.

CD73, also known as ecto-5′-nucleotidase (ecto-5′-NT), is a glycosylphosphatidylinositol (GPI)-anchored ectoenzyme composed of two identical subunits of 70-74 kDa. The mature protein consists of 548 amino acids and corresponds to a molecular mass of ~63 kDa (Airas et al., 1993). The human CD73 gene has been mapped to region q14-q21 of chromosome 6. CD73 is abundantly expressed by vascular endothelial cells (Jalkanen et al., 2008) and by a subpopulation of peripheral blood lymphocytes represented by regulatory T cells and primed uncommitted CD4-positive T cells. Follicular dendritic cells (Airas, 1998), intestinal epithelial cells (Strohmeier et al., 1997), fibroblasts (Nemoto et al., 2004), cardiomyocytes

by the shift from mesenchymal to epithelial markers (Kajiyama et al., 2002).

eight-blade β-propeller domain (residues 59–497), that enclose a large cavity of ~ 30–45A° in diameter. Access to this cavity is provided by a large side opening of ~15A°(Aertgeerts et al., 2004)*.* However, only elongated peptides, or unfolded or partly unfolded protein fragments, can reach the small pocket within this cavity that contains the active site. The main enzymatic activity of CD26 is a serine protease activity with a post-proline dipeptidyl aminopeptidase activity, preferentially cleaving Xaa-Pro or Xaa-Ala dipeptides (where Xaa is any amino acid except Pro) from the N-terminus of polypeptides.

CD26 contains nine potential N-linked glycosylation sites that lie predominantly on the propeller domain, near the dimerization interface (Engel et al., 2003). The human CD26 gene consists of 26 exons and is located on the long arm of chromosome 2 (Tanaka et al., 1992).

#### **4.2 Functions**

CD26 exerts pivotal roles in nutrition, metabolism, immune and endocrine systems, bone marrow mobilization, cancer growth and cell adhesion. CD26 activates or deactivates various bioactive peptides on the cell surface or in the extracellular environment, by cleaving them enzymatically, therefore regulating their availability for adjacent cells. CD26 substrates include cytokines and several chemokines: substance P, chorionic gonadotropin, tumour necrosis factor α (TNF-α), interleukin-2, stromal cell-derived factor 1a, RANTES, neuropeptide Y, peptide YY, glucagon-like peptide (GLP)-1, GLP-2 and glucose-dependent insulinotropic peptide (Gorrell, 2005). Besides its enzymatic activity, CD26 shows a variety of functions, including regulation of inflammatory and immunological responses, signal transduction, interactions with extracellular matrix proteins and apoptosis.

CD26 ligands include adenosine deaminase (ADA) (Morrison et al., 1993)*,* kidney Na+/H+ ion exchanger 3 (Girardi et al., 2001) and fibronectin (Cheng et al., 2003).

CD26 has been consistently associated with cancer since its identification (ten Kate et al., 1984). A number of recent studies have provided evidence that CD26 plays a role in discrete steps of tumour progression, such as cell adhesion, invasion and cell cycle arrest (Pethiyagoda et al., 2000). In selected carcinoma tissues, CD26 is misexpressed and it can function either as an oncogene or as a tumour suppressor gene. Its expression is upregulated and associated with tumour aggressiveness in T and B lymphomas and leukaemias (Bauvois et al., 1999; Carbone et al., 1995; Dang et al., 2003), thyroid follicular tumours (de Micco et al., 2008), papillary carcinomas, astrocytic tumours (Stremenova et al., 2007) and gastrointestinal stromal tumours (Yamaguchi et al., 2008)*.* Conversely, loss of CD26 occurs during malignant transformation of melanocytes into melanoma (Wesley et al., 1999), indicating a possible role of the molecule in suppressing the malignant transformation of melanocytes.

The precise biological mechanism through which CD26 regulates tumour cell progression remains controversial. According to Wesley et al., high CD26 expression leads to a loss of tumorigenicity through its serine protease activity (Wesley, et al., 1999). On the other hand, the suppressive effect of CD26 on melanoma's malignant phenotype is related neither to the protease activity located at the extracellular domain nor to the signal transduction related to the cytoplasmic domain (Pethiyagoda et al., 2000)*.*

In 2002 Kajiyama et al. first described the expression of CD26 in ovarian carcinoma cell lines and tissues. CD26 immunoreactivity was observed on surgically resected ovarian carcinoma of different histotypes, but was not found in stromal cells. CD26 expression in ovarian cancer cell lines is associated with an epithelioid morphology. Indeed, forced expression of CD26 in an ovarian cancer cell line results in marked morphological changes from a fibroblastic/spindle-shaped appearance toward an epithelioid pattern, which is paralleled by the shift from mesenchymal to epithelial markers (Kajiyama et al., 2002).

Exogenous expression of CD26 leads to a significant reduction in the invasive potential in ovarian carcinoma cell lines *in vitro* and an increased E-cadherin expression. Indeed, CD26 expression in ovarian cancer cell lines positively correlates with E-cadherin expression and induces the upregulation of both E-cadherin and β-catenin, which play a key role in the suppression of invasive and metastatic phenotype of cancer cells (Kajiyama et al., 2003). Moreover, in ovarian carcinoma cell lines CD26 expression negatively correlates with MMP-2 expression, and the expression levels of both MMP-2 and MT1-MMP are significantly reduced in CD26-transfected cells. Overexpression of CD26 also increases expression levels of TIMP-1 and TIMP-2, known to be key inhibitors of tumour invasion, angiogenesis and metastasis (Kikkawa et al., 2005).

Overexpression of CD26 reduced intraperitoneal dissemination of carcinoma cells and prolonged survival time *in vivo* in a mouse orthotopic model. Ovarian carcinoma cell lines with higher CD26 expression has significantly less metastatic potential when injected into the abdominal cavity of nude mice than the CD26-negative control cells (Mizutani et al., 2003). Consistent with this, the intensity of CD26 immunohistochemical staining in tissues proved to be stronger in well-differentiated and non-infiltrating ovarian carcinomas, thus indicating that the decrease of CD26 is related to neoplastic transformation and tumour progression (Zhang et al., 2008).

A positive correlation between CD26 expression and sensitivity to paclitaxel has been described in several ovarian carcinoma cell lines. Forced expression of CD26 in a CD26 negative ovarian cancer cell line significantly enhanced sensitivity to paclitaxel by increasing the rate of apoptotic cells through the repression of the transcriptional factor Twist, a master regulator of epithelial-mesenchymal transition, linked to paclitaxel resistance. These data were corroborated by the observation that paclitaxel-resistant NOS-PR cells showed reduced expression of CD26. However, no significant alteration in paclitaxel sensitivity was observed in the presence of a specific inhibitor of DPPIV activity in CD26-transfected or natively CD26-overexpressing cells (Kajiyama et al., 2010).

Further understanding of the anti-invasive effect of CD26 may prove useful in devising new strategies in the control of ovarian cancer and other carcinomas. Like other membranebound peptidases, CD26 may soon be destined for use not only as a new diagnostic/prognostic marker, but also as a molecular target in novel therapeutic strategies.

### **5. CD73**

250 Ovarian Cancer – Basic Science Perspective

eight-blade β-propeller domain (residues 59–497), that enclose a large cavity of ~ 30–45A° in diameter. Access to this cavity is provided by a large side opening of ~15A°(Aertgeerts et al., 2004)*.* However, only elongated peptides, or unfolded or partly unfolded protein fragments, can reach the small pocket within this cavity that contains the active site. The main enzymatic activity of CD26 is a serine protease activity with a post-proline dipeptidyl aminopeptidase activity, preferentially cleaving Xaa-Pro or Xaa-Ala dipeptides (where Xaa

CD26 contains nine potential N-linked glycosylation sites that lie predominantly on the propeller domain, near the dimerization interface (Engel et al., 2003). The human CD26 gene consists of 26 exons and is located on the long arm of chromosome 2 (Tanaka et al., 1992).

CD26 exerts pivotal roles in nutrition, metabolism, immune and endocrine systems, bone marrow mobilization, cancer growth and cell adhesion. CD26 activates or deactivates various bioactive peptides on the cell surface or in the extracellular environment, by cleaving them enzymatically, therefore regulating their availability for adjacent cells. CD26 substrates include cytokines and several chemokines: substance P, chorionic gonadotropin, tumour necrosis factor α (TNF-α), interleukin-2, stromal cell-derived factor 1a, RANTES, neuropeptide Y, peptide YY, glucagon-like peptide (GLP)-1, GLP-2 and glucose-dependent insulinotropic peptide (Gorrell, 2005). Besides its enzymatic activity, CD26 shows a variety of functions, including regulation of inflammatory and immunological responses, signal

CD26 ligands include adenosine deaminase (ADA) (Morrison et al., 1993)*,* kidney Na+/H+

CD26 has been consistently associated with cancer since its identification (ten Kate et al., 1984). A number of recent studies have provided evidence that CD26 plays a role in discrete steps of tumour progression, such as cell adhesion, invasion and cell cycle arrest (Pethiyagoda et al., 2000). In selected carcinoma tissues, CD26 is misexpressed and it can function either as an oncogene or as a tumour suppressor gene. Its expression is upregulated and associated with tumour aggressiveness in T and B lymphomas and leukaemias (Bauvois et al., 1999; Carbone et al., 1995; Dang et al., 2003), thyroid follicular tumours (de Micco et al., 2008), papillary carcinomas, astrocytic tumours (Stremenova et al., 2007) and gastrointestinal stromal tumours (Yamaguchi et al., 2008)*.* Conversely, loss of CD26 occurs during malignant transformation of melanocytes into melanoma (Wesley et al., 1999), indicating a possible role of the molecule in suppressing the malignant transformation of

The precise biological mechanism through which CD26 regulates tumour cell progression remains controversial. According to Wesley et al., high CD26 expression leads to a loss of tumorigenicity through its serine protease activity (Wesley, et al., 1999). On the other hand, the suppressive effect of CD26 on melanoma's malignant phenotype is related neither to the protease activity located at the extracellular domain nor to the signal transduction related to

In 2002 Kajiyama et al. first described the expression of CD26 in ovarian carcinoma cell lines and tissues. CD26 immunoreactivity was observed on surgically resected ovarian carcinoma of different histotypes, but was not found in stromal cells. CD26 expression in ovarian cancer cell lines is associated with an epithelioid morphology. Indeed, forced expression of

is any amino acid except Pro) from the N-terminus of polypeptides.

transduction, interactions with extracellular matrix proteins and apoptosis.

ion exchanger 3 (Girardi et al., 2001) and fibronectin (Cheng et al., 2003).

**4.2 Functions** 

melanocytes.

the cytoplasmic domain (Pethiyagoda et al., 2000)*.*

#### **5.1 Structure and expression**

CD73, also known as ecto-5′-nucleotidase (ecto-5′-NT), is a glycosylphosphatidylinositol (GPI)-anchored ectoenzyme composed of two identical subunits of 70-74 kDa. The mature protein consists of 548 amino acids and corresponds to a molecular mass of ~63 kDa (Airas et al., 1993). The human CD73 gene has been mapped to region q14-q21 of chromosome 6. CD73 is abundantly expressed by vascular endothelial cells (Jalkanen et al., 2008) and by a subpopulation of peripheral blood lymphocytes represented by regulatory T cells and primed uncommitted CD4-positive T cells. Follicular dendritic cells (Airas, 1998), intestinal epithelial cells (Strohmeier et al., 1997), fibroblasts (Nemoto et al., 2004), cardiomyocytes

Ectoenzymes in Epithelial Ovarian

cancer immunotherapy.

**6.1 Structure and expression** 

**6. CD157** 

**6.2 Functions** 

regulation of T cell function (Clayton et al., 2011).

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 253

adenosine levels within the tumour microenvironment and hence participate to the negative

CD73 expressed in ovarian cancer negatively modulates tumour antigen-specific T cell immunity. Indeed, it has been demonstrated that knockdown of CD73 on tumour cells by siRNA improved anti-tumour T cell responses, completely restoring the efficacy of adoptive T cell therapy and leading to long-term tumour-free survival in tumour-bearing mice. Moreover, in a mouse model, host CD73 deficiency decreased the ovarian carcinoma burden and increased mouse survival in a T cell–dependent manner. Accordingly, reduction of both tumour and host CD73 resulted in an optimal anti-tumour effect (Jin et al., 2010). Pharmacological blockade of CD73 using the specific inhibitor α,β-methylene adenosine 5′ diphosphate (APCP) or a blocking anti-CD73 monoclonal antibody inhibited tumour growth and promoted efficacy of adoptive T cell therapy (Zhang, 2010), suggesting that CD73-targeted therapy might be a promising and rational approach to cancer treatment (Häusler SF et al., 2011). In summary, detailed analysis of CD73 expression on tumour cells and/or host cells regulating anti-tumour immunity may have important consequences on our understanding of immunosuppressive mechanisms in the tumour microenvironment that support tumour evasion. Inhibition of CD73 could be a therapeutic adjuvant to improve

CD157/BST-1 is a GPI-anchored glycoprotein encoded by a member of a gene family of NADase/ADP-ribosyl cyclase, which includes CD38. The CD38 and bone marrow stromal cell antigen 1 (BST-1) genes arose by gene duplication before the divergence of humans and rodents (Ferrero et al., 1997). The human CD157 gene is located on chromosome 4p15, it spans ~35 kb and consists of nine exons (Muraoka et al., 1996). Although CD157 was initially characterised as a stromal (Kaisho et al., 1994) and myeloid surface antigen (Goldstein et al., 1993), it is also expressed by certain other cell types that include vascular

CD157 is an ectoenzyme that cleave extracellular nicotinamide adenine dinucleotide (NAD) and NADP+, generating cyclic ADP ribose (cADPR), NAADP+, and ADPR. Beside their role as mediators of intracellular calcium release (Galione, 1994), the products of CD157-operated NAD cleavage can act as extracellular immunomodifiers (Haag et al., 2007). Emerging data indicated that these metabolites can act extracellularly as paracrine factors (Moreschi et al., 2008). Moreover, the catalytic reactions generate substrate for ADP-ribosyl transferases and polymerases involved in cell signalling, DNA repair and apoptosis (Haag et al., 2007). In addition, CD157 possesses receptor activity, indeed, it interacts with other surface molecules thus acquiring the ability to transduce signals (Malavasi et al., 2008). Accumulating evidence indicates that CD157 is a key molecule in the control of leukocyte adhesion, migration and diapedesis (Funaro, 2004; Ortolan, 2006). CD157 establishes a structural interaction with β1 and β2 integrins (Lavagno et al., 2007) and, following antibody-induced cross-linking, promotes their relocation into detergentresistant membrane domains, thus driving the dynamic reorganization of signalling-

endothelial cells (Ortolan et al., 2002) and mesothelial cells (Ross et al., 1998).

(Carneiro-Ramos et al., 2004), neurons, oligodendrocytes (Maienschein et al., 1996) and mesenchymal stem cells (Barry et al., 2001) have been reported to express CD73.

#### **5.2 Functions**

It has been proposed that CD73 behaves as an adhesion molecule modulating lymphocyteendothelial cell interactions (Airas et al., 2000). Furthermore, CD73 is known to play a critical role *in vivo* in maintaining the integrity of the vascular endothelium during hypoxia (Colgan et al., 2006), in mediating efficient entry of lymphocytes into the central nervous system during experimental autoimmune encephalomyelitis and in regulating leukocyteendothelium interaction during cardiac ischemia–reperfusion (Koszalka et al., 2004).

CD73 catalyzes the dephosphorylation of purine and pyrimidine ribo- and deoxyribonucleoside monophosphates to the corresponding nucleoside. This ectoenzymatic cascade operates in tandem with CD39 (ecto-ATPase) and catalyzes the conversion of AMP to bioactive adenosine from adenosine triphosphate (ATP) which is often released into the extracellular environment from damaged or inflamed target cells (Stagg et al., 2010b). Extracellular adenosine induces potent immunosuppressive effects, mainly mediated through four adenosine-binding G protein–coupled receptors. In addition to its enzymatic function, CD73 has been suggested to have a role in T cell signalling (Resta et al., 1998).

The resistance of many solid tumours to host immune responses has been largely attributed to a spectrum of tumour-associated immune-suppressive mechanisms. During tumour progression, tumour cells promote a tolerant microenvironment and activation of multiple immunosuppressive mechanisms, which may act in concert to attenuate an effective immune responses (Rabinovich et al., 2007). It is thought that tipping the balance from an immune-suppressive to an immune-active environment is necessary for effective cancer immunotherapy (Rabinovich et al., 2007). Adenosine is a purine nucleoside reaching high concentrations within solid tumours (Ohta et al., 2006) where it promotes tumour growth through the stimulation of tumour angiogenesis (Stagg & Smyth, 2010b) and inhibition of anti-tumour immune responses (Hoskin et al., 2008). However, the mechanisms whereby adenosine accumulates in solid tumours and the effects resulting from this accumulation are not completely understood.

CD73 expression has been reported in several tumour types (Stagg & Smyth, 2010b), including ovarian cancer (Jin et al., 2010) and its expression has been associated with a prometastatic phenotype in melanoma and breast cancer (Leth-Larsen et al., 2009). Although *in vitro* studies suggested that CD73 expression can enhance breast cancer cell migration and invasion, the underlying mechanisms remain elusive. In breast cancer cells, CD73 expression significantly inhibits endogenous adaptive anti-tumour immunosurveillance, in addition, CD73-derived adenosine enhances tumour cell migration *in vitro* and metastasis *in vivo* through the activation of A2B adenosine receptors (Stagg et al., 2010a). CD73 expression has been shown to be regulated by estrogen receptors, whereby loss of estrogen receptors significantly enhances CD73 expression (Spychala et al., 2004). CD73 is highly expressed in many human solid tumours (Salmi et al., 2011), and its high expression and activity are associated with tumour invasiveness and metastasis (Stagg et al., 2010a) and with shorter patient survival. Recently it has been demonstrated that exosomes released by cancer cells *in vitro* and in biological effusions are able to dephosphorylate exogenous ATP and 5′AMP to form adenosine. These hydrolytic activities have been in part attributed to expression of functional CD39 and CD73 by exosomes. This mechanism may contribute to augmenting adenosine levels within the tumour microenvironment and hence participate to the negative regulation of T cell function (Clayton et al., 2011).

CD73 expressed in ovarian cancer negatively modulates tumour antigen-specific T cell immunity. Indeed, it has been demonstrated that knockdown of CD73 on tumour cells by siRNA improved anti-tumour T cell responses, completely restoring the efficacy of adoptive T cell therapy and leading to long-term tumour-free survival in tumour-bearing mice. Moreover, in a mouse model, host CD73 deficiency decreased the ovarian carcinoma burden and increased mouse survival in a T cell–dependent manner. Accordingly, reduction of both tumour and host CD73 resulted in an optimal anti-tumour effect (Jin et al., 2010).

Pharmacological blockade of CD73 using the specific inhibitor α,β-methylene adenosine 5′ diphosphate (APCP) or a blocking anti-CD73 monoclonal antibody inhibited tumour growth and promoted efficacy of adoptive T cell therapy (Zhang, 2010), suggesting that CD73-targeted therapy might be a promising and rational approach to cancer treatment (Häusler SF et al., 2011). In summary, detailed analysis of CD73 expression on tumour cells and/or host cells regulating anti-tumour immunity may have important consequences on our understanding of immunosuppressive mechanisms in the tumour microenvironment that support tumour evasion. Inhibition of CD73 could be a therapeutic adjuvant to improve cancer immunotherapy.

#### **6. CD157**

252 Ovarian Cancer – Basic Science Perspective

(Carneiro-Ramos et al., 2004), neurons, oligodendrocytes (Maienschein et al., 1996) and

It has been proposed that CD73 behaves as an adhesion molecule modulating lymphocyteendothelial cell interactions (Airas et al., 2000). Furthermore, CD73 is known to play a critical role *in vivo* in maintaining the integrity of the vascular endothelium during hypoxia (Colgan et al., 2006), in mediating efficient entry of lymphocytes into the central nervous system during experimental autoimmune encephalomyelitis and in regulating leukocyte-

CD73 catalyzes the dephosphorylation of purine and pyrimidine ribo- and deoxyribonucleoside monophosphates to the corresponding nucleoside. This ectoenzymatic cascade operates in tandem with CD39 (ecto-ATPase) and catalyzes the conversion of AMP to bioactive adenosine from adenosine triphosphate (ATP) which is often released into the extracellular environment from damaged or inflamed target cells (Stagg et al., 2010b). Extracellular adenosine induces potent immunosuppressive effects, mainly mediated through four adenosine-binding G protein–coupled receptors. In addition to its enzymatic function, CD73 has been suggested to have a role in T cell signalling (Resta et al., 1998). The resistance of many solid tumours to host immune responses has been largely attributed to a spectrum of tumour-associated immune-suppressive mechanisms. During tumour progression, tumour cells promote a tolerant microenvironment and activation of multiple immunosuppressive mechanisms, which may act in concert to attenuate an effective immune responses (Rabinovich et al., 2007). It is thought that tipping the balance from an immune-suppressive to an immune-active environment is necessary for effective cancer immunotherapy (Rabinovich et al., 2007). Adenosine is a purine nucleoside reaching high concentrations within solid tumours (Ohta et al., 2006) where it promotes tumour growth through the stimulation of tumour angiogenesis (Stagg & Smyth, 2010b) and inhibition of anti-tumour immune responses (Hoskin et al., 2008). However, the mechanisms whereby adenosine accumulates in solid tumours and the effects resulting from this accumulation are

CD73 expression has been reported in several tumour types (Stagg & Smyth, 2010b), including ovarian cancer (Jin et al., 2010) and its expression has been associated with a prometastatic phenotype in melanoma and breast cancer (Leth-Larsen et al., 2009). Although *in vitro* studies suggested that CD73 expression can enhance breast cancer cell migration and invasion, the underlying mechanisms remain elusive. In breast cancer cells, CD73 expression significantly inhibits endogenous adaptive anti-tumour immunosurveillance, in addition, CD73-derived adenosine enhances tumour cell migration *in vitro* and metastasis *in vivo* through the activation of A2B adenosine receptors (Stagg et al., 2010a). CD73 expression has been shown to be regulated by estrogen receptors, whereby loss of estrogen receptors significantly enhances CD73 expression (Spychala et al., 2004). CD73 is highly expressed in many human solid tumours (Salmi et al., 2011), and its high expression and activity are associated with tumour invasiveness and metastasis (Stagg et al., 2010a) and with shorter patient survival. Recently it has been demonstrated that exosomes released by cancer cells *in vitro* and in biological effusions are able to dephosphorylate exogenous ATP and 5′AMP to form adenosine. These hydrolytic activities have been in part attributed to expression of functional CD39 and CD73 by exosomes. This mechanism may contribute to augmenting

endothelium interaction during cardiac ischemia–reperfusion (Koszalka et al., 2004).

mesenchymal stem cells (Barry et al., 2001) have been reported to express CD73.

**5.2 Functions** 

not completely understood.

#### **6.1 Structure and expression**

CD157/BST-1 is a GPI-anchored glycoprotein encoded by a member of a gene family of NADase/ADP-ribosyl cyclase, which includes CD38. The CD38 and bone marrow stromal cell antigen 1 (BST-1) genes arose by gene duplication before the divergence of humans and rodents (Ferrero et al., 1997). The human CD157 gene is located on chromosome 4p15, it spans ~35 kb and consists of nine exons (Muraoka et al., 1996). Although CD157 was initially characterised as a stromal (Kaisho et al., 1994) and myeloid surface antigen (Goldstein et al., 1993), it is also expressed by certain other cell types that include vascular endothelial cells (Ortolan et al., 2002) and mesothelial cells (Ross et al., 1998).

#### **6.2 Functions**

CD157 is an ectoenzyme that cleave extracellular nicotinamide adenine dinucleotide (NAD) and NADP+, generating cyclic ADP ribose (cADPR), NAADP+, and ADPR. Beside their role as mediators of intracellular calcium release (Galione, 1994), the products of CD157-operated NAD cleavage can act as extracellular immunomodifiers (Haag et al., 2007). Emerging data indicated that these metabolites can act extracellularly as paracrine factors (Moreschi et al., 2008). Moreover, the catalytic reactions generate substrate for ADP-ribosyl transferases and polymerases involved in cell signalling, DNA repair and apoptosis (Haag et al., 2007). In addition, CD157 possesses receptor activity, indeed, it interacts with other surface molecules thus acquiring the ability to transduce signals (Malavasi et al., 2008). Accumulating evidence indicates that CD157 is a key molecule in the control of leukocyte adhesion, migration and diapedesis (Funaro, 2004; Ortolan, 2006). CD157 establishes a structural interaction with β1 and β2 integrins (Lavagno et al., 2007) and, following antibody-induced cross-linking, promotes their relocation into detergentresistant membrane domains, thus driving the dynamic reorganization of signalling-

Ectoenzymes in Epithelial Ovarian

**7.2 Functions** 

et al., 2001).

necrosis factor kappa B (NF-kB) (Lee et al., 2006).

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 255

ATX is predominantly expressed in brain, kidney, placenta, ovary, small intestine and in body fluids such as plasma (Tokumura et al., 2002), cerebral spinal fluid, saliva, and

Autotaxin is defined as a multi-functional protein producing *(i)* lysophosphatidic acid (LPA) by conversion of lysophosphatidylcholine (LPC), present in human serum or plasma, and *(ii)* cyclic phosphatidic acid (cPA), an LPA analogue with distinct physiological activities. ATX activity accounts for the majority of LPA production in blood (Nakanaga et al., 2010). The biological activity of LPA is largely mediated through the activation of five receptors, LPA1 to LPA5. All of these are type I, rhodopsin-like G protein-coupled receptors with seven-transmembrane alpha helices (Lin et al., 2010). LPA evokes a wide variety of cellular responses in different cell types including Ras-mediated cell proliferation and Rho/Racregulated cell migration (including vascular endothelial cells migration), neurite retraction, platelet aggregation, smooth muscle contraction, actin stress fibers formation and cytokine/chemokine secretion. LPA levels are increased during pathological conditions of the brain (neuropsychiatric disorders such as bipolar disorders, schizophrenia, etc.). Deregulation of LPA signalling is found in cardiovascular diseases: the formation of excess fibrous connective tissues is strongly influenced by receptor-mediated LPA signalling in different organs (for example, lung, kidney and liver) (Lin et al., 2010). Moreover, in both *in vivo* and *in vitro* systems, LPA has been shown to participate in critical events of cancer progression such as cell proliferation, growth, survival, migration, invasion, and promotion of angiogenesis (van Meeteren et al., 2007). Therefore, LPA signalling is worth considering

for its involvement in disease processes as well as in normal physiological functions.

Autotaxin was originally identified as a tumour cell motility factor released in the spent medium of human melanoma cells. When overexpressed in Ras-NIH3T3 cells, ATX promotes tumour aggressiveness, metastasis and angiogenesis in nude mice (Nam et al., 2000). ATX is highly expressed in several human cancers, including glioblastoma, lung and breast cancer, renal cell carcinoma, neuroblastoma, thyroid carcinoma and Hodgkin's lymphoma (Mills et al., 2003). High ATX expression is detected in glioblastoma multiforme, a lethal cancer with a high infiltration rate (Hoelzinger et al., 2005). ATX has also been found upregulated in stromal cells from prostate carcinoma patients (Zhao et al., 2007) and its expression is strongly enhanced by v-Jun oncogene-induced transformation (Black et al., 2004) and by overexpression of cancer-associated α6β4 integrin in breast cancer (Chen et al., 2005). In an *in vivo* angiogenesis model, ATX-transfected Ras-transformed NIH3T3 cells caused more prominent new blood vessel formation than control cells (Nam et al., 2000). In addition, ATX stimulates human vascular endothelial cells grown on Matrigel to form tubules, similarly to the effects induced by vascular endothelial growth factor (VEGF) (Nam

Recent studies have demonstrated the molecular mechanisms underlying the ATX/LPA axis in cancer. ATX-induced motility of melanoma cells is mediated through the activation of focal adhesion kinase (FAK) (Jung et al., 2004) and, in the nucleus, by the DNA binding of

Another finding is that LPA strongly counteracts Taxol-induced death in the MCF-7 breast cancer cell line and in MDA-MB-435 melanoma cells, by activating phosphatidylinositol 3 kinase (PI3K), which antagonizes the Taxol-induced accumulation of cancer cells in the

follicular and amniotic fluids (Giganti et al., 2008; Nishimasu et al., 2011).

competent membrane microdomains. Moreover, CD157 effectively contributes to the integrin-driven signalling network that is critical during leukocyte transmigration (Lo Buono et al., 2011).

Recently, we demonstrated that CD157 is expressed in epithelial ovarian cancer (EOC) primary cell cultures and tissues, and it is involved in interactions among EOC cells, extracellular matrix proteins, and mesothelial cells which ultimately control tumour cell migration and invasion. The results inferred *in vitro* were validated by clinical evidence: CD157 was expressed by 93% of EOC analysed and high CD157 expression was associated with rapid tumour relapse in patients. Moreover, CD157 appears to be a marker of poor prognosis in the serous subtype of ovarian cancer, which is the most frequent and aggressive type. Multivariate survival analysis showed that CD157 is an independent prognostic factor of tumour relapse shortly after surgical debulking of ovarian cancer (Ortolan et al., 2010). Several lines of evidence point to the fact that high levels of CD157 are associated with more aggressive ovarian cancer. First, forced expression of CD157 in CD157 negative NIH:OVCAR-3 cells substantially increased cell motility, a prerequisite for dissemination. Second, blockade of CD157 activity, either by a specific monoclonal antibody *in vitro* or by its weak expression in patients, was associated with reduced invasion and migration by tumour cells. Finally, clinical observations revealed that high CD157 correlated with rapid tumour relapse (Ortolan et al., 2010). However, how CD157 might contribute to a more aggressive ovarian cancer remains to be defined (Annunziata et al., 2010). Our results support the rationale for the future use of CD157 as a potential diagnostic target for EOC, providing the opportunity to develop new strategies using CD157 as a therapeutic target to prevent tumour dissemination in patients with serous ovarian cancer.

#### **7. Autotaxin/CD203c**

#### **7.1 Structure and expression**

Autotaxin (ATX) also known as CD203c or ENPP2 (ectonucleotide pyrophosphatase/phosphodiesterase 2), is a cell motility–stimulating factor originally isolated from human melanoma cells (Stracke et al., 1992). It is a member of the ENPP protein family, which includes membrane-associated or secreted ectoenzymes that hydrolyze pyrophosphate or phosphodiester bonds in various extracellular compounds, such as nucleotides and lysophospholipids (Tokumura et al., 2002). ATX/CD203c is a soluble 125 kDa glycoprotein encoded by a single gene located on human chromosome 8. Three alternatively spliced isoforms have been reported: α, β and γ. Isoform β, considered the canonical form, is the predominant one, and is expressed in peripheral tissues while isoform γ is more highly expressed in the central nervous system. Both β and γ variants are catalytically active, whereas the α isoform is rapidly degraded into smaller inactive forms (Giganti et al., 2008). Autotaxin contains a catalytic domain, which is responsible for enzymatic activity and two additional domains, a somatomedin-B-like domain and a nuclease-like domain, which are located at the N-terminus and C-terminus of the protein, respectively. The somatomedin-B-like domain is rich in cysteine residues and contains an RGD tripeptide motif that is possibly involved in cell-extracellular matrix interactions. The nuclease-like domain contains an EF hand-like motif, structurally similar to the DNA- or RNA-non-specific endonucleases but it is catalytically inactive. All three domains are required for the catalytic activity (Nishimasu et al., 2011).

ATX is predominantly expressed in brain, kidney, placenta, ovary, small intestine and in body fluids such as plasma (Tokumura et al., 2002), cerebral spinal fluid, saliva, and follicular and amniotic fluids (Giganti et al., 2008; Nishimasu et al., 2011).

#### **7.2 Functions**

254 Ovarian Cancer – Basic Science Perspective

competent membrane microdomains. Moreover, CD157 effectively contributes to the integrin-driven signalling network that is critical during leukocyte transmigration (Lo

Recently, we demonstrated that CD157 is expressed in epithelial ovarian cancer (EOC) primary cell cultures and tissues, and it is involved in interactions among EOC cells, extracellular matrix proteins, and mesothelial cells which ultimately control tumour cell migration and invasion. The results inferred *in vitro* were validated by clinical evidence: CD157 was expressed by 93% of EOC analysed and high CD157 expression was associated with rapid tumour relapse in patients. Moreover, CD157 appears to be a marker of poor prognosis in the serous subtype of ovarian cancer, which is the most frequent and aggressive type. Multivariate survival analysis showed that CD157 is an independent prognostic factor of tumour relapse shortly after surgical debulking of ovarian cancer (Ortolan et al., 2010). Several lines of evidence point to the fact that high levels of CD157 are associated with more aggressive ovarian cancer. First, forced expression of CD157 in CD157 negative NIH:OVCAR-3 cells substantially increased cell motility, a prerequisite for dissemination. Second, blockade of CD157 activity, either by a specific monoclonal antibody *in vitro* or by its weak expression in patients, was associated with reduced invasion and migration by tumour cells. Finally, clinical observations revealed that high CD157 correlated with rapid tumour relapse (Ortolan et al., 2010). However, how CD157 might contribute to a more aggressive ovarian cancer remains to be defined (Annunziata et al., 2010). Our results support the rationale for the future use of CD157 as a potential diagnostic target for EOC, providing the opportunity to develop new strategies using CD157 as a therapeutic

target to prevent tumour dissemination in patients with serous ovarian cancer.

required for the catalytic activity (Nishimasu et al., 2011).

Autotaxin (ATX) also known as CD203c or ENPP2 (ectonucleotide pyrophosphatase/phosphodiesterase 2), is a cell motility–stimulating factor originally isolated from human melanoma cells (Stracke et al., 1992). It is a member of the ENPP protein family, which includes membrane-associated or secreted ectoenzymes that hydrolyze pyrophosphate or phosphodiester bonds in various extracellular compounds, such as nucleotides and lysophospholipids (Tokumura et al., 2002). ATX/CD203c is a soluble 125 kDa glycoprotein encoded by a single gene located on human chromosome 8. Three alternatively spliced isoforms have been reported: α, β and γ. Isoform β, considered the canonical form, is the predominant one, and is expressed in peripheral tissues while isoform γ is more highly expressed in the central nervous system. Both β and γ variants are catalytically active, whereas the α isoform is rapidly degraded into smaller inactive forms (Giganti et al., 2008). Autotaxin contains a catalytic domain, which is responsible for enzymatic activity and two additional domains, a somatomedin-B-like domain and a nuclease-like domain, which are located at the N-terminus and C-terminus of the protein, respectively. The somatomedin-B-like domain is rich in cysteine residues and contains an RGD tripeptide motif that is possibly involved in cell-extracellular matrix interactions. The nuclease-like domain contains an EF hand-like motif, structurally similar to the DNA- or RNA-non-specific endonucleases but it is catalytically inactive. All three domains are

Buono et al., 2011).

**7. Autotaxin/CD203c** 

**7.1 Structure and expression** 

Autotaxin is defined as a multi-functional protein producing *(i)* lysophosphatidic acid (LPA) by conversion of lysophosphatidylcholine (LPC), present in human serum or plasma, and *(ii)* cyclic phosphatidic acid (cPA), an LPA analogue with distinct physiological activities. ATX activity accounts for the majority of LPA production in blood (Nakanaga et al., 2010). The biological activity of LPA is largely mediated through the activation of five receptors, LPA1 to LPA5. All of these are type I, rhodopsin-like G protein-coupled receptors with seven-transmembrane alpha helices (Lin et al., 2010). LPA evokes a wide variety of cellular responses in different cell types including Ras-mediated cell proliferation and Rho/Racregulated cell migration (including vascular endothelial cells migration), neurite retraction, platelet aggregation, smooth muscle contraction, actin stress fibers formation and cytokine/chemokine secretion. LPA levels are increased during pathological conditions of the brain (neuropsychiatric disorders such as bipolar disorders, schizophrenia, etc.). Deregulation of LPA signalling is found in cardiovascular diseases: the formation of excess fibrous connective tissues is strongly influenced by receptor-mediated LPA signalling in different organs (for example, lung, kidney and liver) (Lin et al., 2010). Moreover, in both *in vivo* and *in vitro* systems, LPA has been shown to participate in critical events of cancer progression such as cell proliferation, growth, survival, migration, invasion, and promotion of angiogenesis (van Meeteren et al., 2007). Therefore, LPA signalling is worth considering for its involvement in disease processes as well as in normal physiological functions.

Autotaxin was originally identified as a tumour cell motility factor released in the spent medium of human melanoma cells. When overexpressed in Ras-NIH3T3 cells, ATX promotes tumour aggressiveness, metastasis and angiogenesis in nude mice (Nam et al., 2000). ATX is highly expressed in several human cancers, including glioblastoma, lung and breast cancer, renal cell carcinoma, neuroblastoma, thyroid carcinoma and Hodgkin's lymphoma (Mills et al., 2003). High ATX expression is detected in glioblastoma multiforme, a lethal cancer with a high infiltration rate (Hoelzinger et al., 2005). ATX has also been found upregulated in stromal cells from prostate carcinoma patients (Zhao et al., 2007) and its expression is strongly enhanced by v-Jun oncogene-induced transformation (Black et al., 2004) and by overexpression of cancer-associated α6β4 integrin in breast cancer (Chen et al., 2005). In an *in vivo* angiogenesis model, ATX-transfected Ras-transformed NIH3T3 cells caused more prominent new blood vessel formation than control cells (Nam et al., 2000). In addition, ATX stimulates human vascular endothelial cells grown on Matrigel to form tubules, similarly to the effects induced by vascular endothelial growth factor (VEGF) (Nam et al., 2001).

Recent studies have demonstrated the molecular mechanisms underlying the ATX/LPA axis in cancer. ATX-induced motility of melanoma cells is mediated through the activation of focal adhesion kinase (FAK) (Jung et al., 2004) and, in the nucleus, by the DNA binding of necrosis factor kappa B (NF-kB) (Lee et al., 2006).

Another finding is that LPA strongly counteracts Taxol-induced death in the MCF-7 breast cancer cell line and in MDA-MB-435 melanoma cells, by activating phosphatidylinositol 3 kinase (PI3K), which antagonizes the Taxol-induced accumulation of cancer cells in the

Ectoenzymes in Epithelial Ovarian

and may lead to improved tumour cell destruction.

inhibitory effects on the indicated steps.

**8. Conclusion** 

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 257

cancer. The ectopic expression of ATX leads to the activation of a PI3K/Akt-mediated survival pathway, suggesting that ATX can delay carboplatin-induced cell death through the generation of LPA and the subsequent activation of the PI3K/Akt pathway (Vidot et al., 2010). The inhibition of ATX in therapy has the advantage of providing a single extracellular drug target capable of blocking production of LPA. It has been observed that the primary effect of ATX is to delay apoptosis induced by carboplatin; since the exposure of tumour cells to carboplatin in patients is transient, accelerating the induction of apoptosis might be beneficial

Fig. 2. Schematic representation of the role of ectoenzymes in the main steps of ovarian cancer progression. Ectoenzymes in red have stimulating effects, those in green have

Improved understanding of the underlying biology of ovarian tumour progression and chemoresistance has led to the development of molecular targeted therapies. Ectoenzymes are attractive targets for designing new strategies to interfere with ovarian cancer progression and recurrence. This can be achieved by inhibiting the ectoenzymes that promote tumour migration and invasiveness (such as CD157 and CD13) or by inducing the activity of ectoenzymes that normally counteract tumour progression (such as CD26). In many cases, ectoenzymes can be inactivated either by specific monoclonal antibodies that block their function, or by small-molecule enzyme inhibitors. The dual nature of ectoenzymes warrants more detailed and vigorous investigation, because some of their

G2/M phase of the cell cycle (Samadi et al., 2009). Recently it has been demonstrated that the ATX/LPA axis allows breast cancer cells to escape from mitotic arrest following the PI3K-dependent displacement of Taxol from polymerised tubulin (Samadi et al., 2011). Moreover, recent data from *in vivo* experiments indicate that increased expression of ATX, LPA1, LPA2 or LPA3 receptors in mice is associated with enhanced invasiveness of estrogen receptor-positive, metastatic breast cancers (Liu et al., 2009). Finally, the significance of the plasma or serum ATX levels in cancer patients has been reported in patients with follicular lymphoma, where serum ATX levels proved to be significantly higher than those in healthy subjects, to correlate with plasma LPA levels and to change according to patient clinical course (Masuda et al., 2008). Additional studies have reported an impressive and specific increase in serum ATX activity and plasma LPA in patients with chronic hepatitis C (Watanabe et al., 2007) and pancreatic cancer (Nakai et al., 2011).

In the last 20 years several studies have considered the potential role of the ATX/LPA axis in ovarian cancer. The high metastatic potential of ovarian carcinoma was suggested to be related to increased local production of LPA in the peritoneal cavity (Mills & Moolenaar, 2003). Levels of LPA are markedly elevated in the ascites of patients with EOC (Mills et al., 1988) and in the plasma of 90% of stage I ovarian cancer patients, compared with healthy women (Xu et al., 1998).

The outcomes of LPA-driven signalling are determined by the expression level of LPA receptors on the cell surfaces. Indeed, normal ovarian epithelial cells express low levels of mRNA for LPA2 and LPA3, whereas the mRNA levels for LPA2 and particularly LPA3, are elevated in EOC (Fang et al., 2002), suggesting a shift on ovarian cancer cells towards an LPA-dependent phenotype. Moreover >90% of LPA degradation by ovarian cancer cells is caused by the action of lipid phosphate phosphohydrolase-like (LPP-like) enzymes, whose expression differs between normal ovarian epithelium and epithelial ovarian cancer (Imai et al., 2000). This implies that LPA, its receptors and downstream metabolic cascade might be potential targets for the design of novel ovarian cancer therapies.

LPA has been found to induce VEGF expression (Hu et al., 2001) which in turn contributes to malignant ascites formation by increasing peritoneal microvessel permeability (Nagy et al., 1995). A feedback model between ATX, LPA and VEGF in ovarian cancer cells has been recently proposed (Ptaszynska et al., 2008). VEGF activates ATX transcription and subsequent protein secretion through VEGFR2. Increased secretion of ATX leads to an increased level of extracellular LPA. Completing the loop, LPA can stimulate VEGF and VEGFR2 expression through LPA receptor signalling thus enhancing tumour survival and growth. These data indicate that cross-talk between ATX and VEGF may be an important autocrine mechanism in the generation of an aggressive ovarian cancer phenotype. In addition, soluble ATX may be a beneficial target for cancer therapy because of its capacity to control both LPA production and signalling, and VEGF signalling.

The development of drug resistance to cytotoxic therapies such as carboplatin and paclitaxel as well as to newly emerging therapies (Agarwal et al., 2003), remains a high risk factor for ovarian cancer patients. Therefore, the identification of genes which confer drug resistance may offer novel therapeutic targets that can be exploited to develop drugs which re-sensitize tumour cells to chemotherapeutic agents (Richardson et al., 2005). ATX has been linked to chemoresistance due to its ability to inhibit apoptosis induced by paclitaxel in breast cancer cells (Samadi et al., 2009) and LPA can inhibit cell death induced by cisplatin (Frankel et al., 1996). It has been demonstrated that ATX may be a target for treating drug-resistant ovarian

G2/M phase of the cell cycle (Samadi et al., 2009). Recently it has been demonstrated that the ATX/LPA axis allows breast cancer cells to escape from mitotic arrest following the PI3K-dependent displacement of Taxol from polymerised tubulin (Samadi et al., 2011). Moreover, recent data from *in vivo* experiments indicate that increased expression of ATX, LPA1, LPA2 or LPA3 receptors in mice is associated with enhanced invasiveness of estrogen receptor-positive, metastatic breast cancers (Liu et al., 2009). Finally, the significance of the plasma or serum ATX levels in cancer patients has been reported in patients with follicular lymphoma, where serum ATX levels proved to be significantly higher than those in healthy subjects, to correlate with plasma LPA levels and to change according to patient clinical course (Masuda et al., 2008). Additional studies have reported an impressive and specific increase in serum ATX activity and plasma LPA in patients with chronic hepatitis C (Watanabe et al.,

In the last 20 years several studies have considered the potential role of the ATX/LPA axis in ovarian cancer. The high metastatic potential of ovarian carcinoma was suggested to be related to increased local production of LPA in the peritoneal cavity (Mills & Moolenaar, 2003). Levels of LPA are markedly elevated in the ascites of patients with EOC (Mills et al., 1988) and in the plasma of 90% of stage I ovarian cancer patients, compared with healthy

The outcomes of LPA-driven signalling are determined by the expression level of LPA receptors on the cell surfaces. Indeed, normal ovarian epithelial cells express low levels of mRNA for LPA2 and LPA3, whereas the mRNA levels for LPA2 and particularly LPA3, are elevated in EOC (Fang et al., 2002), suggesting a shift on ovarian cancer cells towards an LPA-dependent phenotype. Moreover >90% of LPA degradation by ovarian cancer cells is caused by the action of lipid phosphate phosphohydrolase-like (LPP-like) enzymes, whose expression differs between normal ovarian epithelium and epithelial ovarian cancer (Imai et al., 2000). This implies that LPA, its receptors and downstream metabolic cascade might be

LPA has been found to induce VEGF expression (Hu et al., 2001) which in turn contributes to malignant ascites formation by increasing peritoneal microvessel permeability (Nagy et al., 1995). A feedback model between ATX, LPA and VEGF in ovarian cancer cells has been recently proposed (Ptaszynska et al., 2008). VEGF activates ATX transcription and subsequent protein secretion through VEGFR2. Increased secretion of ATX leads to an increased level of extracellular LPA. Completing the loop, LPA can stimulate VEGF and VEGFR2 expression through LPA receptor signalling thus enhancing tumour survival and growth. These data indicate that cross-talk between ATX and VEGF may be an important autocrine mechanism in the generation of an aggressive ovarian cancer phenotype. In addition, soluble ATX may be a beneficial target for cancer therapy because of its capacity to

The development of drug resistance to cytotoxic therapies such as carboplatin and paclitaxel as well as to newly emerging therapies (Agarwal et al., 2003), remains a high risk factor for ovarian cancer patients. Therefore, the identification of genes which confer drug resistance may offer novel therapeutic targets that can be exploited to develop drugs which re-sensitize tumour cells to chemotherapeutic agents (Richardson et al., 2005). ATX has been linked to chemoresistance due to its ability to inhibit apoptosis induced by paclitaxel in breast cancer cells (Samadi et al., 2009) and LPA can inhibit cell death induced by cisplatin (Frankel et al., 1996). It has been demonstrated that ATX may be a target for treating drug-resistant ovarian

2007) and pancreatic cancer (Nakai et al., 2011).

potential targets for the design of novel ovarian cancer therapies.

control both LPA production and signalling, and VEGF signalling.

women (Xu et al., 1998).

cancer. The ectopic expression of ATX leads to the activation of a PI3K/Akt-mediated survival pathway, suggesting that ATX can delay carboplatin-induced cell death through the generation of LPA and the subsequent activation of the PI3K/Akt pathway (Vidot et al., 2010). The inhibition of ATX in therapy has the advantage of providing a single extracellular drug target capable of blocking production of LPA. It has been observed that the primary effect of ATX is to delay apoptosis induced by carboplatin; since the exposure of tumour cells to carboplatin in patients is transient, accelerating the induction of apoptosis might be beneficial and may lead to improved tumour cell destruction.

Fig. 2. Schematic representation of the role of ectoenzymes in the main steps of ovarian cancer progression. Ectoenzymes in red have stimulating effects, those in green have inhibitory effects on the indicated steps.

#### **8. Conclusion**

Improved understanding of the underlying biology of ovarian tumour progression and chemoresistance has led to the development of molecular targeted therapies. Ectoenzymes are attractive targets for designing new strategies to interfere with ovarian cancer progression and recurrence. This can be achieved by inhibiting the ectoenzymes that promote tumour migration and invasiveness (such as CD157 and CD13) or by inducing the activity of ectoenzymes that normally counteract tumour progression (such as CD26). In many cases, ectoenzymes can be inactivated either by specific monoclonal antibodies that block their function, or by small-molecule enzyme inhibitors. The dual nature of ectoenzymes warrants more detailed and vigorous investigation, because some of their

Ectoenzymes in Epithelial Ovarian

pp. 519-524, ISSN 0006-291X

247-253, ISSN 0019-2805

4971

ISSN 0300-8177

7-8 (Mar), pp. 1042-1048, ISSN 0007-0920

(Dec 9), pp. 2519-2529, ISSN 1533-4406

Vol. 29 N° 2 (Apr), pp. 331-338, ISSN 0105-6263

(Aug 20), pp. 243-252, ISSN 0020-7136

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 259

Barry, F., Boynton, R., Murphy, M., Haynesworth, S., & Zaia, J. (2001) The SH-3 and SH-4

Bauvois, B., De Meester, I., Dumont, J., Rouillard, D., Zhao, H. X., et al. (1999) Constitutive

Bhagwat, S. V., Lahdenranta, J., Giordano, R., Arap, W., Pasqualini, R., et al. (2001)

Black, E. J., Clair, T., Delrow, J., Neiman, P., & Gillespie, D. A. (2004) Microarray analysis

Bowes, M. A., & Kenny, A. J. (1987) An immunohistochemical study of endopeptidase-24.11

Burns, D. M., Walker, B., Gray, J., & Nelson, J. (1999) Breast cancer cell-associated

Cannistra, S. A. (2004) Cancer of the ovary, *New England Journal of Medicine,* Vol. 351 N° 24

Carbone, A., Gloghini, A., Zagonel, V., Aldinucci, D., Gattei, V., et al. (1995) The expression

Carl-McGrath, S., Lendeckel, U., Ebert, M., Wolter, A. B., Roessner, A., et al. (2004) The

Carneiro-Ramos, M. S., da Silva, V. B., Coutinho, M. B., Jr., Battastini, A. M., Sarkis, J. J., et al.

Chang, Y. W., Chen, S. C., Cheng, E. C., Ko, Y. P., Lin, Y. C., et al. (2005) CD13

Chen, L., Gao, Z., Zhu, J., & Rodgers, G. P. (2007) Identification of CD13+CD36+ cells as a

*Experimental Hematology,* Vol. 35 N° 7 (Jul), pp. 1047-1055, ISSN 0301-472X

formation, *Blood,* Vol. 97 N° 3 (Feb 1), pp. 652-659, ISSN 0006-4971

*Oncogene,* Vol. 23 N° 13 (Mar 25), pp. 2357-2366, ISSN 0950-9232

*Journal of Cancer,* Vol. 79 N° 2 (Jan), pp. 214-220, ISSN 0007-0920

antibodies recognize distinct epitopes on CD73 from human mesenchymal stem cells, *Biochemical and Biophysical Research Communications,* Vol. 289 N° 2 (Nov 30),

expression of CD26/dipeptidylpeptidase IV on peripheral blood B lymphocytes of patients with B chronic lymphocytic leukaemia, *British Journal of Cancer,* Vol. 79 N°

CD13/APN is activated by angiogenic signals and is essential for capillary tube

identifies Autotaxin, a tumour cell motility and angiogenic factor with lysophospholipase D activity, as a specific target of cell transformation by v-Jun,

and aminopeptidase N in lymphoid tissues, *Immunology,* Vol. 60 N° 2 (Feb), pp.

endopeptidase EC 24.11 modulates proliferative response to bombesin, *British* 

of CD26 and CD40 ligand is mutually exclusive in human T-cell non-Hodgkin's lymphomas/leukemias, *Blood,* Vol. 86 N° 12 (Dec 15), pp. 4617-4626, ISSN 0006-

ectopeptidases CD10, CD13, CD26, and CD143 are upregulated in gastric cancer, *International Journal of Oncology,* Vol. 25 N° 5 (Nov), pp. 1223-1232, ISSN 1019-6439 Carlsson, L., Ronquist, G., Eliasson, R., Egberg, N., & Larsson, A. (2006) Flow cytometric

technique for determination of prostasomal quantity, size and expression of CD10, CD13, CD26 and CD59 in human seminal plasma, *International Journal of Andrology,* 

(2004) Thyroid hormone stimulates 5'-ecto-nucleotidase of neonatal rat ventricular myocytes, *Molecular and Cellular Biochemistry,* Vol. 265 N° 1-2 (Oct), pp. 195-201,

(aminopeptidase N) can associate with tumour-associated antigen L6 and enhance the motility of human lung cancer cells, *International Journal of Cancer,* Vol. 116 N° 2

common progenitor for erythroid and myeloid lineages in human bone marrow,

functions seem to be independent of their enzymatic activities. It is conceivable that the large extracellular domains of ectoenzymes and their lateral interaction with other membrane proteins can mediate responses without involvement of their catalytic activity. However, many of the non-substrate ligands of ectoenzymes remain to be identified. Moreover, increasing evidence indicates that a number of ectoenzymes orchestrate the immune mechanisms underlying tumour progression and outcome and it is now evident that ovarian cancer features a number of different tumour evasion mechanisms. The future challenge will be to use a combinatorial approach to increase the existing anti-tumour response, dampen tumour evasion mechanisms and target crucial environmental players.

#### **9. Acknowledgement**

This work was supported by grants from the Italian Association for Cancer Research (MFAG 6312 to E.O.) and the Italian Ministry for University and Scientific Research (PRIN 2008 to A.F.). The Fondazione Internazionale Ricerche Medicina Sperimentale (FIRMS) provided financial and administrative assistance. N.LB. S.M. and A.G. are members of the Ph.D. program in "Complexity in Post-Genomic Biology" and R.P. is a member of the Ph.D. program in "Human Genetics", all at the University of Torino, Torino, Italy.

#### **10. References**


functions seem to be independent of their enzymatic activities. It is conceivable that the large extracellular domains of ectoenzymes and their lateral interaction with other membrane proteins can mediate responses without involvement of their catalytic activity. However, many of the non-substrate ligands of ectoenzymes remain to be identified. Moreover, increasing evidence indicates that a number of ectoenzymes orchestrate the immune mechanisms underlying tumour progression and outcome and it is now evident that ovarian cancer features a number of different tumour evasion mechanisms. The future challenge will be to use a combinatorial approach to increase the existing anti-tumour response, dampen tumour evasion mechanisms and target crucial environmental players.

This work was supported by grants from the Italian Association for Cancer Research (MFAG 6312 to E.O.) and the Italian Ministry for University and Scientific Research (PRIN 2008 to A.F.). The Fondazione Internazionale Ricerche Medicina Sperimentale (FIRMS) provided financial and administrative assistance. N.LB. S.M. and A.G. are members of the Ph.D. program in "Complexity in Post-Genomic Biology" and R.P. is a member of the Ph.D.

Aertgeerts, K., Ye, S., Shi, L., Prasad, S. G., Witmer, D., et al. (2004) N-linked glycosylation of

Agarwal, R., & Kaye, S. B. (2003) Ovarian cancer: strategies for overcoming resistance to

Airas, L., Salmi, M., & Jalkanen, S. (1993) Lymphocyte-vascular adhesion protein-2 is a novel

*Journal of Immunology,* Vol. 151 N° 8 (Oct 15), pp. 4228-4238, ISSN 0022-1767 Airas, L. (1998) CD73 and adhesion of B-cells to follicular dendritic cells, *Leukemia and* 

Airas, L., Niemela, J., & Jalkanen, S. (2000) CD73 engagement promotes lymphocyte binding

Annunziata, C. M., & Birrer, M. J. (2010) CD157 in ovarian carcinoma: how does it help us?,

Antczak, C., De Meester, I., & Bauvois, B. (2001) Ectopeptidases in pathophysiology,

Bagnato, A., Salani, D., Di Castro, V., Wu-Wong, J. R., Tecce, R., et al. (1999) Expression of

dipeptidyl peptidase IV (CD26): effects on enzyme activity, homodimer formation, and adenosine deaminase binding, *Protein Science,* Vol. 13 N° 1 (Jan), pp. 145-154,

chemotherapy, *Nature Reviews. Cancer,* Vol. 3 N° 7 (Jul), pp. 502-516, ISSN 1474-

70-kDa molecule involved in lymphocyte adhesion to vascular endothelium,

to endothelial cells via a lymphocyte function-associated antigen-1-dependent mechanism, *Journal of Immunology,* Vol. 165 N° 10 (Nov 15), pp. 5411-5417, ISSN

*Journal of the National Cancer Institute,* Vol. 102 N° 15 (Aug 4), pp. 1104-1105, ISSN

endothelin 1 and endothelin A receptor in ovarian carcinoma: evidence for an autocrine role in tumour growth, *Cancer Research,* Vol. 59 N° 3 (Feb 1), pp. 720-727,

program in "Human Genetics", all at the University of Torino, Torino, Italy.

*Lymphoma,* Vol. 29 N° 1-2 (Mar), pp. 37-47, ISSN 1042-8194

*Bioessays,* Vol. 23 N° 3 (Mar), pp. 251-260, ISSN 0265-9247

**9. Acknowledgement** 

**10. References** 

175X

0022-1767

1460-2105

ISSN 0008-5472

ISSN 0961-8368


Ectoenzymes in Epithelial Ovarian

151, ISSN 0892-6638

0262-0898

0303-7207

46677, ISSN 0021-9258

218, ISSN 0001-281

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 261

Engel, M., Hoffmann, T., Wagner, L., Wermann, M., Heiser, U., et al. (2003) The crystal

Fang, X., Schummer, M., Mao, M., Yu, S., Tabassam, F. H., et al. (2002) Lysophosphatidic

Feki, A., Berardi, P., Bellingan, G., Major, A., Krause, K. H., et al. (2009) Dissemination of

Ferrero, E., & Malavasi, F. (1997) Human CD38, a leukocyte receptor and ectoenzyme, is a

Frankel, A., & Mills, G. B. (1996) Peptide and lipid growth factors decrease cis-

Fujii, H., Nakajima, M., Saiki, I., Yoneda, J., Azuma, I., et al. (1995) Human melanoma

Fujimoto, Y., Nakanishi, Y., Sekine, S., Yoshimura, K., Akasu, T., et al. (2005) CD10

Galione, A. (1994) Cyclic ADP-ribose, the ADP-ribosyl cyclase pathway and calcium

Giganti, A., Rodriguez, M., Fould, B., Moulharat, N., Coge, F., et al. (2008) Murine and

Girardi, A. C., Degray, B. C., Nagy, T., Biemesderfer, D., & Aronson, P. S. (2001) Association

Goldstein, S. C., & Todd, R. F., 3rd. (1993) Structural and biosynthetic features of the Mo5

*Colon and Rectum,* Vol. 48 N° 10 (Oct), pp. 1883-1889, ISSN 0012-3706 Funaro, A., Ortolan, E., Ferranti, B., Gargiulo, L., Notaro, R., et al. (2004) CD157 is an

(Dec 15), pp. 4269-4278, ISSN 0006-4971

283 N° 12 (Mar 21), pp. 7776-7789, ISSN 0021-9258

*Oncology/Hematology,* Vol. 72 N° 1 (Oct), pp. 1-9, ISSN 1879-0461

*Immunology,* Vol. 159 N° 8 (Oct 15), pp. 3858-3865, ISSN 0022-1767

*Clinical Cancer Research,* Vol. 2 N° 8 (Aug), pp. 1307-1313, 1078-0432

1582 N° 1-3 (May 23), pp. 257-264, ISSN 0006-3002

*States of America,* Vol. 100 N° 9 (Apr 29), pp. 5063-5068, ISSN 0027-8424 Erdos, E. G., & Skidgel, R. A. (1989) Neutral endopeptidase 24.11 (enkephalinase) and

structure of dipeptidyl peptidase IV (CD26) reveals its functional regulation and enzymatic mechanism, *Proceedings of the National Academy of Sciences of the United* 

related regulators of peptide hormones, *FASEB Journal,* Vol. 3 N° 2 (Feb), pp. 145-

acid is a bioactive mediator in ovarian cancer, *Biochimica et Biophysica Acta,* Vol.

intraperitoneal ovarian cancer: Discussion of mechanisms and demonstration of lymphatic spreading in ovarian cancer model, *Critical Reviews in* 

member of a novel eukaryotic gene family of nicotinamide adenine dinucleotide+ converting enzymes: extensive structural homology with the genes for murine bone marrow stromal cell antigen 1 and aplysian ADP-ribosyl cyclase, *Journal of* 

diamminedichloroplatinum-induced cell death in human ovarian cancer cells,

invasion and metastasis enhancement by high expression of aminopeptidase N/CD13, *Clinical and Experimental Metastasis,* Vol. 13 N° 5 (Sep), pp. 337-344, ISSN

expression in colorectal carcinoma correlates with liver metastasis, *Diseases of the* 

important mediator of neutrophil adhesion and migration, *Blood*, Vol. 104 N° 13

signalling, *Molecular and Cellular Endocrinology,* Vol. 98 N° 2 (Jan), pp. 125-131, ISSN

human autotaxin alpha, beta, and gamma isoforms: gene organization, tissue distribution, and biochemical characterization, *Journal of Biological Chemistry,* Vol.

of Na(+)-H(+) exchanger isoform NHE3 and dipeptidyl peptidase IV in the renal proximal tubule, *Journal of Biological Chemistry,* Vol. 276 N° 49 (Dec 7), pp. 46671-

human myeloid differentiation antigen, *Tissue Antigens,* Vol. 41 N° 4 (Apr), pp. 214-


Chen, M., & O'Connor, K. L. (2005) Integrin alpha6beta4 promotes expression of

Cheng, H. C., Abdel-Ghany, M., & Pauli, B. U. (2003) A novel consensus motif in fibronectin

Clayton A, Al-Taei S, Webber, D., M., Tabi, M., et al. (2011) Cancer Exosomes Express CD39

Cohen, A. J., Bunn, P. A., Franklin, W., Magill-Solc, C., Hartmann, C., et al. (1996) Neutral

Colgan, S. P., Eltzschig, H. K., Eckle, T., & Thompson, L. F. (2006) Physiological roles for

Connelly, J. C., Skidgel, R. A., Schulz, W. W., Johnson, A. R., & Erdos, E. G. (1985) Neutral

D'Adamio, L., Shipp, M. A., Masteller, E. L., & Reinherz, E. L. (1989) Organization of the

Dai, J., Shen, R., Sumitomo, M., Goldberg, J. S., Geng, Y., et al. (2001) Tumour-suppressive

*Clinical Cancer Research,* Vol. 7 N° 5 (May), pp. 1370-1377, ISSN 1078-0432 Dang, N. H., Aytac, U., Sato, K., O'Brien, S., Melenhorst, J., et al. (2003) T-large granular

*Journal of Haematology,* Vol. 121 N° 6 (Jun), pp. 857-865, ISSN 0007-1048 Danielsen, E. M., & van Deurs, B. (1997) Galectin-4 and small intestinal brush border

Danziger, R. S. (2008) Aminopeptidase N in arterial hypertension, *Heart Failure Reviews,* Vol.

de Micco, C., Savchenko, V., Giorgi, R., Sebag, F., & Henry, J. F. (2008) Utility of malignancy

Dunphy, C. H., Gardner, L. J., Manes, J. L., Bee, C. S., & Taysi, K. (2000) CD30+ anaplastic

*Chemistry,* Vol. 278 N° 27 (Jul 4), pp. 24600-24607, ISSN 0021-9258

24 N° 32 (Jul 28), pp. 5125-5130, ISSN 0950-9232

*Immunology,* Vol. 187 N° pp. 676-683

N° 24 (Dec), pp. 8737-8741, ISSN 0027-8424

N° 18 (Sep), pp. 7103-7107, ISSN 0027-8424

13 N° 3 (Sep), pp. 293-298. ISSN 1382-4147

pp. 831-839, ISSN 0008-5472

2251, ISSN 1059-1524

0007-0920

0887-8013

1573-9538

autotaxin/ENPP2 autocrine motility factor in breast carcinoma cells, *Oncogene,* Vol.

mediates dipeptidyl peptidase IV adhesion and metastasis, *Journal of Biological* 

and CD73, Which Suppress T Cells through Adenosine Production, *Journal of* 

endopeptidase: variable expression in human lung, inactivation in lung cancer, and modulation of peptide-induced calcium flux, *Cancer Research,* Vol. 56 N° 4 (Feb 15),

ecto-5'-nucleotidase (CD73), *Purinergic Signal,* Vol. 2 N° 2 (Jun), pp. 351-360, ISSN

endopeptidase 24.11 in human neutrophils: cleavage of chemotactic peptide, *Proceedings of the National Academy of Sciences of the United States of America,* Vol. 82

gene encoding common acute lymphoblastic leukemia antigen (neutral endopeptidase 24.11): multiple miniexons and separate 5' untranslated regions, *Proceedings of the National Academy of Sciences of the United States of America,* Vol. 86

effects of neutral endopeptidase in androgen-independent prostate cancer cells,

lymphocyte lymphoproliferative disorder: expression of CD26 as a marker of clinically aggressive disease and characterization of marrow inhibition, *British* 

enzymes form clusters, *Molecular Biology of the Cell,* Vol. 8 N° 11 (Nov), pp. 2241-

markers in fine-needle aspiration cytology of thyroid nodules: comparison of Hector Battifora mesothelial antigen-1, thyroid peroxidase and dipeptidyl aminopeptidase IV, *British Journal of Cancer,* Vol. 98 N° 4 (Feb 26), pp. 818-823, ISSN

large-cell lymphoma with aberrant expression of CD13: case report and review of the literature, *Journal of Clinical Laboratory Analysis,* Vol. 14 N° 6 pp. 299-304, ISSN


Ectoenzymes in Epithelial Ovarian

1 (Jan), pp. 18-26, ISSN 1524-4636

N° 12 (Jun 7), pp. 5325-5329, ISSN 0027-8424

Vol. 101 N° 2 (Feb), pp. 347-354. ISSN 1349-7006

Vol. 63 N° 23 (Dec 1), pp. 8500-8506. ISSN 0008-5472

2753-2757, ISSN 0008-5472

ISSN 1078-0432

0277-1691

45-51, ISSN 0006-3002

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 263

Ishii, K., Usui, S., Yamamoto, H., Sugimura, Y., Tatematsu, M., et al. (2001b) Decreases of

Jalkanen, S., & Salmi, M. (2008) VAP-1 and CD73, endothelial cell surface enzymes in

Jin, D., Fan, J., Wang, L., Thompson, L. F., Liu, A., et al. (2010) CD73 on tumour cells impairs

*Journal of Biochemistry,* Vol. 271 N° 8 (Apr), pp. 1557-1565, ISSN 0014-2956 Kaisho, T., Ishikawa, J., Oritani, K., Inazawa, J., Tomizawa, H., et al. (1994) BST-1, a surface

Kajiyama, H., Kikkawa, F., Suzuki, T., Shibata, K., Ino, K., et al. (2002) Prolonged survival

Kajiyama, H., Kikkawa, F., Khin, E., Shibata, K., Ino, K., et al. (2003) Dipeptidyl peptidase IV

cells, *Cancer Research,* Vol. 63 N° 9 (May 1), pp. 2278-2283, ISSN 0008-5472 Kajiyama, H., Shibata, K., Terauchi, M., Morita, T., Ino, K., et al. (2005) Neutral

Kajiyama, H., Shibata, K., Ino, K., Mizutani, S., Nawa, A., et al. (2010) The expression of

Kehlen, A., Lendeckel, U., Dralle, H., Langner, J., & Hoang-Vu, C. (2003) Biological

Khin, E. E., Kikkawa, F., Ino, K., Suzuki, T., Shibata, K., et al. (2003) Neutral

Kikkawa, F., Kajiyama, H., Shibata, K., Ino, K., Nomura, S., et al. (2005) Dipeptidyl peptidase

Kitamura, Y., Watanabe, M., Komatsubara, S., & Sakata, Y. (1990) [Urinary excretion of

*Biochemistry,* Vol. 129 N° 2 (Feb), pp. 253-258, ISSN 0021-924X

metallothionein and aminopeptidase N in renal cancer tissues, *Journal of* 

leukocyte extravasation, *Arteriosclerosis, Thrombosis, and Vascular Biology,* Vol. 28 N°

antitumour T-cell responses: a novel mechanism of tumour-induced immune suppression, *Cancer Research,* Vol. 70 N° 6 (Mar 15), pp. 2245-2255, ISSN 1538-7445 Jung, I. D., Lee, J., Lee, K. B., Park, C. G., Kim, Y. K., et al. (2004) Activation of p21-activated

kinase 1 is required for lysophosphatidic acid-induced focal adhesion kinase phosphorylation and cell motility in human melanoma A2058 cells, *European* 

molecule of bone marrow stromal cell lines that facilitates pre-B-cell growth, *Proceedings of the National Academy of Sciences of the United States of America,* Vol. 91

and decreased invasive activity attributable to dipeptidyl peptidase IV overexpression in ovarian carcinoma, *Cancer Research,* Vol. 62 N° 10 (May 15), pp.

overexpression induces up-regulation of E-cadherin and tissue inhibitors of matrix metalloproteinases, resulting in decreased invasive potential in ovarian carcinoma

endopeptidase 24.11/CD10 suppresses progressive potential in ovarian carcinoma in vitro and in vivo, *Clinical Cancer Research,* Vol. 11 N° 5 (Mar 1), pp. 1798-1808,

dipeptidyl peptidase IV (DPPIV/CD26) is associated with enhanced chemosensitivity to paclitaxel in epithelial ovarian carcinoma cells, *Cancer Science,* 

significance of aminopeptidase N/CD13 in thyroid carcinomas, *Cancer Research,* 

endopeptidase/CD10 expression in the stroma of epithelial ovarian carcinoma, *International Journal of Gynecological Pathology,* Vol. 22 N° 2 (Apr), pp. 175-180, ISSN

IV in tumour progression, *Biochimica et Biophysica Acta,* Vol. 1751 N° 1 (Aug 1), pp.

glycine.prolile dipeptidile aminopeptidase, N-acetyl-beta-D-glucosaminidase, alanine aminopeptidase and low molecular protein in patients with renal cell


Gorrell, M. D. (2005) Dipeptidyl peptidase IV and related enzymes in cell biology and liver disorders, *Clinical Science,* Vol. 108 N° 4 (Apr), pp. 277-292, ISSN 0143-5221 Greaves, M. F., Hariri, G., Newman, R. A., Sutherland, D. R., Ritter, M. A., et al. (1983)

Haag, F., Adriouch, S., Brass, A., Jung, C., Moller, S., et al. (2007) Extracellular NAD and

Hashida, H., Takabayashi, A., Kanai, M., Adachi, M., Kondo, K., et al. (2002)

Häusler SF, Montalbán Del Barrio I, Strohschein J, Anoop Chandran P, Engel JB, et al. (2011)

Hoelzinger, D. B., Mariani, L., Weis, J., Woyke, T., Berens, T. J., et al. (2005) Gene expression

Hoffmann-Fezer, G., Knapp, W., & Thierfelder, S. (1982) Anatomical distribution of call

Hoskin, D. W., Mader, J. S., Furlong, S. J., Conrad, D. M., & Blay, J. (2008) Inhibition of T cell

Hu, Y. L., Tee, M. K., Goetzl, E. J., Auersperg, N., Mills, G. B., et al. (2001) Lysophosphatidic

Ikeda, N., Nakajima, Y., Tokuhara, T., Hattori, N., Sho, M., et al. (2003) Clinical significance

Imai, A., Furui, T., Tamaya, T., & Mills, G. B. (2000) A gonadotropin-releasing hormone-

Ino, K., Suzuki, T., Uehara, C., Nagasaka, T., Okamoto, T., et al. (2000) The expression and

Ishii, K., Usui, S., Sugimura, Y., Yamamoto, H., Yoshikawa, K., et al. (2001a) Inhibition of

*Pharmaceutical Bulletin,* Vol. 24 N° 3 (Mar), pp. 226-230, ISSN 0918-6158

*Cancer Research,* Vol. 9 N° 4 (Apr), pp. 1503-1508, ISSN 1078-0432

targets, *Neoplasia,* Vol. 7 N° 1 (Jan), pp. 7-16, ISSN 1522-8002

*Research,* Vol. 6 N° 6 pp. 761-767, ISSN 0145-2126

Vol. 85 N° 9 (Sep), pp. 3370-3375, ISSN 0021-972X

(Apr), pp. 628-639, ISSN 0006-4971

pp. 71-81, ISSN 1573-9546

(Oct), pp. 1405-1418, ISSN 0340-7004

(Mar), pp. 527-535, ISSN 1019-6439

768, ISSN 0027-8874

ISSN 0023-6837

386, ISSN 0016-5085

Selective expression of the common acute lymphoblastic leukemia (gp 100) antigen on immature lymphoid cells and their malignant counterparts, *Blood,* Vol. 61 N° 4

ATP: Partners in immune cell modulation, *Purinergic Signal,* Vol. 3 N° 1-2 (Mar),

Aminopeptidase N is involved in cell motility and angiogenesis: its clinical significance in human colon cancer, *Gastroenterology,* Vol. 122 N° 2 (Feb), pp. 376-

Ectonucleotidases CD39 and CD73 on OvCA cells are potent adenosine-generating enzymes responsible for adenosine receptor 2A-dependent suppression of T cell function and NK cell cytotoxicity. *Cancer Immunology, Immunotherapy,* Vol.60 N°10

profile of glioblastoma multiforme invasive phenotype points to new therapeutic

antigen expressing cells in normal lymphatic tissue and in lymphomas, *Leukemia* 

and natural killer cell function by adenosine and its contribution to immune evasion by tumour cells (Review), *International Journal of Oncology,* Vol. 32 N° 3

acid induction of vascular endothelial growth factor expression in human ovarian cancer cells, *Journal of the National Cancer Institute,* Vol. 93 N° 10 (May 16), pp. 762-

of aminopeptidase N/CD13 expression in human pancreatic carcinoma, *Clinical* 

responsive phosphatase hydrolyses lysophosphatidic acid within the plasma membrane of ovarian cancer cells, *Journal of Clinical Endocrinology and Metabolism,* 

localization of neutral endopeptidase 24.11/CD10 in human gestational trophoblastic diseases, *Laboratory Investigation,* Vol. 80 N° 11 (Nov), pp. 1729-1738,

aminopeptidase N (AP-N) and urokinase-type plasminogen activator (uPA) by zinc suppresses the invasion activity in human urological cancer cells, *Biological and* 


Ectoenzymes in Epithelial Ovarian

ISSN 0022-1767

2141

pp. 429-438, ISSN 0306-4522

(Oct 15), pp. 445-449, ISSN 0264-6021

12341-12352, ISSN 0021-9258

pp. 1066-1071, ISSN 0008-5472

pp. 1008-1017, ISSN 0741-5400

pp. 344-355, ISSN 0143-4160

ISSN 0065-2598

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 265

Maienschein, V., & Zimmermann, H. (1996) Immunocytochemical localization of ecto-5'-

Malavasi, F., Deaglio, S., Funaro, A., Ferrero, E., Horenstein, A. L., et al. (2008) Evolution

Masuda, A., Nakamura, K., Izutsu, K., Igarashi, K., Ohkawa, R., et al. (2008) Serum autotaxin

Matsas, R., Stephenson, S. L., Hryszko, J., Kenny, A. J., & Turner, A. J. (1985) The metabolism

Metzgar, R. S., Borowitz, M. J., Jones, N. H., & Dowell, B. L. (1981) Distribution of common

*Experimental Medicine,* Vol. 154 N° 4 (Oct 1), pp. 1249-1254, ISSN 0022-1007 Miki, T., Takegami, Y., Okawa, K., Muraguchi, T., Noda, M., et al. (2007) The reversion-

Mills, G. B., May, C., McGill, M., Roifman, C. M., & Mellors, A. (1988) A putative new

Mills, G. B., & Moolenaar, W. H. (2003) The emerging role of lysophosphatidic acid in cancer, *Nature Reviews. Cancer,* Vol. 3 N° 8 (Aug), pp. 582-591. 1474-175X Mina-Osorio, P., & Ortega, E. (2005) Aminopeptidase N (CD13) functionally interacts with

Mina-Osorio, P., Shapiro, L. H., & Ortega, E. (2006) CD13 in cell adhesion: aminopeptidase

Mizutani, S., Kajiyama, H., Suzuki, T., Shibata, K., Ino, K., et al. (2003) Survival time and

Moreschi, I., Bruzzone, S., Bodrato, N., Usai, C., Guida, L., et al. (2008) NAADP+ is an

*Biology,* Vol. 79 N° 4 (Apr), pp. 719-730, ISSN 0741-5400

nucleotidase in cultures of cerebellar granule cells, *Neuroscience,* Vol. 70 N° 2 (Jan),

and function of the ADP ribosyl cyclase/CD38 gene family in physiology and pathology, *Physiological Reviews,* Vol. 88 N° 3 (Jul), pp. 841-886, ISSN 0031-9333 Massaia, M., Pileri, A., Boccadoro, M., Bianchi, A., Palumbo, A., et al. (1988) The generation

of alloreactive cytotoxic T lymphocytes requires the expression of ecto-5'nucleotidase activity, *Journal of Immunology,* Vol. 141 N° 11 (Dec 1), pp. 3768-3775,

measurement in haematological malignancies: a promising marker for follicular lymphoma, *British Journal of Haematology,* Vol. 143 N° 1 (Oct), pp. 60-70, ISSN 1365-

of neuropeptides. Phase separation of synaptic membrane preparations with Triton X-114 reveals the presence of aminopeptidase N, *Biochemical Journal,* Vol. 231 N° 2

acute lymphoblastic leukemia antigen in nonhematopoietic tissues, *Journal of* 

inducing cysteine-rich protein with Kazal motifs (RECK) interacts with membrane type 1 matrix metalloproteinase and CD13/aminopeptidase N and modulates their endocytic pathways, *Journal of Biological Chemistry,* Vol. 282 N° 16 (Apr 20), pp.

growth factor in ascitic fluid from ovarian cancer patients: identification, characterization, and mechanism of action, *Cancer Research,* Vol. 48 N° 5 (Mar 1),

FcgammaRs in human monocytes, *Journal of Leukocyte Biology,* Vol. 77 N° 6 (Jun),

N (CD13) mediates homotypic aggregation of monocytic cells, *Journal of Leukocyte* 

invasive activity due to dipeptidyl peptidase IV overexpression in ovarian carcinoma, *Advances in Experimental Medicine and Biology,* Vol. 524 N° pp. 253-256,

agonist of the human P2Y11 purinergic receptor, *Cell Calcium,* Vol. 43 N° 4 (Apr),

carcinoma], *Hinyokika Kiyo (Acta Urologica Japonica),* Vol. 36 N° 5 (May), pp. 535-539, ISSN 0018-1994


Knopfel, M., Davies, J. P., Duong, P. T., Kvaerno, L., Carreira, E. M., et al. (2007) Multiple

Larsen, S. L., Pedersen, L. O., Buus, S., & Stryhn, A. (1996) T cell responses affected by

Lavagno, L., Ferrero, E., Ortolan, E., Malavasi, F., & Funaro, A. (2007) CD157 is part of a

*Journal of Biological Regulators and Homeostatic Agents,* Vol. 21 N° 1-2 pp. 5-11. Lee, J., Duk Jung, I., Gyo Park, C., Han, J. W., & Young Lee, H. (2006) Autotaxin stimulates

Lerche, C., Vogel, L. K., Shapiro, L. H., Noren, O., & Sjostrom, H. (1996) Human

Leth-Larsen, R., Lund, R., Hansen, H. V., Laenkholm, A. V., Tarin, D., et al. (2009)

Lin, M. E., Herr, D. R., & Chun, J. (2010) Lysophosphatidic acid (LPA) receptors: signaling

Liu, S., Umezu-Goto, M., Murph, M., Lu, Y., Liu, W., et al. (2009) Expression of autotaxin

Loke, S. L., Leung, C. Y., Chiu, K. Y., Yau, W. L., Cheung, K. N., et al. (1990) Localisation of

Madara, J. L., Patapoff, T. W., Gillece-Castro, B., Colgan, S. P., Parkos, C. A., et al. (1993) 5'-

*Clinical Pathology,* Vol. 43 N° 8 (Aug), pp. 654-656, ISSN 0021-9746

*Investigation,* Vol. 91 N° 5 (May), pp. 2320-2325, ISSN 0021-9738

*Proteomics,* Vol. 8 N° 6 (Jun), pp. 1436-1449, ISSN 1535-9484

N° 3-4 (Apr), pp. 130-138, ISSN 1098-8823

*et Biophysica Acta,* Vol. 1771 N° 9 (Sep), pp. 1140-1147, ISSN 0006-3002 Koszalka, P., Ozuyaman, B., Huo, Y., Zernecke, A., Flogel, U., et al. (2004) Targeted

ISSN 0018-1994

821, ISSN 1524-4571

183-189, ISSN 0022-1007

pp. 712-713, ISSN 0938-8990

carcinoma], *Hinyokika Kiyo (Acta Urologica Japonica),* Vol. 36 N° 5 (May), pp. 535-539,

plasma membrane receptors but not NPC1L1 mediate high-affinity, ezetimibesensitive cholesterol uptake into the intestinal brush border membrane, *Biochimica* 

disruption of cd73/ecto-5'-nucleotidase alters thromboregulation and augments vascular inflammatory response, *Circulation Research,* Vol. 95 N° 8 (Oct 15), pp. 814-

aminopeptidase N (CD13)-mediated trimming of major histocompatibility complex class II-bound peptides, *Journal of Experimental Medicine,* Vol. 184 N° 1 (Jul 1), pp.

supramolecular complex with CD11b/CD18 on the human neutrophil cell surface,

urokinase-type plasminogen activator expression through phosphoinositide 3 kinase-Akt-nuclear [corrected] factor kappa B signaling cascade in human melanoma cells, *Melanoma Research,* Vol. 16 N° 5 (Oct), pp. 445-452, ISSN 0960-8931

aminopeptidase N is encoded by 20 exons, *Mammalian Genome,* Vol. 7 N° 9 (Sep),

Metastasis-related plasma membrane proteins of human breast cancer cells identified by comparative quantitative mass spectrometry, *Molecular and Cellular* 

properties and disease relevance, *Prostaglandins and Other Lipid Mediators,* Vol. 91

and lysophosphatidic acid receptors increases mammary tumourigenesis, invasion, and metastases, *Cancer Cell,* Vol. 15 N° 6 (Jun 2), pp. 539-550, ISSN 1878-3686 Lo Buono, N., Parrotta, R., Morone, S., Bovino, P., Nacci, G., et al. (2011) The CD157-integrin

partnership controls transendothelial migration and adhesion of human monocytes *Journal of Biological Chemistry,* Vol. 286, N°21 (May) pp. 18681-18691, ISSN 0021-9258

CD10 to biliary canaliculi by immunoelectron microscopical examination, *Journal of* 

adenosine monophosphate is the neutrophil-derived paracrine factor that elicits chloride secretion from T84 intestinal epithelial cell monolayers, *Journal of Clinical* 


Ectoenzymes in Epithelial Ovarian

13132-13137, ISSN 0027-8424

pp. 4214-4222, ISSN 0006-4971

ISSN 1078-8956

1541-7786

2896

N° 4 (Dec), pp. 309-322, ISSN 0263-6484

Vol. 18 N° 5 pp. 391-400, ISSN 0262-0898

N° pp. 267-296, ISSN 0732-0582

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 267

Olsen, J., Cowell, G. M., Konigshofer, E., Danielsen, E. M., Moller, J., et al. (1988) Complete

Ortolan, E., Tibaldi, E. V., Ferranti, B., Lavagno, L., Garbarino, G., et al. (2006) CD157 plays a

Ortolan, E., Arisio, R., Morone, S., Bovino, P., Lo-Buono, N., et al. (2010) Functional Role and

Pesando, J. M., Tomaselli, K. J., Lazarus, H., & Schlossman, S. F. (1983) Distribution and

Pethiyagoda, C. L., Welch, D. R., & Fleming, T. P. (2000) Dipeptidyl peptidase IV (DPPIV)

Pfleiderer, G., & Celliers, P. G. (1963) [Isolation of an Aminopeptidase from Kidney Particles], *Biochemische Zeitschrift,* Vol. 339 N° (Dec 3), pp. 186-189. ISSN 0366-0753 Ptaszynska, M. M., Pendrak, M. L., Bandle, R. W., Stracke, M. L., & Roberts, D. D. (2008)

Rabinovich, G. A., Gabrilovich, D., & Sotomayor, E. M. (2007) Immunosuppressive

Razak, K., & Newland, A. C. (1992) The significance of aminopeptidases and haematopoietic cell differentiation, *Blood Reviews,* Vol. 6 N° 4 (Dec), pp. 243-250, ISSN 0268-960X Resta, R., Yamashita, Y., & Thompson, L. F. (1998) Ecto-enzyme and signaling functions of

Richardson, A., & Kaye, S. B. (2005) Drug resistance in ovarian cancer: the emerging

Ritz, J., Pesando, J. M., Notis-McConarty, J., Lazarus, H., & Schlossman, S. F. (1980) A

*Resistance Updates,* Vol. 8 N° 5 (Oct), pp. 311-321, ISSN 1368-7646

Vol. 283 N° 5747 (Feb 7), pp. 583-585, ISSN 0028-0836

*Immunology,* Vol. 131 N° 4 (Oct), pp. 2038-2045, ISSN 0022-1767

*Cancer Institute,* Vol. 105 N° 16 (Jul 16), pp. 1160-1177, ISSN 1460-2105 Papandreou, C. N., Usmani, B., Geng, Y., Bogenrieder, T., Freeman, R., et al. (1998) Neutral

*Academy of Sciences of the United States of America,* Vol. 103 N° 35 (Aug 29), pp.

amino acid sequence of human intestinal aminopeptidase N as deduced from cloned cDNA, *FEBS Letters,* Vol. 238 N° 2 (Oct 10), pp. 307-314, ISSN 0014-5793 Ortolan, E., Vacca, P., Capobianco, A., Armando, E., Crivellin, F., et al. (2002) CD157, the

Janus of CD38 but with a unique personality, *Cell Biochemistry and Function,* Vol. 20

pivotal role in neutrophil transendothelial migration, *Blood*, Vol. 108 N° 13 (Dec 15),

Prognostic Significance of CD157 in Ovarian Carcinoma, *Journal of the National* 

endopeptidase 24.11 loss in metastatic human prostate cancer contributes to androgen-independent progression, *Nature Medicine,* Vol. 4 N° 1 (Jan), pp. 50-57,

modulation of a human leukemia-associated antigen (CALLA), *Journal of* 

inhibits cellular invasion of melanoma cells, *Clinical and Experimental Metastasis,* 

Positive feedback between vascular endothelial growth factor-A and autotaxin in ovarian cancer cells, *Molecular Cancer Research,* Vol. 6 N° 3 (Mar), pp. 352-363, ISSN

strategies that are mediated by tumour cells, *Annual Review of Immunology,* Vol. 25

lymphocyte CD73, *Immunological Reviews,* Vol. 161 N° (Feb), pp. 95-109, ISSN 0105-

importance of gene transcription and spatio-temporal regulation of resistance, *Drug* 

monoclonal antibody to human acute lymphoblastic leukaemia antigen, *Nature,* 


Morrison, M. E., Vijayasaradhi, S., Engelstein, D., Albino, A. P., & Houghton, A. N. (1993) A

Muraoka, O., Tanaka, H., Itoh, M., Ishihara, K., & Hirano, T. (1996) Genomic structure of human BST-1, *Immunology Letters,* Vol. 54 N° 1 (Dec 1), pp. 1-4, ISSN 0165-2478 Nagy, J. A., Masse, E. M., Herzberg, K. T., Meyers, M. S., Yeo, K. T., et al. (1995)

Nakai, Y., Ikeda, H., Nakamura, K., Kume, Y., Fujishiro, M., et al. (2011) Specific increase in

Nakanaga, K., Hama, K., & Aoki, J. (2010) Autotaxin--an LPA producing enzyme with diverse functions, *J Biochem,* Vol. 148 N° 1 (Jul), pp. 13-24, ISSN 1756-2651 Nam, S. W., Clair, T., Campo, C. K., Lee, H. Y., Liotta, L. A., et al. (2000) Autotaxin (ATX), a

Naora, H., & Montell, D. J. (2005) Ovarian cancer metastasis: integrating insights from

Nemoto, E., Kunii, R., Tada, H., Tsubahara, T., Ishihata, H., et al. (2004) Expression of

Nishimasu, H., Okudaira, S., Hama, K., Mihara, E., Dohmae, N., et al. (2011) Crystal

mice, *FEBS Letters,* Vol. 401 N° 2-3 (Jan 20), pp. 227-229, ISSN 0014-5793 Nomura, R., Kiyota, A., Suzaki, E., Kataoka, K., Ohe, Y., et al. (2004) Human coronavirus

Noren, O. (1986) The enzymes of the enterocyte plasma membrane, In: *Molecular and cellular basis of digestion,* Desnuelle P., 133-167, Elsevier, Amsterdam, Holland. O'Hare, M. J., Ormerod, M. G., Monaghan, P., Lane, E. B., & Gusterson, B. A. (1991)

Ohta, A., Gorelik, E., Prasad, S. J., Ronchese, F., Lukashev, D., et al. (2006) A2A adenosine

Vol. 78 N° 16 (Aug), pp. 8701-8708, ISSN 0022-538X

ISSN 0022-1007

ISSN 1474-175X

10-19, ISSN 0022-3484

221, ISSN 0301-4681

(Jan 15), pp. 360-368, ISSN 0008-5472

Vol. 44 N° 8-9 (Jun), pp. 576-581, ISSN 1873-2933

N° 18 (Sep 15), pp. 6938-6944, ISSN 0008-5472

marker for neoplastic progression of human melanocytes is a cell surface ectopeptidase, *Journal of Experimental Medicine,* Vol. 177 N° 4 (Apr 1), pp. 1135-1143,

Pathogenesis of ascites tumour growth: vascular permeability factor, vascular hyperpermeability, and ascites fluid accumulation, *Cancer Research,* Vol. 55 N° 2

serum autotaxin activity in patients with pancreatic cancer, *Clinical Biochemistry,* 

potent tumour motogen, augments invasive and metastatic potential of rastransformed cells, *Oncogene,* Vol. 19 N° 2 (Jan 13), pp. 241-247, ISSN 0950-9232 Nam, S. W., Clair, T., Kim, Y. S., McMarlin, A., Schiffmann, E., et al. (2001) Autotaxin (NPP-

2), a metastasis-enhancing motogen, is an angiogenic factor, *Cancer Research,* Vol. 61

disparate model organisms, *Nature Reviews. Cancer,* Vol. 5 N° 5 (May), pp. 355-366,

CD73/ecto-5'-nucleotidase on human gingival fibroblasts and contribution to the inhibition of interleukin-1alpha-induced granulocyte-macrophage colony stimulating factor production, *Journal of Periodontal Research,* Vol. 39 N° 1 (Feb), pp.

structure of autotaxin and insight into GPCR activation by lipid mediators, *Nature Structural and Molecular Biology,* Vol. 18 N° 2 (Feb), pp. 205-212, ISSN 1545-9985 Noble, F., & Roques, B. P. (1997) Association of aminopeptidase N and endopeptidase 24.15

inhibitors potentiate behavioral effects mediated by nociceptin/orphanin FQ in

229E binds to CD13 in rafts and enters the cell through caveolae, *Journal of Virology,* 

Characterization in vitro of luminal and myoepithelial cells isolated from the human mammary gland by cell sorting, *Differentiation,* Vol. 46 N° 3 (Apr), pp. 209-

receptor protects tumours from antitumour T cells, *Proceedings of the National* 

*Academy of Sciences of the United States of America,* Vol. 103 N° 35 (Aug 29), pp. 13132-13137, ISSN 0027-8424


Ectoenzymes in Epithelial Ovarian

1547-1552, ISSN 1091-6490

pp. 2524-2529, ISSN 0021-9258

pp. 785-792, ISSN 1019-6439

2601, ISSN 0021-9738

5472

ISSN 1078-0432

0163-7827

Carcinoma: Potential Diagnostic Markers and Therapeutic Targets 269

Stagg, J., & Smyth, M. J. (2010b) Extracellular adenosine triphosphate and adenosine in cancer, *Oncogene,* Vol. 29 N° 39 (Sep 30), pp. 5346-5358, ISSN 1476-5594 Stracke, M. L., Krutzsch, H. C., Unsworth, E. J., Arestad, A., Cioce, V., et al. (1992)

Stremenova, J., Krepela, E., Mares, V., Trim, J., Dbaly, V., et al. (2007) Expression and

Strohmeier, G. R., Lencer, W. I., Patapoff, T. W., Thompson, L. F., Carlson, S. L., et al. (1997)

Suzuki, T., Kikkawa, F., Ino, K., Nagasaka, T., Tamakoshi, K., et al. (2001) Imbalance

endometrial carcinoma, *Oncology,* Vol. 60 N° 3 pp. 258-267, ISSN 0030-2414 Tanaka, T., Camerini, D., Seed, B., Torimoto, Y., Dang, N. H., et al. (1992) Cloning and

ten Kate, J., Wijnen, J. T., van der Goes, R. G., Quadt, R., Griffioen, G., et al. (1984)

Terauchi, M., Kajiyama, H., Shibata, K., Ino, K., Nawa, A., et al. (2007) Inhibition of

Tokumura, A., Majima, E., Kariya, Y., Tominaga, K., Kogure, K., et al. (2002) Identification of

van Hensbergen, Y., Broxterman, H. J., Hanemaaijer, R., Jorna, A. S., van Lent, N. A., et al.

van Hensbergen, Y., Broxterman, H. J., Rana, S., van Diest, P. J., Duyndam, M. C., et al.

van Meeteren, L. A., & Moolenaar, W. H. (2007) Regulation and biological activities of the

Vol. 149 N° 2 (Jul 15), pp. 481-486, ISSN 0022-1767

*BMC Cancer,* Vol. 7 N° pp. 140, ISSN 1471-2407

Vol. 277 N° 42 (Oct 18), pp. 39436-39442, ISSN 0021-9258

Vol. 10 N° 3 (Feb 1), pp. 1180-1191, ISSN 1078-0432

*National Academy of Sciences of the United States of America,* Vol. 107 N° 4 (Jan 26), pp.

Identification, purification, and partial sequence analysis of autotaxin, a novel motility-stimulating protein, *Journal of Biological Chemistry,* Vol. 267 N° 4 (Feb 5),

enzymatic activity of dipeptidyl peptidase-IV in human astrocytic tumours are associated with tumour grade, *International Journal of Oncology,* Vol. 31 N° 4 (Oct),

Surface expression, polarization, and functional significance of CD73 in human intestinal epithelia, *Journal of Clinical Investigation,* Vol. 99 N° 11 (Jun 1), pp. 2588-

between neutral endopeptidase 24.11 and endothelin-1 expression in human

functional expression of the T cell activation antigen CD26, *Journal of Immunology,* 

Quantitative changes in adenosine deaminase isoenzymes in human colorectal adenocarcinomas, *Cancer Research,* Vol. 44 N° 10 (Oct), pp. 4688-4692, ISSN 0008-

APN/CD13 leads to suppressed progressive potential in ovarian carcinoma cells,

human plasma lysophospholipase D, a lysophosphatidic acid-producing enzyme, as autotaxin, a multifunctional phosphodiesterase, *Journal of Biological Chemistry,* 

(2002) Soluble aminopeptidase N/CD13 in malignant and nonmalignant effusions and intratumoural fluid, *Clinical Cancer Research,* Vol. 8 N° 12 (Dec), pp. 3747-3754,

(2004) Reduced growth, increased vascular area, and reduced response to cisplatin in CD13-overexpressing human ovarian cancer xenografts, *Clinical Cancer Research,* 

autotaxin-LPA axis, *Progress in Lipid Research,* Vol. 46 N° 2 (Mar), pp. 145-160, ISSN


Ross, J. A., Ansell, I., Hjelle, J. T., Anderson, J. D., Miller-Hjelle, M. A., et al. (1998)

Saiki, I., Fujii, H., Yoneda, J., Abe, F., Nakajima, M., et al. (1993) Role of aminopeptidase N

Salles, G., Chen, C. Y., Reinherz, E. L., & Shipp, M. A. (1992) CD10/NEP is expressed on

Salmi, M., & Jalkanen, S. (2011) Homing-associated molecules CD73 and VAP-1 as targets to

Samadi, N., Gaetano, C., Goping, I. S., & Brindley, D. N. (2009) Autotaxin protects MCF-7

Samadi, N., Bekele, R. T., Goping, I. S., Schang, L. M., & Brindley, D. N. (2011)

Semenza, G. (1986) Anchoring and biosynthesis of stalked brush border membrane proteins:

Shipp, M. A., Richardson, N. E., Sayre, P. H., Brown, N. R., Masteller, E. L., et al. (1988)

Shipp, M. A., Vijayaraghavan, J., Schmidt, E. V., Masteller, E. L., D'Adamio, L., et al. (1989)

Shipp, M. A., & Look, A. T. (1993) Hematopoietic differentiation antigens that are

Spychala, J., Lazarowski, E., Ostapkowicz, A., Ayscue, L. H., Jin, A., et al. (2004) Role of

*Oncogene,* Vol. 28 N° 7 (Feb 19), pp. 1028-1039, ISSN 1476-5594

Vol. 201 N° 1 (Apr 10), pp. 22-32, ISSN 0008-8749

*Biology,* Vol. 2 N° pp. 255-313, ISSN 0743-4634

Vol. 86 N° 1 (Jan), pp. 297-301, ISSN 0027-8424

1052-1070, ISSN 0006-4971

*Journal of Cancer,* Vol. 54 N° 1 (Apr 22), pp. 137-143, ISSN 0020-7136 Salani, D., Di Castro, V., Nicotra, M. R., Rosano, L., Tecce, R., et al. (2000) Role of endothelin-

Vol. 14 N° pp. 25-30. ISSN 1197-8554

N° 5 (Nov), pp. 1537-1547, ISSN 0002-9440

6), pp. 1543-1550, ISSN 1873-3468

4971

8424

Phenotypic mapping of human mesothelial cells, *Advances in Peritoneal Dialysis,* 

(CD13) in tumour-cell invasion and extracellular matrix degradation, *International* 

1 in neovascularization of ovarian carcinoma, *American Journal of Pathology,* Vol. 157

Thy-1low B220+ murine B-cell progenitors and functions to regulate stromal celldependent lymphopoiesis, *Blood,* Vol. 80 N° 8 (Oct 15), pp. 2021-2029, ISSN 0006-

prevent harmful inflammations and cancer spread, *FEBS Letters,* Vol. 585 N° 11 (Jun

breast cancer and MDA-MB-435 melanoma cells against Taxol-induced apoptosis,

Lysophosphatidate induces chemo-resistance by releasing breast cancer cells from taxol-induced mitotic arrest, *PLoS ONE,* Vol. 6 N° 5 pp. e20608, ISSN 1932-6203 Santos, A. N., Langner, J., Herrmann, M., & Riemann, D. (2000) Aminopeptidase N/CD13 is

directly linked to signal transduction pathways in monocytes, *Cellular Immunology,* 

glycosidases and peptidases of enterocytes and renal tubuli, *Annual Review of Cell* 

Molecular cloning of the common acute lymphoblastic leukemia antigen (CALLA) identifies a type II integral membrane protein, *Proceedings of the National Academy of Sciences of the United States of America,* Vol. 85 N° 13 (Jul), pp. 4819-4823, ISSN 0027-

Common acute lymphoblastic leukemia antigen (CALLA) is active neutral endopeptidase 24.11 ("enkephalinase"): direct evidence by cDNA transfection analysis, *Proceedings of the National Academy of Sciences of the United States of America,* 

membrane-associated enzymes: cutting is the key!, *Blood,* Vol. 82 N° 4 (Aug 15), pp.

estrogen receptor in the regulation of ecto-5'-nucleotidase and adenosine in breast cancer, *Clinical Cancer Research,* Vol. 10 N° 2 (Jan 15), pp. 708-717, ISSN 1078-0432 Stagg, J., Divisekera, U., McLaughlin, N., Sharkey, J., Pommey, S., et al. (2010a) Anti-CD73

antibody therapy inhibits breast tumour growth and metastasis, *Proceedings of the* 

*National Academy of Sciences of the United States of America,* Vol. 107 N° 4 (Jan 26), pp. 1547-1552, ISSN 1091-6490


**14** 

*Italy* 

**P53 Network in Ovarian Cancer** 

P53 (Tp53, tumor protein p53) is one of the most relevant human oncosuppressor genes. Accordingly, inactivation of p53 by direct mutation of the gene is one of the most frequent

In Ovarian Carcinoma (OC), p53 is altered in 30–80% of cases. Molecular and genetic studies have further confirmed the relevance of p53 in the development and progression of OC. Several studied have attempted to establish the p53 status as a marker of clinicopathological features. However, the predictive value of p53 alterations is still ambiguous, suggesting that multiple factors contribute to define p53 function. One of these factors may be related to the recent discovery of p53 variants in OC that may modulate, or even antagonize wild-type p53

Additional studies in molecular oncology have revealed alternative routes of p53 inactivation through deregulation of its negative regulators, MDM2 and MDM4. MDM family members are key regulators of p53 activity and levels, by acting as repressors of p53 transcriptional function and as a complex for the degradation of p53 protein. Their overexpression has been observed in many human tumors characterized by wild-type p53 status, supporting the model of multiple ways of p53 inactivation in tumor cells. In fact, p53 dysfunction measured as pathogenic mutations or altered copy number of MDM2 and MDM4, approaches 100% of confirmed high-grade serous carcinoma (Ahmed et al., 2010). Recent data, also from our group, have contributed to define an even higher level of complexity in the p53 network. Indeed, it has been shown that the canonical inhibitors MDM2 and MDM4 may actually exhibit a dual mode of action (Shmueli & Oren, 2007; Mancini et al., 2009a). Particularly, following DNA damage, MDM4 functions as a cooperative factor in p53 apoptosis and promotes cell death in cisplatinum-treated ovarian cancer cells. Accordingly, MDM4 levels/p53 status correlates significantly with

Interestingly, various studies have evidenced that the estrogen signalling pathway has a profound impact on the activity of MDM2/MDM4/p53 network (Bond & Levine, 2007)

Overall, these data suggest that a combined signature of p53 network may be a better prognostic factor for clinicopathological properties of ovarian cancer in agreement to what it

In this chapter, we will summarize all these data and try to compose potential scenario for

suggesting the relevance of hormonal status too in the prediction of p53 function.

**1. Introduction** 

genetic lesions in human tumors.

function (Hofstetter et al., 2010).

chemosensitivity of OC (Mancini et al., 2009b).

has been recently published (Kalloger et al., 2011).

novel predicting properties of p53 network in ovarian cancer.

Fabiola Moretti and Francesca Mancini *Institute of Cell Biology and Neurobiology National Research Council of Italy (CNR)* 


## **P53 Network in Ovarian Cancer**

Fabiola Moretti and Francesca Mancini

*Institute of Cell Biology and Neurobiology National Research Council of Italy (CNR) Italy* 

#### **1. Introduction**

270 Ovarian Cancer – Basic Science Perspective

Vidot, S., Witham, J., Agarwal, R., Greenhough, S., Bamrah, H. S., et al. (2010) Autotaxin

Watanabe, N., Ikeda, H., Nakamura, K., Ohkawa, R., Kume, Y., et al. (2007) Both plasma

Wesley, U. V., Albino, A. P., Tiwari, S., & Houghton, A. N. (1999) A role for dipeptidyl

*of Experimental Medicine,* Vol. 190 N° 3 (Aug 2), pp. 311-322, ISSN 0022-1007 Xu, Y., Shen, Z., Wiper, D. W., Wu, M., Morton, R. E., et al. (1998) Lysophosphatidic acid as a

Yamaguchi, U., Nakayama, R., Honda, K., Ichikawa, H., Hasegawa, T., et al. (2008) Distinct

Zhang, B. (2010) CD73: a novel target for cancer immunotherapy, *Cancer Research,* Vol. 70 N°

Zhang, M. Z., Qiao, Y. H., & Suo, Z. H. (2008) [Correlation of DPPIV expression with

Zhao, H., Ramos, C. F., Brooks, J. D., & Peehl, D. M. (2007) Distinctive gene expression of

*Cellular Physiology,* Vol. 210 N° 1 (Jan), pp. 111-121, ISSN 0021-9541

*Clinical Oncology,* Vol. 26 N° 25 (Sep 1), pp. 4100-4108, ISSN 1527-7755 Yamashita, M., Kajiyama, H., Terauchi, M., Shibata, K., Ino, K., et al. (2007) Involvement of

Vol. 22 N° 6 (Jun), pp. 926-935, ISSN 1873-3913

8 (Aug 26), pp. 719-723, ISSN 0098-7484

16 (Aug 15), pp. 6407-6411, ISSN 1538-7445

2243-2250, ISSN 0020-7136

848-852, ISSN 0253-3766

delays apoptosis induced by carboplatin in ovarian cancer cells, *Cellular Signalling,* 

lysophosphatidic acid and serum autotaxin levels are increased in chronic hepatitis C, *Journal of Clinical Gastroenterology,* Vol. 41 N° 6 (Jul), pp. 616-623, ISSN 0192-0790

peptidase IV in suppressing the malignant phenotype of melanocytic cells, *Journal* 

potential biomarker for ovarian and other gynecologic cancers, *JAMA,* Vol. 280 N°

gene expression-defined classes of gastrointestinal stromal tumour, *Journal of* 

aminopeptidase N in enhanced chemosensitivity to paclitaxel in ovarian carcinoma in vitro and in vivo, *International Journal of Cancer,* Vol. 120 N° 10 (May 15), pp.

clinicopathological features and prognosis in epithelial ovarian carcinoma], *Zhonghua Zhong Liu Za Zhi (Chinese Journal of Oncology),* Vol. 30 N° 11 (Nov), pp.

prostatic stromal cells cultured from diseased versus normal tissues, *Journal of* 

P53 (Tp53, tumor protein p53) is one of the most relevant human oncosuppressor genes. Accordingly, inactivation of p53 by direct mutation of the gene is one of the most frequent genetic lesions in human tumors.

In Ovarian Carcinoma (OC), p53 is altered in 30–80% of cases. Molecular and genetic studies have further confirmed the relevance of p53 in the development and progression of OC. Several studied have attempted to establish the p53 status as a marker of clinicopathological features. However, the predictive value of p53 alterations is still ambiguous, suggesting that multiple factors contribute to define p53 function. One of these factors may be related to the recent discovery of p53 variants in OC that may modulate, or even antagonize wild-type p53 function (Hofstetter et al., 2010).

Additional studies in molecular oncology have revealed alternative routes of p53 inactivation through deregulation of its negative regulators, MDM2 and MDM4. MDM family members are key regulators of p53 activity and levels, by acting as repressors of p53 transcriptional function and as a complex for the degradation of p53 protein. Their overexpression has been observed in many human tumors characterized by wild-type p53 status, supporting the model of multiple ways of p53 inactivation in tumor cells. In fact, p53 dysfunction measured as pathogenic mutations or altered copy number of MDM2 and MDM4, approaches 100% of confirmed high-grade serous carcinoma (Ahmed et al., 2010).

Recent data, also from our group, have contributed to define an even higher level of complexity in the p53 network. Indeed, it has been shown that the canonical inhibitors MDM2 and MDM4 may actually exhibit a dual mode of action (Shmueli & Oren, 2007; Mancini et al., 2009a). Particularly, following DNA damage, MDM4 functions as a cooperative factor in p53 apoptosis and promotes cell death in cisplatinum-treated ovarian cancer cells. Accordingly, MDM4 levels/p53 status correlates significantly with chemosensitivity of OC (Mancini et al., 2009b).

Interestingly, various studies have evidenced that the estrogen signalling pathway has a profound impact on the activity of MDM2/MDM4/p53 network (Bond & Levine, 2007) suggesting the relevance of hormonal status too in the prediction of p53 function.

Overall, these data suggest that a combined signature of p53 network may be a better prognostic factor for clinicopathological properties of ovarian cancer in agreement to what it has been recently published (Kalloger et al., 2011).

In this chapter, we will summarize all these data and try to compose potential scenario for novel predicting properties of p53 network in ovarian cancer.

P53 Network in Ovarian Cancer 273

The cooperative action of p53 in BRCA1-driven tumorigenesis and in the induction of hereditary ovarian cancer is further strengthened by the phenotype of knock-out mice. *Brca1*−*/*− mouse embryos are embryonic lethal at embryonic day 6.5; if embryos are deleted simultaneously for both *Brca1* and *p53*, the embryonic lethality is delayed (Scully & Livingston, 2000). This suggests that p53 function antagonizes genome instability induced by BRCA1 loss, causing embryo lethality. Therefore, in order to promote tumor development, p53 activity must be lost so that the cell transformation process can go on

According to this model, in ovaries removed prophylactically from women heterozygote for BRCA1, alteration of p53 was observed in all early stage I serous carcinomas as well as in the adjacent dysplastic surface epithelium (Pothuir et al., 2001). Although sporadic ovarian carcinomas were not analyzed in this study, the clinical and pathological features of BRCAassociated ovarian carcinomas and their sporadic counterparts are indistinguishable,

Overall, these observations support a general model in which *p53* inactivation is required

Alterations of p53 pathway are one of the most frequent events in sporadic epithelial ovarian cancer (EOC). The majority of p53 mutations at its locus 17p13.1 are missense mutations that cause single residue changes, largely occurring in the DNA binding domain (Figure 1) (Sigal & Rotter, 2000). The p53 Web Site (http://p53.free.fr/index.html) reports that the most representative mutations found in ovarian cancers occur in the canonical hot

Although p53 mutations have been detected in all histological types of EOC, they are more strongly associated with high grade serous carcinomas then with low grade or borderline serous carcinomas (Kupryjanczyk et al., 1993; Kupryjanczyk et al., 1995; Skomedal et al., 1997; Zheng et al., 1995). The percentage of p53 gene mutations was reported to be lower also in others tumor types as endometrioid, mucinous, and clear-cell ovarian tumors (28%,

The pathogenesis of ovarian carcinoma lacks of a defined tumor progression model. According to Kurman and Shih, the surface epithelial tumors are divided into two categories designated type I and type II tumors that correspond to two main pathways of tumorigenesis. Type I tumors tend to be low-grade neoplasms that arise in a stepwise manner from borderline tumors whereas type II tumors are high-grade neoplasms for which morphologically recognizable precursor lesions have not been identified (*de novo*  development). According to this classification, high-grade serous carcinoma is the prototypic type II tumor whereas low-grade serous carcinoma and all other histological types are the prototypic type I tumor. Importantly, p53 gene mutation is the most common single genetic alteration observed in high-grade serous carcinomas, clinically the most

Recently, The Cancer Genome Atlas project has evidenced the presence of p53 mutations in almost all analyzed high-grade serous ovarian adenocarcinomas (96%) (Cancer Genome Atlas Research Network, 2011). Similarly, Ahmed et al., reported the presence of p53 mutation in 96.7% of high-grade serous carcinoma. Interestingly, molecular and pathological review of mutation-negative cases showed in these cases copy number gain of

not only for tumor progression but also for the early development of OC.

spots of p53 gene, namely residues 273, 248, and 175 (ranging from 8% to 5%).

important histological subtype of ovarian cancer (Kurman & Shih, 2011).

suggesting that their histogenesis may be similar.

16%, and 10%, respectively) (Skilling et al., 1996).

**2.2 P53 mutation and ovarian cancer** 

easily.

#### **2. P53**

P53 is a central hub in the cellular response to a variety of stress signals, including DNA damage, hypoxia and aberrant proliferative signals, such as oncogene activation. Its activation results in the fulfillment of key cellular processes as cell-cycle arrest, senescence and, most importantly for tumor clearance, apoptosis.

P53 is a transcriptional factor able to bind specific DNA sequences and to modulate transcription of several targets by its transactivation domain. P53 transcriptional activities are mediated by its oligomerization (Figure 1).

Fig. 1. P53 protein domains. TAD=Trans-Activation domain; PRD=Proline-Rich domain; DBD=DNA-binding domain; OD=Oligomerization domain; NES=Nuclear export signal; NLS=Nuclear localization signal.

The traditional view describing p53 activation in response to cellular stress comprises three basic steps: stabilization of p53, sequence-specific DNA binding, and transcriptional activation of target genes (Yee & Vousden, 2005). Promoter selection is dictated by numerous factors, including posttranslational modifications of p53 that can influence the recruitment of p53 binding proteins to specific promoters.

In addition to these nuclear activities, p53 possesses also cytosolic activities that can induce apoptosis in a transcription-independent manner (Green & Kroemer, 2009). Specifically, in response to various cell death signals, such as ionizing radiation, p53 rapidly localizes to the mitochondria where induces mitochondrial outer membrane permeabilization (MOMP) leading to the release of pro-apoptotic factors.

The relevance of this function in the tumor suppression has been demonstrated by mouse models expressing mitochondrial-targeted p53 variants (Galluzzi et al., 2008).

Because of its potent tumor suppressive activity, it is widely assumed that the complete molecular understanding of p53 action will produce fundamental insights into the natural processes that limit tumorigenesis and will contribute to identify key molecular targets for therapeutic intervention.

#### **2.1 P53 role in ovarian cancer**

Most of the epithelial ovarian cancers (EOC) are thought to arise from a single cell of the ovarian surface epithelium (OSE) which accumulates different genetic and epigenetic alterations which in turn lead to the malignant phenotype. The molecular events underlying this transformation are poorly understood. The inactivation of p53 oncosuppressor function seems to be an early event in the induction of hereditary ovarian cancer characterized by germ-line mutations of the BRCA1 tumor suppressor (Werness et al., 2000), suggesting that the loss of p53 function is required for a transformed cell to tolerate the loss of the BRCA1 function. Consistent with this, familial ovarian cancers have high frequency of p53 mutations (Ramus et al., 1999).

P53 is a central hub in the cellular response to a variety of stress signals, including DNA damage, hypoxia and aberrant proliferative signals, such as oncogene activation. Its activation results in the fulfillment of key cellular processes as cell-cycle arrest, senescence

P53 is a transcriptional factor able to bind specific DNA sequences and to modulate transcription of several targets by its transactivation domain. P53 transcriptional activities

Fig. 1. P53 protein domains. TAD=Trans-Activation domain; PRD=Proline-Rich domain; DBD=DNA-binding domain; OD=Oligomerization domain; NES=Nuclear export signal;

The traditional view describing p53 activation in response to cellular stress comprises three basic steps: stabilization of p53, sequence-specific DNA binding, and transcriptional activation of target genes (Yee & Vousden, 2005). Promoter selection is dictated by numerous factors, including posttranslational modifications of p53 that can influence the

In addition to these nuclear activities, p53 possesses also cytosolic activities that can induce apoptosis in a transcription-independent manner (Green & Kroemer, 2009). Specifically, in response to various cell death signals, such as ionizing radiation, p53 rapidly localizes to the mitochondria where induces mitochondrial outer membrane permeabilization (MOMP)

The relevance of this function in the tumor suppression has been demonstrated by mouse

Because of its potent tumor suppressive activity, it is widely assumed that the complete molecular understanding of p53 action will produce fundamental insights into the natural processes that limit tumorigenesis and will contribute to identify key molecular targets for

Most of the epithelial ovarian cancers (EOC) are thought to arise from a single cell of the ovarian surface epithelium (OSE) which accumulates different genetic and epigenetic alterations which in turn lead to the malignant phenotype. The molecular events underlying this transformation are poorly understood. The inactivation of p53 oncosuppressor function seems to be an early event in the induction of hereditary ovarian cancer characterized by germ-line mutations of the BRCA1 tumor suppressor (Werness et al., 2000), suggesting that the loss of p53 function is required for a transformed cell to tolerate the loss of the BRCA1 function. Consistent with this, familial ovarian cancers have high frequency of p53

models expressing mitochondrial-targeted p53 variants (Galluzzi et al., 2008).

and, most importantly for tumor clearance, apoptosis.

recruitment of p53 binding proteins to specific promoters.

leading to the release of pro-apoptotic factors.

are mediated by its oligomerization (Figure 1).

NLS=Nuclear localization signal.

therapeutic intervention.

**2.1 P53 role in ovarian cancer** 

mutations (Ramus et al., 1999).

**2. P53** 

The cooperative action of p53 in BRCA1-driven tumorigenesis and in the induction of hereditary ovarian cancer is further strengthened by the phenotype of knock-out mice. *Brca1*−*/*− mouse embryos are embryonic lethal at embryonic day 6.5; if embryos are deleted simultaneously for both *Brca1* and *p53*, the embryonic lethality is delayed (Scully & Livingston, 2000). This suggests that p53 function antagonizes genome instability induced by BRCA1 loss, causing embryo lethality. Therefore, in order to promote tumor development, p53 activity must be lost so that the cell transformation process can go on easily.

According to this model, in ovaries removed prophylactically from women heterozygote for BRCA1, alteration of p53 was observed in all early stage I serous carcinomas as well as in the adjacent dysplastic surface epithelium (Pothuir et al., 2001). Although sporadic ovarian carcinomas were not analyzed in this study, the clinical and pathological features of BRCAassociated ovarian carcinomas and their sporadic counterparts are indistinguishable, suggesting that their histogenesis may be similar.

Overall, these observations support a general model in which *p53* inactivation is required not only for tumor progression but also for the early development of OC.

#### **2.2 P53 mutation and ovarian cancer**

Alterations of p53 pathway are one of the most frequent events in sporadic epithelial ovarian cancer (EOC). The majority of p53 mutations at its locus 17p13.1 are missense mutations that cause single residue changes, largely occurring in the DNA binding domain (Figure 1) (Sigal & Rotter, 2000). The p53 Web Site (http://p53.free.fr/index.html) reports that the most representative mutations found in ovarian cancers occur in the canonical hot spots of p53 gene, namely residues 273, 248, and 175 (ranging from 8% to 5%).

Although p53 mutations have been detected in all histological types of EOC, they are more strongly associated with high grade serous carcinomas then with low grade or borderline serous carcinomas (Kupryjanczyk et al., 1993; Kupryjanczyk et al., 1995; Skomedal et al., 1997; Zheng et al., 1995). The percentage of p53 gene mutations was reported to be lower also in others tumor types as endometrioid, mucinous, and clear-cell ovarian tumors (28%, 16%, and 10%, respectively) (Skilling et al., 1996).

The pathogenesis of ovarian carcinoma lacks of a defined tumor progression model. According to Kurman and Shih, the surface epithelial tumors are divided into two categories designated type I and type II tumors that correspond to two main pathways of tumorigenesis. Type I tumors tend to be low-grade neoplasms that arise in a stepwise manner from borderline tumors whereas type II tumors are high-grade neoplasms for which morphologically recognizable precursor lesions have not been identified (*de novo*  development). According to this classification, high-grade serous carcinoma is the prototypic type II tumor whereas low-grade serous carcinoma and all other histological types are the prototypic type I tumor. Importantly, p53 gene mutation is the most common single genetic alteration observed in high-grade serous carcinomas, clinically the most important histological subtype of ovarian cancer (Kurman & Shih, 2011).

Recently, The Cancer Genome Atlas project has evidenced the presence of p53 mutations in almost all analyzed high-grade serous ovarian adenocarcinomas (96%) (Cancer Genome Atlas Research Network, 2011). Similarly, Ahmed et al., reported the presence of p53 mutation in 96.7% of high-grade serous carcinoma. Interestingly, molecular and pathological review of mutation-negative cases showed in these cases copy number gain of

P53 Network in Ovarian Cancer 275

All these data underline the necessity to assess the global functionality of p53 pathway and to distinguish the p53 genetic status in order to improve its prediction sensitivity in OC.

Many cancer-associated genes, including the tumor suppressor p53, exhibit alternative premRNA splicing. These variants may derive from canonical splice sites or by mutations that

In ovarian cancers cell lines and in primary ovarian cancers, different p53 splice variants were recently identified, some of them previously reported and others as novel cancer-

Fig. 2. Exon structure of the human p53 gene and p53 splice variants encoding C-terminally truncated proteins. Coding sequence (continuous lines); non-coding sequence (dashed

Alternative splicing at the C terminus gives rise to p53, containing 133 additional base pairs (bp) from intron 9 (exon 9b), and p53, retaining the distal 58 bp of exon 9b. These insertions result in a frameshift that introduces a premature termination codon. The p53 and p53 proteins lack the basic regulatory domain and most of the oligomerization domain (OD) and possess unique short C-terminal tails of 10 and 15 new residues, respectively. Another splice variant, p53E6, is missing exon 6 and encodes a C-terminally truncated p53 protein that lacks part of the DNA binding domain and the entire OD (Jolly et al., 1994). The three novel p53 splice variants identified in OC are p53, p53 and p53, arising from alternative splicing of exon 6 or intron 9. P53 splice variants were present in 18 of 34 ovarian cancer cell lines analysed (52.9%) and 134 of 245 primary ovarian cancers (54.7%). In this study, p53 expression was associated with impaired response to primary platinum-based chemotherapy and its expression constituted an independent prognostic marker for recurrence-free and overall survival. P53 expression was associated with adverse clinicopathologic markers, that is, serous and poorly differentiated cancers and correlated with worse recurrence-free survival in patients exhibiting functionally active p53 (Hofstetter

**2.4 P53 splicing variants and p53 targets** 

specific forms (Hofstetter et al., 2010) (Figure 2).

introduce new aberrant splicing sites.

lines).

MDM2 or MDM4, confirming the potential role of p53 network in contributing to p53 dysfunction (Ahmed et al., 2010). In this tumor context, therefore p53 mutation appears to be a driver pathogenetic event.

#### **2.3 P53 predictive value in ovarian cancer**

According to previous observations, several studies have tried to define the association of p53 with clinicopathological features of the OC. Because p53 mutation is almost invariably present in high-grade serous carcinoma, it is not of substantial prognostic or predictive significance in this tumor type.

On the contrary, considering the tumor stage, the prevalence of p53 genetic alterations appears to rise with increasing stage. Indeed, p53 gene mutations occur more often in stage III and IV ovarian cancers when compared to stage I and II, i.e., 58% versus 37% respectively (reviewed by Shelling et al., 1995), suggesting a positive selection of p53 mutation along tumor progression.

In the same direction, Bernardini et al. evidenced a correlation of the type of p53 mutation with the stage of the tumor. Early stage cancers have a significantly higher rate of null mutations (frameshift or chain terminating mutations that cause the lack of p53 protein) in comparison to late stage disease (38% vs. 8%) (Bernardini et al., 2010). These data suggest that along tumor progression p53 missense mutations are positively selected compared to null mutation, probably due to the "gain of function" of some p53 mutants that promote cell proliferation, tumor formation and invasion.

Accordingly, p53 overexpression (a specific feature of mutant p53 protein in cancer cells), has been associated with poor prognosis, poor overall survival and altered sensitivity to chemotherapy in patients with ovarian cancer (Fujita et al., 1994; Ferrandina et al., 1999; Sengupta et al., 2000; Reles et al., 2001; Hashiguchi et al., 2001; Tachibana et al., 2003; Bali et al., 2004; Bartel et al., 2008; Bernardini et al., 2010; Lee et al., 2011). However, others studies showed that overexpression of p53 is not associated with patient outcome (Havrilesky et al., 2003), has no prognostic value (Laframboise et al., 2000; Fallows et al., 2001) and is not predictive for responsiveness to platinum-based chemotherapy (Bauerschlag et al., 2010). Recently, a meta-analysis of studies on the prognostic value of p53 expression, showed that aberrant p53 status is associated only with poor overall survival (de Graeffet al., 2009), although there was ample heterogeneity among studies.

A number of factors can affect the predictive value of p53 alterations in OC. At first, it is increasingly evident that the overexpression of p53 protein, as usually detected by immunohistochemistry, is not strictly linked to its mutation. Indeed, Bartel et al. described a group of patients with p53 overexpression in which 49% of samples retain wild-type p53 (Bartel et al., 2008).

Moreover, the mere expression of p53 protein or its mutational status could not be sufficient to explain its behavior in the tumor context. P53 regulators, such as MDM2 and MDM4, can be altered (by overexpression or mutation) and differently modulate p53 wild-type functions (see next paragraphs).

Moreover, besides p53 regulators, other p53 alterations, as p53 alternative splicing (see next paragraph) can affect p53 function and, of note, create false results depending on the methodology of p53 detection. Particularly, immunohistochemical analysis cannot easily distinguish the protein form and the genetic status of a positive p53 staining.

MDM2 or MDM4, confirming the potential role of p53 network in contributing to p53 dysfunction (Ahmed et al., 2010). In this tumor context, therefore p53 mutation appears to

According to previous observations, several studies have tried to define the association of p53 with clinicopathological features of the OC. Because p53 mutation is almost invariably present in high-grade serous carcinoma, it is not of substantial prognostic or predictive

On the contrary, considering the tumor stage, the prevalence of p53 genetic alterations appears to rise with increasing stage. Indeed, p53 gene mutations occur more often in stage III and IV ovarian cancers when compared to stage I and II, i.e., 58% versus 37% respectively (reviewed by Shelling et al., 1995), suggesting a positive selection of p53 mutation along

In the same direction, Bernardini et al. evidenced a correlation of the type of p53 mutation with the stage of the tumor. Early stage cancers have a significantly higher rate of null mutations (frameshift or chain terminating mutations that cause the lack of p53 protein) in comparison to late stage disease (38% vs. 8%) (Bernardini et al., 2010). These data suggest that along tumor progression p53 missense mutations are positively selected compared to null mutation, probably due to the "gain of function" of some p53 mutants that promote cell

Accordingly, p53 overexpression (a specific feature of mutant p53 protein in cancer cells), has been associated with poor prognosis, poor overall survival and altered sensitivity to chemotherapy in patients with ovarian cancer (Fujita et al., 1994; Ferrandina et al., 1999; Sengupta et al., 2000; Reles et al., 2001; Hashiguchi et al., 2001; Tachibana et al., 2003; Bali et al., 2004; Bartel et al., 2008; Bernardini et al., 2010; Lee et al., 2011). However, others studies showed that overexpression of p53 is not associated with patient outcome (Havrilesky et al., 2003), has no prognostic value (Laframboise et al., 2000; Fallows et al., 2001) and is not predictive for responsiveness to platinum-based chemotherapy (Bauerschlag et al., 2010). Recently, a meta-analysis of studies on the prognostic value of p53 expression, showed that aberrant p53 status is associated only with poor overall survival (de Graeffet al., 2009),

A number of factors can affect the predictive value of p53 alterations in OC. At first, it is increasingly evident that the overexpression of p53 protein, as usually detected by immunohistochemistry, is not strictly linked to its mutation. Indeed, Bartel et al. described a group of patients with p53 overexpression in which 49% of samples retain wild-type p53

Moreover, the mere expression of p53 protein or its mutational status could not be sufficient to explain its behavior in the tumor context. P53 regulators, such as MDM2 and MDM4, can be altered (by overexpression or mutation) and differently modulate p53 wild-type

Moreover, besides p53 regulators, other p53 alterations, as p53 alternative splicing (see next paragraph) can affect p53 function and, of note, create false results depending on the methodology of p53 detection. Particularly, immunohistochemical analysis cannot easily

distinguish the protein form and the genetic status of a positive p53 staining.

be a driver pathogenetic event.

significance in this tumor type.

tumor progression.

(Bartel et al., 2008).

functions (see next paragraphs).

**2.3 P53 predictive value in ovarian cancer** 

proliferation, tumor formation and invasion.

although there was ample heterogeneity among studies.

All these data underline the necessity to assess the global functionality of p53 pathway and to distinguish the p53 genetic status in order to improve its prediction sensitivity in OC.

#### **2.4 P53 splicing variants and p53 targets**

Many cancer-associated genes, including the tumor suppressor p53, exhibit alternative premRNA splicing. These variants may derive from canonical splice sites or by mutations that introduce new aberrant splicing sites.

In ovarian cancers cell lines and in primary ovarian cancers, different p53 splice variants were recently identified, some of them previously reported and others as novel cancerspecific forms (Hofstetter et al., 2010) (Figure 2).

Fig. 2. Exon structure of the human p53 gene and p53 splice variants encoding C-terminally truncated proteins. Coding sequence (continuous lines); non-coding sequence (dashed lines).

Alternative splicing at the C terminus gives rise to p53, containing 133 additional base pairs (bp) from intron 9 (exon 9b), and p53, retaining the distal 58 bp of exon 9b. These insertions result in a frameshift that introduces a premature termination codon. The p53 and p53 proteins lack the basic regulatory domain and most of the oligomerization domain (OD) and possess unique short C-terminal tails of 10 and 15 new residues, respectively. Another splice variant, p53E6, is missing exon 6 and encodes a C-terminally truncated p53 protein that lacks part of the DNA binding domain and the entire OD (Jolly et al., 1994). The three novel p53 splice variants identified in OC are p53, p53 and p53, arising from alternative splicing of exon 6 or intron 9. P53 splice variants were present in 18 of 34 ovarian cancer cell lines analysed (52.9%) and 134 of 245 primary ovarian cancers (54.7%). In this study, p53 expression was associated with impaired response to primary platinum-based chemotherapy and its expression constituted an independent prognostic marker for recurrence-free and overall survival. P53 expression was associated with adverse clinicopathologic markers, that is, serous and poorly differentiated cancers and correlated with worse recurrence-free survival in patients exhibiting functionally active p53 (Hofstetter

P53 Network in Ovarian Cancer 277

leading to the concept that MDM2 overexpression may be an alternative way of p53 inactivation in human tumors (Marine & Lozano, 2010). Accordingly, MDM2 overexpression has been observed in many human cancers (Momand et al., 1998). Recent data have led to reconsider MDM2 not only as a p53 inhibitor but also as a modifier of p53 response. Indeed, after stress, MDM2 contributes to lower the protein levels of some proapoptotic factors (i.e. HIPK2, TIP60) that assist p53 in activating its apoptotic function. Therefore, increase or decline of MDM2 levels would affect p53 choice between growth arrest and apoptosis respectively (Shmueli & Oren., 2007). The relevance of this model in the oncosuppressive activity of p53 as well as in its role in chemosensitivity remains to be

MDM2 aberrant expression has been reported in human tumors, including ovarian cancer. Several ways of MDM2 aberrant expression have been recognized. The first way is the amplification of the gene. The human MDM2 gene (also HDM2) resides on chromosome 12q13-14 and is amplified in a large cohort of human tumors (about 7% in a survey of 28 tumor types). MDM2 overall amplification frequency in all ovarian cancer was reported to be 3.1% (Momand, 1998). However, analysing specific tumor subtype, MDM2 amplification has been recognized in 80% of serous borderline tumors (Mayr and al., 2006) often associated to co-expression of p21WAF1/CIP1 suggesting that in this histotype these cell

cycle control proteins might be important for cancer phenotype (Palazzo et al., 2000).

mechanism of MDM2 stabilization in the ovarian cancer too.

In addition, MDM2 levels can be upregulated independently of gene amplification. Both enhanced MDM2 protein levels as well as high levels of MDM2 transcripts have been reported in different tumor histotypes although the molecular mechanisms that underlie such alterations have not been completely characterized. In OC, MDM2 overexpression has been reported by various reports (varying among 17%, 33%, and 47, 5%) (Baekelandt et al., 1999; Dogan et al., 2005; Cho et al., 2006). In one study, it has been demonstrated the independency from amplification events (Foulkes et al., 1995) confirming the existence of

More recently, two single nucleotide polymorphisms (SNP) in the P2 promoter of the MDM2 gene able to modify MDM2 levels have been identified. The first one, at the 309th nucleotide in the first intron, alters the affinity of the transcriptional activator Sp1 resulting in different levels of MDM2 mRNA and protein. Particularly, the T to G nucleotide change extends the length of one Sp1 DNA binding site, increasing the transcription of the MDM2 gene. This in turn results in attenuation of the p53 activity and accelerated tumor formation (Bond et al., 2004). The presence of this polymorphism has been considered an oncogenic predisposing factor. Indeed the authors found out a significant correlation of SNP309G with earlier age of onset in a group of sporadic soft tissue sarcoma. Subsequently, the SNP309G effects appeared to be mediated by the hormonal status, being effective in the presence of an active estrogen signalling pathway (Bond & Levine, 2007). Therefore, the role of this polymorphism has been especially studied in breast and ovarian cancer. However, relative studies have reported controversial results indicating both association between SNP309G and OC risk (Yarden et al., 2008) or earlier age of onset in estrogen receptor-overexpressing FIGO stage III patients (Bartel et al., 2008) as well as the lack of its association with OC (Campbell et al., 2006) or cancer risk (Krekac et a., 2008). Recently, an important study has solved these controversies. It has been identified an additional SNP (at nucleotide 285)

elucidated.

**3.1 MDM2 alterations and ovarian cancer** 

et al., 2010). The other p53 splice variants differ in their clinical relevance, implicating that they possess different functions in vivo. The exact molecular function of these variants has not been completely ascertained; however, it has been hypothesized that they can modulate wild-type p53 function as well as be endowed of autonomous activity.

Overall, the discovery of the high frequency of p53 splice variants in ovarian cancer increases the complexity of the deregulation of p53 pathway in OC and therefore the understanding of p53 contribution to the pathogenesis of ovarian carcinoma.

Another layer of complexity in the prediction of p53 function, is represented by the analysis of some p53 targets. One of the most relevant and more studied is p21waf1/cip1. It is a cyclin-dependent kinase inhibitor that is usually induced through a p53-related pathway. P21waf1/cip1 has been shown to be integral to the control of the cell cycle after DNA damage. Indeed, up-regulation of p21waf1/cip1 by p53 is essential to sustain cell cycle arrest after DNA damage.

Although p21waf1/cip1 has been studied in EOC, the role of this protein as a prognostic indicator is still controversial (Sengupta et al., 2000; Geisler et al., 2001). Some studies confirm the importance of the combination of p21 and p53 staining in determining EOC prognosis. Indeed, expression of p53 protein in the absence of p21waf1/cip1 was a better marker of poor prognosis than either p53 or p21waf1/cip1 expression alone (Bali et al., 2004; Geisler et al., 2001; Werness et al., 1999).

Cai et al. suggested that since p21 expression may be an indicator of wild-type p53 function, lack of p21 in the presence of p53 expression may be predictive of an inactivated status of p53. Given that p53 inactivation precedes morphological transformation of the ovarian surface epithelium in most cases, the double analysis of these proteins might constitute an early marker of pre-neoplastic lesions (Cai et al., 2009).

Another important group of p53 targets involved in ovarian cancer was recently identified in the MicroRNAs molecules. MicroRNAs (miRNA) are a recently discovered class of noncoding RNAs that negatively regulate gene expression. Evidence indicates that miRNAs play an important role in cancer development. MiR-34b and miR-34c are the miRNAs most significantly affected by p53 and have been shown to cooperate in suppressing proliferation and transformation of neoplastic epithelial ovarian cells (Corney et al., 2007). Analyzing a group of EOC, Corney et al., showed that miR-34b/c expression is decreased in 72% of tumors with p53 mutation. Furthermore, expression of miR-34b/c is significantly reduced in stage IV tumors compared to stage III. These data suggest that miR-34 family plays an important role in EOC pathogenesis and that reduced expression of miR-34b/c may be particularly important for tumor progression to the most advanced stages (Corney et al., 2010).

Overall, these data highlight the importance of the analysis of p53 and of its targets as a tool with improved prediction properties in OC.

#### **3. MDM2**

MDM2 (for transformed mouse 3T3 cell double minute 2) is the first and best known negative regulator of p53. It has been isolated from a spontaneous transformed BALB/c 3T3 mouse cell line in 1992.

MDM2 interacts physically with p53 and brings this oncosuppressor to degradation besides to inhibit its transcriptional function masking the p53 activation domain. Molecular and genetic studies have confirmed the crucial role of MDM2 in the inhibition of p53 function,

et al., 2010). The other p53 splice variants differ in their clinical relevance, implicating that they possess different functions in vivo. The exact molecular function of these variants has not been completely ascertained; however, it has been hypothesized that they can modulate

Overall, the discovery of the high frequency of p53 splice variants in ovarian cancer increases the complexity of the deregulation of p53 pathway in OC and therefore the

Another layer of complexity in the prediction of p53 function, is represented by the analysis of some p53 targets. One of the most relevant and more studied is p21waf1/cip1. It is a cyclin-dependent kinase inhibitor that is usually induced through a p53-related pathway. P21waf1/cip1 has been shown to be integral to the control of the cell cycle after DNA damage. Indeed, up-regulation of p21waf1/cip1 by p53 is essential to sustain cell cycle

Although p21waf1/cip1 has been studied in EOC, the role of this protein as a prognostic indicator is still controversial (Sengupta et al., 2000; Geisler et al., 2001). Some studies confirm the importance of the combination of p21 and p53 staining in determining EOC prognosis. Indeed, expression of p53 protein in the absence of p21waf1/cip1 was a better marker of poor prognosis than either p53 or p21waf1/cip1 expression alone (Bali et al., 2004;

Cai et al. suggested that since p21 expression may be an indicator of wild-type p53 function, lack of p21 in the presence of p53 expression may be predictive of an inactivated status of p53. Given that p53 inactivation precedes morphological transformation of the ovarian surface epithelium in most cases, the double analysis of these proteins might constitute an

Another important group of p53 targets involved in ovarian cancer was recently identified in the MicroRNAs molecules. MicroRNAs (miRNA) are a recently discovered class of noncoding RNAs that negatively regulate gene expression. Evidence indicates that miRNAs play an important role in cancer development. MiR-34b and miR-34c are the miRNAs most significantly affected by p53 and have been shown to cooperate in suppressing proliferation and transformation of neoplastic epithelial ovarian cells (Corney et al., 2007). Analyzing a group of EOC, Corney et al., showed that miR-34b/c expression is decreased in 72% of tumors with p53 mutation. Furthermore, expression of miR-34b/c is significantly reduced in stage IV tumors compared to stage III. These data suggest that miR-34 family plays an important role in EOC pathogenesis and that reduced expression of miR-34b/c may be particularly important for tumor progression to the most advanced stages (Corney et al.,

Overall, these data highlight the importance of the analysis of p53 and of its targets as a tool

MDM2 (for transformed mouse 3T3 cell double minute 2) is the first and best known negative regulator of p53. It has been isolated from a spontaneous transformed BALB/c 3T3

MDM2 interacts physically with p53 and brings this oncosuppressor to degradation besides to inhibit its transcriptional function masking the p53 activation domain. Molecular and genetic studies have confirmed the crucial role of MDM2 in the inhibition of p53 function,

wild-type p53 function as well as be endowed of autonomous activity.

arrest after DNA damage.

2010).

**3. MDM2** 

mouse cell line in 1992.

Geisler et al., 2001; Werness et al., 1999).

with improved prediction properties in OC.

early marker of pre-neoplastic lesions (Cai et al., 2009).

understanding of p53 contribution to the pathogenesis of ovarian carcinoma.

leading to the concept that MDM2 overexpression may be an alternative way of p53 inactivation in human tumors (Marine & Lozano, 2010). Accordingly, MDM2 overexpression has been observed in many human cancers (Momand et al., 1998). Recent data have led to reconsider MDM2 not only as a p53 inhibitor but also as a modifier of p53 response. Indeed, after stress, MDM2 contributes to lower the protein levels of some proapoptotic factors (i.e. HIPK2, TIP60) that assist p53 in activating its apoptotic function. Therefore, increase or decline of MDM2 levels would affect p53 choice between growth arrest and apoptosis respectively (Shmueli & Oren., 2007). The relevance of this model in the oncosuppressive activity of p53 as well as in its role in chemosensitivity remains to be elucidated.

#### **3.1 MDM2 alterations and ovarian cancer**

MDM2 aberrant expression has been reported in human tumors, including ovarian cancer. Several ways of MDM2 aberrant expression have been recognized. The first way is the amplification of the gene. The human MDM2 gene (also HDM2) resides on chromosome 12q13-14 and is amplified in a large cohort of human tumors (about 7% in a survey of 28 tumor types). MDM2 overall amplification frequency in all ovarian cancer was reported to be 3.1% (Momand, 1998). However, analysing specific tumor subtype, MDM2 amplification has been recognized in 80% of serous borderline tumors (Mayr and al., 2006) often associated to co-expression of p21WAF1/CIP1 suggesting that in this histotype these cell cycle control proteins might be important for cancer phenotype (Palazzo et al., 2000).

In addition, MDM2 levels can be upregulated independently of gene amplification. Both enhanced MDM2 protein levels as well as high levels of MDM2 transcripts have been reported in different tumor histotypes although the molecular mechanisms that underlie such alterations have not been completely characterized. In OC, MDM2 overexpression has been reported by various reports (varying among 17%, 33%, and 47, 5%) (Baekelandt et al., 1999; Dogan et al., 2005; Cho et al., 2006). In one study, it has been demonstrated the independency from amplification events (Foulkes et al., 1995) confirming the existence of mechanism of MDM2 stabilization in the ovarian cancer too.

More recently, two single nucleotide polymorphisms (SNP) in the P2 promoter of the MDM2 gene able to modify MDM2 levels have been identified. The first one, at the 309th nucleotide in the first intron, alters the affinity of the transcriptional activator Sp1 resulting in different levels of MDM2 mRNA and protein. Particularly, the T to G nucleotide change extends the length of one Sp1 DNA binding site, increasing the transcription of the MDM2 gene. This in turn results in attenuation of the p53 activity and accelerated tumor formation (Bond et al., 2004). The presence of this polymorphism has been considered an oncogenic predisposing factor. Indeed the authors found out a significant correlation of SNP309G with earlier age of onset in a group of sporadic soft tissue sarcoma. Subsequently, the SNP309G effects appeared to be mediated by the hormonal status, being effective in the presence of an active estrogen signalling pathway (Bond & Levine, 2007). Therefore, the role of this polymorphism has been especially studied in breast and ovarian cancer. However, relative studies have reported controversial results indicating both association between SNP309G and OC risk (Yarden et al., 2008) or earlier age of onset in estrogen receptor-overexpressing FIGO stage III patients (Bartel et al., 2008) as well as the lack of its association with OC (Campbell et al., 2006) or cancer risk (Krekac et a., 2008). Recently, an important study has solved these controversies. It has been identified an additional SNP (at nucleotide 285)

P53 Network in Ovarian Cancer 279

lines by altering the p53 apoptotic response. This scenario is even more complicated by the observation that under stress, MDM2 may degrade MDM4 and therefore inhibit MDM4 mediated proapoptotic function. These molecular data lead to a reconsideration of the role of MDM4 and/or MDM2 in p53 suppression. Their relative balance rather than the single molecules could be relevant for their function in the regulation of p53 (Mancini et al., 2010). Interestingly, a recent mathematical model provided support for this hypothesis: it shows that MDM4 may stabilize or even amplify DNA damage-induced p53 response, depending on the balance with MDM2, the main regulator of MDM4 levels (Kim et al., 2010) (Figure 3).

Fig. 3. P53 network following different DNA damages.

To date, MDM4 overexpression or amplification have not been reported in OC. On the contrary, a significant downregulation of MDM4 mRNA and protein levels has been observed in a group of wt-p53 carrying OC characterized by resistance to platinum-derived

**4.1 MDM4 and ovarian cancer** 

whose activity profoundly impacts on the activity of SNP309G. In vitro, SNP285C strongly reduces the Sp1 binding to MDM2 promoter therefore counteracting the inhibitory activity of SNP309 towards p53 pathway. Indeed, the authors demonstrated that the presence of SNP285C antagonizes the activity of the SNP309G lowering the risk and the age of appearance of ovarian cancer. Interestingly, SNP285C has been evidenced in Caucasian individuals only, while being absent in Chinese population (Knappskog et al., 2011).

An additional way of MDM2 deregulation is the expression of MDM2 splicing variants. Indeed, besides full-length (fl) mRNA, more than 40 different splice variants of MDM2 transcripts have been identified in normal tissues and tumors including OC (Sigalas et al., 1996; Bartel et al., 2002), and tumorigenicity of some of these variants has been in vivo and in vitro assessed. Although the specific role of MDM2 variants in OC has not been studied, their presence may lead to a misinterpretation of MDM2 expression in tumor samples. Indeed, MDM2 detection by immunohistochemistry often lacks the sufficient specificity to distinguish wild type protein from splicing forms. This is of relevance taking into consideration the fact that many of these variants show a p53-independent function as they have lost, at least in part, the p53-binding domain. Therefore, their presence should be clearly ascertained when considering the p53 network.

#### **3.2 MDM2 predictive value in ovarian cancer**

Given the frequent alteration of MDM2 in OC, several studies have investigated the association of its expression with ovarian carcinoma properties. Conflicting results have been reported, suggesting that the analysis of sole MDM2 as well as of sole p53 are not good predictors. A recent study has supported this hypothesis demonstrating that a 9 marker set (including MDM2, CDKN2A, DKK1, HNF1B, PGR, TFF3, TP53, VIM and WT1) is the most predictive factor of ovarian cancer subtype (high-grade serous, clear cell, endometrioid, mucinous and low-grade serous) in a 322 archival ovarian carcinoma by tissue microarrays (Kalloger et al., 2011). Validation of this panel in two independent series of 81 cases demonstrated good to excellent ability to predict subtype (k=0.85 and 0.78). These data point to multiple immunohistochemical analysis as the gold standard for diagnostic accuracy in the future.

#### **4. MDM4**

Murine Mdm4 (for transformed mouse 3T3 cell double minute 4, also Mdmx) and human ortholog MDM4 (also HDMX) have been identified as the closest analogues of Mdm2 in 1996 (Marine et al., 2007).

Similarly to MDM2, MDM4 inhibits p53 transcriptional function although less efficiently than MDM2. However, at variance with MDM2, MDM4 is unable to degrade p53 protein or other targets. In the human cell, MDM4 heterodimerizes with MDM2 and their complex is considered the effective controller of p53 activity. In agreement with its inhibitory function, MDM4 overexpression has been observed in some human cancer, in some cases associated to simultaneous MDM2 overexpression (Macchiarulo et al., 2011).

However, recent evidence indicates that, in analogy to MDM2, MDM4 is not only a p53 inhibitor. It has been demonstrated that upon stress, MDM4 contributes to p53 activation by stabilizing its levels and promoting the mitochondrial apoptotic response (Mancini et al., 2009a). Noteworthy, this MDM4 activity is able to modify chemosensitivity of cancer cell

whose activity profoundly impacts on the activity of SNP309G. In vitro, SNP285C strongly reduces the Sp1 binding to MDM2 promoter therefore counteracting the inhibitory activity of SNP309 towards p53 pathway. Indeed, the authors demonstrated that the presence of SNP285C antagonizes the activity of the SNP309G lowering the risk and the age of appearance of ovarian cancer. Interestingly, SNP285C has been evidenced in Caucasian

An additional way of MDM2 deregulation is the expression of MDM2 splicing variants. Indeed, besides full-length (fl) mRNA, more than 40 different splice variants of MDM2 transcripts have been identified in normal tissues and tumors including OC (Sigalas et al., 1996; Bartel et al., 2002), and tumorigenicity of some of these variants has been in vivo and in vitro assessed. Although the specific role of MDM2 variants in OC has not been studied, their presence may lead to a misinterpretation of MDM2 expression in tumor samples. Indeed, MDM2 detection by immunohistochemistry often lacks the sufficient specificity to distinguish wild type protein from splicing forms. This is of relevance taking into consideration the fact that many of these variants show a p53-independent function as they have lost, at least in part, the p53-binding domain. Therefore, their presence should be

Given the frequent alteration of MDM2 in OC, several studies have investigated the association of its expression with ovarian carcinoma properties. Conflicting results have been reported, suggesting that the analysis of sole MDM2 as well as of sole p53 are not good predictors. A recent study has supported this hypothesis demonstrating that a 9 marker set (including MDM2, CDKN2A, DKK1, HNF1B, PGR, TFF3, TP53, VIM and WT1) is the most predictive factor of ovarian cancer subtype (high-grade serous, clear cell, endometrioid, mucinous and low-grade serous) in a 322 archival ovarian carcinoma by tissue microarrays (Kalloger et al., 2011). Validation of this panel in two independent series of 81 cases demonstrated good to excellent ability to predict subtype (k=0.85 and 0.78). These data point to multiple immunohistochemical analysis as the gold standard for diagnostic accuracy in

Murine Mdm4 (for transformed mouse 3T3 cell double minute 4, also Mdmx) and human ortholog MDM4 (also HDMX) have been identified as the closest analogues of Mdm2 in

Similarly to MDM2, MDM4 inhibits p53 transcriptional function although less efficiently than MDM2. However, at variance with MDM2, MDM4 is unable to degrade p53 protein or other targets. In the human cell, MDM4 heterodimerizes with MDM2 and their complex is considered the effective controller of p53 activity. In agreement with its inhibitory function, MDM4 overexpression has been observed in some human cancer, in some cases associated

However, recent evidence indicates that, in analogy to MDM2, MDM4 is not only a p53 inhibitor. It has been demonstrated that upon stress, MDM4 contributes to p53 activation by stabilizing its levels and promoting the mitochondrial apoptotic response (Mancini et al., 2009a). Noteworthy, this MDM4 activity is able to modify chemosensitivity of cancer cell

to simultaneous MDM2 overexpression (Macchiarulo et al., 2011).

individuals only, while being absent in Chinese population (Knappskog et al., 2011).

clearly ascertained when considering the p53 network.

**3.2 MDM2 predictive value in ovarian cancer** 

the future.

**4. MDM4** 

1996 (Marine et al., 2007).

lines by altering the p53 apoptotic response. This scenario is even more complicated by the observation that under stress, MDM2 may degrade MDM4 and therefore inhibit MDM4 mediated proapoptotic function. These molecular data lead to a reconsideration of the role of MDM4 and/or MDM2 in p53 suppression. Their relative balance rather than the single molecules could be relevant for their function in the regulation of p53 (Mancini et al., 2010). Interestingly, a recent mathematical model provided support for this hypothesis: it shows that MDM4 may stabilize or even amplify DNA damage-induced p53 response, depending on the balance with MDM2, the main regulator of MDM4 levels (Kim et al., 2010) (Figure 3).

Fig. 3. P53 network following different DNA damages.

#### **4.1 MDM4 and ovarian cancer**

To date, MDM4 overexpression or amplification have not been reported in OC. On the contrary, a significant downregulation of MDM4 mRNA and protein levels has been observed in a group of wt-p53 carrying OC characterized by resistance to platinum-derived

P53 Network in Ovarian Cancer 281

P53 is a central hub in the stress response and its function plays a major role in human

In ovarian cancer, it seems to be a key determinant in the appearance as well as in the progression of the tumor. In addition, its status affects the response to the chemotherapy. Despite this, the numerous studies aimed to use p53 detection as a marker for prediction of clinicopathological features of OC have provided some conflicting results. Accordingly, clinical trials based on the status of p53 are not currently in progress. Increasing evidence from literature suggests that the assessment of p53 function and therefore its predictive/diagnostic value might be foreseen more effectively by the analysis of its network, particularly of its regulators MDM4 and MDM2 and of some of its targets, as p21 and Bcl2. The recent work by Kalloger (Kalloger et al., 2011) using tissue microarray gave strong support to this hypothesis. Moreover, it is assuming increased relevance integrated genomic analyses for simultaneous analysis of mRNA, miRNA and promoter methylation to delineate transcriptional subtype associated to clinicopathological properties of tumor

In the future, the integration of disease-specific transcriptional profile analysis and protein

Abdel-Fatah, TM., Powe, DG., Agboola, J., Adamowicz-Brice, M., Blamey, RW., Lopez-

Ahmed, AA., Etemadmoghadam, D., Temple, J., Lynch, AG., Riad, M., Sharma, R., Stewart,

Atwal, GS., Kirchhoff, T., Bond, EE., Montagna, M., Menin, C., Bertorelle, R., Scaini, MC.,

Baekelandt, M., Kristensen, GB., Nesland, JM., Tropé, CG. & Holm, R. (1999) Clinical

Bali, A., O'Brien, PM., Edwards, LS., Sutherland, RL., Hacker, NF. & Henshall, SM. (2004).

outcome in serous epithelial ovarian cancer. Clin Cancer Res 10:5168–5177. Bartel, F., Taubert, H. & Harris, LC. (2002). Alternative and aberrant splicing of MDM2

Bartel, F., Jung, J., Böhnke, A., Gradhand, E., Zeng, K., Thomssen, C. & Hauptmann, S.

Garcia, MA., Green, AR., Reis-Filho, JS. & Ellis, IO. (2010). The biological, clinical and prognostic implications of p53 transcriptional pathways in breast cancers. *J* 

C., Fereday, S., Caldas, C., Defazio, A., Bowtell, D. & Brenton, JD. (2010). Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. *J* 

Bartel, F., Böhnke, A., Pempe, C., Gradhand, E., Hauptmann, S., Offit, K., Levine, AJ. & Bond, GL. (2009). Altered tumor formation and evolutionary selection of genetic variants in the human MDM4 oncogene. *Proc Natl Acad Sci U S A* 106:

significance of apoptosis-related factors p53, Mdm2, and Bcl-2 in advanced ovarian

Cyclin D1, p53, and p21Waf1/Cip1 expression is predictive of poor clinical

(2008). Both germ line and somatic genetics of the p53 pathway affect ovarian

**5. Conclusion** 

**6. References** 

(Cancer Genome Atlas Research Network, 2011).

*Pathol.* 220: 419-34.

*Pathol.* 221:49-56.

cancer. J Clin Oncol. 17: 2061.

mRNA in human cancer. Cancer Cell 2: 9-15.

cancer incidence and survival. Clin Cancer Res. 14:89-96.

10236-41.

detection could represent the optimum for patient diagnosis and cure.

oncogenesis.

therapy in comparison to the responsive ones (Mancini et al., 2009b). These data have been correlated to the ability of MDM4 to promote p53-dependent mitochondrial apoptosis. Although few data have been reported on the role of mitochondrial apoptosis in the sensitivity to particular chemotherapeutic drugs, it has been shown that the expression of mitochondrial proteins BCL2 (antiapoptotic) and BAX (proapoptotic) have predictive value in ovarian cancer patients treated with platinum-based chemotherapy ( Kupryjańczyk et al., 2003), confirming that the mitochondrial pathway may have a relevant function in the response to this chemotherapy in these tumors. Of note, these data may contribute to explain the difficulties to correlate p53 status with chemotherapy response. Indeed, not only p53 status but also the status of its regulators may profoundly affect the chemosensitivity of ovarian cancer.

These findings are supported by evidence that in other human tumours MDM4 levels are significantly downregulated in association with more aggressive features (Prodosmo et al., 2008) and that in breast cancer MDM4 presence is considered a positive prognostic factor (Abdel-Fatah et al., 2010).

Further studies about the role of MDM4 in OC have highlighted that the estrogen pathway is an important modifier, in analogy to what observed for MDM2. Indeed, it has been recognized a SNP at position 34091 in the 3' untranslated region (UTR) of MDM4, just 32 nucleotides downstream of the stop codon. SNP34091C introduces an illegitimate binding site for a miRNA, miR-191, that is ubiquitously expressed in human normal and cancer tissues.

The presence of SNP34091C is correlated to a decrease in the MDM4 levels. Interestingly, SNP34091A correlates with increased MDM4 expression in a group of 66 primary ovarian carcinomas and with significant decreased overall survival and increased risk of tumorrelated death (Wynendaele et al., 2010). Noteworthy, this occurs only in patients negative for estrogen receptor (ER) expression suggesting the MDM4 oncogenic function is modified by ER signalling pathway although no ER binding sites are present in the MDM4 gene. Intriguingly, ER status affects MDM4 and MDM2 in an opposite way. It counteracts oncogenic activity of MDM4 while potentiates that of MDM2. The understanding of the molecular mechanism underlying these effects will further clarify the role of these proteins in the development and progression of ovarian cancer. It has to be emphasized that SNP34091A does not correlate with p53 status suggesting that MDM4 may exert oncogenic function independently of p53 too (Wynendaele et al., 2010).

In a second genetic study, the authors identified additional SNP that confer an earlier age of onset of familial and sporadic OC in 3 different populations of Caucasian of different ethnic background and in 1 population of African Americans. However, the effects of these SNP on MDM4 levels and/or activity were not identified as well as any relationship with estrogen signalling (Atwal et al., 2009).

Finally, in analogy to MDM2, alternative splicing of MDM4 has been described as well (Mancini et al., 2009c). Particularly, a tumor-specific form, MDM4-211, derived from an aberrant splicing between exon 2 and exon 11, is frequently present in OC (unpublished data). This form lacks the p53-binding domain and therefore cannot directly modulate p53 function. However, it can stabilize MDM2 and in turn inhibit p53 function (Giglio et al., 2006). The relevance of this variant in OC features remains to be elucidated. However, as previously described, the presence of these variants suggests measures of caution in the interpretation of MDM4 IHC positive results.

#### **5. Conclusion**

280 Ovarian Cancer – Basic Science Perspective

therapy in comparison to the responsive ones (Mancini et al., 2009b). These data have been correlated to the ability of MDM4 to promote p53-dependent mitochondrial apoptosis. Although few data have been reported on the role of mitochondrial apoptosis in the sensitivity to particular chemotherapeutic drugs, it has been shown that the expression of mitochondrial proteins BCL2 (antiapoptotic) and BAX (proapoptotic) have predictive value in ovarian cancer patients treated with platinum-based chemotherapy ( Kupryjańczyk et al., 2003), confirming that the mitochondrial pathway may have a relevant function in the response to this chemotherapy in these tumors. Of note, these data may contribute to explain the difficulties to correlate p53 status with chemotherapy response. Indeed, not only p53 status but also the status of its regulators may profoundly affect the chemosensitivity of

These findings are supported by evidence that in other human tumours MDM4 levels are significantly downregulated in association with more aggressive features (Prodosmo et al., 2008) and that in breast cancer MDM4 presence is considered a positive prognostic factor

Further studies about the role of MDM4 in OC have highlighted that the estrogen pathway is an important modifier, in analogy to what observed for MDM2. Indeed, it has been recognized a SNP at position 34091 in the 3' untranslated region (UTR) of MDM4, just 32 nucleotides downstream of the stop codon. SNP34091C introduces an illegitimate binding site for a miRNA, miR-191, that is ubiquitously expressed in human normal and cancer

The presence of SNP34091C is correlated to a decrease in the MDM4 levels. Interestingly, SNP34091A correlates with increased MDM4 expression in a group of 66 primary ovarian carcinomas and with significant decreased overall survival and increased risk of tumorrelated death (Wynendaele et al., 2010). Noteworthy, this occurs only in patients negative for estrogen receptor (ER) expression suggesting the MDM4 oncogenic function is modified by ER signalling pathway although no ER binding sites are present in the MDM4 gene. Intriguingly, ER status affects MDM4 and MDM2 in an opposite way. It counteracts oncogenic activity of MDM4 while potentiates that of MDM2. The understanding of the molecular mechanism underlying these effects will further clarify the role of these proteins in the development and progression of ovarian cancer. It has to be emphasized that SNP34091A does not correlate with p53 status suggesting that MDM4 may exert oncogenic

In a second genetic study, the authors identified additional SNP that confer an earlier age of onset of familial and sporadic OC in 3 different populations of Caucasian of different ethnic background and in 1 population of African Americans. However, the effects of these SNP on MDM4 levels and/or activity were not identified as well as any relationship with estrogen

Finally, in analogy to MDM2, alternative splicing of MDM4 has been described as well (Mancini et al., 2009c). Particularly, a tumor-specific form, MDM4-211, derived from an aberrant splicing between exon 2 and exon 11, is frequently present in OC (unpublished data). This form lacks the p53-binding domain and therefore cannot directly modulate p53 function. However, it can stabilize MDM2 and in turn inhibit p53 function (Giglio et al., 2006). The relevance of this variant in OC features remains to be elucidated. However, as previously described, the presence of these variants suggests measures of caution in the

function independently of p53 too (Wynendaele et al., 2010).

ovarian cancer.

tissues.

(Abdel-Fatah et al., 2010).

signalling (Atwal et al., 2009).

interpretation of MDM4 IHC positive results.

P53 is a central hub in the stress response and its function plays a major role in human oncogenesis.

In ovarian cancer, it seems to be a key determinant in the appearance as well as in the progression of the tumor. In addition, its status affects the response to the chemotherapy.

Despite this, the numerous studies aimed to use p53 detection as a marker for prediction of clinicopathological features of OC have provided some conflicting results. Accordingly, clinical trials based on the status of p53 are not currently in progress. Increasing evidence from literature suggests that the assessment of p53 function and therefore its predictive/diagnostic value might be foreseen more effectively by the analysis of its network, particularly of its regulators MDM4 and MDM2 and of some of its targets, as p21 and Bcl2. The recent work by Kalloger (Kalloger et al., 2011) using tissue microarray gave strong support to this hypothesis. Moreover, it is assuming increased relevance integrated genomic analyses for simultaneous analysis of mRNA, miRNA and promoter methylation to delineate transcriptional subtype associated to clinicopathological properties of tumor (Cancer Genome Atlas Research Network, 2011).

In the future, the integration of disease-specific transcriptional profile analysis and protein detection could represent the optimum for patient diagnosis and cure.

#### **6. References**


P53 Network in Ovarian Cancer 283

Foulkes, WD., Stamp, GW., Afzal, S., Lalani, N., McFarlane, CP., Trowsdale, J. & Campbell,

Fujita, M., Enomoto, T., Inoue, M., Tanizawa, O., Ozaki, M., Rice, JM. & Nomura, T. (1994).

Geisler, HE., Geisler, JP., Miller, GA., Geisler, MJ., Wiemann, MC., Zhou, Z. & Crabtree, W.

Giglio, S., Mancini, F., Gentiletti, F., Sparaco, G., Felicioni, L., Barassi, F., Martella, C.,

Green, DR. & Kroemer, G. (2009). Cytoplasmic functions of the tumour suppressor p53.

Hashiguchi, Y., Tsuda, H., Yamamoto, K., Inoue, T., Ishiko, O. & Ogita, S. (2001). Combined

Havrilesky, L., Darcy, M., Hamdan, H., Priore, RL., Leon, J., Bell, J. & Berchuck, A. (2003).

Hofstetter, G., Berger, A., Fiegl, H., Slade, N., Zorić, A., Holzer, B., Schuster, E., Mobus, VJ.,

Kim, S., Aladjem, MI., McFadden, GB. & Kohn, KW. (2010). Predicted functions of MdmX in fine-tuning the response of p53 to DNA damage. PLoS Comput Biol. 6: e1000665. Knappskog, S., Bjørnslett, M., Myklebust, LM., Huijts, PE., Vreeswijk, MP., Edvardsen, H.,

risk for breast and ovarian cancer in Caucasians. Cancer Cell 19: 273-82. Krekac, D., Brozkova, K., Knoflickova, D., Hrstka, R., Muller, P., Nenutil, R. & Vojtesek, B.

epithelial tumors of the human ovary. Jpn J Cancer Res 85:1247–1256. Galluzzi, L., Morselli, E., Kepp, O., Tajeddine, N. & Kroemer, G. (2008). Targeting p53 to

mitochondria for cancer therapy. Cell Cycle 7: 1949-55.

mutation status. Br J Cancer. 72: 883-8.

than either alone. Cancer 92:781–6.

prediction. Mod Pathol. 24: 512-21.

or breast cancer risk. Oncology 74: 84-7.

65: 9687-94.

996.

3814-25.

Nature 458: 1127-30.

IG. (1995). MDM2 overexpression is rare in ovarian carcinoma irrespective of TP53

Alteration of the p53 tumor suppressor gene occurs independently of K-ras activation and more frequently in serous adenocarcinomas than in other common

(2001) p21 and p53 in ovarian carcinoma: their combined staining is more valuable

Prodosmo, A., Iacovelli, S., Buttitta, F., Farsetti, A., Soddu, S., Marchetti, A., Sacchi, A., Pontecorvi, A. & Moretti F. (2005). Identification of an aberrantly spliced form of HDMX in human tumors: a new mechanism for HDM2 stabilization. Cancer Res.

analysis of p53 and RB pathways in epithelial ovarian cancer. Hum Pathol. 32: 988–

Prognostic significance of p53 mutation and p53 overexpression in advanced epithelial ovarian cancer: a Gynecologic Oncology Group Study. J Clin Oncol. 21:

Reimer, D., Daxenbichler, G., Marth, C., Zeimet, AG., Concin, N. & Zeillinger, R. (2010). Alternative splicing of p53 and p73: the novel p53 splice variant p53delta is an independent prognostic marker in ovarian cancer. Oncogene. 29: 1997-2004. Kalloger, SE., Köbel, M., Leung, S., Mehl, E., Gao, D., Marcon, KM., Chow, C., Clarke, BA.,

Huntsman, DG. & Gilks, CB. (2011). Calculator for ovarian carcinoma subtype

Guo, Y., Zhang, X., Yang, M., Ylisaukko-Oja, SK., Alhopuro, P., Arola, J., Tollenaar, RA., van Asperen, CJ., Seynaeve, C., Staalesen, V., Chrisanthar, R., Løkkevik, E., Salvesen, HB., Evans, DG., Newman, WG., Lin, D., Aaltonen, LA., Børresen-Dale, AL. Tell, GS. Stoltenberg, C. Romundstad, P. Hveem, K. Lillehaug, JR., Vatten, L., Devilee, P., Dørum, A. & Lønning, PE. (2011). The MDM2 promoter SNP285C/309G haplotype diminishes Sp1 transcription factor binding and reduces

(2008). MDM2SNP309 does not associate with elevated MDM2 protein expression


Bauerschlag, DO., Schem, C., Weigel, MT., Von Kaisenberg, C., Strauss, A., Bauknecht, T.,

Bernardini, MQ., Baba, T., Lee, PS., Barnett, JC., Sfakianos, GP., Secord, AA., Murphy, SK.,

Bond, GL., Hu, W., Bond, EE., Robins, H., Lutzker, SG., Arva, NC., Bargonetti, J., Bartel, F.,

Bond, GL. & Levine, AJ. (2007). A single nucleotide polymorphism in the p53 pathway

Cai, KQ., Wu, H., Klein-Szanto, AJ. & Xu, XX. (2009). Acquisition of a second mutation of

Campbell, IG., Eccles, DM. & Choong, DY. (2006). No association of the MDM2 SNP309 polymorphism with risk of breast or ovarian cancer. Cancer Lett. 240:195-7. Cancer Genome Atlas Research Network (2011). Integrated genomic analyses of ovarian

Cho, EY., Choi, YL., Chae, SW., Sohn, JH. & Ahn, GH. (2006). Relationship between p53-

Corney, DC., Flesken-Nikitin, A., Godwin, AK., Wang, W. & Nikitin, AY. (2007).

cell proliferation and adhesion-independent growth. Cancer Res. 67: 8433-8. Corney, DC., Hwang, CI., Matoso, A., Vogt, M., Flesken-Nikitin, A., Godwin, AK., Kamat,

de Graeff, P., Crijns, AP., de Jong, S., Boezen, M., Post, WJ., de Vries, EG., van der Zee, AG.

Dogan, E., Saygili, U., Tuna, B., Gol, M., Gürel, D., Acar, B. & Koyuncuoğlu, M. (2005). p53

Fallows, S., Price, J., Atkinson, RJ., Johnston, PG., Hickey, I. & Russell, SE. (2001). P53

Ferrandina, G., Fagotti, A., Salerno, MG., Natali, PG., Mottolese, M., Maneschi, F., De

epithelial ovarian cancer: a meta-analysis. Br J Cancer. 101: 149-59.

multivariate analysis. Gynecol Oncol. 97: 46-52.

ovarian cancer. Br J Cancer 81: 733–740.

mutations in serous ovarian cancers. BMC Cancer. 10: 237.

136:79-88.

humans. Cell 119: 591-602.

carcinoma. Nature 474:609-15.

Gynecol Cancer. 16: 1000-6.

1119-28.

68-75.

cancer in humans. Oncogene. 26:1317-23.

ovarian tumorigenicity. Gynecol Oncol. 114:18-25.

Maass, N. & Meinhold-Heerlein I. (2010). The role of p53 as a surrogate marker for chemotherapeutical responsiveness in ovarian cancer. J Cancer Res Clin Oncol.

Iversen, E., Marks, JR. & Berchuck, A. (2010). Expression signatures of TP53

Taubert, H., Wuerl, P., Onel, K., Yip, L., Hwang, SJ., Strong, LC., Lozano, G. & Levine, AJ. (2004). A single nucleotide polymorphism in the MDM2 promoter attenuates the p53 tumor suppressor pathway and accelerates tumor formation in

interacts with gender, environmental stresses and tumor genetics to influence

the Tp53 alleles immediately precedes epithelial morphological transformation in

associated proteins and estrogen receptor status in ovarian serous neoplasms. Int J

MicroRNA-34b and MicroRNA-34c are targets of p53 and cooperate in control of

AA., Sood, AK., Ellenson, LH., Hermeking, H. & Nikitin, AY. (2010). Frequent downregulation of miR-34 family in human ovarian cancers. Clin Cancer Res. 16:

& de Bock, GH. (2009). Modest effect of p53, EGFR and HER-2/neu on prognosis in

and mdm2 as prognostic indicators in patients with epithelial ovarian cancer: a

mutation does not affect prognosis in ovarian epithelial malignancies. J Pathol. 194:

Pasqua, A., Benedetti-Panici, P., Mancuso, S. & Scambia, G. (1999). p53 overexpression is associated with cyto-reduction and response to chemotherapy in


P53 Network in Ovarian Cancer 285

Palazzo, JP., Monzon, F., Burke, M., Hyslop, T., Dunton, C., Barusevicius, A., Capuzzi, D. &

Pothuir, B., Leitao, M., Barakat, R., Akram, M., Bogomolniy, F., Olvera, N. & Lin, O. (2001).

Prodosmo, A., Giglio, S., Moretti, S., Mancini, F., Barbi, F., Avenia, N., Di Conza, G.,

Ramus, SJ., Bobrow, LG., Pharoah, PD., Finnigan, DS., Fishman, A., Altaras, M., Harrington,

Reles, A., Wen, WH., Schmider, A., Gee, C., Runnebaum, IB., Kilian, U., Jones, LA., El-

Scully, R. & Livingston, DM. (2000). In search of the tumour-suppressor functions of BRCA1

Sengupta, PS., McGown, AT., Bajaj, V., Blackhall, F., Swindell, R., Bromley, M., Shanks, JH.,

Sigal, A. & Rotter, V. (2000). Oncogenic mutations of the p53 tumor suppressor: the demons

Sigalas, I., Calvert, AH., Anderson, JJ., Neal, DE. & Lunec J. (1996) Alternatively spliced

Skilling, JS., Sood, A., Niemann, T., Lager, DJ. & Buller, RE. (1996). An abundance of p53

Skomedal, H., Kristensen, GB., Abeler, VM., Borresen-Dale, AL., Trope, C. & Holm, R.

Tachibana, M., Watanabe, J., Matsushima, Y., Nishida, K., Kobayashi, Y., Fujimura, M. &

related proteins in epithelial ovarian cancer. Eur J Cancer 36: 2317–28. Shelling, AN., Cooke, IE. & Ganesan, TS. (1995). The genetic analysis of ovarian cancer. Br J

Shmueli, A. & Oren, M. (2007). Mdm2: p53's lifesaver? Mol Cell. 25:794-6.

of the guardian of the genome. Cancer Res. 60: 6788–6793.

and frequent detection in human cancer. Nat Med. 2: 912-7.

null mutations in ovarian carcinoma. Oncogene 13: 117-23.

serous borderline ovarian tumors. Hum Pathol. 31: 698-704.

Oncologists 32nd Annual Meeting.

and BRCA2. Nature 408: 429–432.

96.

Cancer 25: 91-6.

Cancer. 72: 521-7.

181: 158–165.

598–606.

2984-97.

Kovatich, AJ. (2000). Overexpression of p21WAF1/CIP1 and MDM2 characterizes

Genetic analysis of ovarian carcinoma histogenesis. Society of Gynecologic

Schünemann, HJ., Pistola, L., Ludovini, V., Sacchi, A., Pontecorvi, A., Puxeddu, E. & Moretti, F. (2008). Analysis of human MDM4 variants in papillary thyroid carcinomas reveals new potential markers of cancer properties. J Mol Med. 86: 585-

PA., Gayther, SA., Ponder, BA. & Friedman, LS. (1999). Increased frequency of TP53 mutations in BRCA1 and BRCA2 ovarian tumours. Genes Chromosomes

Nagga,r A., Minguillon, C., Schönborn, I., Reich, O., Kreienberg, R., Lichtenegger, W. & Press, MF. (2001). Correlation of p53 mutations with resistance to platinumbased chemotherapy and shortened survival in ovarian cancer. Clin Cancer Res. 7:

Ward, T., Buckley, CH., Reynolds, K., Slade, RJ. & Jayson, GC. (2000). p53 and

mdm2 transcripts with loss of p53 binding domain sequences: transforming ability

(1997). TP53 protein accumulation and gene mutation in relation to overexpression of MDM2 protein in ovarian borderline tumours and stage I carcinomas. J Pathol.

Shiromizu, K. (2003). Independence of the prognostic value of tumor suppressor protein expression in ovarian adenocarcinomas: A multivariate analysis of expression of p53, retinoblastoma, and related proteins. Int J Gynecol Cancer 13:


Kupryjanczyk, J., Thor, AD., Beauchamp, R., Merritt, V., Edgerton, SM., Bell, DA. & Yandell,

Kupryjanczyk, J., Bell, DA., Dimeo, D., Beauchamp, R., Thor, AD. & Yandell, DW. (1995).

Kupryjańczyk, J., Szymańska, T., Madry, R., Timorek, A., Stelmachów, J., Karpińska, G.,

Kurman, RJ. & Shih, IeM. (2011). Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer-Shifting the paradigm. Hum Pathol. 42:918-31. Laframboise, S., Chapman, W., McLaughlin, J. & Andrulis, IL. (2000). p53 mutations in

Lee, YH., Heo, JH., Kim, TH., Kang, H., Kim, G., Kim, J., Cho, SH. & An, HJ. (2011).

Mancini, F. & Moretti, F. (2009a). Mitochondrial MDM4 (MDMX): an unpredicted role in the

Mancini, F., Di Conza, G., Pellegrino, M., Rinaldo, C., Prodosmo, A., Giglio, S., D'Agnano, I.,

Mancini, F., Di Conza, G., Monti, O., Macchiarulo, A., Pellicciari, R., Pontecorvi, A. &

Marine, JC., Dyer, MA. & Jochemsen, AG. (2007). MDMX: from bench to bedside. J Cell Sci.

Marine, JC. & Lozano, G. (2010). Mdm2-mediated ubiquitylation: p53 and beyond. Cell

Mayr, D., Kanitz, V., Anderegg, B., Luthardt, B., Engel, J., Löhrs, U., Amann, G. & Diebold J.

p53-mediated intrinsic apoptotic pathway. Cell Cycle 8:3854-9.

biomarkers in ovarian epithelial tumors. Int J Gynecol Pathol. 30: 205-17. Macchiarulo, A., Giacchè, N., Carotti, A., Moretti, F. & Pellicciari, R. (2011) Expanding the

cancer. Proc Natl Acad Sci U S A. 90: 4961–4965.

Pathol. 26: 387–392.

Cancer 88: 848-54.

Chem Commun. 2: 455-465.

Curr Genomics 10: 42-50.

Death Differ. 17: 93-102.

database. Nucleic Acids Res. 26: 3453-9.

302-8.

1080-3.

120: 371-8.

DW. (1993). p53 gene mutations and protein accumulation in human ovarian

p53 gene analysis of ovarian borderline tumors and stage I carcinomas. Hum

Rembiszewska, A., Ziółkowska, I., Kraszewska, E., Debniak, J., Emerich, J., Ułańska, M., Płuzańska, A., Jedryka, M., Goluda, M., Chudecka-Głaz, A., Rzepka-Górska, I., Klimek, M., Urbański, K., Breborowicz, J., Zieliński, J. & Markowska J. (2003). Evaluation of clinical significance of TP53, BCL-2, BAX and MEK1 expression in 229 ovarian carcinomas treated with platinum-based regimen. Br J

epithelial ovarian cancers: possible role in predicting chemoresistance. Cancer J. 6:

Significance of cell cycle regulatory proteins as malignant and prognostic

Horizon of Chemotherapeutic Targets: From MDM2 to MDMX (MDM4). Med

Florenzano, F., Felicioni, L., Buttitta, F., Marchetti, A., Sacchi, A., Pontecorvi, A., Soddu, S. & Moretti, F. (2009b). MDM4 (MDMX) localizes at the mitochondria and facilitates the p53-mediated intrinsic-apoptotic pathway. EMBO J. 28: 1926-39. Mancini, F., Di Conza, G. & Moretti, F. (2009c). MDM4 (MDMX) and its Transcript Variants.

Moretti F. (2010). Puzzling over MDM4-p53 network. Int J Biochem Cell Biol. 42:

(2006). Analysis of gene amplification and prognostic markers in ovarian cancer using comparative genomic hybridization for microarrays and immunohistochemical analysis for tissue microarrays. Am J Clin Pathol.126: 101-9. Momand, J., Jung, D., Wilczynski, S. & Niland, J. (1998). The MDM2 gene amplification


**15** 

*USA* 

Stéphanie Gaillard

*Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine Baltimore* 

**Gene Amplification in Ovarian Carcinomas:** 

**Lessons from Selected Amplified Gene Families** 

Ovarian cancer is the most malignant gynecologic cancer causing an estimated 140,000 deaths per year worldwide(Jemal et al 2011). In greater than 75% of incident cases, the disease is detected only after it has reached an advanced stage (stage III and IV) when standard therapy is unlikely to be curative. Even after maximal cytoreductive surgery followed by platinum-based chemotherapy, the survival rate at 5 years is only 15-30% (Kosary 1994). Epithelial ovarian cancer is a heterogeneous disease that can be subdivided into four histological categories: serous, clear cell, endometrial, and mucinous. The pathogenesis of the individual subtypes relies on different molecular and pathway aberrations and thus will likely respond with different sensitivities to systemic and targeted therapies(Kurman and Shih Ie 2008). The identification of critical molecular and pathway aberrations specific to each subtype could provide key insights into the mechanisms driving

Tumors characteristically display alterations in gene expression that lead to the acquisition of the hallmark features of cancer: uncontrolled proliferation, evasion of growth suppression and of the immune system, resistance to death signals, unlimited replicative potential, development of a supportive microenvironment (including angiogenesis), and ability to invade and metastasize(Hanahan and Weinberg 2011). Aberrant gene expression is manifest through a number of different mechanisms including DNA copy number alterations (amplifications, deletions, gains and losses of whole chromosomes resulting in aneuploidy), epigenetic regulation via methylation or histone acetylation, fusion proteins and individual gene mutations. Amplifications that are critical to tumorigenesis likely are essential because they result in the overexpression of gene products on which the tumor is dependent. These are often referred to as "driver" genes, as dysregulated expression leads to the activation of oncogenic pathways, while other genes in the amplified region may or may not be overexpressed and instead are "passenger" genes. Analysis of individual amplifications have elucidated driver pathways of cancer and revealed potential targets for drug development. For example, amplification of the Her-2/neu gene occurs in 25-30% of breast cancers and is associated with a more aggressive phenotype(Slamon et al 1989). However, treatment with HER-2 targeted therapy, in particular trastuzumab, has dramatically improved the natural history of HER2-positive breast cancer(Ferretti et al 2007). Similarly,

tumorigenesis and direct efforts in the development of targeted therapies.

**1. Introduction** 


### **Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families**

#### Stéphanie Gaillard

*Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine Baltimore USA* 

#### **1. Introduction**

286 Ovarian Cancer – Basic Science Perspective

Werness, BA., Freedman, AN., Piver, MS., Romero-Gutierrez, M. & Petrow, E. (1999).

Werness, BA., Parvatiyar, P., Ramus, SJ., Whittemore, AS., Garlinghouse-Jones, K., Oakley-

heterozygosity at BRCA1 and TP53. J Natl Cancer Inst. 92: 1088–1091. Wynendaele, J., Böhnke, A., Leucci, E., Nielsen, SJ., Lambertz, I., Hammer, S., Sbrzesny, N.,

epithelialcancers of the ovary. Gynecol Oncol. 75: 413–8.

1317-22.

87: 1146–1153.

Prognostic significance of p53 and p21(waf1/cip1) immunoreactivity in

Girvan, I., DiCioccio, RA., Wiest, J., Tsukada, Y., Ponder, BA. & Piver, MS. (2000). Ovarian carcinoma in situ with germline BRCA1 mutation and loss of

Kubitza, D., Wolf, A., Gradhand, E., Balschun,K., Braicu, I., Sehouli, J., Darb-Esfahani, S., Denkert, C. Thomssen, C., Hauptmann, S., Lund, A., Marine, JC. & Bartel, F. (2010). An illegitimate microRNA target site within the 3' UTR of MDM4 affects ovarian cancer progression and chemosensitivity. Cancer Res. 70: 9641-9. Yarden, RI., Friedman, E., Metsuyanim, S., Olender, T., Ben-Asher, E. & Papa, MZ. (2008).

MDM2 SNP309 accelerates breast and ovarian carcinogenesis in BRCA1 and BRCA2 carriers of Jewish-Ashkenazi descent. Breast Cancer Res Treat. 111:497-504.

Dubeau, L. (1995). Genetic disparity between morphologically benign cysts contiguous to ovarian carcinomas and solitary cystadenomas. J Natl Cancer Inst.

Yee, KS. & Vousden, KH. (2005). Complicating the complexity of p53. Carcinogenesis 26:

Zheng, J., Benedict, WF., Xu, HJ., Hu, SX., Kim, TM., Velicescu, M., Wan, M., Cofer, KF. &

Ovarian cancer is the most malignant gynecologic cancer causing an estimated 140,000 deaths per year worldwide(Jemal et al 2011). In greater than 75% of incident cases, the disease is detected only after it has reached an advanced stage (stage III and IV) when standard therapy is unlikely to be curative. Even after maximal cytoreductive surgery followed by platinum-based chemotherapy, the survival rate at 5 years is only 15-30% (Kosary 1994). Epithelial ovarian cancer is a heterogeneous disease that can be subdivided into four histological categories: serous, clear cell, endometrial, and mucinous. The pathogenesis of the individual subtypes relies on different molecular and pathway aberrations and thus will likely respond with different sensitivities to systemic and targeted therapies(Kurman and Shih Ie 2008). The identification of critical molecular and pathway aberrations specific to each subtype could provide key insights into the mechanisms driving tumorigenesis and direct efforts in the development of targeted therapies.

Tumors characteristically display alterations in gene expression that lead to the acquisition of the hallmark features of cancer: uncontrolled proliferation, evasion of growth suppression and of the immune system, resistance to death signals, unlimited replicative potential, development of a supportive microenvironment (including angiogenesis), and ability to invade and metastasize(Hanahan and Weinberg 2011). Aberrant gene expression is manifest through a number of different mechanisms including DNA copy number alterations (amplifications, deletions, gains and losses of whole chromosomes resulting in aneuploidy), epigenetic regulation via methylation or histone acetylation, fusion proteins and individual gene mutations. Amplifications that are critical to tumorigenesis likely are essential because they result in the overexpression of gene products on which the tumor is dependent. These are often referred to as "driver" genes, as dysregulated expression leads to the activation of oncogenic pathways, while other genes in the amplified region may or may not be overexpressed and instead are "passenger" genes. Analysis of individual amplifications have elucidated driver pathways of cancer and revealed potential targets for drug development. For example, amplification of the Her-2/neu gene occurs in 25-30% of breast cancers and is associated with a more aggressive phenotype(Slamon et al 1989). However, treatment with HER-2 targeted therapy, in particular trastuzumab, has dramatically improved the natural history of HER2-positive breast cancer(Ferretti et al 2007). Similarly,

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 289

ovarian cancer and reveal it to be a heterogeneous group of diseases(Gorringe et al 2010, Meinhold-Heerlein et al 2005, Nakayama et al 2007, Staebler et al 2002). Recent studies of the genomic alterations between invasive serous carcinomas and low grade or borderline serous tumors have identified dramatic differences in DNA copy number changes (Meinhold-Heerlein et al 2005, Nakayama et al 2007, Staebler et al 2002). High-grade serous carcinomas uniformly exhibited more extensive DNA copy number variations than borderline tumors or low-grade serous carcinomas (Figure 2). The frequency and amplitude of changes was higher in invasive serous carcinomas and involve the majority of chromosomes through gain or loss of discrete subchromosomal regions, chromosome arms, or whole chromosomes. By contrast, low-grade tumors exhibit significantly fewer copy number gains and few chromosomal losses. The pervasive changes seen within the chromosomes of highgrade serous ovarian carcinomas suggest that significant genomic instability is a critical

Fig. 2. Genome-wide distribution of DNA copy number changes in low-grade and highgrade ovarian serous carcinomas. Each column represents an individual tumor sample. DNA copy number changes are represented as pseudocolor gradients corresponding to the folds of increase (red boxes) and decrease (blue boxes), as compared to pooled normal

samples. Reproduced with permission (Nakayama et al 2007).

feature of this disease.

non-small cell lung cancers with mutations in or amplification of the EGFR gene benefit from EGFR inhibitors. Several amplified genes have been identified in epithelial ovarian cancers. The Cancer Genome Atlas (TCGA) project recently published their results from a multicenter comprehensive effort to characterize the molecular abnormalities in high-grade serous ovarian carcinomas. In this study 489 clinically annotated stage II-IV high-grade serous ovarian cancer samples were analyzed for changes in mRNA expression, microRNA expression, DNA copy number, and DNA promoter methylation. Interestingly, the TCGA found a relatively low rate of recurrent mutations while copy number changes were relatively abundant(Cancer Genome Atlas Research Network, 2011). In light of the recent results of the TCGA, this chapter will discuss the major pathways (Figure 1) frequently amplified in ovarian cancers and review the clinical efficacy of therapeutic agents targeting these genes.

Fig. 1. Pathways amplified in epithelial ovarian cancer. \*represents targetable pathways discussed in this chapter.

#### **2. Global assessment of copy number variation in ovarian cancer**

DNA copy number variations can be identified using several techniques including cytogenetics, fluorescence in situ hybridization (FISH), comparative genomic hybridization (CGH), and single nucleotide polymorphism (SNP) arrays. The latter two have the advantage of providing an unbiased genome wide assessment of copy number variation and have been widely used to characterize the complex genomic alterations attributable to

non-small cell lung cancers with mutations in or amplification of the EGFR gene benefit from EGFR inhibitors. Several amplified genes have been identified in epithelial ovarian cancers. The Cancer Genome Atlas (TCGA) project recently published their results from a multicenter comprehensive effort to characterize the molecular abnormalities in high-grade serous ovarian carcinomas. In this study 489 clinically annotated stage II-IV high-grade serous ovarian cancer samples were analyzed for changes in mRNA expression, microRNA expression, DNA copy number, and DNA promoter methylation. Interestingly, the TCGA found a relatively low rate of recurrent mutations while copy number changes were relatively abundant(Cancer Genome Atlas Research Network, 2011). In light of the recent results of the TCGA, this chapter will discuss the major pathways (Figure 1) frequently amplified in ovarian cancers and review the clinical efficacy of therapeutic agents targeting

Fig. 1. Pathways amplified in epithelial ovarian cancer. \*represents targetable pathways

DNA copy number variations can be identified using several techniques including cytogenetics, fluorescence in situ hybridization (FISH), comparative genomic hybridization (CGH), and single nucleotide polymorphism (SNP) arrays. The latter two have the advantage of providing an unbiased genome wide assessment of copy number variation and have been widely used to characterize the complex genomic alterations attributable to

**2. Global assessment of copy number variation in ovarian cancer** 

these genes.

discussed in this chapter.

ovarian cancer and reveal it to be a heterogeneous group of diseases(Gorringe et al 2010, Meinhold-Heerlein et al 2005, Nakayama et al 2007, Staebler et al 2002). Recent studies of the genomic alterations between invasive serous carcinomas and low grade or borderline serous tumors have identified dramatic differences in DNA copy number changes (Meinhold-Heerlein et al 2005, Nakayama et al 2007, Staebler et al 2002). High-grade serous carcinomas uniformly exhibited more extensive DNA copy number variations than borderline tumors or low-grade serous carcinomas (Figure 2). The frequency and amplitude of changes was higher in invasive serous carcinomas and involve the majority of chromosomes through gain or loss of discrete subchromosomal regions, chromosome arms, or whole chromosomes. By contrast, low-grade tumors exhibit significantly fewer copy number gains and few chromosomal losses. The pervasive changes seen within the chromosomes of highgrade serous ovarian carcinomas suggest that significant genomic instability is a critical feature of this disease.

Fig. 2. Genome-wide distribution of DNA copy number changes in low-grade and highgrade ovarian serous carcinomas. Each column represents an individual tumor sample. DNA copy number changes are represented as pseudocolor gradients corresponding to the folds of increase (red boxes) and decrease (blue boxes), as compared to pooled normal samples. Reproduced with permission (Nakayama et al 2007).

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 291

model of ovarian serous carcinogenesis in which high-grade and low-grade ovarian serous tumors develop along distinctly different molecular pathways(Kurman and Shih Ie 2008). Pathway activation through PIK3CA can occur through either amplification or activating mutation of the catalytic subunit. Mutations of PIK3CA are typically associated with endometrioid and clear cell subtypes and are associated with lower tumor stage and grade(Campbell et al 2004, Kolasa et al 2009, Willner et al 2007). Amplifications, on the other hand, have been detected in all histological subtypes, though there was an association with poorer differentiation. PIK3CA amplification has been reported in 13-24% of ovarian carcinomas and is associated with increased expression of phosphorylated AKT indicating that amplification results in increased activation of the pathway(Campbell et al 2004, Kolasa

Clinical data is lacking in the majority of these studies and the prognostic role of AKT and mTOR in ovarian cancer is unclear. The median survival of patients with normal levels of AKT2 was longer than in patients whose tumors harbored AKT2 amplifications (45 versus 22 months, respectively), however the study was limited by the small number of patients for which survival data was available and did not reach statistical significance(Bellacosa et al 1995). The activation of AKT and increased downstream mTOR expression has been associated with more aggressive disease and shorter patient survival(Bunkholt Elstrand et al 2010). The effect of PIK3CA amplification on survival is also unclear with some studies showing no influence of amplification on overall survival while another showed that PIK3CA amplification was associated with shorter survival(Kolasa et al 2009, Willner et al

PIK3-AKT2 pathway activation may affect response to therapy. PIK3CA amplification was identified more frequently in patients who were platinum resistant and in patients who did not achieve a complete remission to chemotherapy(Kolasa et al 2009). Disease recurrence was increased in the group with amplifications, however this study was limited by its small size and overall survival was not affected. Further studies in ovarian cancer cell lines with acquired cisplatin resistance shown that the cells harbor increased activation of the Akt/mTOR survival pathway and that inhibition of the pathway resensitizes the cells to cisplatin treatment(Lee et al 2005b, Peng et al 2010). However, whether they can be used as

Given the relatively common activation of this pathway in tumorigenesis, there has been considerable interest in developing therapeutic drugs to target the PTEN/PIK3/AKT pathway for use in multiple cancers. The most successful approach thus far has been the development of mTOR inhibitors, which have been approved for use in renal cell carcinomas and pancreatic neuroendocrine tumors. Rapamycin, and its derivative inhibitors (temsirolimus, everolimus, and ridaforolimus) are currently in use in multiple clinical trials specifically evaluating their effectiveness for the treatment of advanced ovarian cancer. The current progress of the development of these drugs for ovarian cancer was the topic of a recent excellent review (Mabuchi et al 2011). Preclinical data suggest that these agents may be effective both as monotherapy as well as in combination with traditional cytotoxic chemotherapy and may even be effective as preventative agents. The majority of these studies are ongoing and have not completed recruitment, however the results of a few have been published (Table 1). In a phase I clinical trial designed to determine the recommended phase II dose of weekly temsirolimus and topotecan for the treatment of advanced and/or recurrent gynecologic malignancies, the toxicities of the combination were dose-limiting (Temkin et al 2010). Seven participants with ovarian cancer were enrolled in the study but

et al 2009, Nakayama et al 2006b, Willner et al 2007, Woenckhaus et al 2007).

predictors of therapeutic response has not been established.

2007, Woenckhaus et al 2007).

Similar results were found in the TCGA analysis of the molecular aberrations in high-grade serous ovarian carcinomas. The project identified only 9 significant recurrently mutated genes, of which TP53, BRCA1, and BRCA2 were the most common(Cancer Genome Atlas Research Network, 2011). In contrast, copy number aberrations were abundant. One hundred and thirteen significant focal DNA copy number aberrations, including 8 regional recurrent gains, 22 regional recurrent losses, and 63 regions of focal amplification, were identified. Five of the regional gains were present in >50% of tumors. Analysis of the focal amplifications identified a number of genes that were highly amplified and potential therapeutic targets.

The results of these studies clearly highlight the complex molecular and genetic changes that are harbored by ovarian serous carcinomas. Copy number alteration alone, however, does not necessarily indicate that the region plays a causal role in tumorigenesis. One of the challenges with these studies is identifying the potential oncogenes or oncogenic pathways within the affected chromosomal regions that are likely to be responsible for the pathogenesis of ovarian cancer and/or should be a focus for drug development. In the following sections, we will discuss some of the candidate genes that have been identified and are being evaluated in clinical practice.

#### **3. PIK3CA and AKT2**

The phosphoinositide 3-kinase (PIK3)-AKT2 signaling pathway regulates diverse cellular functions including cellular proliferation, migration, metabolic homeostasis, apoptosis and survival, and the dysregulation of this pathway has been implicated in the tumorigenesis of a variety of cancers(Karakas et al 2006, Stokoe 2005). AKT2 is a serine/threonine protein kinase containing SH2-like (Src homology 2-like) domains and is a member of the AKT subfamily. It was originally identified as one of the putative human homologs of the v-akt oncogene of the retrovirus AKT8 (Staal 1987). AKT2 is activated by its upstream regulator PI3K. PIK3CA is the 110kD component of the catalytic subunit of PIK3 and aberrations in normal signaling of PIK3CA and AKT2 have been implicated in ovarian cancer pathogenesis making them potential targets for drug development(Cheng et al 1992, Dancey 2004, Hu et al 2005). Overexpression of activated PIK3CA results in phosphorylation of AKT and cellular transformation and inactivation of AKT by dominant negative mutants abrogates the survival advantage conferred by activated PI3K (Kang et al 2005, Link et al 2005). PTEN (phosphatase and tensin homologue deleted on chromosome 10) is a dual lipid and protein phosphatase that targets PIP3 (phosphatidylinositol-3,4,5- triphosphate), the target of PIK3. This pathway may be aberrantly activated by amplification or mutation of AKT2 or PIK3CA, or deletion, promoter methylation, or functional loss of PTEN which can lead to the excessive activation of downstream effectors, such as mTOR(Altomare et al 2004, Gao et al 2004, Mabuchi et al 2009).

AKT2 amplification has been reported in 5-29% of ovarian cancer cases(Bellacosa et al 1995, Cheng et al 1992, Courjal et al 1996, Nakayama et al 2006b, Park et al 2006). In comparison, AKT2 was not amplified in benign or borderline ovarian tumors(Bellacosa et al 1995, Nakayama et al 2006b). Similarly, low-level amplifications were present in PIK3CA in highgrade carcinomas but not in serous borderline tumors. Twenty seven percent of cases showed amplification in either gene emphasizing how frequently components of this pathway are amplified in ovarian cancer and coamplification of the two genes was seen in a small subset(Nakayama et al 2006b). The findings of this study also support the dualistic

Similar results were found in the TCGA analysis of the molecular aberrations in high-grade serous ovarian carcinomas. The project identified only 9 significant recurrently mutated genes, of which TP53, BRCA1, and BRCA2 were the most common(Cancer Genome Atlas Research Network, 2011). In contrast, copy number aberrations were abundant. One hundred and thirteen significant focal DNA copy number aberrations, including 8 regional recurrent gains, 22 regional recurrent losses, and 63 regions of focal amplification, were identified. Five of the regional gains were present in >50% of tumors. Analysis of the focal amplifications identified a number of genes that were highly amplified and potential

The results of these studies clearly highlight the complex molecular and genetic changes that are harbored by ovarian serous carcinomas. Copy number alteration alone, however, does not necessarily indicate that the region plays a causal role in tumorigenesis. One of the challenges with these studies is identifying the potential oncogenes or oncogenic pathways within the affected chromosomal regions that are likely to be responsible for the pathogenesis of ovarian cancer and/or should be a focus for drug development. In the following sections, we will discuss some of the candidate genes that have been identified

The phosphoinositide 3-kinase (PIK3)-AKT2 signaling pathway regulates diverse cellular functions including cellular proliferation, migration, metabolic homeostasis, apoptosis and survival, and the dysregulation of this pathway has been implicated in the tumorigenesis of a variety of cancers(Karakas et al 2006, Stokoe 2005). AKT2 is a serine/threonine protein kinase containing SH2-like (Src homology 2-like) domains and is a member of the AKT subfamily. It was originally identified as one of the putative human homologs of the v-akt oncogene of the retrovirus AKT8 (Staal 1987). AKT2 is activated by its upstream regulator PI3K. PIK3CA is the 110kD component of the catalytic subunit of PIK3 and aberrations in normal signaling of PIK3CA and AKT2 have been implicated in ovarian cancer pathogenesis making them potential targets for drug development(Cheng et al 1992, Dancey 2004, Hu et al 2005). Overexpression of activated PIK3CA results in phosphorylation of AKT and cellular transformation and inactivation of AKT by dominant negative mutants abrogates the survival advantage conferred by activated PI3K (Kang et al 2005, Link et al 2005). PTEN (phosphatase and tensin homologue deleted on chromosome 10) is a dual lipid and protein phosphatase that targets PIP3 (phosphatidylinositol-3,4,5- triphosphate), the target of PIK3. This pathway may be aberrantly activated by amplification or mutation of AKT2 or PIK3CA, or deletion, promoter methylation, or functional loss of PTEN which can lead to the excessive activation of downstream effectors, such as mTOR(Altomare et al 2004, Gao et

AKT2 amplification has been reported in 5-29% of ovarian cancer cases(Bellacosa et al 1995, Cheng et al 1992, Courjal et al 1996, Nakayama et al 2006b, Park et al 2006). In comparison, AKT2 was not amplified in benign or borderline ovarian tumors(Bellacosa et al 1995, Nakayama et al 2006b). Similarly, low-level amplifications were present in PIK3CA in highgrade carcinomas but not in serous borderline tumors. Twenty seven percent of cases showed amplification in either gene emphasizing how frequently components of this pathway are amplified in ovarian cancer and coamplification of the two genes was seen in a small subset(Nakayama et al 2006b). The findings of this study also support the dualistic

therapeutic targets.

**3. PIK3CA and AKT2** 

al 2004, Mabuchi et al 2009).

and are being evaluated in clinical practice.

model of ovarian serous carcinogenesis in which high-grade and low-grade ovarian serous tumors develop along distinctly different molecular pathways(Kurman and Shih Ie 2008).

Pathway activation through PIK3CA can occur through either amplification or activating mutation of the catalytic subunit. Mutations of PIK3CA are typically associated with endometrioid and clear cell subtypes and are associated with lower tumor stage and grade(Campbell et al 2004, Kolasa et al 2009, Willner et al 2007). Amplifications, on the other hand, have been detected in all histological subtypes, though there was an association with poorer differentiation. PIK3CA amplification has been reported in 13-24% of ovarian carcinomas and is associated with increased expression of phosphorylated AKT indicating that amplification results in increased activation of the pathway(Campbell et al 2004, Kolasa et al 2009, Nakayama et al 2006b, Willner et al 2007, Woenckhaus et al 2007).

Clinical data is lacking in the majority of these studies and the prognostic role of AKT and mTOR in ovarian cancer is unclear. The median survival of patients with normal levels of AKT2 was longer than in patients whose tumors harbored AKT2 amplifications (45 versus 22 months, respectively), however the study was limited by the small number of patients for which survival data was available and did not reach statistical significance(Bellacosa et al 1995). The activation of AKT and increased downstream mTOR expression has been associated with more aggressive disease and shorter patient survival(Bunkholt Elstrand et al 2010). The effect of PIK3CA amplification on survival is also unclear with some studies showing no influence of amplification on overall survival while another showed that PIK3CA amplification was associated with shorter survival(Kolasa et al 2009, Willner et al 2007, Woenckhaus et al 2007).

PIK3-AKT2 pathway activation may affect response to therapy. PIK3CA amplification was identified more frequently in patients who were platinum resistant and in patients who did not achieve a complete remission to chemotherapy(Kolasa et al 2009). Disease recurrence was increased in the group with amplifications, however this study was limited by its small size and overall survival was not affected. Further studies in ovarian cancer cell lines with acquired cisplatin resistance shown that the cells harbor increased activation of the Akt/mTOR survival pathway and that inhibition of the pathway resensitizes the cells to cisplatin treatment(Lee et al 2005b, Peng et al 2010). However, whether they can be used as predictors of therapeutic response has not been established.

Given the relatively common activation of this pathway in tumorigenesis, there has been considerable interest in developing therapeutic drugs to target the PTEN/PIK3/AKT pathway for use in multiple cancers. The most successful approach thus far has been the development of mTOR inhibitors, which have been approved for use in renal cell carcinomas and pancreatic neuroendocrine tumors. Rapamycin, and its derivative inhibitors (temsirolimus, everolimus, and ridaforolimus) are currently in use in multiple clinical trials specifically evaluating their effectiveness for the treatment of advanced ovarian cancer. The current progress of the development of these drugs for ovarian cancer was the topic of a recent excellent review (Mabuchi et al 2011). Preclinical data suggest that these agents may be effective both as monotherapy as well as in combination with traditional cytotoxic chemotherapy and may even be effective as preventative agents. The majority of these studies are ongoing and have not completed recruitment, however the results of a few have been published (Table 1). In a phase I clinical trial designed to determine the recommended phase II dose of weekly temsirolimus and topotecan for the treatment of advanced and/or recurrent gynecologic malignancies, the toxicities of the combination were dose-limiting (Temkin et al 2010). Seven participants with ovarian cancer were enrolled in the study but

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 293

2206, an allosteric AKT inhibitor, showed preclinical efficacy in ovarian cancer cell lines with synergistic responses when combined with other cytotoxic agents such as doxorubicin, docetaxel, and carboplatin. It is currently under investigation in a phase II trial evaluating its efficacy as monotherapy specifically in ovarian cancers exhibiting defects in the PI3K/AKT pathway while several other phase I trials are evaluating its safety in combination with other chemotherapeutic agents(Hirai et al 2010). The results of these and

Everolimus mTOR inhibitor Under evaluation in Phase I and II

AZD-8055 ATP-competitive mTOR inhibitor Dual mTORC1/mTORC2 inhibitor,

CH5132799 Selective class I PI3K inhibitor Anti-tumor activity in vitro and in

GDC-0941 PIK3CA inhibitor One ovarian cancer patient (PTEN

BEZ235 Dual PI3K/mTOR inhibitor Anti-tumor activity in mouse

MK-2206 Allosteric AKT inhibitor Currently being evaluated in

Table 2. Other PI3K-AKT pathway inhibitors with pre-clinical efficacy in ovarian cancer.

The epidermal growth factor receptor (EGFR) family of receptor tyrosine kinases has been implicated in the oncogenic transformation of a number of cancers. This family of genes encodes for four transmembrane tyrosine kinase receptors commonly referred to as EGFR (HER1/erbB1), HER2/neu (erbB2), HER3 (erbB2) and HER4 (erbB4). They each consist of a ligand-binding extracellular domain, an intracellular kinase domain, and a C-terminal signaling tail. The receptors are activated by binding to one of more than 30 ligands that then allow the formation of homodimers or heterodimers; except HER2 has no known ligand but is able to form heterodimers with other ligand-bound EGFR family members. Interestingly, HER3 lacks intrinsic kinase activity and therefore must form a heterodimer to be active and its preferred binding partner is HER2/neu. Activated dimers recruit signaling molecules through a phosphorylated cytoplasmic domain that initiates a signaling cascade leading to the activation of downstream pathways such as PI3K-AKT and MAPK that

trials for ovarian cancer

animal models

et al 2011)

prevents feedback activation of AKT observed with rapalogues

negative) showed 30% response by PET & 80% by CA-125, stayed on study for ~5 months(Moreno Garcia

model, undergoing evaluation as monotherapy and in combination with cytotoxic chemotherapy

recurrent Grade 2 or 3 ovarian, fallopian tube, or primary

peritoneal cancer with evidence of a defect in the PI3K/AKT pathway

other ongoing studies of PI3K-AKT pathway inhibitors are eagerly awaited.

Drug Target Comments

OSI-027 ATP-competitive mTOR inhibitor

**4. Epidermal growth factor receptors** 

the authors do not report the best response for these participants; nine of the 11 evaluable participants on the study had stable disease. In a Phase I study of temsirolimus, carboplatin, and paclitaxel in patients with endometrial and ovarian cancers, the combination was well tolerated and a recommended phase II dose was established(Oza et al 2009). In addition, 22 of the 26 participants with follow-up data showed either partial response (38.5%) or stable disease (46%) for a median duration of 7 months. In a phase II trial combining targeted therapies, temsirolimus and bevacizumab, a monoclonal antibody targeting VEGF-A, were given to patients with recurrent epithelial ovarian cancer who had received ≤2 chemotherapy regimens for recurrent disease. This study met its first stage goal of 14 participants remaining progression free at 6 months and has been reopened for second stage accrual(Morgan et al 2011). Rapamycin and its analogues predominantly inhibit mTOR complex 1 (mTORC1) without affecting the activity of mTORC2. A novel ATP-competitive inhibitor of mTOR kinase activity, AZD8055, inhibits both the mTORC1/mTORC2 and prevents the feedback activation of AKT that is observed with the rapalogues and has completed phase I clinical trial in advanced solid malignancies(Banerji et al 2011, Chresta et al 2010).


Table 1. Selected Clinical Trials of mTOR inhibitors in Ovarian Cancer.

Several other PI3K-AKT pathway inhibitors (Table 2) are in early clinical development. Of these, GDC-0941, an inhibitor of PIK3CA, has shown early signs of possible clinical efficacy in an ovarian cancer patient with a PTEN negative tumor(Moreno Garcia et al 2011). MK-

the authors do not report the best response for these participants; nine of the 11 evaluable participants on the study had stable disease. In a Phase I study of temsirolimus, carboplatin, and paclitaxel in patients with endometrial and ovarian cancers, the combination was well tolerated and a recommended phase II dose was established(Oza et al 2009). In addition, 22 of the 26 participants with follow-up data showed either partial response (38.5%) or stable disease (46%) for a median duration of 7 months. In a phase II trial combining targeted therapies, temsirolimus and bevacizumab, a monoclonal antibody targeting VEGF-A, were given to patients with recurrent epithelial ovarian cancer who had received ≤2 chemotherapy regimens for recurrent disease. This study met its first stage goal of 14 participants remaining progression free at 6 months and has been reopened for second stage accrual(Morgan et al 2011). Rapamycin and its analogues predominantly inhibit mTOR complex 1 (mTORC1) without affecting the activity of mTORC2. A novel ATP-competitive inhibitor of mTOR kinase activity, AZD8055, inhibits both the mTORC1/mTORC2 and prevents the feedback activation of AKT that is observed with the rapalogues and has completed phase I clinical trial in advanced solid malignancies(Banerji et al 2011, Chresta et

Therapy Phase # Pts Selection Criteria Outcome Comments

advanced or recurrent gynecologic malignancy refractory to curative therapy

malignancies suitable for carboplatin and paclitaxel chemotherapy who had not received more than 2 prior lines of chemotherapy

epithelial OC who had received

chemotherapy regimens for recurrent disease

Several other PI3K-AKT pathway inhibitors (Table 2) are in early clinical development. Of these, GDC-0941, an inhibitor of PIK3CA, has shown early signs of possible clinical efficacy in an ovarian cancer patient with a PTEN negative tumor(Moreno Garcia et al 2011). MK-

≤ 2

9/11 SD Toxicities of the

10/26 PR 12/26 SD

3/25 PR 9/25 SD combination were dose limiting, intolerable in pts previously treated with radiation

Median duration of response 7 months

Met first stage goal, reopened for second stage accrual

(NCT01010126)

al 2010).

2010)

Temsirolimus + Topotecan (Temkin et al

Temsirolimus + Carboplatin + Paclitaxel (Oza et al 2009)

Temsirolimus + Bevacizumab (Morgan et al

2011)

I 15

(7 ovarian cancer)

I 31 advanced solid

II 31 recurrent

Table 1. Selected Clinical Trials of mTOR inhibitors in Ovarian Cancer.

2206, an allosteric AKT inhibitor, showed preclinical efficacy in ovarian cancer cell lines with synergistic responses when combined with other cytotoxic agents such as doxorubicin, docetaxel, and carboplatin. It is currently under investigation in a phase II trial evaluating its efficacy as monotherapy specifically in ovarian cancers exhibiting defects in the PI3K/AKT pathway while several other phase I trials are evaluating its safety in combination with other chemotherapeutic agents(Hirai et al 2010). The results of these and other ongoing studies of PI3K-AKT pathway inhibitors are eagerly awaited.


Table 2. Other PI3K-AKT pathway inhibitors with pre-clinical efficacy in ovarian cancer.

#### **4. Epidermal growth factor receptors**

The epidermal growth factor receptor (EGFR) family of receptor tyrosine kinases has been implicated in the oncogenic transformation of a number of cancers. This family of genes encodes for four transmembrane tyrosine kinase receptors commonly referred to as EGFR (HER1/erbB1), HER2/neu (erbB2), HER3 (erbB2) and HER4 (erbB4). They each consist of a ligand-binding extracellular domain, an intracellular kinase domain, and a C-terminal signaling tail. The receptors are activated by binding to one of more than 30 ligands that then allow the formation of homodimers or heterodimers; except HER2 has no known ligand but is able to form heterodimers with other ligand-bound EGFR family members. Interestingly, HER3 lacks intrinsic kinase activity and therefore must form a heterodimer to be active and its preferred binding partner is HER2/neu. Activated dimers recruit signaling molecules through a phosphorylated cytoplasmic domain that initiates a signaling cascade leading to the activation of downstream pathways such as PI3K-AKT and MAPK that

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 295

Schilder et al 2005), 0% for CI-1033 an irreversible EGFR inhibitor(Campos et al 2005)). TKIs combined with cytotoxic chemotherapy, anti-angiogenic therapy, or hormonal therapy have also shown limited clinical efficacy and in some cases excessive toxicity(Campos et al 2010, Chambers et al 2010, Nimeiri et al 2008, Vasey et al 2008). The reason behind the relative failure of EGFR targeted therapies is not understood, but may be related to constitutive activation of downstream pathways, overexpression of ligands, or activation of alternative signaling pathways (reviewed in (Bianco et al 2007, Siwak et al 2010)). Despite the promising preclinical results based on the amplification data, these therapeutic agents cannot be

recommended outside of a clinical trial setting for the treatment of ovarian cancer.

Therapy Phase # Pts Selection Criteria Outcome Comments

ovarian or primary peritoneal carcinoma

ovarian, or primary peritoneal cancer

relapsed platinumsensitive ovarian or primary peritoneal

1/25 PR 9/25 SD

6/37 SD

3/28 CR 6/28 PR 8/28 SD

Median PFS 14.4 mths, PFS at 18 mths 38.8%

Median progression free survival 1.8

Did not meet criteria for a second stage of

No prolongation of PFS when compared to historical data

(NCT01296035)

(NCT00861120)

(NCT01388621)

months

accrual

Ongoing

Ongoing

Opening soon

II 25 Persistent/recurrent

positive

carcinoma

stage III or IV,

debulked tumor, EGFR positive by IHC

platinum-resistant epithelial ovarian, primary peritoneal or fallopian tube cancer

epithelial primary ovarian, primary fallopian or primary peritoneal cancer

recurrent epithelial ovarian cancer, primary peritoneal carcinomatosis or fallopian tube cancer, KRAS wild type

II 40 Initial treatment of

II Persistent/recurrent

II Platinum resistant

II Platinum-sensitive

Table 3. Anti-EGFR monoclonal antibodies.

II 37 recurrent, EGFR-

II 28 (26

EGFR +)

Cetuximab (Schilder et al 2009)

Matuzumab (Seiden et al 2007)

Cetuximab + Carboplatin (Secord et al 2008)

Cetuximab + Carboplatin + Paclitaxel (Konner et al

Panitumumab

Gemcitabine

Panitumumab + Pegylated Liposomal Doxorubicin

Panitumumab

Carboplatin + Pegylated Liposomal Doxorubicin

2008)

+

+

ultimately regulate cellular proliferation, migration, invasion, and apoptosis. Two recent excellent reviews have been published on the role of these receptors in ovarian cancer(Sheng and Liu 2011, Siwak et al 2010); herein we will focus on the clinical implications of EGFR, HER2/neu and HER3, the three receptors found to be amplified in ovarian cancers.

Amplification of the EGFR gene has been identified in 4-22% of ovarian cancers and, for the most part, amplification correlates with overexpression(Dimova et al 2006, Lassus et al 2006, Stadlmann et al 2006, Vermeij et al 2008). Some studies have delineated the level of amplification into high and low categories. While high level amplification occurs in a small percentage of tumors (4-12%), low level gain has been reported in as many as 43% of cases(Dimova et al 2006, Lassus et al 2006). High-level amplifications have been associated with malignant tumors and worse histologic grade. Results are mixed on the influence of EGFR overexpression on patient outcome. Several studies showed no association with survival, while EGFR overexpression was found to be a strong prognostic indicator in other studies(Baekelandt et al 1999, Elie et al 2004, Lassus et al 2006, Lee et al 2005a, Nicholson et al 2001). The discrepancy may be related to different methodologies used in staining and analysis.

Preclinical data suggests that targeting EGFR is an effective approach to treating ovarian cancer. Ovarian cancer cells treated with antisense RNA or dominant-negative approaches showed reduced proliferation, invasion, and tumorigenicity in a rat ovarian tumor model(Alper et al 2000, Alper et al 2001, Chan et al 2005). A human-mouse chimeric anti-EGFR monoclonal antibody (C225, cetuximab) resulted in decreased activity of cyclin dependent kinases and inhibition of ovarian cancer cellular proliferation by 40-50% and when combined with cytotoxic chemotherapy enhanced the efficacy of those agents(Ye et al 1999). However, the results have been inconsistent and targeting of EGFR with either gefitinib or cetuximab in several ovarian cancer cell lines showed minimal response(Bull Phelps et al 2008).

Two types of EGFR inhibitors are currently in clinical use: monoclonal antibodies (Table 3) and small molecule tyrosine kinase inhibitors (TKIs), and several have been evaluated for the treatment of ovarian cancer. The studies have taken different strategies, some requiring EGFR immunohistochemical positivity as an inclusion criterion, while others evaluated EGFR expression only after enrollment. Overall the results have been disappointing with some studies showing, at best, modest response. In the two studies using single agent EGFR monoclonal antibodies, cetuximab and matuzumab, overall response rates were 4% and 0%, respectively(Schilder et al 2009, Seiden et al 2007). There are five trials evaluating EGFR monoclonal antibodies in combination with cytotoxic chemotherapy, with three ongoing. Of the two involving cetuximab, a phase II trial of cetuximab in combination with carboplatin in recurrent, platinum-sensitive disease yielded an objective response rate of 34.6%, a rate that was too low to warrant further evaluation(Secord et al 2008). The other Phase II study that evaluated the combination of cetuximab, paclitaxel, and carboplatin in the initial treatment of advanced-stage ovarian, primary peritoneal, or fallopian tube cancers did not show an increase in progression free survival compared to historical controls(Konner et al 2008). Three separate phase II trials are evaluating panitumumab with cytotoxic chemotherapy; the results of these studies are not yet available but are eagerly awaited.

Small molecule tyrosine kinase inhibitors (TKI) targeting EGFR activity have been investigated in several trials specifically focused on ovarian cancer (Table 4). Single agent TKI did not show any substantial clinical benefit (0-9% for gefitinib(Posadas et al 2007,

ultimately regulate cellular proliferation, migration, invasion, and apoptosis. Two recent excellent reviews have been published on the role of these receptors in ovarian cancer(Sheng and Liu 2011, Siwak et al 2010); herein we will focus on the clinical implications of EGFR,

Amplification of the EGFR gene has been identified in 4-22% of ovarian cancers and, for the most part, amplification correlates with overexpression(Dimova et al 2006, Lassus et al 2006, Stadlmann et al 2006, Vermeij et al 2008). Some studies have delineated the level of amplification into high and low categories. While high level amplification occurs in a small percentage of tumors (4-12%), low level gain has been reported in as many as 43% of cases(Dimova et al 2006, Lassus et al 2006). High-level amplifications have been associated with malignant tumors and worse histologic grade. Results are mixed on the influence of EGFR overexpression on patient outcome. Several studies showed no association with survival, while EGFR overexpression was found to be a strong prognostic indicator in other studies(Baekelandt et al 1999, Elie et al 2004, Lassus et al 2006, Lee et al 2005a, Nicholson et al 2001). The discrepancy may be related to different methodologies used in staining and

Preclinical data suggests that targeting EGFR is an effective approach to treating ovarian cancer. Ovarian cancer cells treated with antisense RNA or dominant-negative approaches showed reduced proliferation, invasion, and tumorigenicity in a rat ovarian tumor model(Alper et al 2000, Alper et al 2001, Chan et al 2005). A human-mouse chimeric anti-EGFR monoclonal antibody (C225, cetuximab) resulted in decreased activity of cyclin dependent kinases and inhibition of ovarian cancer cellular proliferation by 40-50% and when combined with cytotoxic chemotherapy enhanced the efficacy of those agents(Ye et al 1999). However, the results have been inconsistent and targeting of EGFR with either gefitinib or cetuximab in several ovarian cancer cell lines showed minimal response(Bull

Two types of EGFR inhibitors are currently in clinical use: monoclonal antibodies (Table 3) and small molecule tyrosine kinase inhibitors (TKIs), and several have been evaluated for the treatment of ovarian cancer. The studies have taken different strategies, some requiring EGFR immunohistochemical positivity as an inclusion criterion, while others evaluated EGFR expression only after enrollment. Overall the results have been disappointing with some studies showing, at best, modest response. In the two studies using single agent EGFR monoclonal antibodies, cetuximab and matuzumab, overall response rates were 4% and 0%, respectively(Schilder et al 2009, Seiden et al 2007). There are five trials evaluating EGFR monoclonal antibodies in combination with cytotoxic chemotherapy, with three ongoing. Of the two involving cetuximab, a phase II trial of cetuximab in combination with carboplatin in recurrent, platinum-sensitive disease yielded an objective response rate of 34.6%, a rate that was too low to warrant further evaluation(Secord et al 2008). The other Phase II study that evaluated the combination of cetuximab, paclitaxel, and carboplatin in the initial treatment of advanced-stage ovarian, primary peritoneal, or fallopian tube cancers did not show an increase in progression free survival compared to historical controls(Konner et al 2008). Three separate phase II trials are evaluating panitumumab with cytotoxic chemotherapy; the results of these studies are not yet available but are eagerly awaited. Small molecule tyrosine kinase inhibitors (TKI) targeting EGFR activity have been investigated in several trials specifically focused on ovarian cancer (Table 4). Single agent TKI did not show any substantial clinical benefit (0-9% for gefitinib(Posadas et al 2007,

HER2/neu and HER3, the three receptors found to be amplified in ovarian cancers.

analysis.

Phelps et al 2008).

Schilder et al 2005), 0% for CI-1033 an irreversible EGFR inhibitor(Campos et al 2005)). TKIs combined with cytotoxic chemotherapy, anti-angiogenic therapy, or hormonal therapy have also shown limited clinical efficacy and in some cases excessive toxicity(Campos et al 2010, Chambers et al 2010, Nimeiri et al 2008, Vasey et al 2008). The reason behind the relative failure of EGFR targeted therapies is not understood, but may be related to constitutive activation of downstream pathways, overexpression of ligands, or activation of alternative signaling pathways (reviewed in (Bianco et al 2007, Siwak et al 2010)). Despite the promising preclinical results based on the amplification data, these therapeutic agents cannot be recommended outside of a clinical trial setting for the treatment of ovarian cancer.


Table 3. Anti-EGFR monoclonal antibodies.

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 297

Expression and amplification levels of Her2/neu in ovarian cancer have been extensively evaluated, however the data is inconsistent and its significance is still controversial. Early studies showed amplification in 26% with corresponding overexpression and an analysis of the subset with available survival data showed a significantly longer median overall survival in women whose tumors did not exhibit Her2 amplification (1879, 959, and 243 days for women having one copy, 2-5 copies and >5 copies of Her2/neu gene, respectively, p <0.0001)(Slamon et al 1989). In subsequent studies, observed rates of Her2/neu amplification in ovarian cancer has been reported in up to 66% of epithelial ovarian cancers with overexpression reported in up to 76%(Camilleri-Broet et al 2004, Press et al 1990, Ross et al 1999, Serrano-Olvera et al 2006, Slamon et al 1989, Tuefferd et al 2007, Vermeij et al 2008). Levels of amplification differ with low copy number amplification (<2) observed in as many as 79%, 3-5 copies in 14%, >5 copies in 6.8%, and >10 copies in 1.8%(Lassus et al 2004). The level of amplification in general has correlated with level of overexpression by IHC, however this too has been called into question(Lassus et al 2004, Mano et al 2004, Pegram et al 1997, Wu et al 2004) and may be reflective of other mechanisms responsible for

Several studies have shown an association between Her2/neu overexpression/ amplification and poor response to therapy and prognosis, however more recent reports refute this association(Berchuck et al 1990, Bookman et al 2003, Farley et al 2009, Pegram et al 1997, Rubin et al 1994, Tuefferd et al 2007). In a recent Gynecologic Oncology Group study that evaluated Her2/neu amplification in 133 epithelial ovarian cancers, amplification (>2 copies) was only identified in 7% and was not an independent prognostic factor for progression free survival or overall survival(Farley et al 2009). A phase II trial evaluating the efficacy of trastuzumab, a monoclonal humanized anti-Her2 antibody, in patients with recurrent ovarian cancer showed that only 11% of tumor samples exhibited elevated expression of Her2 by immunohistochemistry. Of the participants treated with trastuzumab, the overall response rate was only 7% with a progression free interval of 2 months(Bookman et al 2003). Overall, it does not appear that Her2/neu amplification has predictive or prognostic value in epithelial ovarian cancer and the value of treatment with HER2 directed monotherapy is limited (Table 5). Despite, preclinical evidence of effectiveness(Gordon et al 2006), pertuzumab, a recombinant, humanized monoclonal antibody that binds the HER2 dimerization domain impeding dimerization of HER2 with other family members and thus prevents activation of downstream pathways, has shown similarly low response rates in clinical trials in the treatment of ovarian cancer. As a single agent, the response rate was only 4.3% and in a randomized phase II study the addition of pertuzumab to gemcitabine improved the objective response rate to 13.8% from 4.6%(Gordon et al 2006, Makhija et al 2010). Treatment response appeared to correlate with Her2 phosphorylation status in one study and low Her3 expression in another, however these markers have not yet been validated in further studies. Lapatinib, a dual EGFR/HER2 TKI, has also shown limited clinical response and excessive toxicity(Joly et al 2009, Kimball et al 2008). Preliminary results of a phase I/II trial combining lapatinib with carboplatin and paclitaxel showed promising preliminary results, but the final results of the trial have not been published(Rivkin et al 2008). Further studies will be necessary to determine whether lapatinib may be a useful

overexpression other than amplification.

agent in ovarian cancer.


Table 4. Anti-EGFR small molecule inhibitors.

18/52 SD at highest dose level

1/23 CR 14/23 SD

2/34 PR 15/34 SD

7/23 PR

8/39 PR 10/39 SD

1/13 CR 1/13 PR 7/13 SD

median PFS 2.2 mths, median OS 9.1 mths at highest dose level

>50%, however not associated with clinical

1/27 PR 4 pts with PFS ≥6 mths, trial

EGFR positive pts

16/56 SD Tumor did not need to be

expression

alone

ORR not improved compared to historical controls of Bevacizumab

to single-agent

Combination not superior

Bevacizumab, rate of GI perforation a concern

IHC

decreased during therapy in

did not continue to second stage, responder had activating EGFR mutation, trend towards response in

positive for ER or EGFR by

Objective response rate (52%) lower than in historical controls (59%), unselected for EGFR

9/24 SD EGFR and pEGFR levels

benefit

Therapy Phase # Pts Selection Criteria Outcome Comments

epithelial ovarian

ovarian cancer

epithelial ovarian or primary peritoneal

peritoneal or tubal carcinoma, ER and/or PR positive

recurrent epithelial ovarian cancer

recurrent, ovarian cancer, EGFR positive by IHC

Ib 45 Chemonaive 5/23 CR

II 40 Platinum resistant 1/39 CR

primary peritoneal or fallopian tube

II 13 Recurrent ovarian,

cancer

Table 4. Anti-EGFR small molecule inhibitors.

II 105 Persistent/recurrent

cancer

II 24 Recurrent epithelial

II 27 Persistent/recurrent

II 35 Recurrent ovarian,

by IHC

II 56 Refractory,

II 34 Refractory,

carcinoma

CI-1033/ Canertinib (Campos et al 2005)

Gefitinib (Posadas et al 2007)

Gefitinib (Schilder et al 2005)

Gefitinib + Anastrazole (Krasner et al 2005)

Gefitinib + Tamoxifen (Wagner et al 2007)

Erlotinib (Gordon et al 2005)

Erlotinib + Carboplatin

Docetaxel (Vasey et al 2008)

Erlotinib + Bevacizumab (Chambers et al 2010)

Erlotinib + Bevacizumab (Nimeiri et al 2008)

+

Expression and amplification levels of Her2/neu in ovarian cancer have been extensively evaluated, however the data is inconsistent and its significance is still controversial. Early studies showed amplification in 26% with corresponding overexpression and an analysis of the subset with available survival data showed a significantly longer median overall survival in women whose tumors did not exhibit Her2 amplification (1879, 959, and 243 days for women having one copy, 2-5 copies and >5 copies of Her2/neu gene, respectively, p <0.0001)(Slamon et al 1989). In subsequent studies, observed rates of Her2/neu amplification in ovarian cancer has been reported in up to 66% of epithelial ovarian cancers with overexpression reported in up to 76%(Camilleri-Broet et al 2004, Press et al 1990, Ross et al 1999, Serrano-Olvera et al 2006, Slamon et al 1989, Tuefferd et al 2007, Vermeij et al 2008). Levels of amplification differ with low copy number amplification (<2) observed in as many as 79%, 3-5 copies in 14%, >5 copies in 6.8%, and >10 copies in 1.8%(Lassus et al 2004). The level of amplification in general has correlated with level of overexpression by IHC, however this too has been called into question(Lassus et al 2004, Mano et al 2004, Pegram et al 1997, Wu et al 2004) and may be reflective of other mechanisms responsible for overexpression other than amplification.

Several studies have shown an association between Her2/neu overexpression/ amplification and poor response to therapy and prognosis, however more recent reports refute this association(Berchuck et al 1990, Bookman et al 2003, Farley et al 2009, Pegram et al 1997, Rubin et al 1994, Tuefferd et al 2007). In a recent Gynecologic Oncology Group study that evaluated Her2/neu amplification in 133 epithelial ovarian cancers, amplification (>2 copies) was only identified in 7% and was not an independent prognostic factor for progression free survival or overall survival(Farley et al 2009). A phase II trial evaluating the efficacy of trastuzumab, a monoclonal humanized anti-Her2 antibody, in patients with recurrent ovarian cancer showed that only 11% of tumor samples exhibited elevated expression of Her2 by immunohistochemistry. Of the participants treated with trastuzumab, the overall response rate was only 7% with a progression free interval of 2 months(Bookman et al 2003). Overall, it does not appear that Her2/neu amplification has predictive or prognostic value in epithelial ovarian cancer and the value of treatment with HER2 directed monotherapy is limited (Table 5). Despite, preclinical evidence of effectiveness(Gordon et al 2006), pertuzumab, a recombinant, humanized monoclonal antibody that binds the HER2 dimerization domain impeding dimerization of HER2 with other family members and thus prevents activation of downstream pathways, has shown similarly low response rates in clinical trials in the treatment of ovarian cancer. As a single agent, the response rate was only 4.3% and in a randomized phase II study the addition of pertuzumab to gemcitabine improved the objective response rate to 13.8% from 4.6%(Gordon et al 2006, Makhija et al 2010). Treatment response appeared to correlate with Her2 phosphorylation status in one study and low Her3 expression in another, however these markers have not yet been validated in further studies. Lapatinib, a dual EGFR/HER2 TKI, has also shown limited clinical response and excessive toxicity(Joly et al 2009, Kimball et al 2008). Preliminary results of a phase I/II trial combining lapatinib with carboplatin and paclitaxel showed promising preliminary results, but the final results of the trial have not been published(Rivkin et al 2008). Further studies will be necessary to determine whether lapatinib may be a useful agent in ovarian cancer.

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 299

HER4 has been variably reported in ovarian cancer, ranging from nearly absent to almost ubiquitously expressed(Sheng and Liu 2011). Interestingly, overexpression of HER4 in ovarian cancer was associated with a trend toward improved progression free and overall survival, an effect that has also been seen in breast cancer possibly by promoting differentiation(Pejovic et al 2009, Rajkumar et al 1996). However, these results have not been

The Notch signaling pathway is an evolutionarily conserved pathway that regulates cellular differentiation, proliferation, and apoptosis. The family of Notch receptors (Notch 1-4) are large transmembrane proteins that consist of an extracellular ligand binding domain, a transmembrane domain, and an intracellular domain. Activation of the receptors is a multistep process consisting of an initial cleavage event allowing the extracellular domain to heterodimerize with transmembrane ligands (Delta-like 1, 3, 4 and Jagged 1 and 2). Following ligand binding a second cleavage event releases the Notch extracellular domain (ECD) causing the ECD and the ligand to be endocytosed. Cleavage by gamma secretase following endocytosis releases the active Notch intracellular domain (NICD) allowing for translocation to the nucleus and heterodimerization to transcription factors and recruitment of coactivators to form a functionally active transcriptional complex(Rose 2009). Of the Notch receptors, Notch1 and Notch3 have been implicated in ovarian cancer. Reports of Notch1 expression in ovarian cancer are inconsistent with some showing increased expression in carcinomas compared to benign tumor or normal ovarian surface epithelium, while others showed decreased mRNA expression in carcinomas(Hopfer et al 2005, Rose et

The association between Notch3 and ovarian cancer has been more extensively studied. High level Notch3 amplification has been observed in 7.8% of high-grade serous carcinomas (Nakayama et al 2007), while high level protein overexpression was found in 63% of serous carcinomas and was significantly correlated with advanced stage, likelihood of metastasis, chemoresistance and poor overall survival(Jung et al 2010). Overexpression of the Notch ligands, Jagged-1 and Jagged-2, has also been identified in ovarian tumor cells lending support that activation of the Notch pathway promotes ovarian cancer proliferation and that inhibition of this pathway may be a viable therapeutic approach(Choi et al 2008, Hopfer et al 2005). Similarly, the TCGA identified alterations in the Notch pathway in 22% of high-grade serous ovarian carcinoma samples, which included amplification/mutation of Notch3, amplification of Jagged-1 and Jagged-2, and amplification/mutation of MAML1-3, a family of Notch transcriptional coactivators(Cancer Genome Atlas Research Network, 2011). Inactivation of Notch signaling through targeting Jagged-1 or direct inhibition of Notch by preventing cleavage with a gamma-secretase inhibitor decreases the proliferative potential of and increases apoptosis in ovarian cancer cell lines and xenograft models(Park et al 2006, Steg et al 2011). Targeting Jagged-1 also resulted in decreased microvessel density in xenografts

Notch pathway inhibitors have recently moved into clinical trials. Early reports of a phase I clinical trial of RO4929097, a selective oral gamma-secretase inhibitor, showed prolonged

confirmed and the role of HER4 in ovarian cancer is still undefined.

suggesting Notch signaling may play a role in angiogenesis.

**5. Notch signaling pathway** 

al 2010, Wang et al 2010).


Table 5. Selected Clinical Trials of HER2/neu Targeted Agents in Ovarian Cancer.

The roles of HER3 and HER4 in ovarian cancer have been less extensively studied(Sheng and Liu 2011). HER3 amplification and overexpression in ovarian cancer has been described and in one study was significantly associated with poor survival (median survival time 3.3 years vs. 1.8 years for patients with low vs. high HER3 expression)(Sheng and Liu 2011, Tanner et al 2006, Tsuda et al 2004). Antibodies directed against the extracellular domain of HER3 diminished HER2 activity and attenuated the activation of downstream effectors(van der Horst et al 2005). Compensatory overexpression of HER3 has also been implicated as a mechanism of resistance to other EGFR inhibitors(Sheng and Liu 2011). These data suggest that targeting HER3 may be an effective treatment strategy and three monoclonal antibodies that target HER3 are being tested in early phase clinical trials for advanced solid tumors (U3-1287, MM-121, and MM-111 which targets both HER2 and HER3). The expression of HER4 has been variably reported in ovarian cancer, ranging from nearly absent to almost ubiquitously expressed(Sheng and Liu 2011). Interestingly, overexpression of HER4 in ovarian cancer was associated with a trend toward improved progression free and overall survival, an effect that has also been seen in breast cancer possibly by promoting differentiation(Pejovic et al 2009, Rajkumar et al 1996). However, these results have not been confirmed and the role of HER4 in ovarian cancer is still undefined.

#### **5. Notch signaling pathway**

298 Ovarian Cancer – Basic Science Perspective

1/41 CR 2/41 PR 16/41 SD

5 PR 8 SD

9/65 PR (combo) 3/65 PR (placebo)

0/2 PR 7/9 SD

3/11 PR 3/11 SD

CR 21% PR 29% SD 29%

serum HER2 was not associated with clinical outcome

Median PFS 6.6 wks, trend toward

improved PFS for pts with pHER2+ disease

Low HER3 mRNA expression may predict pertuzumab clinical benefit

Prematurely stopped for lack of efficacy

unacceptable toxicities, excessive treatment delays and limited clinical responses

final results not published

Therapy Phase # Pts Selection Criteria Outcome Comments

IHC

Recurrent

advanced,

cancer

cancer

months

Recurrent

I/II 25 Recurrent ovarian

cancer

Table 5. Selected Clinical Trials of HER2/neu Targeted Agents in Ovarian Cancer.

The roles of HER3 and HER4 in ovarian cancer have been less extensively studied(Sheng and Liu 2011). HER3 amplification and overexpression in ovarian cancer has been described and in one study was significantly associated with poor survival (median survival time 3.3 years vs. 1.8 years for patients with low vs. high HER3 expression)(Sheng and Liu 2011, Tanner et al 2006, Tsuda et al 2004). Antibodies directed against the extracellular domain of HER3 diminished HER2 activity and attenuated the activation of downstream effectors(van der Horst et al 2005). Compensatory overexpression of HER3 has also been implicated as a mechanism of resistance to other EGFR inhibitors(Sheng and Liu 2011). These data suggest that targeting HER3 may be an effective treatment strategy and three monoclonal antibodies that target HER3 are being tested in early phase clinical trials for advanced solid tumors (U3-1287, MM-121, and MM-111 which targets both HER2 and HER3). The expression of

epithelial ovarian

platinum-resistant epithelial ovarian, fallopian tube, or primary peritoneal

Ovarian cancer relapsed w/in 12

platinum sensitive epithelial ovarian carcinoma

persistent or recurrent epithelial ovarian cancer, 2/3+ HER2 by

Trastuzumab (Bookman et al 2003)

Pertuzumab (Gordon et al

Pertuzumab

Gemcitabine vs Placebo + Gemcitabine (Makhija et al

Lapatinib + Topotecan (Joly et al 2009)

Lapatinib + Carboplatin (Kimball et al

Lapatinib + Carboplatin + Paclitaxel (Rivkin et al 2008)

2008)

2006)

2010)

+

II 41

II 117

65 (combo) 65 (placebo)

39 (37 ovarian cancer)

II

II

I 12

The Notch signaling pathway is an evolutionarily conserved pathway that regulates cellular differentiation, proliferation, and apoptosis. The family of Notch receptors (Notch 1-4) are large transmembrane proteins that consist of an extracellular ligand binding domain, a transmembrane domain, and an intracellular domain. Activation of the receptors is a multistep process consisting of an initial cleavage event allowing the extracellular domain to heterodimerize with transmembrane ligands (Delta-like 1, 3, 4 and Jagged 1 and 2). Following ligand binding a second cleavage event releases the Notch extracellular domain (ECD) causing the ECD and the ligand to be endocytosed. Cleavage by gamma secretase following endocytosis releases the active Notch intracellular domain (NICD) allowing for translocation to the nucleus and heterodimerization to transcription factors and recruitment of coactivators to form a functionally active transcriptional complex(Rose 2009). Of the Notch receptors, Notch1 and Notch3 have been implicated in ovarian cancer. Reports of Notch1 expression in ovarian cancer are inconsistent with some showing increased expression in carcinomas compared to benign tumor or normal ovarian surface epithelium, while others showed decreased mRNA expression in carcinomas(Hopfer et al 2005, Rose et al 2010, Wang et al 2010).

The association between Notch3 and ovarian cancer has been more extensively studied. High level Notch3 amplification has been observed in 7.8% of high-grade serous carcinomas (Nakayama et al 2007), while high level protein overexpression was found in 63% of serous carcinomas and was significantly correlated with advanced stage, likelihood of metastasis, chemoresistance and poor overall survival(Jung et al 2010). Overexpression of the Notch ligands, Jagged-1 and Jagged-2, has also been identified in ovarian tumor cells lending support that activation of the Notch pathway promotes ovarian cancer proliferation and that inhibition of this pathway may be a viable therapeutic approach(Choi et al 2008, Hopfer et al 2005). Similarly, the TCGA identified alterations in the Notch pathway in 22% of high-grade serous ovarian carcinoma samples, which included amplification/mutation of Notch3, amplification of Jagged-1 and Jagged-2, and amplification/mutation of MAML1-3, a family of Notch transcriptional coactivators(Cancer Genome Atlas Research Network, 2011). Inactivation of Notch signaling through targeting Jagged-1 or direct inhibition of Notch by preventing cleavage with a gamma-secretase inhibitor decreases the proliferative potential of and increases apoptosis in ovarian cancer cell lines and xenograft models(Park et al 2006, Steg et al 2011). Targeting Jagged-1 also resulted in decreased microvessel density in xenografts suggesting Notch signaling may play a role in angiogenesis.

Notch pathway inhibitors have recently moved into clinical trials. Early reports of a phase I clinical trial of RO4929097, a selective oral gamma-secretase inhibitor, showed prolonged

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 301

1998, Mayr et al 2006, Nakayama et al 2007, Nakayama et al 2010, Park et al 2006, Schraml et al 2003a). Cyclin E expression has been found in as many as 97% of ovarian cancer/primary peritoneal cancer samples(Davidson et al 2006). In suboptimally debulked advanced epithelial ovarian cancers obtained from women enrolled in GOG111, the expression level of cyclin E correlated with a 6 month shorter median survival and worse overall survival(Farley et al 2003). Analysis of the subset of patients with serous carcinomas (72% of total study) showed an 11 month difference in median survival and suggested that the role of cyclin E was limited to the serous histology as nonserous tumors showed no statistically significant difference in survival based on cyclin E expression. The association between cyclin E amplification and poor outcome has also been identified in recent German and Japanese studies, although the correlation was not statistically significant in the latter(Mayr et al 2006, Nakayama et al 2010). Two independent labs have also suggested that amplification of the cyclin E gene was associated with primary treatment resistance and targeting cyclin E expression with siRNA reduced cell viability and increased apoptosis(Etemadmoghadam et al 2009, Etemadmoghadam et al 2010, Nakayama et al 2010). These studies suggest that cyclin E amplification/expression may serve as both a prognostic and predictive factor in ovarian cancer as well as a therapeutic target in the

Several studies have evaluated the expression levels of many other cell cycle regulatory proteins, however few appear to show gene amplification. Although overexpression of cyclin D has been reported, levels of expression did not correlate with clinical outcome and the mechanism of overexpression was not through amplification of the gene(Courjal et al 1996, Dhar et al 1999, Hung et al 1996, Masciullo et al 1997). High copy number amplification of cdk2 was found in only 4-6% of cases(Cancer Genome Atlas Research Network, 2011, Marone et al 1998). Genomic loss of the region containing the retinoblastoma (Rb) gene and loss of heterozygosity of Rb has been described, however loss of expression occurred in few cases leading the investigators to conclude that Rb did not play a significant role in high-grade ovarian carcinomas(Dodson et al 1994, Kim et al 1994, Li et al 1991). Recently, two families of mitotic kinases have been implicated in ovarian cancer: the Pololike kinases and Aurora kinases. Overexpression of both has been associated with a shortened survival time in patients with ovarian cancer and these targets have been the focus of recent clinical trials, however only the Aurora A gene was found to be amplified (in 15-27% of ovarian carcinomas)(Chen et al 2009, Mendiola et al 2009, Tanner et al 2000, Weichert et al 2004). Level of amplification of the Aurora A gene has been inconsistent with regards to tumor characteristics (histology or grade), level of expression, or patient outcome, with reports of greater association with early stage and low grade ovarian cancers as well as

Many cell cycle associated kinase inhibitors are in early phase development (reviewed in (De Falco and De Luca 2010)), but few have been tested in ovarian cancer (Table 6). Interestingly, a mitotic regulatory inhibitor that affects the polo-like kinases (among others), had clinical benefit for a chemorefractory ovarian cancer patient for 24 months(Jimeno et al 2008). Preliminary results with MLN8237, an Aurora A kinase inhibitor, in a phase I trial showed one long term response (>1.5 yrs) in a patient with platinum refractory ovarian cancer(Dees et al 2010). A phase II study of ENMD-2076, an oral small molecule kinase inhibitor with activity against aurora kinases among other

treatment of ovarian cancer.

an association with poor prognosis(Fu et al 2006).

stable disease in 3 ovarian cancer patients (Table 6)(Tolcher et al 2010). Combination therapy is being evaluated in two ongoing early phase clinical trials in which RO4929097 is combined with either cediranib, a VEGF inhibitor, or GDC-0449, a hedgehog inhibitor. Whether this will be a useful agent in treating ovarian cancer remains to be seen.


Table 6. Other pathway inhibitors with pre-clinical efficacy in ovarian cancer.

### **6. Cell cycle regulatory proteins**

Sustaining proliferative signaling through disruption of cell cycle regulatory checkpoints is one of the hallmarks of cancer(Hanahan and Weinberg 2011). Aberrant expression of cyclins, cyclin dependent kinases (Cdks), and cyclin-Cdk inhibitors has been linked to tumorigenesis in multiple cancer models(Deshpande et al 2005, Hwang and Clurman 2005). Studies in epithelial ovarian cancer have shown inconsistent associations between individual cell cycle regulatory protein expression and patient outcome (reviewed in Nam and Kim(Nam and Kim 2008)). Among the best studied in ovarian cancer is cyclin E. Amplification of the cyclin E gene occurs in 7-65% of ovarian cancers, typically resulting in overexpression of the cyclin E protein(Cancer Genome Atlas Research Network, 2011, Courjal et al 1996, Marone et al

stable disease in 3 ovarian cancer patients (Table 6)(Tolcher et al 2010). Combination therapy is being evaluated in two ongoing early phase clinical trials in which RO4929097 is combined with either cediranib, a VEGF inhibitor, or GDC-0449, a hedgehog inhibitor.

PD 0332991 CDK4/6 inhibitor Current being tested in NCT01037790

Preliminary efficacy in 3 ovarian cancer patients(Tolcher et al 2010). Two early phase combination trials ongoing: NCT01131234 (+ cediranib), NCT01154452 (+ GDC-0449)

which includes ovarian germ cell tumors

Ongoing phase II trial in combination with cisplatin in epithelial ovarian

refractory ovarian cancer pt, maintained

3/46 PR, 27/46 SD in preliminary report from phase II trial in platinum resistant ovarian cancer(Matulonis et al 2011)

Durable response in a platinum-

progression free for 24 months

et al 2010), ongoing phase II in combination with paclitaxel

Durable response (PR) in a pt with platinum-refractory ovarian cancer with continued treatment over 1.5 years(Dees

cancers (NCT00083122)

(Jimeno et al 2008)

(NCT01091428)

Whether this will be a useful agent in treating ovarian cancer remains to be seen.

Drug Target Comments

BMS-387032 (SNS-032) CDK2 inhibitor

ON 01910.Na Polo-Like Kinase 1

MLN8237 Aurora A kinase

ENMD-2076 Aurora kinase

**6. Cell cycle regulatory proteins** 

Selective oral gamma-secretase inhibitor of Notch

Multi-CDK inhibitor

inhibitor

inhibitor

inhibitor

Table 6. Other pathway inhibitors with pre-clinical efficacy in ovarian cancer.

Sustaining proliferative signaling through disruption of cell cycle regulatory checkpoints is one of the hallmarks of cancer(Hanahan and Weinberg 2011). Aberrant expression of cyclins, cyclin dependent kinases (Cdks), and cyclin-Cdk inhibitors has been linked to tumorigenesis in multiple cancer models(Deshpande et al 2005, Hwang and Clurman 2005). Studies in epithelial ovarian cancer have shown inconsistent associations between individual cell cycle regulatory protein expression and patient outcome (reviewed in Nam and Kim(Nam and Kim 2008)). Among the best studied in ovarian cancer is cyclin E. Amplification of the cyclin E gene occurs in 7-65% of ovarian cancers, typically resulting in overexpression of the cyclin E protein(Cancer Genome Atlas Research Network, 2011, Courjal et al 1996, Marone et al

R04929097

Flavopiridol (Alvocidib)

1998, Mayr et al 2006, Nakayama et al 2007, Nakayama et al 2010, Park et al 2006, Schraml et al 2003a). Cyclin E expression has been found in as many as 97% of ovarian cancer/primary peritoneal cancer samples(Davidson et al 2006). In suboptimally debulked advanced epithelial ovarian cancers obtained from women enrolled in GOG111, the expression level of cyclin E correlated with a 6 month shorter median survival and worse overall survival(Farley et al 2003). Analysis of the subset of patients with serous carcinomas (72% of total study) showed an 11 month difference in median survival and suggested that the role of cyclin E was limited to the serous histology as nonserous tumors showed no statistically significant difference in survival based on cyclin E expression. The association between cyclin E amplification and poor outcome has also been identified in recent German and Japanese studies, although the correlation was not statistically significant in the latter(Mayr et al 2006, Nakayama et al 2010). Two independent labs have also suggested that amplification of the cyclin E gene was associated with primary treatment resistance and targeting cyclin E expression with siRNA reduced cell viability and increased apoptosis(Etemadmoghadam et al 2009, Etemadmoghadam et al 2010, Nakayama et al 2010). These studies suggest that cyclin E amplification/expression may serve as both a prognostic and predictive factor in ovarian cancer as well as a therapeutic target in the treatment of ovarian cancer.

Several studies have evaluated the expression levels of many other cell cycle regulatory proteins, however few appear to show gene amplification. Although overexpression of cyclin D has been reported, levels of expression did not correlate with clinical outcome and the mechanism of overexpression was not through amplification of the gene(Courjal et al 1996, Dhar et al 1999, Hung et al 1996, Masciullo et al 1997). High copy number amplification of cdk2 was found in only 4-6% of cases(Cancer Genome Atlas Research Network, 2011, Marone et al 1998). Genomic loss of the region containing the retinoblastoma (Rb) gene and loss of heterozygosity of Rb has been described, however loss of expression occurred in few cases leading the investigators to conclude that Rb did not play a significant role in high-grade ovarian carcinomas(Dodson et al 1994, Kim et al 1994, Li et al 1991). Recently, two families of mitotic kinases have been implicated in ovarian cancer: the Pololike kinases and Aurora kinases. Overexpression of both has been associated with a shortened survival time in patients with ovarian cancer and these targets have been the focus of recent clinical trials, however only the Aurora A gene was found to be amplified (in 15-27% of ovarian carcinomas)(Chen et al 2009, Mendiola et al 2009, Tanner et al 2000, Weichert et al 2004). Level of amplification of the Aurora A gene has been inconsistent with regards to tumor characteristics (histology or grade), level of expression, or patient outcome, with reports of greater association with early stage and low grade ovarian cancers as well as an association with poor prognosis(Fu et al 2006).

Many cell cycle associated kinase inhibitors are in early phase development (reviewed in (De Falco and De Luca 2010)), but few have been tested in ovarian cancer (Table 6). Interestingly, a mitotic regulatory inhibitor that affects the polo-like kinases (among others), had clinical benefit for a chemorefractory ovarian cancer patient for 24 months(Jimeno et al 2008). Preliminary results with MLN8237, an Aurora A kinase inhibitor, in a phase I trial showed one long term response (>1.5 yrs) in a patient with platinum refractory ovarian cancer(Dees et al 2010). A phase II study of ENMD-2076, an oral small molecule kinase inhibitor with activity against aurora kinases among other

Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 303

Despite the identification of several amplified pathways, the results of the clinical trials of therapeutic agents targeting these pathways in ovarian cancer have been disappointing. There are several potential reasons for the poor response rates. The majority of studies of new targeted agents enroll patients with advanced disease often after several lines of standard cytotoxic therapy have failed. Even when used in combination with cytotoxic chemotherapy, these agents may not be able to overcome the mechanisms of resistance that the tumor has developed. Of interest would be evaluating these drugs in low-volume or early (marker only) recurrent disease or in combination with initial chemotherapy. Another strategy would be to test these typically cytostatic agents as maintenance therapy in patients

Resistance to targeted agents is mediated through a variety of mechanisms including mutation of the target, constitutive activation of downstream effectors, or activation of compensatory pathways. Defining the mechanisms of constitutive or acquired resistance requires thorough investigation in cellular and animal models. Emphasis should be placed on characterizing resistance mechanisms and developing better predictive markers to

Targeting codependent pathways, rather than the amplified genes directly, may be another approach to cancer treatment. Cancer cells typically co-opt metabolic and stress response pathways becoming functionally reliant on them for continued proliferation while normal cells are not dependent on their function. Raj et al. recently used this strategy to preferentially eliminate cancer cells by targeting the oxidative stress response pathway(Raj et al 2011). This approach is similar to the synthetic lethality seen with PARP inhibitors in

In summary, while at present there is not a clear role for targeting the amplified pathways in ovarian cancer outside of a clinical trial, elucidating strategies of tumor resistance and compensatory mechanisms may allow for the development of novel therapeutic agents or the rational combination of existing agents to improve the prognosis of patients with

Special thanks to Drs. Ie-Ming Shih and Tian-Li Wang for their help in the preparation of

(Cancer Genome Atlas Research Network, 2011). Integrated genomic analyses of ovarian

Alper O, De Santis ML, Stromberg K, Hacker NF, Cho-Chung YS, Salomon DS (2000). Anti-

Alper O, Bergmann-Leitner ES, Bennett TA, Hacker NF, Stromberg K, Stetler-Stevenson WG

ovarian carcinoma cells. *J Natl Cancer Inst* 93: 1375-1384.

sense suppression of epidermal growth factor receptor expression alters cellular proliferation, cell-adhesion and tumorigenicity in ovarian cancer cells. *Int J Cancer*

(2001). Epidermal growth factor receptor signaling and the invasive phenotype of

identify subsets of patients who are more likely to respond to therapy.

**8. Conclusion** 

who are in a complete clinical remission.

tumors with BRCA mutations.

ovarian cancer.

this manuscript.

**10. References** 

**9. Acknowledgement** 

88: 566-574.

carcinoma. *Nature* 474: 609-615.

kinases, showed modest activity in platinum-resistant ovarian cancer(Matulonis et al 2011). Inhibition of aurora kinase has been reported to sensitize cells to treatment with paclitaxel(Hata et al 2005, Scharer et al 2008) and the combination of paclitaxel and MLN8237 is being evaluated in a phase II randomized clinical trial. Results from these clinical trials are eagerly awaited.

#### **7. Chromatin remodeling and transcription**

Epigenetic modifications, such as DNA methylation and histone modifications, interact to remodel chromatin and result in the dysregulation of genes and pathways leading to uncontrolled cell growth. These mechanisms are primarily under the regulation of DNA methyltransferases (DNMTs) and histone decetylases (HDACs) and therapeutic agents inhibiting these epigenetic modifiers are currently in clinical use for the treatment of certain hematologic malignancies and are being evaluated in clinical trials for ovarian cancer (reviewed in Matei and Nephew(Matei and Nephew 2010)). Other chromatin remodeling proteins are emerging as potentially important in the pathogenesis of ovarian cancer and may be useful therapeutic targets. Amplification of the chromosome 11q13.5 locus is frequently detected in human cancers, including ovarian carcinomas. This region was amplified in 13-16% of high grade ovarian carcinomas but not in any of the normal ovarian tissues, benign ovarian tumors, or low grade ovarian carcinomas analyzed(Nakayama et al 2007, Shih Ie et al 2005). The only gene within the amplicon that showed consistent overexpression was the gene encoding HBXAP/Rsf-1, a subunit of the RSF chromatin assembly complex. Patients whose tumors harbored amplification of Rsf-1 had a shorter overall survival compared with those without amplification(Nakayama et al 2007, Sheu et al 2010, Shih Ie et al 2005). Rsf-1 amplification (and ensuing overexpression) was identified as an independent prognostic factor based on multivariate analysis and this may be secondary to its ability to confer resistance to treatment with paclitaxel(Choi et al 2009). Elevated levels of Rsf-1 was shown to induce chromosomal instability, and in non-transformed cells, induced growth arrest and activated DNA damage response pathways. However in the presence of an inactivated p53, long-term overexpression of Rsf-1 stimulated cellular proliferation. While Rsf-1 is only amplified in a subset of highgrade ovarian serous carcinomas, inactivation or disruption of the RSF complex may be a useful therapeutic approach for tumors that depend on this protein for a proliferative advantage.

Other genes, such as MYC, NACC1 (which encodes Nac1), EMSY, MECOM, and PAK1 involved in chromatin remodeling and transcription, have also been shown to be amplified in ovarian carcinomas(Dimova et al 2009, Schraml et al 2003b, Shih Ie et al 2011). The expression of some, such as Nac1, has been associated with poor progression-free survival and paclitaxel resistance(Davidson et al 2007, Jinawath et al 2009, Nakayama et al 2006a). For others, such as MYC and EMSY, the significance of the amplification in high grade serous carcinoma is unclear and they may not be the oncogenic driver within the amplicon(Shih Ie et al 2005). Others are likely only relevant for a subtype, as in ARID-1A in clear cell carcinomas. A number of amplified genes identified by the TCGA and others have potential drugs currently in preclinical development or early phase clinical trials. However further work is necessary to determine whether any of these are prognostic markers or predictive of response to therapy.

#### **8. Conclusion**

302 Ovarian Cancer – Basic Science Perspective

kinases, showed modest activity in platinum-resistant ovarian cancer(Matulonis et al 2011). Inhibition of aurora kinase has been reported to sensitize cells to treatment with paclitaxel(Hata et al 2005, Scharer et al 2008) and the combination of paclitaxel and MLN8237 is being evaluated in a phase II randomized clinical trial. Results from these

Epigenetic modifications, such as DNA methylation and histone modifications, interact to remodel chromatin and result in the dysregulation of genes and pathways leading to uncontrolled cell growth. These mechanisms are primarily under the regulation of DNA methyltransferases (DNMTs) and histone decetylases (HDACs) and therapeutic agents inhibiting these epigenetic modifiers are currently in clinical use for the treatment of certain hematologic malignancies and are being evaluated in clinical trials for ovarian cancer (reviewed in Matei and Nephew(Matei and Nephew 2010)). Other chromatin remodeling proteins are emerging as potentially important in the pathogenesis of ovarian cancer and may be useful therapeutic targets. Amplification of the chromosome 11q13.5 locus is frequently detected in human cancers, including ovarian carcinomas. This region was amplified in 13-16% of high grade ovarian carcinomas but not in any of the normal ovarian tissues, benign ovarian tumors, or low grade ovarian carcinomas analyzed(Nakayama et al 2007, Shih Ie et al 2005). The only gene within the amplicon that showed consistent overexpression was the gene encoding HBXAP/Rsf-1, a subunit of the RSF chromatin assembly complex. Patients whose tumors harbored amplification of Rsf-1 had a shorter overall survival compared with those without amplification(Nakayama et al 2007, Sheu et al 2010, Shih Ie et al 2005). Rsf-1 amplification (and ensuing overexpression) was identified as an independent prognostic factor based on multivariate analysis and this may be secondary to its ability to confer resistance to treatment with paclitaxel(Choi et al 2009). Elevated levels of Rsf-1 was shown to induce chromosomal instability, and in non-transformed cells, induced growth arrest and activated DNA damage response pathways. However in the presence of an inactivated p53, long-term overexpression of Rsf-1 stimulated cellular proliferation. While Rsf-1 is only amplified in a subset of highgrade ovarian serous carcinomas, inactivation or disruption of the RSF complex may be a useful therapeutic approach for tumors that depend on this protein for a proliferative

Other genes, such as MYC, NACC1 (which encodes Nac1), EMSY, MECOM, and PAK1 involved in chromatin remodeling and transcription, have also been shown to be amplified in ovarian carcinomas(Dimova et al 2009, Schraml et al 2003b, Shih Ie et al 2011). The expression of some, such as Nac1, has been associated with poor progression-free survival and paclitaxel resistance(Davidson et al 2007, Jinawath et al 2009, Nakayama et al 2006a). For others, such as MYC and EMSY, the significance of the amplification in high grade serous carcinoma is unclear and they may not be the oncogenic driver within the amplicon(Shih Ie et al 2005). Others are likely only relevant for a subtype, as in ARID-1A in clear cell carcinomas. A number of amplified genes identified by the TCGA and others have potential drugs currently in preclinical development or early phase clinical trials. However further work is necessary to determine whether any of these are prognostic markers or

clinical trials are eagerly awaited.

advantage.

predictive of response to therapy.

**7. Chromatin remodeling and transcription** 

Despite the identification of several amplified pathways, the results of the clinical trials of therapeutic agents targeting these pathways in ovarian cancer have been disappointing. There are several potential reasons for the poor response rates. The majority of studies of new targeted agents enroll patients with advanced disease often after several lines of standard cytotoxic therapy have failed. Even when used in combination with cytotoxic chemotherapy, these agents may not be able to overcome the mechanisms of resistance that the tumor has developed. Of interest would be evaluating these drugs in low-volume or early (marker only) recurrent disease or in combination with initial chemotherapy. Another strategy would be to test these typically cytostatic agents as maintenance therapy in patients who are in a complete clinical remission.

Resistance to targeted agents is mediated through a variety of mechanisms including mutation of the target, constitutive activation of downstream effectors, or activation of compensatory pathways. Defining the mechanisms of constitutive or acquired resistance requires thorough investigation in cellular and animal models. Emphasis should be placed on characterizing resistance mechanisms and developing better predictive markers to identify subsets of patients who are more likely to respond to therapy.

Targeting codependent pathways, rather than the amplified genes directly, may be another approach to cancer treatment. Cancer cells typically co-opt metabolic and stress response pathways becoming functionally reliant on them for continued proliferation while normal cells are not dependent on their function. Raj et al. recently used this strategy to preferentially eliminate cancer cells by targeting the oxidative stress response pathway(Raj et al 2011). This approach is similar to the synthetic lethality seen with PARP inhibitors in tumors with BRCA mutations.

In summary, while at present there is not a clear role for targeting the amplified pathways in ovarian cancer outside of a clinical trial, elucidating strategies of tumor resistance and compensatory mechanisms may allow for the development of novel therapeutic agents or the rational combination of existing agents to improve the prognosis of patients with ovarian cancer.

#### **9. Acknowledgement**

Special thanks to Drs. Ie-Ming Shih and Tian-Li Wang for their help in the preparation of this manuscript.

#### **10. References**


Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 305

Chambers SK, Clouser MC, Baker AF, Roe DJ, Cui H, Brewer MA, Hatch KD, Gordon MS,

Chan JK, Pham H, You XJ, Cloven NG, Burger RA, Rose GS, Van Nostrand K, Korc M,

Chen YJ, Chen CM, Twu NF, Yen MS, Lai CR, Wu HH, Wang PH, Yuan CC (2009).

Cheng JQ, Godwin AK, Bellacosa A, Taguchi T, Franke TF, Hamilton TC, Tsichlis PN, Testa

Choi JH, Park JT, Davidson B, Morin PJ, Shih Ie M, Wang TL (2008). Jagged-1 and Notch3

Choi JH, Sheu JJ, Guan B, Jinawath N, Markowski P, Wang TL, Shih Ie M (2009). Functional

Chresta CM, Davies BR, Hickson I, Harding T, Cosulich S, Critchlow SE, Vincent JP, Ellston

Courjal F, Louason G, Speiser P, Katsaros D, Zeillinger R, Theillet C (1996). Cyclin gene

Dancey JE (2004). Molecular targeting: PI3 kinase pathway. *Ann Oncol* 15 Suppl 4: iv233-239. Davidson B, Zhang Z, Kleinberg L, Li M, Florenes VA, Wang TL, Shih Ie M (2006). Gene

diffuse malignant peritoneal mesothelioma. *Clin Cancer Res* 12: 5944-5950. Davidson B, Berner A, Trope CG, Wang TL, Shih Ie M (2007). Expression and clinical role of

De Falco M, De Luca A (2010). Cell cycle as a target of antineoplastic drugs. *Curr Pharm Des*

Dees EC, Infante JR, Burris HA, Astsaturov IA, Stinchcombe T, Liu H, Galvin K,

Deshpande A, Sicinski P, Hinds PW (2005). Cyclins and cdks in development and cancer: a

ovarian carcinoma effusions. *Hum Pathol* 38: 1030-1036.

(pts) with solid tumors. *J Clin Oncol* 28: Abstr 3010.

perspective. *Oncogene* 24: 2909-2915.

paclitaxel resistance in ovarian cancer. *Cancer Res* 69: 1407-1415.

erlotinib in resistant ovarian cancer. *Clin Cancer Res* 16: 5320-5328.

in a rat model. *Cancer Res* 65: 3243-3248.

*Sci U S A* 89: 9267-9271.

5723.

253.

16: 1417-1426.

cancer patients. *Virchows Arch* 455: 431-440.

antitumor activity. *Cancer Res* 70: 288-298.

Janicek MF, Isaacs JD, Gordon AN, Nagle RB, Wright HM, Cohen JL, Alberts DS (2010). Overexpression of tumor vascular endothelial growth factor A may portend an increased likelihood of progression in a phase II trial of bevacizumab and

Disaia PJ, Fan H (2005). Suppression of ovarian cancer cell tumorigenicity and evasion of Cisplatin resistance using a truncated epidermal growth factor receptor

Overexpression of Aurora B is associated with poor prognosis in epithelial ovarian

JR (1992). AKT2, a putative oncogene encoding a member of a subfamily of proteinserine/threonine kinases, is amplified in human ovarian carcinomas. *Proc Natl Acad* 

juxtacrine loop regulates ovarian tumor growth and adhesion. *Cancer Res* 68: 5716-

analysis of 11q13.5 amplicon identifies Rsf-1 (HBXAP) as a gene involved in

R, Jones D, Sini P, James D, Howard Z, Dudley P, Hughes G, Smith L *et al* (2010). AZD8055 is a potent, selective, and orally bioavailable ATP-competitive mammalian target of rapamycin kinase inhibitor with in vitro and in vivo

amplification and overexpression in breast and ovarian cancers: evidence for the selection of cyclin D1 in breast and cyclin E in ovarian tumors. *Int J Cancer* 69: 247-

expression signatures differentiate ovarian/peritoneal serous carcinoma from

the bric-a-brac tramtrack broad complex/poxvirus and zinc protein NAC-1 in

Venkatakrishnan K, Fingert HJ, Cohen RB (2010). Phase I study of the investigational drug MLN8237, an Aurora A kinase (AAK) inhibitor, in patients


Altomare DA, Wang HQ, Skele KL, De Rienzo A, Klein-Szanto AJ, Godwin AK, Testa JR

Banerji U, Aghajanian C, Raymond E, Kurzrock R, Blanco-Codesido M, Oelmann E,

Berchuck A, Kamel A, Whitaker R, Kerns B, Olt G, Kinney R, Soper JT, Dodge R, Clarke-

resistance to inhibitors of EGFR signaling. *Curr Pharm Des* 13: 3358-3367. Bookman MA, Darcy KM, Clarke-Pearson D, Boothby RA, Horowitz IR (2003). Evaluation of

Bull Phelps SL, Schorge JO, Peyton MJ, Shigematsu H, Xiang LL, Miller DS, Lea JS (2008).

Bunkholt Elstrand M, Dong HP, Odegaard E, Holth A, Elloul S, Reich R, Trope CG,

Camilleri-Broet S, Hardy-Bessard AC, Le Tourneau A, Paraiso D, Levrel O, Leduc B, Bain S,

Campbell IG, Russell SE, Choong DY, Montgomery KG, Ciavarella ML, Hooi CS, Cristiano

Campos S, Hamid O, Seiden MV, Oza A, Plante M, Potkul RK, Lenehan PF, Kaldjian EP,

Campos SM, Berlin ST, Parker LM, Chen WY, Bunnell CA, Atkinson T, Lee J, Matulonis U,

in metastatic serous ovarian carcinoma. *Hum Pathol* 41: 794-804.

multicenter study of the GINECO group. *Ann Oncol* 15: 104-112.

breast cancer. *Cancer Res* 64: 7678-7681.

cancer. *J Clin Oncol* 23: 5597-5604.

cancer. *Int J Clin Oncol* 15: 390-398.

ovarian cancer. *Anticancer Res* 19: 4469-4474.

carcinomas. *Int J Cancer* 64: 280-285.

*Oncol* 21: 283-290.

109: 411-417.

(2004). AKT and mTOR phosphorylation is frequently detected in ovarian cancer and can be targeted to disrupt ovarian tumor cell growth. *Oncogene* 23: 5853-5857. Baekelandt M, Kristensen GB, Trope CG, Nesland JM, Holm R (1999). Epidermal growth

factor receptor expression has no independent prognostic significance in advanced

Grinsted L, Burke W, Kaye SB, Naing A (2011). First results from a phase I trial of AZD8055, a dual mTORC1 and mTORC2 inhibitor. *J Clin Oncol* 29: Abstr 3096. Bellacosa A, de Feo D, Godwin AK, Bell DW, Cheng JQ, Altomare DA, Wan M, Dubeau L,

Scambia G, Masciullo V, Ferrandina G, Benedetti Panici P, Mancuso S, Neri G, Testa JR (1995). Molecular alterations of the AKT2 oncogene in ovarian and breast

Pearson DL, Marks P, et al. (1990). Overexpression of HER-2/neu is associated with poor survival in advanced epithelial ovarian cancer. *Cancer Res* 50: 4087-4091. Bianco R, Damiano V, Gelardi T, Daniele G, Ciardiello F, Tortora G (2007). Rational

combination of targeted therapies as a strategy to overcome the mechanisms of

monoclonal humanized anti-HER2 antibody, trastuzumab, in patients with recurrent or refractory ovarian or primary peritoneal carcinoma with overexpression of HER2: a phase II trial of the Gynecologic Oncology Group. *J Clin* 

Implications of EGFR inhibition in ovarian cancer cell proliferation. *Gynecol Oncol*

Davidson B (2010). Mammalian target of rapamycin is a biomarker of poor survival

Orfeuvre H, Audouin J, Pujade-Lauraine E (2004). HER-2 overexpression is an independent marker of poor prognosis of advanced primary ovarian carcinoma: a

BE, Pearson RB, Phillips WA (2004). Mutation of the PIK3CA gene in ovarian and

Varterasian ML, Jordan C, Charbonneau C, Hirte H (2005). Multicenter, randomized phase II trial of oral CI-1033 for previously treated advanced ovarian

Hirsch MS, Harris L, Krasner CN (2010). Phase I trial of liposomal doxorubicin and ZD1839 in patients with refractory gynecological malignancies or metastatic breast


Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 307

Gordon MS, Matei D, Aghajanian C, Matulonis UA, Brewer M, Fleming GF, Hainsworth JD,

relationship with tumor HER2 activation status. *J Clin Oncol* 24: 4324-4332. Gorringe KL, George J, Anglesio MS, Ramakrishna M, Etemadmoghadam D, Cowin P,

Hanahan D, Weinberg RA (2011). Hallmarks of cancer: the next generation. *Cell* 144: 646-

Hata T, Furukawa T, Sunamura M, Egawa S, Motoi F, Ohmura N, Marumoto T, Saya H,

Hirai H, Sootome H, Nakatsuru Y, Miyama K, Taguchi S, Tsujioka K, Ueno Y, Hatch H,

Hopfer O, Zwahlen D, Fey MF, Aebi S (2005). The Notch pathway in ovarian carcinomas

Hu L, Hofmann J, Jaffe RB (2005). Phosphatidylinositol 3-kinase mediates angiogenesis and

Hung WC, Chai CY, Huang JS, Chuang LY (1996). Expression of cyclin D1 and c-Ki-ras gene product in human epithelial ovarian tumors. *Hum Pathol* 27: 1324-1328. Hwang HC, Clurman BE (2005). Cyclin E in normal and neoplastic cell cycles. *Oncogene* 24:

Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011). Global cancer statistics. *CA* 

Jimeno A, Li J, Messersmith WA, Laheru D, Rudek MA, Maniar M, Hidalgo M, Baker SD,

Jinawath N, Vasoontara C, Yap KL, Thiaville MM, Nakayama K, Wang TL, Shih IM

Joly F, Weber B, Pautier P, Fabbro M, Selle F, Krieger S, Leconte A, Bourgeois H, Henry-

French FEDEGYN-FNCLCC phase II trial. *J Clin Oncol* 27: Abstr 5555.

Donehower RC (2008). Phase I study of ON 01910.Na, a novel modulator of the Polo-like kinase 1 pathway, in adult patients with solid tumors. *J Clin Oncol* 26:

(2009). NAC-1, a potential stem cell pluripotency factor, contributes to paclitaxel resistance in ovarian cancer through inactivating Gadd45 pathway. *Oncogene* 28:

Amar M (2009). Combined topotecan and lapatinib in patients with early recurrent ovarian or peritoneal cancer after first line of platinum-based chemotherapy: A

interactions between genomic loci in ovarian cancer. *PLoS One* 5.

targeted drugs in vitro and in vivo. *Mol Cancer Ther* 9: 1956-1967.

792.

674.

8212.

2776-2786.

5504-5510.

1941-1948.

*Cancer J Clin* 61: 69-90.

*Cancer Res* 65: 2899-2905.

and adenomas. *Br J Cancer* 93: 709-718.

carcinoma: results from a phase II multicenter study. *Int J Gynecol Cancer* 15: 785-

Garcia AA, Pegram MD, Schilder RJ, Cohn DE, Roman L, Derynck MK, Ng K, Lyons B *et al* (2006). Clinical activity of pertuzumab (rhuMAb 2C4), a HER dimerization inhibitor, in advanced ovarian cancer: potential predictive

Sridhar A, Williams LH, Boyle SE, Yanaihara N, Okamoto A, Urashima M, Smyth GK, Campbell IG, Bowtell DD (2010). Copy number analysis identifies novel

Horii A (2005). RNA interference targeting aurora kinase a suppresses tumor growth and enhances the taxane chemosensitivity in human pancreatic cancer cells.

Majumder PK, Pan BS, Kotani H (2010). MK-2206, an allosteric Akt inhibitor, enhances antitumor efficacy by standard chemotherapeutic agents or molecular

vascular permeability associated with ovarian carcinoma. *Clin Cancer Res* 11: 8208-


Dhar KK, Branigan K, Parkes J, Howells RE, Hand P, Musgrove C, Strange RC, Fryer AA,

Dimova I, Zaharieva B, Raitcheva S, Dimitrov R, Doganov N, Toncheva D (2006). Tissue

Dimova I, Raicheva S, Dimitrov R, Doganov N, Toncheva D (2009). Coexistence of copy

Dodson MK, Cliby WA, Xu HJ, DeLacey KA, Hu SX, Keeney GL, Li J, Podratz KC, Jenkins

Etemadmoghadam D, deFazio A, Beroukhim R, Mermel C, George J, Getz G, Tothill R,

Etemadmoghadam D, George J, Cowin PA, Cullinane C, Kansara M, Gorringe KL, Smyth

Farley J, Smith LM, Darcy KM, Sobel E, O'Connor D, Henderson B, Morrison LE, Birrer MJ

Farley J, Fuchiuji S, Darcy KM, Tian C, Hoskins WJ, McGuire WP, Hanjani P, Warshal D,

Ferretti G, Felici A, Papaldo P, Fabi A, Cognetti F (2007). HER2/neu role in breast cancer: from a prognostic foe to a predictive friend. *Curr Opin Obstet Gynecol* 19: 56-62. Fu S, Hu W, Kavanagh JJ, Bast RC, Jr. (2006). Targeting Aurora kinases in ovarian cancer.

Gao N, Flynn DC, Zhang Z, Zhong XS, Walker V, Liu KJ, Shi X, Jiang BH (2004). G1 cell

Gordon AN, Finkler N, Edwards RP, Garcia AA, Crozier M, Irwin DH, Barrett E (2005).

cycle progression and the expression of G1 cyclins are regulated by PI3K/AKT/mTOR/p70S6K1 signaling in human ovarian cancer cells. *Am J Physiol* 

Efficacy and safety of erlotinib HCl, an epidermal growth factor receptor (HER1/EGFR) tyrosine kinase inhibitor, in patients with advanced ovarian

protein in epithelial ovarian tumour cells. *Br J Cancer* 81: 1174-1181.

*Int J Gynecol Cancer* 16: 145-151.

(GINECO group). *Br J Cancer* 91: 470-475.

carcinomas. *Clin Cancer Res* 15: 1417-1427.

ovarian cancer. *PLoS One* 5: e15498.

study. *Cancer Res* 63: 1235-1241.

Study. *Gynecol Oncol* 113: 341-347.

*Expert Opin Ther Targets* 10: 77-85.

*Cell Physiol* 287: C281-291.

*Onkologie* 32: 405-410.

Redman CW, Hoban PR (1999). Expression and subcellular localization of cyclin D1

microarray analysis of EGFR and erbB2 copy number changes in ovarian tumors.

number increases of c-Myc, ZNF217, CCND1, ErbB1 and ErbB2 in ovarian cancers.

RB, Benedict WF (1994). Evidence of functional RB protein in epithelial ovarian carcinomas despite loss of heterozygosity at the RB locus. *Cancer Res* 54: 610-613. Elie C, Geay JF, Morcos M, Le Tourneau A, Girre V, Broet P, Marmey B, Chauvenet L,

Audouin J, Pujade-Lauraine E, Camilleri-Broet S (2004). Lack of relationship between EGFR-1 immunohistochemical expression and prognosis in a multicentre clinical trial of 93 patients with advanced primary ovarian epithelial cancer

Okamoto A, Raeder MB, Harnett P, Lade S, Akslen LA, Tinker AV, Locandro B, Alsop K *et al* (2009). Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian

GK, Bowtell DD (2010). Amplicon-dependent CCNE1 expression is critical for clonogenic survival after cisplatin treatment and is correlated with 20q11 gain in

(2003). Cyclin E expression is a significant predictor of survival in advanced, suboptimally debulked ovarian epithelial cancers: a Gynecologic Oncology Group

Greer BE, Belinson J, Birrer MJ (2009). Associations between ERBB2 amplification and progression-free survival and overall survival in advanced stage, suboptimally-resected epithelial ovarian cancers: a Gynecologic Oncology Group carcinoma: results from a phase II multicenter study. *Int J Gynecol Cancer* 15: 785- 792.


Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 309

Li SB, Schwartz PE, Lee WH, Yang-Feng TL (1991). Allele loss at the retinoblastoma locus in

Link W, Rosado A, Fominaya J, Thomas JE, Carnero A (2005). Membrane localization of all

Mabuchi S, Kawase C, Altomare DA, Morishige K, Sawada K, Hayashi M, Tsujimoto M,

resistant clear cell carcinoma of the ovary. *Clin Cancer Res* 15: 5404-5413. Mabuchi S, Hisamatsu T, Kimura T (2011). Targeting mTOR Signaling Pathway in Ovarian

Makhija S, Amler LC, Glenn D, Ueland FR, Gold MA, Dizon DS, Paton V, Lin CY, Januario

tube cancer, or primary peritoneal cancer. *J Clin Oncol* 28: 1215-1223. Mano MS, Awada A, Di Leo A, Durbecq V, Paesmans M, Cardoso F, Larsimont D, Piccart M

expression in epithelial ovarian carcinoma. *Gynecol Oncol* 92: 887-895. Marone M, Scambia G, Giannitelli C, Ferrandina G, Masciullo V, Bellacosa A, Benedetti-

Masciullo V, Scambia G, Marone M, Giannitelli C, Ferrandina G, Bellacosa A, Benedetti

Matei DE, Nephew KP (2010). Epigenetic therapies for chemoresensitization of epithelial

Matulonis U, Tew WP, Matei D, Behbakht K, Fleming GF, Oza AM (2011). A phase II

Mayr D, Kanitz V, Anderegg B, Luthardt B, Engel J, Lohrs U, Amann G, Diebold J (2006).

Meinhold-Heerlein I, Bauerschlag D, Hilpert F, Dimitrov P, Sapinoso LM, Orlowska-Volk

Mendiola M, Barriuso J, Marino-Enriquez A, Redondo A, Dominguez-Caceres A,

prognostic biomarkers in ovarian carcinoma. *Hum Pathol* 40: 631-638. Moreno Garcia V, Baird RD, Shah KJ, Basu B, Tunariu N, Blanco M, Cassier PA, Pedersen

amplification and RNA overexpression. *Int J Cancer* 75: 34-39.

analysis for tissue microarrays. *Am J Clin Pathol* 126: 101-109.

ovarian carcinomas. *Int J Cancer* 74: 390-395.

ovarian cancer. *Gynecol Oncol* 116: 195-201.

5021.

1053-1065.

class I PI 3-kinase isoforms suppresses c-Myc-induced apoptosis in Rat1 fibroblasts

Yamoto M, Klein-Szanto AJ, Schilder RJ, Ohmichi M, Testa JR, Kimura T (2009). mTOR is a promising therapeutic target both in cisplatin-sensitive and cisplatin-

T, Ng K, Strauss A, Kelsey S, Sliwkowski MX, Matulonis U (2010). Clinical activity of gemcitabine plus pertuzumab in platinum-resistant ovarian cancer, fallopian

(2004). Rates of topoisomerase II-alpha and HER-2 gene amplification and

Panici P, Mancuso S (1998). Analysis of cyclin E and CDK2 in ovarian cancer: gene

Panici P, Mancuso S (1997). Altered expression of cyclin D1 and CDK4 genes in

study of ENMD-2076 in platinum-resistant ovarian cancer. *J Clin Oncol* 29: Abstr

Analysis of gene amplification and prognostic markers in ovarian cancer using comparative genomic hybridization for microarrays and immunohistochemical

M, Bauknecht T, Park TW, Jonat W, Jacobsen A, Sehouli J, Luttges J, Krajewski M, Krajewski S, Reed JC *et al* (2005). Molecular and prognostic distinction between serous ovarian carcinomas of varying grade and malignant potential. *Oncogene* 24:

Hernandez-Cortes G, Perez-Fernandez E, Sanchez-Navarro I, Vara JA, Suarez A, Espinosa E, Gonzalez-Baron M, Palacios J, Hardisson D (2009). Aurora kinases as

JV, Puglisi M, Sarker D, Papadatos-Pastos D, Omlin AG, Biondo A, Ware JA, Koeppen H *et al* (2011). A phase I study evaluating GDC-0941, an oral

human ovarian cancer. *J Natl Cancer Inst* 83: 637-640.

via Akt. *J Cell Biochem* 95: 979-989.

Cancer. *Curr Med Chem* 18: 2960-2968.


Jung SG, Kwon YD, Song JA, Back MJ, Lee SY, Lee C, Hwang YY, An HJ (2010). Prognostic

Kang S, Bader AG, Vogt PK (2005). Phosphatidylinositol 3-kinase mutations identified in

Karakas B, Bachman KE, Park BH (2006). Mutation of the PIK3CA oncogene in human

Kim TM, Benedict WF, Xu HJ, Hu SX, Gosewehr J, Velicescu M, Yin E, Zheng J, D'Ablaing

Kolasa IK, Rembiszewska A, Felisiak A, Ziolkowska-Seta I, Murawska M, Moes J, Timorek

Konner J, Schilder RJ, DeRosa FA, Gerst SR, Tew WP, Sabbatini PJ, Hensley ML, Spriggs DR,

Kosary CL (1994). FIGO stage, histology, histologic grade, age and race as prognostic factors

Krasner CN, Debernardo RL, Findley M, Penson R, Matulonis U, Atkinson T, Roche M,

Kurman RJ, Shih Ie M (2008). Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. *Int J Gynecol Pathol* 27: 151-160. Lassus H, Leminen A, Vayrynen A, Cheng G, Gustafsson JA, Isola J, Butzow R (2004).

Lassus H, Sihto H, Leminen A, Joensuu H, Isola J, Nupponen NN, Butzow R (2006). Gene

Lee CH, Huntsman DG, Cheang MC, Parker RL, Brown L, Hoskins P, Miller D, Gilks CB

in advanced stage ovarian carcinoma. *Int J Gynecol Pathol* 24: 147-152. Lee S, Choi EJ, Jin C, Kim DH (2005b). Activation of PI3K/Akt pathway by PTEN reduction

outcome in serous ovarian carcinoma. *Gynecol Oncol* 92: 31-39.

in serous ovarian carcinoma. *J Mol Med (Berl)* 84: 671-681.

cancer cell line. *Gynecol Oncol* 97: 26-34.

human cancer are oncogenic. *Proc Natl Acad Sci U S A* 102: 802-807.

1977-1983.

cancers. *Br J Cancer* 94: 455-459.

*Gynecol Oncol* 111: 95-101.

tube cancer. *Gynecol Oncol* 110: 140-145.

*ASCO Annual Meeting Proceedings* 23: Abstr 5063.

vagina. *Semin Surg Oncol* 10: 31-46.

*Ther* 8: 21-26.

significance of Notch 3 gene expression in ovarian serous carcinoma. *Cancer Sci* 101:

G, Dubeau L (1994). Loss of heterozygosity on chromosome 13 is common only in the biologically more aggressive subtypes of ovarian epithelial tumors and is associated with normal retinoblastoma gene expression. *Cancer Res* 54: 605-609. Kimball KJ, Numnum TM, Kirby TO, Zamboni WC, Estes JM, Barnes MN, Matei DE, Koch

KM, Alvarez RD (2008). A phase I study of lapatinib in combination with carboplatin in women with platinum sensitive recurrent ovarian carcinoma.

A, Dansonka-Mieszkowska A, Kupryjanczyk J (2009). PIK3CA amplification associates with resistance to chemotherapy in ovarian cancer patients. *Cancer Biol* 

Aghajanian CA (2008). A phase II study of cetuximab/paclitaxel/carboplatin for the initial treatment of advanced-stage ovarian, primary peritoneal, or fallopian

in determining survival for cancers of the female gynecological system: an analysis of 1973-87 SEER cases of cancers of the endometrium, cervix, ovary, vulva, and

Seiden MV (2005). Phase II Trial of Anastrazole in Combination with Gefitinib in Women with Asymptomatic Mullerian Cancer. *Journal of Clinical Oncology, 2005* 

ERBB2 amplification is superior to protein expression status in predicting patient

amplification, mutation, and protein expression of EGFR and mutations of ERBB2

(2005a). Assessment of Her-1, Her-2, And Her-3 expression and Her-2 amplification

and PIK3CA mRNA amplification contributes to cisplatin resistance in an ovarian


Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 311

Posadas EM, Liel MS, Kwitkowski V, Minasian L, Godwin AK, Hussain MM, Espina V,

Press MF, Jones LA, Godolphin W, Edwards CL, Slamon DJ (1990). HER-2/neu oncogene

Raj L, Ide T, Gurkar AU, Foley M, Schenone M, Li X, Tolliday NJ, Golub TR, Carr SA, Shamji

Rajkumar T, Stamp GW, Hughes CM, Gullick WJ (1996). c-erbB3 protein expression in

Rivkin SE, Muller C, Iriarte D, Arthur J, Canoy A, Reid H (2008). Phase I/II lapatinib plus

Rose SL (2009). Notch signaling pathway in ovarian cancer. *Int J Gynecol Cancer* 19: 564-

Rose SL, Kunnimalaiyaan M, Drenzek J, Seiler N (2010). Notch 1 signaling is active in

Ross JS, Yang F, Kallakury BV, Sheehan CE, Ambros RA, Muraca PJ (1999). HER-2/neu

Rubin SC, Finstad CL, Federici MG, Scheiner L, Lloyd KO, Hoskins WJ (1994). Prevalence

Scharer CD, Laycock N, Osunkoya AO, Logani S, McDonald JF, Benigno BB, Moreno CS

Schilder RJ, Sill MW, Chen X, Darcy KM, Decesare SL, Lewandowski G, Lee RB, Arciero CA,

Schilder RJ, Pathak HB, Lokshin AE, Holloway RW, Alvarez RD, Aghajanian C, Min H,

Schraml P, Bucher C, Bissig H, Nocito A, Haas P, Wilber K, Seelig S, Kononen J, Mihatsch

Schraml P, Schwerdtfeger G, Burkhalter F, Raggi A, Schmidt D, Ruffalo T, King W, Wilber

critical oncogene target in ovarian carcinoma. *Am J Pathol* 163: 985-992.

Gynecologic Oncology Group Study. *Clin Cancer Res* 11: 5539-5548.

ovarian cancer. *Clin Mol Pathol* 49: M199-202.

ovarian cancer. *Gynecol Oncol* 117: 130-133.

the ovary. *Am J Clin Pathol* 111: 311-316.

ovarian cancer cells. *J Transl Med* 6: 79.

human tumours. *J Pathol* 200: 375-382.

109: 1323-1330.

209-221.

231-234.

566.

*Oncol* 26: Abstr 5556.

73: 1456-1459.

113: 21-27.

Wood BJ, Steinberg SM, Kohn EC (2007). A phase II and pharmacodynamic study of gefitinib in patients with refractory or recurrent epithelial ovarian cancer. *Cancer*

amplification and expression in breast and ovarian cancers. *Prog Clin Biol Res* 354A:

AF, Stern AM, Mandinova A, Schreiber SL, Lee SW (2011). Selective killing of cancer cells by a small molecule targeting the stress response to ROS. *Nature* 475:

carboplatin and paclitaxel in stage III or IV relapsed ovarian cancer patients. *J Clin* 

oncogene amplification by fluorescence in situ hybridization in epithelial tumors of

and significance of HER-2/neu expression in early epithelial ovarian cancer. *Cancer*

(2008). Aurora kinase inhibitors synergize with paclitaxel to induce apoptosis in

Wu H, Godwin AK (2005). Phase II study of gefitinib in patients with relapsed or persistent ovarian or primary peritoneal carcinoma and evaluation of epidermal growth factor receptor mutations and immunohistochemical expression: a

Devarajan K, Ross E, Drescher CW, Godwin AK (2009). Phase II trial of single agent cetuximab in patients with persistent or recurrent epithelial ovarian or primary peritoneal carcinoma with the potential for dose escalation to rash. *Gynecol Oncol*

MJ, Dirnhofer S, Sauter G (2003a). Cyclin E overexpression and amplification in

K, Mihatsch MJ, Moch H (2003b). Combined array comparative genomic hybridization and tissue microarray analysis suggest PAK1 at 11q13.5-q14 as a

phosphoinositide-3 kinase (PI3K) inhibitor, in patients with advanced solid tumors or multiple myeloma. *J Clin Oncol* 29: Abstr 3021.


Morgan R, Oza AM, Qin R, Laumann KM, Mackay H, Strevel EL, Welch S, Sullivan D,

Nakayama K, Nakayama N, Kurman RJ, Cope L, Pohl G, Samuels Y, Velculescu VE, Wang

Nam EJ, Kim YT (2008). Alteration of cell-cycle regulation in epithelial ovarian cancer. *Int J* 

Nicholson RI, Gee JM, Harper ME (2001). EGFR and cancer prognosis. *Eur J Cancer* 37 Suppl

Nimeiri HS, Oza AM, Morgan RJ, Friberg G, Kasza K, Faoro L, Salgia R, Stadler WM, Vokes

Oza AM, Kollmannsberger C, Group NCT, Hirte H, Welch S, Siu L, Mazurka J, Sederias J,

Park JT, Li M, Nakayama K, Mao TL, Davidson B, Zhang Z, Kurman RJ, Eberhart CG, Shih

Pegram MD, Finn RS, Arzoo K, Beryt M, Pietras RJ, Slamon DJ (1997). The effect of HER-

Pejovic T, Pande NT, Mori M, Mhawech-Fauceglia P, Harrington C, Mongoue-Tchokote S,

candidate genes for early neoplastic changes. *Transl Oncol* 2: 341-349. Peng DJ, Wang J, Zhou JY, Wu GS (2010). Role of the Akt/mTOR survival pathway in

genes in purified ovarian serous neoplasms. *Cancer Biol Ther* 5: 779-785. Nakayama K, Nakayama N, Jinawath N, Salani R, Kurman RJ, Shih Ie M, Wang TL (2007). Amplicon profiles in ovarian serous carcinomas. *Int J Cancer* 120: 2613-2617. Nakayama N, Nakayama K, Shamima Y, Ishikawa M, Katagiri A, Iida K, Miyazaki K (2010).

Cancer (CCCP) N01 Consortia NCI#8233. *J Clin Oncol* 29: abstr 5015. Nakayama K, Nakayama N, Davidson B, Sheu JJ, Jinawath N, Santillan A, Salani R, Bristow

or multiple myeloma. *J Clin Oncol* 29: Abstr 3021.

*Proc Natl Acad Sci U S A* 103: 18739-18744.

target in ovarian cancer. *Cancer* 116: 2621-2634.

*Gynecol Cancer* 18: 1169-1182.

IND 179. *J Clin Oncol* 27: abstr 3558.

ovarian cancer cells. *Oncogene* 15: 537-547.

4: S9-15.

55.

6312-6318.

605.

phosphoinositide-3 kinase (PI3K) inhibitor, in patients with advanced solid tumors

Wenham RM, Chen HX, Doyle LA, Gandara DR, Erlichman C (2011). A phase II trial of temsirolimus and bevacizumab in patients with endometrial, ovarian, hepatocellular carcinoma, carcinoid, or islet cell cancer: Ovarian cancer (OC) subset—A study of the Princess Margaret, Mayo, Southeast phase II, and California

RE, Morin PJ, Kurman RJ, Wang TL, Shih Ie M (2006a). A BTB/POZ protein, NAC-1, is related to tumor recurrence and is essential for tumor growth and survival.

TL, Shih Ie M (2006b). Sequence mutations and amplification of PIK3CA and AKT2

Gene amplification CCNE1 is related to poor survival and potential therapeutic

EE, Fleming GF (2008). Efficacy and safety of bevacizumab plus erlotinib for patients with recurrent ovarian, primary peritoneal, and fallopian tube cancer: a trial of the Chicago, PMH, and California Phase II Consortia. *Gynecol Oncol* 110: 49-

Doyle LA, Eisenhauer E (2009). Phase I study of temsirolimus (CCI-779), carboplatin, and paclitaxel in patients (pts) with advanced solid tumors: NCIC CTG

Ie M, Wang TL (2006). Notch3 gene amplification in ovarian cancer. *Cancer Res* 66:

2/neu overexpression on chemotherapeutic drug sensitivity in human breast and

Dim D, Andrews C, Beck A, Tarumi Y, Djilas J, Cappuccini F, Caballero O, Huang J, Levy S *et al* (2009). Expression profiling of the ovarian surface kinome reveals

cisplatin resistance in ovarian cancer cells. *Biochem Biophys Res Commun* 394: 600-


Gene Amplification in Ovarian Carcinomas: Lessons from Selected Amplified Gene Families 313

Tanner B, Hasenclever D, Stern K, Schormann W, Bezler M, Hermes M, Brulport M, Bauer

Temkin SM, Yamada SD, Fleming GF (2010). A phase I study of weekly temsirolimus and

Tolcher AW, Mikulski SM, Messersmith WA, Kwak EL, Gibbon D, Boylan J, Xu ZX,

Tsuda H, Birrer MJ, Ito YM, Ohashi Y, Lin M, Lee C, Wong WH, Rao PH, Lau CC, Berkowitz

Tuefferd M, Couturier J, Penault-Llorca F, Vincent-Salomon A, Broet P, Guastalla JP,

van der Horst EH, Murgia M, Treder M, Ullrich A (2005). Anti-HER-3 MAbs inhibit HER-3-

Vasey PA, Gore M, Wilson R, Rustin G, Gabra H, Guastalla JP, Lauraine EP, Paul J, Carty K,

Wagner U, du Bois A, Pfisterer J, Huober J, Loibl S, Luck HJ, Sehouli J, Gropp M, Stahle A,

Wang M, Wang J, Wang L, Wu L, Xin X (2010). Notch1 expression correlates with tumor differentiation status in ovarian carcinoma. *Med Oncol* 27: 1329-1335. Weichert W, Denkert C, Schmidt M, Gekeler V, Wolf G, Kobel M, Dietel M, Hauptmann S

Willner J, Wurz K, Allison KH, Galic V, Garcia RL, Goff BA, Swisher EM (2007). Alternate

Woenckhaus J, Steger K, Sturm K, Munstedt K, Franke FE, Fenic I (2007). Prognostic value of

fallopian tube and primary peritoneal cancers. *Br J Cancer* 98: 1774-1780. Vermeij J, Teugels E, Bourgain C, Xiangming J, in 't Veld P, Ghislain V, Neyns B, De Greve J

proportion of invasive epithelial ovarian cancers. *BMC Cancer* 8: 3.

*Cancer Res* 6: 1833-1839.

2502.

malignancies. *Gynecol Oncol* 117: 473-476.

*Cancer Genet Cytogenet* 155: 97-107.

*PLoS One* 2: e1138.

*J Cancer* 115: 519-527.

2.6). *Gynecol Oncol* 105: 132-137.

carcinoma. *Br J Cancer* 90: 815-821.

*Hum Pathol* 38: 607-613.

387-395.

A, Schiffer IB, Gebhard S, Schmidt M, Steiner E, Sehouli J, Edelmann J, Lauter J *et al* (2006). ErbB-3 predicts survival in ovarian cancer. *J Clin Oncol* 24: 4317-4323. Tanner MM, Grenman S, Koul A, Johannsson O, Meltzer P, Pejovic T, Borg A, Isola JJ (2000).

Frequent amplification of chromosomal region 20q12-q13 in ovarian cancer. *Clin* 

topotecan in the treatment of advanced and/or recurrent gynecologic

DeMario M, Wheler JJ (2010). A phase I study of RO4929097, a novel gamma secretase inhibitor, in patients with advanced solid tumors. *J Clin Oncol* 28: Abstr

RS, Wong KK, Mok SC (2004). Identification of DNA copy number changes in microdissected serous ovarian cancer tissue using a cDNA microarray platform.

Allouache D, Combe M, Weber B, Pujade-Lauraine E, Camilleri-Broet S (2007). HER2 status in ovarian carcinomas: a multicenter GINECO study of 320 patients.

mediated signaling in breast cancer cell lines resistant to anti-HER-2 antibodies. *Int* 

Kaye S (2008). A phase Ib trial of docetaxel, carboplatin and erlotinib in ovarian,

(2008). Genomic activation of the EGFR and HER2-neu genes in a significant

Schmalfeldt B, Meier W, Jackisch C (2007). Gefitinib in combination with tamoxifen in patients with ovarian cancer refractory or resistant to platinum-taxane based therapy--a phase II trial of the AGO Ovarian Cancer Study Group (AGO-OVAR

(2004). Polo-like kinase isoform expression is a prognostic factor in ovarian

molecular genetic pathways in ovarian carcinomas of common histological types.

PIK3CA and phosphorylated AKT expression in ovarian cancer. *Virchows Arch* 450:


Secord AA, Blessing JA, Armstrong DK, Rodgers WH, Miner Z, Barnes MN, Lewandowski

Seiden MV, Burris HA, Matulonis U, Hall JB, Armstrong DK, Speyer J, Weber JD, Muggia F

Serrano-Olvera A, Duenas-Gonzalez A, Gallardo-Rincon D, Candelaria M, De la Garza-

Sheng Q, Liu J (2011). The therapeutic potential of targeting the EGFR family in epithelial

Sheu JJ, Guan B, Choi JH, Lin A, Lee CH, Hsiao YT, Wang TL, Tsai FJ, Shih Ie M (2010). Rsf-

Shih Ie M, Sheu JJ, Santillan A, Nakayama K, Yen MJ, Bristow RE, Vang R, Parmigiani G,

Shih Ie M, Nakayama K, Wu G, Nakayama N, Zhang J, Wang TL (2011). Amplification of

Siwak DR, Carey M, Hennessy BT, Nguyen CT, McGahren Murray MJ, Nolden L, Mills GB

Slamon DJ, Godolphin W, Jones LA, Holt JA, Wong SG, Keith DE, Levin WJ, Stuart SG,

Staal SP (1987). Molecular cloning of the akt oncogene and its human homologues AKT1

Stadlmann S, Gueth U, Reiser U, Diener PA, Zeimet AG, Wight E, Mirlacher M, Sauter G,

Staebler A, Heselmeyer-Haddad K, Bell K, Riopel M, Perlman E, Ried T, Kurman RJ (2002).

proliferative serous tumors and serous carcinomas. *Hum Pathol* 33: 47-59. Steg AD, Katre AA, Goodman BW, Han HD, Nick AM, Stone RL, Coleman RE, Alvarez RD,

current knowledge and future challenges. *J Oncol* 2010: 568938.

human breast and ovarian cancer. *Science* 244: 707-712.

and recurrent ovarian cancer. *Mod Pathol* 19: 607-610.

*Proc Natl Acad Sci U S A* 84: 5034-5037.

invasive epithelial ovarian cancer. *Cancer Treat Rev* 32: 180-190.

peritoneal malignancies. *Gynecol Oncol* 104: 727-731.

ovarian cancer. *Br J Cancer* 104: 1241-1245.

instability. *J Biol Chem* 285: 38260-38269.

102: 14004-14009.

638-645.

1-22.

499.

G, Mannel RS (2008). Phase II trial of cetuximab and carboplatin in relapsed platinum-sensitive ovarian cancer and evaluation of epidermal growth factor receptor expression: a Gynecologic Oncology Group study. *Gynecol Oncol* 108: 493-

(2007). A phase II trial of EMD72000 (matuzumab), a humanized anti-EGFR monoclonal antibody, in patients with platinum-resistant ovarian and primary

Salazar J (2006). Prognostic, predictive and therapeutic implications of HER2 in

1, a chromatin remodeling protein, induces DNA damage and promotes genomic

Kurman RJ, Trope CG, Davidson B, Wang TL (2005). Amplification of a chromatin remodeling gene, Rsf-1/HBXAP, in ovarian carcinoma. *Proc Natl Acad Sci U S A*

the ch19p13.2 NACC1 locus in ovarian high-grade serous carcinoma. *Mod Pathol* 24:

(2010). Targeting the epidermal growth factor receptor in epithelial ovarian cancer:

Udove J, Ullrich A, et al. (1989). Studies of the HER-2/neu proto-oncogene in

and AKT2: amplification of AKT1 in a primary human gastric adenocarcinoma.

Mihatsch MJ, Singer G (2006). Epithelial growth factor receptor status in primary

Micropapillary serous carcinoma of the ovary has distinct patterns of chromosomal imbalances by comparative genomic hybridization compared with atypical

Lopez-Berestein G, Sood AK, Landen CN (2011). Targeting the Notch Ligand Jagged1 in Both Tumor Cells and Stroma in Ovarian Cancer. *Clin Cancer Res*. Stokoe D (2005). The phosphoinositide 3-kinase pathway and cancer. *Expert Rev Mol Med* 7:


**16** 

*1,2,4USA 3China* 

**Nitric Oxide/Protein Kinase G-Iα** 

Ronald R. Fiscus1,2, Elaine L. Leung1,2,3, Janica C. Wong1,2,4 and Mary G. Johlfs1,2

*Hong Kong Special Administrative Region,* 

**Promotes c-Src Activation, Proliferation** 

**and Chemoresistance in Ovarian Cancer** 

*1Center for Diabetes and Obesity Prevention, Treatment, Research and Education, and the College of Pharmacy, Roseman University of* 

*Science and Technology, Taipa, Macau Special Administrative Region,* 

*Research, School of Chinese Medicine, Hong Kong Baptist University, and Department of Pathology, Faculty of Medicine, The University of Hong Kong,* 

*Health Sciences (Formerly University of Southern Nevada), Henderson, Nevada, 2Cancer Molecular Biology Section, Nevada Cancer Institute, Las Vegas, Nevada, 3State Key Laboratory for Quality Research in Chinese Medicines, Macau University of* 

*and Shum Yiu Foon Shum Bik Chuen Memorial Centre for Cancer and Inflammation* 

*4Department of Chemistry, University of Nevada Las Vegas, Las Vegas, Nevada,* 

Ovarian cancer is often associated with the development of resistance to chemotherapeutic agents (chemoresistance) and a recurrence of tumor growth, making this type of cancer especially difficult to treat by chemotherapy (Chien *et al*, 2007). The exaggerated cell growth and chemoresistance of ovarian cancer cells involve the dysregulation of multiple cell signaling (signal transduction) pathways that normally regulate cell proliferation and cell survival (Fraser *et al*, 2003). One particular cell signaling pathway that has recently become recognized as playing a central role in promoting cell proliferation and chemoresistance in human ovarian cancer cells is the nitric oxide (NO)/cyclic GMP (cGMP)/cGMP-dependent protein kinase [protein kinase G (PKG)] pathway (Fraser *et al*, 2006; Leung *et al*, 2008; Leung *et al*, 2010). Interestingly, two of the risk factors for developing ovarian cancer, i.e. advanced age and diabetes/obesity (Kulie *et al*, 2011), are known to cause dramatic dysregulations at multiple steps in the NO/cGMP/PKG signaling pathway in various organ systems, most notably the cardiovascular system (e.g. severe impairment of NO-mediated vasodilation and anti-hypertensive effects of the neuropeptide CGRP) (Chan & Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000; Fung *et al*, 2005) and the male reproductive system (e.g. diminished penile erection normally mediated by NO/cGMP/PKG signaling) (Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000; Fung *et al*, 2005). This dysregulation of the

**1. Introduction** 


### **Nitric Oxide/Protein Kinase G-Iα Promotes c-Src Activation, Proliferation and Chemoresistance in Ovarian Cancer**

Ronald R. Fiscus1,2, Elaine L. Leung1,2,3, Janica C. Wong1,2,4 and Mary G. Johlfs1,2

*1Center for Diabetes and Obesity Prevention, Treatment, Research and Education, and the College of Pharmacy, Roseman University of Health Sciences (Formerly University of Southern Nevada), Henderson, Nevada, 2Cancer Molecular Biology Section, Nevada Cancer Institute, Las Vegas, Nevada, 3State Key Laboratory for Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau Special Administrative Region, and Shum Yiu Foon Shum Bik Chuen Memorial Centre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, and Department of Pathology, Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, 4Department of Chemistry, University of Nevada Las Vegas, Las Vegas, Nevada, 1,2,4USA* 

*3China* 

#### **1. Introduction**

314 Ovarian Cancer – Basic Science Perspective

Wu Y, Soslow RA, Marshall DS, Leitao M, Chen B (2004). Her-2/neu expression and

Ye D, Mendelsohn J, Fan Z (1999). Augmentation of a humanized anti-HER2 mAb 4D5

570-575.

C225. *Oncogene* 18: 731-738.

amplification in early stage ovarian surface epithelial neoplasms. *Gynecol Oncol* 95:

induced growth inhibition by a human-mouse chimeric anti-EGF receptor mAb

Ovarian cancer is often associated with the development of resistance to chemotherapeutic agents (chemoresistance) and a recurrence of tumor growth, making this type of cancer especially difficult to treat by chemotherapy (Chien *et al*, 2007). The exaggerated cell growth and chemoresistance of ovarian cancer cells involve the dysregulation of multiple cell signaling (signal transduction) pathways that normally regulate cell proliferation and cell survival (Fraser *et al*, 2003). One particular cell signaling pathway that has recently become recognized as playing a central role in promoting cell proliferation and chemoresistance in human ovarian cancer cells is the nitric oxide (NO)/cyclic GMP (cGMP)/cGMP-dependent protein kinase [protein kinase G (PKG)] pathway (Fraser *et al*, 2006; Leung *et al*, 2008; Leung *et al*, 2010). Interestingly, two of the risk factors for developing ovarian cancer, i.e. advanced age and diabetes/obesity (Kulie *et al*, 2011), are known to cause dramatic dysregulations at multiple steps in the NO/cGMP/PKG signaling pathway in various organ systems, most notably the cardiovascular system (e.g. severe impairment of NO-mediated vasodilation and anti-hypertensive effects of the neuropeptide CGRP) (Chan & Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000; Fung *et al*, 2005) and the male reproductive system (e.g. diminished penile erection normally mediated by NO/cGMP/PKG signaling) (Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000; Fung *et al*, 2005). This dysregulation of the

Nitric Oxide/Protein Kinase G-I Promotes c-Src

Activation, Proliferation and Chemoresistance in Ovarian Cancer 317

Endogenous NO [originally referred to as EDRF (endothelium-derived relaxant factor)] in the cardiovascular system was first shown to play an important biological role in regulating arterial diameter [for reviews, see (Fiscus, 1988; Fiscus, 2002; Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003)]. In healthy arteries (i.e. arteries from individuals that are young and without diabetes, obesity or hypertension), endogenous NO is produced at physiological levels, now estimated to be in the range of 0.01 – 10 nanomolar (Batchelor *et al*, 2010; Sato *et al*, 2006), by the endothelial-form NO-synthase (eNOS) within the endothelial cells lining the arteries. Because of its high lipid solubility, NO readily diffuses into nearby cells, importantly vascular smooth muscle cells in blood vessels, where NO, via binding to the heme group of soluble guanylyl cyclase, enhances cGMP synthesis,

Early studies throughout the 1970's and early 1980's, using purified PKG in *in vitro* experiments, had shown that the addition of cGMP to the purified PKG could enhance its kinase activity, suggesting that cGMP may be the intracellular chemical that serves as the allosteric activator of PKG within cells, similar to the role of cAMP in activating cAMPdependent protein kinase (protein kinase A, PKA) [reviewed in (Fiscus, 1988; Fiscus, 2002; Francis *et al*, 1988; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003)]. Thus, early on, it was suggested that NO, via its ability to elevate intracellular cGMP levels, may be causing vascular effects by activating PKG within the smooth muscle cells. However, the early attempts to prove this were found to be exceedingly difficult because of the uniquely unstable nature of the PKG activation that occurs within mammalian cells (Fiscus, 1988; Fiscus, 2002; Fiscus & Murad, 1988; Fiscus *et al*, 1983; Fiscus *et al*, 1984). It was not until 1983 and 1984 that NO was first shown to significantly stimulate the intracellular activation state

As a serine/threonine kinase, PKG phosphorylates numerous downstream target proteins in vascular smooth muscle cells, which ultimately results in the suppression of arterial vasoconstriction (i.e. vasodilation) (Fiscus, 1988; Fiscus, 2002; Fiscus & Murad, 1988; Francis *et al*, 1988; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003). The NO/cGMP/PKG signaling pathway in vascular smooth muscle cells plays an essential role in preventing vasospasms and maintaining normal blood pressure and blood flow. Interestingly, even the basal release of NO from healthy endothelial cells, now estimated to generate a local concentration of NO of 0.01 – 0.1 nanomolar (Batchelor *et al*, 2010; Sato *et al*, 2006), was shown to cause significant increases in the intracellular PKG activation in vascular smooth muscle cells (Fiscus *et al*, 1983). This "basal activation" of PKG, induced by the basal, low-level (0.01 – 0.1 nanomolar) NO is now recognized to play a key role in protecting against the development of hypertension and other cardiovascular pathologies. Advanced age (Chan & Fiscus, 2002; Fiscus, 1988; Fiscus, 2002; Fiscus & Ming, 2000; Fung *et al*, 2005) or diabetes and/or obesity in younger individuals (Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000) results in the dysregulation of NO production and NO's ability to activate PKG in both the cardiovascular system and the male reproductive system (Chang *et al*, 2004). This diminished capacity to generate the physiological levels of NO and for NO to activate PKG within cells is now recognized to play a key role in aging- and diabetes/obesity-induced pathological complications, including hypertension, athero-

sclerosis (with increased risk of heart attack and stroke) and erectile dysfunction.

**2. Early studies identifying the important roles of NO, cGMP and PKG in controlling blood pressure and blood flow and mediating penile erection** 

resulting in elevation of intracellular levels of cGMP and activation of PKG.

of PKG in mammalian cells (Fiscus *et al*, 1983; Fiscus *et al*, 1984).

NO/cGMP/PKG pathway contributes to the pathogenesis of various aging- and diabetes/ obesity-induced pathological complications, including hypertension, atherosclerosis and erectile dysfunction (Fiscus, 1988; Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000; Fung *et al*, 2005). However, until recently, the role of this signaling pathway in the pathogenesis of ovarian cancer had remained unrecognized. Recent data from our laboratory have now shown that the NO/cGMP/PKG signaling pathway, specifically involving the PKG type-I (PKG-I) isoform of PKG and its interaction with the c-Src tyrosine kinase pathway, plays a key role in promoting the exaggerated cell proliferation and chemoresistance in human ovarian cancer cells (Fraser *et al*, 2006; Leung *et al*, 2008; Leung *et al*, 2010).

Many of the early studies of NO in ovarian cancer had suggested that this small molecule mediates tumoricidal activity, including showing that NO donors added to ovarian cancer cells *in vitro* or the induction of high-level NO synthesis within the ovarian cancer cells by exposure to pro-inflammatory cytokines results in increased levels of apoptotic cell death (Cantuaria *et al*, 2000; Garban & Bonavida, 1999; Rieder *et al*, 2001). Furthermore, NO, released from activated macrophages, inhibits the growth of tumors in an animal model of ovarian cancer (Farias-Eisner *et al*, 1994). However, the molecular mechanisms mediating these tumoricidal actions of NO were not determined in the early studies. It is now recognized that NO has multiple actions in mammalian cells, including both toxic and cytoprotective effects, which greatly depend on the local concentration of NO and the microenvironmental conditions (e.g. whether there is a co-presence of elevated levels of superoxide, which can combine with high-level NO to form peroxynitrite, a toxic prooxidant mediating both oxidative and nitrosative stress) (Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Leung *et al*, 2008; Wong & Fiscus, 2010; Wong & Fiscus, 2011).

At lower physiological levels (0.01 – 1 nanomolar), NO selectively activates the PKG-I isoform (Batchelor *et al*, 2010; Nausch *et al*, 2008; Sato *et al*, 2006), which our laboratory has shown results in cytoprotection (i.e. inhibition of multiple steps in pro-apoptosis pathways, preventing both spontaneous and toxin-induced apoptosis) as well as stimulation of DNA synthesis and cell proliferation in many mammalian cells, including human ovarian cancer cells (Chan & Fiscus, 2003; Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Fraser *et al*, 2006; Fung *et al*, 2005; Leung *et al*, 2008; Leung *et al*, 2010; Wong & Fiscus, 2010; Wong & Fiscus, 2011). Although earlier studies had reported that "PKG expression is lost" when ovarian epithelial cells transform into cancer cells (Hou *et al*, 2006; Wong *et al*, 2001), our studies show that human ovarian cancer cells do indeed express PKG-I, which contributes to exaggerated cell proliferation and chemoresistance (Leung *et al*, 2008; Leung *et al*, 2010).

At higher concentrations (>100 nanomolar), as used in the earlier studies mentioned above, NO causes cytotoxic effects in many types of mammalian cells, both normal and cancer cells, which can involve both the direct S-nitrosylation of cysteine residues of proteins, altering their function (Nakamura & Lipton, 2010; Seth & Stamler, 2011), and the indirect nitration of tyrosine residues in proteins (via formation of peroxynitrite), further altering protein function (e.g. interferring with tyrosine phosphorylation) (Beckman & Koppenol, 1996; Fiscus, 2002; Fiscus *et al*, 2002; Ridnour *et al*, 2008; Thomas *et al*, 2008). Details of these cellular/molecular mechanisms mediating the concentration-dependent cytoprotective and cytotoxic actions of NO are discussed below and illustrated in the following figures.

NO/cGMP/PKG pathway contributes to the pathogenesis of various aging- and diabetes/ obesity-induced pathological complications, including hypertension, atherosclerosis and erectile dysfunction (Fiscus, 1988; Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000; Fung *et al*, 2005). However, until recently, the role of this signaling pathway in the pathogenesis of ovarian cancer had remained unrecognized. Recent data from our laboratory have now shown that the NO/cGMP/PKG signaling pathway, specifically involving the PKG type-I (PKG-I) isoform of PKG and its interaction with the c-Src tyrosine kinase pathway, plays a key role in promoting the exaggerated cell proliferation and chemoresistance in human

Many of the early studies of NO in ovarian cancer had suggested that this small molecule mediates tumoricidal activity, including showing that NO donors added to ovarian cancer cells *in vitro* or the induction of high-level NO synthesis within the ovarian cancer cells by exposure to pro-inflammatory cytokines results in increased levels of apoptotic cell death (Cantuaria *et al*, 2000; Garban & Bonavida, 1999; Rieder *et al*, 2001). Furthermore, NO, released from activated macrophages, inhibits the growth of tumors in an animal model of ovarian cancer (Farias-Eisner *et al*, 1994). However, the molecular mechanisms mediating these tumoricidal actions of NO were not determined in the early studies. It is now recognized that NO has multiple actions in mammalian cells, including both toxic and cytoprotective effects, which greatly depend on the local concentration of NO and the microenvironmental conditions (e.g. whether there is a co-presence of elevated levels of superoxide, which can combine with high-level NO to form peroxynitrite, a toxic prooxidant mediating both oxidative and nitrosative stress) (Cheng Chew *et al*, 2003; Fiscus,

2002; Fiscus *et al*, 2002; Leung *et al*, 2008; Wong & Fiscus, 2010; Wong & Fiscus, 2011).

(Leung *et al*, 2008; Leung *et al*, 2010).

following figures.

At lower physiological levels (0.01 – 1 nanomolar), NO selectively activates the PKG-I isoform (Batchelor *et al*, 2010; Nausch *et al*, 2008; Sato *et al*, 2006), which our laboratory has shown results in cytoprotection (i.e. inhibition of multiple steps in pro-apoptosis pathways, preventing both spontaneous and toxin-induced apoptosis) as well as stimulation of DNA synthesis and cell proliferation in many mammalian cells, including human ovarian cancer cells (Chan & Fiscus, 2003; Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Fraser *et al*, 2006; Fung *et al*, 2005; Leung *et al*, 2008; Leung *et al*, 2010; Wong & Fiscus, 2010; Wong & Fiscus, 2011). Although earlier studies had reported that "PKG expression is lost" when ovarian epithelial cells transform into cancer cells (Hou *et al*, 2006; Wong *et al*, 2001), our studies show that human ovarian cancer cells do indeed express PKG-I, which contributes to exaggerated cell proliferation and chemoresistance

At higher concentrations (>100 nanomolar), as used in the earlier studies mentioned above, NO causes cytotoxic effects in many types of mammalian cells, both normal and cancer cells, which can involve both the direct S-nitrosylation of cysteine residues of proteins, altering their function (Nakamura & Lipton, 2010; Seth & Stamler, 2011), and the indirect nitration of tyrosine residues in proteins (via formation of peroxynitrite), further altering protein function (e.g. interferring with tyrosine phosphorylation) (Beckman & Koppenol, 1996; Fiscus, 2002; Fiscus *et al*, 2002; Ridnour *et al*, 2008; Thomas *et al*, 2008). Details of these cellular/molecular mechanisms mediating the concentration-dependent cytoprotective and cytotoxic actions of NO are discussed below and illustrated in the

ovarian cancer cells (Fraser *et al*, 2006; Leung *et al*, 2008; Leung *et al*, 2010).

#### **2. Early studies identifying the important roles of NO, cGMP and PKG in controlling blood pressure and blood flow and mediating penile erection**

Endogenous NO [originally referred to as EDRF (endothelium-derived relaxant factor)] in the cardiovascular system was first shown to play an important biological role in regulating arterial diameter [for reviews, see (Fiscus, 1988; Fiscus, 2002; Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003)]. In healthy arteries (i.e. arteries from individuals that are young and without diabetes, obesity or hypertension), endogenous NO is produced at physiological levels, now estimated to be in the range of 0.01 – 10 nanomolar (Batchelor *et al*, 2010; Sato *et al*, 2006), by the endothelial-form NO-synthase (eNOS) within the endothelial cells lining the arteries. Because of its high lipid solubility, NO readily diffuses into nearby cells, importantly vascular smooth muscle cells in blood vessels, where NO, via binding to the heme group of soluble guanylyl cyclase, enhances cGMP synthesis, resulting in elevation of intracellular levels of cGMP and activation of PKG.

Early studies throughout the 1970's and early 1980's, using purified PKG in *in vitro* experiments, had shown that the addition of cGMP to the purified PKG could enhance its kinase activity, suggesting that cGMP may be the intracellular chemical that serves as the allosteric activator of PKG within cells, similar to the role of cAMP in activating cAMPdependent protein kinase (protein kinase A, PKA) [reviewed in (Fiscus, 1988; Fiscus, 2002; Francis *et al*, 1988; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003)]. Thus, early on, it was suggested that NO, via its ability to elevate intracellular cGMP levels, may be causing vascular effects by activating PKG within the smooth muscle cells. However, the early attempts to prove this were found to be exceedingly difficult because of the uniquely unstable nature of the PKG activation that occurs within mammalian cells (Fiscus, 1988; Fiscus, 2002; Fiscus & Murad, 1988; Fiscus *et al*, 1983; Fiscus *et al*, 1984). It was not until 1983 and 1984 that NO was first shown to significantly stimulate the intracellular activation state of PKG in mammalian cells (Fiscus *et al*, 1983; Fiscus *et al*, 1984).

As a serine/threonine kinase, PKG phosphorylates numerous downstream target proteins in vascular smooth muscle cells, which ultimately results in the suppression of arterial vasoconstriction (i.e. vasodilation) (Fiscus, 1988; Fiscus, 2002; Fiscus & Murad, 1988; Francis *et al*, 1988; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003). The NO/cGMP/PKG signaling pathway in vascular smooth muscle cells plays an essential role in preventing vasospasms and maintaining normal blood pressure and blood flow. Interestingly, even the basal release of NO from healthy endothelial cells, now estimated to generate a local concentration of NO of 0.01 – 0.1 nanomolar (Batchelor *et al*, 2010; Sato *et al*, 2006), was shown to cause significant increases in the intracellular PKG activation in vascular smooth muscle cells (Fiscus *et al*, 1983). This "basal activation" of PKG, induced by the basal, low-level (0.01 – 0.1 nanomolar) NO is now recognized to play a key role in protecting against the development of hypertension and other cardiovascular pathologies.

Advanced age (Chan & Fiscus, 2002; Fiscus, 1988; Fiscus, 2002; Fiscus & Ming, 2000; Fung *et al*, 2005) or diabetes and/or obesity in younger individuals (Fiscus, 2002; Fiscus *et al*, 2001; Fiscus & Ming, 2000) results in the dysregulation of NO production and NO's ability to activate PKG in both the cardiovascular system and the male reproductive system (Chang *et al*, 2004). This diminished capacity to generate the physiological levels of NO and for NO to activate PKG within cells is now recognized to play a key role in aging- and diabetes/obesity-induced pathological complications, including hypertension, atherosclerosis (with increased risk of heart attack and stroke) and erectile dysfunction.

Nitric Oxide/Protein Kinase G-I Promotes c-Src

Activation, Proliferation and Chemoresistance in Ovarian Cancer 319

Fiscus, 2002; Fiscus *et al*, 2002) , as illustrated in Figure 1. In both cases, this can lead to

cytotoxicity, with resulting inhibition of cell proliferation and induction of apoptosis.

Fig. 1. Multiple cellular and molecular actions of NO in human ovarian cancer cells that occur at very different (local micro-environmental) NO concentrations [in nanomolar (nM)].

#### **3. Anti-apoptotic effects mediated by the low-level-NO/cGMP/PKG-I signaling pathway in neural cells, uterine epithelial cells and human ovarian cancer cells**

Further studies from our laboratory have shown that other types of mammalian cells, including uterine epithelial cells and many types of neural cells, possess all of the components of the NO/cGMP/PKG signaling pathway and that this pathway is biologically functional (Barger *et al*, 1995; Chan & Fiscus, 2003; Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Leung *et al*, 2010). However, in contrast to the contractile-regulatory role of the NO/cGMP/PKG pathway identified in vascular smooth muscle cells, these other mammalian cells utilize this pathway to regulate a very different biological function, i.e. cell survival. Activation of this signaling pathway by low, basal levels of endogenous NO (generated by either eNOS or neural-form NOS (nNOS) within these cells) results in the suppression of both spontaneous apoptosis and toxin-induced apoptosis in uterine epithelial cells and in the many different types of neural cells.

We had hypothesized that, if present in ovarian cancer cells, the NO/cGMP/PKG signaling pathway may also suppress the apoptosis of tumor cells, potentially contributing to the resistance to chemotherapeutic agents (i.e. chemoresistance). Our data have shown that human ovarian cancer cells do indeed express all of the key components of the NO/cGMP/PKG signaling pathway, including all three isoforms of NOS, i.e. eNOS (also called NOS3), nNOS (also called NOS1) and inducible NOS (iNOS, also called NOS2), thus providing an endogenous source of NO (Leung *et al*, 2008). Furthermore, ovarian cancer cells continuously produce NO at low physiological levels, which tonically activates the heme-dependent soluble guanylyl cyclase (Fraser *et al*, 2006), elevating cGMP levels sufficiently enough to cause continuous high-level activation of PKG (Leung *et al*, 2010). We have found that the type-I splice variant of PKG-I (i.e. PKG-I) appears to represent the predominant isoform of PKG expressed in two types of human ovarian cancer cells, OV2008 cells (possessing wild-type p53 and sensitivity to the toxic/pro-apoptotic effects of cisplatin) and A2780cp cells (possessing mutated p53 and resistance to the toxic/pro-apoptotic effects of cisplatin). The PKG-I isoform is the most sensitive of all of the three isoforms of PKG (PKG-I, PKG-I and PKG –II) to stimulation by cGMP and thus is likely to be the only PKG isoform that is substantially activated by the presence of the lower physiological levels (0.01 – 1 nanomolar) of NO (see model in Figure 1).

#### **4. Low-level-NO/cGMP/PKG-I pathway also promotes DNA synthesis and cell proliferation, which are biological responses opposite of those induced by high-level NO**

Figure 1 illustrates the multiple (in some cases, opposite) biological effects of NO in human ovarian cancer cells, which greatly depend on the concentration [shown in nanomolar (nM)] of NO and the experimental conditions (e.g. whether there are elevated levels of superoxide, which is capable of reacting with high-level NO to form peroxynitrite). Most previous studies of NO's role in ovarian cancer have focused on the toxic effects of NO that occur at the higher concentrations (Cantuaria *et al*, 2000; Farias-Eisner *et al*, 1994; Garban & Bonavida, 1999; Rieder *et al*, 2001), which would lead to direct S-nitrosylation of cysteine residues of proteins (Nakamura & Lipton, 2010; Seth & Stamler, 2011) and indirect nitration (via production of peroxynitrite) of tyrosine residues of proteins (Beckman & Koppenol, 1996;

Further studies from our laboratory have shown that other types of mammalian cells, including uterine epithelial cells and many types of neural cells, possess all of the components of the NO/cGMP/PKG signaling pathway and that this pathway is biologically functional (Barger *et al*, 1995; Chan & Fiscus, 2003; Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Leung *et al*, 2010). However, in contrast to the contractile-regulatory role of the NO/cGMP/PKG pathway identified in vascular smooth muscle cells, these other mammalian cells utilize this pathway to regulate a very different biological function, i.e. cell survival. Activation of this signaling pathway by low, basal levels of endogenous NO (generated by either eNOS or neural-form NOS (nNOS) within these cells) results in the suppression of both spontaneous apoptosis and toxin-induced apoptosis in uterine

We had hypothesized that, if present in ovarian cancer cells, the NO/cGMP/PKG signaling pathway may also suppress the apoptosis of tumor cells, potentially contributing to the resistance to chemotherapeutic agents (i.e. chemoresistance). Our data have shown that human ovarian cancer cells do indeed express all of the key components of the NO/cGMP/PKG signaling pathway, including all three isoforms of NOS, i.e. eNOS (also called NOS3), nNOS (also called NOS1) and inducible NOS (iNOS, also called NOS2), thus providing an endogenous source of NO (Leung *et al*, 2008). Furthermore, ovarian cancer cells continuously produce NO at low physiological levels, which tonically activates the heme-dependent soluble guanylyl cyclase (Fraser *et al*, 2006), elevating cGMP levels sufficiently enough to cause continuous high-level activation of PKG (Leung *et al*, 2010). We have found that the type-I splice variant of PKG-I (i.e. PKG-I) appears to represent the predominant isoform of PKG expressed in two types of human ovarian cancer cells, OV2008 cells (possessing wild-type p53 and sensitivity to the toxic/pro-apoptotic effects of cisplatin) and A2780cp cells (possessing mutated p53 and resistance to the toxic/pro-apoptotic effects of cisplatin). The PKG-I isoform is the most sensitive of all of the three isoforms of PKG (PKG-I, PKG-I and PKG –II) to stimulation by cGMP and thus is likely to be the only PKG isoform that is substantially activated by the presence of the lower physiological levels (0.01

**4. Low-level-NO/cGMP/PKG-I pathway also promotes DNA synthesis and cell proliferation, which are biological responses opposite of those induced** 

Figure 1 illustrates the multiple (in some cases, opposite) biological effects of NO in human ovarian cancer cells, which greatly depend on the concentration [shown in nanomolar (nM)] of NO and the experimental conditions (e.g. whether there are elevated levels of superoxide, which is capable of reacting with high-level NO to form peroxynitrite). Most previous studies of NO's role in ovarian cancer have focused on the toxic effects of NO that occur at the higher concentrations (Cantuaria *et al*, 2000; Farias-Eisner *et al*, 1994; Garban & Bonavida, 1999; Rieder *et al*, 2001), which would lead to direct S-nitrosylation of cysteine residues of proteins (Nakamura & Lipton, 2010; Seth & Stamler, 2011) and indirect nitration (via production of peroxynitrite) of tyrosine residues of proteins (Beckman & Koppenol, 1996;

**3. Anti-apoptotic effects mediated by the low-level-NO/cGMP/PKG-I signaling pathway in neural cells, uterine epithelial cells and human** 

epithelial cells and in the many different types of neural cells.

– 1 nanomolar) of NO (see model in Figure 1).

**by high-level NO** 

**ovarian cancer cells** 

Fiscus, 2002; Fiscus *et al*, 2002) , as illustrated in Figure 1. In both cases, this can lead to cytotoxicity, with resulting inhibition of cell proliferation and induction of apoptosis.

Fig. 1. Multiple cellular and molecular actions of NO in human ovarian cancer cells that occur at very different (local micro-environmental) NO concentrations [in nanomolar (nM)].

Nitric Oxide/Protein Kinase G-I Promotes c-Src

*al*, 2010).

proliferation.

**cell proliferation and apoptosis** 

Fiscus, 2010) and illustrated in Figure 2].

Activation, Proliferation and Chemoresistance in Ovarian Cancer 321

2010), an oncogenic protein often overexpressed and/or hyperactivated in many types of cancer cells, including ovarian cancer cells. The key role of PKG-I in activating c-Src and promoting cell proliferation was determined using siRNA gene knockdown techniques, which specifically silences the gene expression of PKG-I, and two types of pharmacological inhibitors, ODQ (1H-[1,2,4]oxadiazolo[4,3-a]quinozalin-1-one, an inhibitor of endogenous NO-induced, heme-dependent activation of soluble guanylyl cyclase) and DT-2 or DT-3 (two highly-specific inhibitors of the serine/threonine kinase activity of PKG-I) (Leung *et* 

Epidermal growth factor (EGF)-induced activation of c-Src tyrosine kinase activity was found to cause tyrosine phosphorylation of PKG-I, increasing the serine/threonine kinase activity of PKG-I and its growth-promoting effects in ovarian cancer cells (Leung *et al*, 2010). Furthermore, we have found that PKG-I directly phosphorylates c-Src at serine-17, which enhances the tyrosine kinase activity of c-Src in both *in vitro* and intact-cell experiments (Fiscus & Johlfs, 2011). In human ovarian cancer cells, the c-Src-mediated tyrosine-phosphorylation of the EGF receptor was found to be highly dependent on PKG-I kinase activity (Leung *et al*, 2010). Thus, there appears to be a novel interaction between PKG-I and c-Src in human ovarian cancer cells. This interaction causes reciprocal phosphorylation, i.e. each protein kinase phosphorylating the other, potentially setting up an "oncogenic reinforcement" resulting in exaggerated DNA synthesis and cell

**6. Opposite effects of the two PKG-I splice variants, PKG-I and PKG-I on** 

Our studies of mammalian cells expressing both PKG-Iand PKG-I isoforms, such as vascular smooth muscle cells, show that exposure of these cells to NO in a wide concentration range results in biphasic responses of cell proliferation and apoptosis (Wong & Fiscus, 2010). For example, exposure to low physiological levels of NO tends to promote cell proliferation and suppress apoptosis (i.e. promote cytoprotection), whereas exposure to higher levels of NO has the opposite effects, suppressing cell proliferation and promoting apoptosis. Similar biphasic responses to different concentrations of NO have been reported in vascular endothelial cells (Isenberg *et al*, 2005). These opposite effects induced by the low and high levels of NO likely involve the many cellular and molecular mechanisms illustrated in the model of Figure 1. Of particular interest in our laboratory is the role played by the two splice variants of PKG-I, since both isozymes are expressed in vascular smooth muscle cells (Wong & Fiscus, 2010). At low levels, NO would selectively stimulate the kinase activity of PKG-I (but not PKG-I), because the PKG-I isoform has a much higher sensitivity to NO and the cGMP-induced allosteric activation (illustrated in Figure 2). At higher levels, NO would activate both PKG-I isoforms. Importantly, PKG-I requires at least 10-times higher levels of cGMP for activation (indicated by Kact), compared with PKG-I (Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003). Activation of PKG-I by the high-level NO likely contributes to suppression of cell proliferation and induction of apoptosis [(Wong &

Figure 1 also illustrates how NO, at intermediate levels (i.e. 50 – 300 nanomolar), binds to the heme group of cytochrome C oxidase in the mitochondria, inhibiting the activity of this important metabolic enzyme (Bellamy *et al*, 2002). The biological role of this response to NO is currently unclear, but likely would result in decreased mitochondrial respiration and oxygen consumption and may possibly contribute to the hypoxic phenotype of cancer cells. At 1 – 50 nanomolar concentration, which is at the transition between the toxicological levels and the higher physiological levels, NO would cause high-level activation of soluble guanylyl cyclase, causing large increases in the intracellular levels of cGMP. Recently, this high-level activation was shown to be dependent not only on the binding of NO to the heme group of soluble guanylyl cyclase (which occurs at much lower concentrations, i.e. 0.01 – 1 nM, of NO) but also on the binding of higher-level NO to cysteine residues of soluble guanylyl cyclase (Fernhoff *et al*, 2009). It is not clear at present what effect this would have in human ovarian cancer cells. However, studies with other types of cells have shown that larger increases in cGMP levels would activate both the PKG-I and PKG-I isoforms of PKG, and thus may result in biological responses that are very different from those mediated by the low-level-NO/cGMP/PKG-I pathway (for details, see Section 6. below). The large increases in cGMP caused by NO at 1 – 50 nanomolar may also regulate the activity of other potential cGMP-target proteins, including the olfactory- and rod-type CNG channels (cyclic nucleotide-gated cation channels), various phosphodiesterases and PKA (via "cross-activation" mediated by high-level cGMP binding to the cAMP-activation sites of PKA) [reviewed in (Fiscus, 2002; Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003)].

At the lower physiological levels (0.01 – 1 nanomolar) NO would selectively activate the PKG-Iisoform of PKG in human ovarian cancer cells, which stimulates DNA synthesis/cell proliferation and suppresses apoptosis (promoting chemoresistance) (Leung *et al*, 2008; Leung *et al*, 2010), responses opposite to those of the high/toxic concentrations of NO. Studies in our laboratory, using both normal and malignant cells, including vascular smooth muscle cells, bone marrow-derived stromal cells and neuroblastoma cells, have suggested that a major role of the low-level-NO/cGMP/PKG-I signaling pathway is to protect these cells against the toxic/pro-apoptotic effects of high-level NO, as might occur during inflammation and exposure of cells to proinflammatory cytokines (Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Wong & Fiscus, 2010; Wong & Fiscus, 2011).

Recent evidence from our laboratory also suggests that the basal activation of PKG-I leads to increased attachment of cells to the extracellular matrix and increased cell migration, shown in bone marrow-derived stromal cells (Wong & Fiscus, 2011) as well as mesothelioma and non-small cell lung cancer cells (Fiscus & Johlfs, 2011). If similar attachment and migration responses occur in ovarian cancer cells, these PKG-I-mediated cellular effects could lead to increased invasion and metastasis. Further experiments are currently underway to test this possibility in models of ovarian cancer.

#### **5. Interaction between c-Src and PKG-I in promoting DNA synthesis and cell proliferation**

Studies from our laboratory suggest that the growth-promoting effect of PKG-I in ovarian cancer cells involves the enhancement of the tyrosine kinase activity of c-Src (Leung *et al*,

Figure 1 also illustrates how NO, at intermediate levels (i.e. 50 – 300 nanomolar), binds to the heme group of cytochrome C oxidase in the mitochondria, inhibiting the activity of this important metabolic enzyme (Bellamy *et al*, 2002). The biological role of this response to NO is currently unclear, but likely would result in decreased mitochondrial respiration and oxygen consumption and may possibly contribute to the hypoxic phenotype of cancer cells. At 1 – 50 nanomolar concentration, which is at the transition between the toxicological levels and the higher physiological levels, NO would cause high-level activation of soluble guanylyl cyclase, causing large increases in the intracellular levels of cGMP. Recently, this high-level activation was shown to be dependent not only on the binding of NO to the heme group of soluble guanylyl cyclase (which occurs at much lower concentrations, i.e. 0.01 – 1 nM, of NO) but also on the binding of higher-level NO to cysteine residues of soluble guanylyl cyclase (Fernhoff *et al*, 2009). It is not clear at present what effect this would have in human ovarian cancer cells. However, studies with other types of cells have shown that larger increases in cGMP levels would activate both the PKG-I and PKG-I isoforms of PKG, and thus may result in biological responses that are very different from those mediated by the low-level-NO/cGMP/PKG-I pathway (for details, see Section 6. below). The large increases in cGMP caused by NO at 1 – 50 nanomolar may also regulate the activity of other potential cGMP-target proteins, including the olfactory- and rod-type CNG channels (cyclic nucleotide-gated cation channels), various phosphodiesterases and PKA (via "cross-activation" mediated by high-level cGMP binding to the cAMP-activation sites of PKA) [reviewed in (Fiscus, 2002; Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001;

At the lower physiological levels (0.01 – 1 nanomolar) NO would selectively activate the PKG-Iisoform of PKG in human ovarian cancer cells, which stimulates DNA synthesis/cell proliferation and suppresses apoptosis (promoting chemoresistance) (Leung *et al*, 2008; Leung *et al*, 2010), responses opposite to those of the high/toxic concentrations of NO. Studies in our laboratory, using both normal and malignant cells, including vascular smooth muscle cells, bone marrow-derived stromal cells and neuroblastoma cells, have suggested that a major role of the low-level-NO/cGMP/PKG-I signaling pathway is to protect these cells against the toxic/pro-apoptotic effects of high-level NO, as might occur during inflammation and exposure of cells to proinflammatory cytokines (Cheng Chew *et al*, 2003; Fiscus, 2002; Fiscus *et al*, 2002; Wong &

Recent evidence from our laboratory also suggests that the basal activation of PKG-I leads to increased attachment of cells to the extracellular matrix and increased cell migration, shown in bone marrow-derived stromal cells (Wong & Fiscus, 2011) as well as mesothelioma and non-small cell lung cancer cells (Fiscus & Johlfs, 2011). If similar attachment and migration responses occur in ovarian cancer cells, these PKG-I-mediated cellular effects could lead to increased invasion and metastasis. Further experiments are

**5. Interaction between c-Src and PKG-I in promoting DNA synthesis and cell** 

Studies from our laboratory suggest that the growth-promoting effect of PKG-I in ovarian cancer cells involves the enhancement of the tyrosine kinase activity of c-Src (Leung *et al*,

currently underway to test this possibility in models of ovarian cancer.

Pilz & Casteel, 2003)].

**proliferation** 

Fiscus, 2010; Wong & Fiscus, 2011).

2010), an oncogenic protein often overexpressed and/or hyperactivated in many types of cancer cells, including ovarian cancer cells. The key role of PKG-I in activating c-Src and promoting cell proliferation was determined using siRNA gene knockdown techniques, which specifically silences the gene expression of PKG-I, and two types of pharmacological inhibitors, ODQ (1H-[1,2,4]oxadiazolo[4,3-a]quinozalin-1-one, an inhibitor of endogenous NO-induced, heme-dependent activation of soluble guanylyl cyclase) and DT-2 or DT-3 (two highly-specific inhibitors of the serine/threonine kinase activity of PKG-I) (Leung *et al*, 2010).

Epidermal growth factor (EGF)-induced activation of c-Src tyrosine kinase activity was found to cause tyrosine phosphorylation of PKG-I, increasing the serine/threonine kinase activity of PKG-I and its growth-promoting effects in ovarian cancer cells (Leung *et al*, 2010). Furthermore, we have found that PKG-I directly phosphorylates c-Src at serine-17, which enhances the tyrosine kinase activity of c-Src in both *in vitro* and intact-cell experiments (Fiscus & Johlfs, 2011). In human ovarian cancer cells, the c-Src-mediated tyrosine-phosphorylation of the EGF receptor was found to be highly dependent on PKG-I kinase activity (Leung *et al*, 2010). Thus, there appears to be a novel interaction between PKG-I and c-Src in human ovarian cancer cells. This interaction causes reciprocal phosphorylation, i.e. each protein kinase phosphorylating the other, potentially setting up an "oncogenic reinforcement" resulting in exaggerated DNA synthesis and cell proliferation.

#### **6. Opposite effects of the two PKG-I splice variants, PKG-I and PKG-I on cell proliferation and apoptosis**

Our studies of mammalian cells expressing both PKG-Iand PKG-I isoforms, such as vascular smooth muscle cells, show that exposure of these cells to NO in a wide concentration range results in biphasic responses of cell proliferation and apoptosis (Wong & Fiscus, 2010). For example, exposure to low physiological levels of NO tends to promote cell proliferation and suppress apoptosis (i.e. promote cytoprotection), whereas exposure to higher levels of NO has the opposite effects, suppressing cell proliferation and promoting apoptosis. Similar biphasic responses to different concentrations of NO have been reported in vascular endothelial cells (Isenberg *et al*, 2005). These opposite effects induced by the low and high levels of NO likely involve the many cellular and molecular mechanisms illustrated in the model of Figure 1. Of particular interest in our laboratory is the role played by the two splice variants of PKG-I, since both isozymes are expressed in vascular smooth muscle cells (Wong & Fiscus, 2010). At low levels, NO would selectively stimulate the kinase activity of PKG-I (but not PKG-I), because the PKG-I isoform has a much higher sensitivity to NO and the cGMP-induced allosteric activation (illustrated in Figure 2). At higher levels, NO would activate both PKG-I isoforms. Importantly, PKG-I requires at least 10-times higher levels of cGMP for activation (indicated by Kact), compared with PKG-I (Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001; Pilz & Casteel, 2003). Activation of PKG-I by the high-level NO likely contributes to suppression of cell proliferation and induction of apoptosis [(Wong & Fiscus, 2010) and illustrated in Figure 2].

Nitric Oxide/Protein Kinase G-I Promotes c-Src

role of PKG in regulating cell proliferation and apoptosis.

opposite biological responses mediated by the two PKG-I isoforms.

In the model of PKG-I shown in Figure 2, we have also included our current concept about the natural endogenous activator of PKG-I within cancer cells, which we believe likely involves high-level NO generated by iNOS, either within the cancer cells themselves or within invading white blood cells (e.g. monocyte/macrophages), following their exposure to pro-inflammatory cytokines, such as interleukin-1 (IL-1), tumor necrosis factor- (TNF- ) and interferon- (IFN-). These pro-inflammatory cytokines are known to induce the gene expression of iNOS (Chan & Fiscus, 2004; Kleinert *et al*, 2003), which causes high-level

Activation, Proliferation and Chemoresistance in Ovarian Cancer 323

and suppression of apoptosis in a variety of mammalian cells, notably human ovarian cancer cells (Fiscus, 2002; Fiscus *et al*, 2002; Fraser *et al*, 2006; Leung *et al*, 2008; Leung *et al*, 2010; Wong & Fiscus, 2010; Wong & Fiscus, 2011). In contrast, activation of both isoforms of PKG-I, following the exposure to higher-level NO in cells expressing both PKG-I isoforms, results in suppression of DNA synthesis/cell proliferation and induction of apoptosis (Wong & Fiscus, 2010). It appears that when PKG-Iis activated by the higher levels of NO, the growth-inhibitory and pro-apoptotic effects of PKG-I predominate over the growthstimulatory and anti-apoptotic effects mediated by PKG-I. Because of these differences in the biological responses mediated by the two isoforms of PKG-I and because most previous studies have used higher levels of NO, there has been confusion in the literature about the

Figure 2 illustrates the opposite biological effects of PKG-I and PKG-I on cell proliferation and apoptosis, using, as examples, two types of cells that selectively express one isoform of PKG-I or the other. As stated above, our studies have shown that human ovarian cancer cells appear to express predominantly the PKG-I isoform, and that the activation of this kinase by endogenous low-level NO generated by eNOS and nNOS within ovarain cancer cells promotes DNA synthesis/cell proliferation and suppresses apoptosis, contributing to chemoresistance (i.e. interfering with the toxic/cancer-cell-killing effects of cisplatin) (Leung *et al*, 2008; Leung *et al*, 2010), shown in the upper model of Figure 2. The lower model in Figure 2 shows the regulation and downstream target proteins of PKG-I, based in part on published data from the laboratories of I.B. Weinstein and W.J. Thompson. Their laboratories have shown that PKG-I is the predominant isoform of PKG-I expressed in colon cancer cells and that, when activated [following the large increases in intracellular cGMP levels induced by Exisulind, a type-2/type-5 phosphodiesterase (PDE2/PDE5) inhibitor], PKG-I phosphorylates two downstream target proteins, -catenin and MEKK1, resulting in inhibition of cell proliferation and induction of apoptosis (Deguchi *et al*, 2004; Soh *et al*, 2000; Thompson *et al*, 2000). They also showed that the overexpression of PKG-I in colon cancer cells causes a large suppression of cell proliferation and induction of apoptosis. Although their studies further showed that transfection of colon cancer cells with vectors causing overexpression of PKG-I also caused a small inhibition of cell proliferation, it appears likely that this forced overexpression of PKG-I may have resulted in the unnatural exposure of PKG-Ito target proteins that normally would not be phosphorylated by naturally-expressed PKG-I (but rather by PKG-I), leading to biological responses more like those of the PKG-I isoform. Overall, the combined data from our laboratory using human ovarian cancer cells and the data from the laboratories of I.B. Weinstein and W.J. Thompson using colon cancer cells suggest that the two isoforms of PKG-I have opposite effects on cell proliferation and apoptosis. However, further studies will be needed to determine if cell-type differences may have also played a role in the

#### **Opposite effects of the two PKG-I splice variants on apoptosis and proliferation**

Fig. 2. Model of the two splice variants of PKG-I, illustrating their activation by different concentration ranges of NO and the downstream phosphorylation of different sets of target proteins. Also, the model for PKG-I illustrates the effects of growth factors (e.g. EGF), which stimulates both the PI3K/Akt pathway, enhancing eNOS activity and low-level NO generation, and c-Src activation, catalyzing downstream tyrosine-phosphorylation of PKG-I, enhancing its sensitivity to allosteric activation by cGMP (represented by the Kact values).

In human ovarian cancer cells, we found that endogenous PKG-I is tyrosinephosphorylated, which depends on the high-level tyrosine kinase activity of c-Src (Leung *et al*, 2010). The tyrosine phosphorylation of PKG-I is known to cause a 3-fold decrease in the Kact for cGMP-induced activation, resulting in a substantial sensitization of the PKG-I to its activation by cGMP (illustrated in Figure 2). Earlier *in vitro* experiments, using purified PKG-I in a kinase reaction mixture with v-Src, the viral form of Src, had shown that tyrosine-phosphorylation of PKG-I results in a 3-fold shift downwards in the Kact (LaFevre-Bernt *et al*, 1998). The human form of c-Src, used in our studies, has the same catalytic domain as v-Src and thus catalyzes the same type of tyrosine-phosphorylation of PKG-I (Leung *et al*, 2010). Thus, in human ovarian cancer cells the tyrosine-phosphorylated PKG-I would be dramatically sensitized to the activation by basal intracellular cGMP levels, resulting in high-level activation (i.e. hyperactivation, estimated to be 90% of maximal activity) of PKG-I within the ovarian cancer cells.

Our laboratory has shown that activation of PKG-I, which occurs tonically in cells that express either eNOS or nNOS, results in stimulation of DNA synthesis/cell proliferation

**Opposite effects of the two PKG-I splice variants on apoptosis and proliferation**

**GF receptor**

Regulatory Domain

Kact = 0.1 M for cGMP-induced activation (not tyrosine-phosphorylated)

**Cell proliferation NO cGMP**

P

Kact = 0.03 M for cGMP-induced activation in c-Src-tyrosine-phosphorylated PKG-I

Catalytic Domain

Catalytic Domain

Phosphorylation Function

**Apoptosis (Chemoresistance)**

**Apoptosis**

**Cell proliferation**

Bad CREB c-Src

VASP


MEKK1/ JNK1

**PKG-I**

Leucine Zipper/ Protein-protein interaction Domain ( encoded by exon I )

Leucine Zipper/ Protein-protein interaction Domain ( encoded by exon I )

R.R. Fiscus' lab: **Human ovarian cancer cells** 

High physiological / low pathological levels (1 – 50 nM) of NO

Low physiological levels (0.01 - 1 nM) of NO

**Growth/survival factors (e.g. EGF)**

**eNOS**

**Akt HSP90**

**PI3K PTEN**

**NO cGMP**

**TNF- IFN-**

**Exisulind** (AptosynTM, Sulindac sulfone) from OSI I.B. Weinstein's lab W.J. Thompson's lab

**nNOS**

P

Ser-1177

**iNOS**

**IL-1**

**PKG-I**

**Colon cancer cells**

Fig. 2. Model of the two splice variants of PKG-I, illustrating their activation by different concentration ranges of NO and the downstream phosphorylation of different sets of target proteins. Also, the model for PKG-I illustrates the effects of growth factors (e.g. EGF), which stimulates both the PI3K/Akt pathway, enhancing eNOS activity and low-level NO generation, and c-Src activation, catalyzing downstream tyrosine-phosphorylation of PKG-I, enhancing its sensitivity to allosteric activation by cGMP (represented by the Kact values). In human ovarian cancer cells, we found that endogenous PKG-I is tyrosinephosphorylated, which depends on the high-level tyrosine kinase activity of c-Src (Leung *et al*, 2010). The tyrosine phosphorylation of PKG-I is known to cause a 3-fold decrease in the Kact for cGMP-induced activation, resulting in a substantial sensitization of the PKG-I to its activation by cGMP (illustrated in Figure 2). Earlier *in vitro* experiments, using purified PKG-I in a kinase reaction mixture with v-Src, the viral form of Src, had shown that tyrosine-phosphorylation of PKG-I results in a 3-fold shift downwards in the Kact (LaFevre-Bernt *et al*, 1998). The human form of c-Src, used in our studies, has the same catalytic domain as v-Src and thus catalyzes the same type of tyrosine-phosphorylation of PKG-I (Leung *et al*, 2010). Thus, in human ovarian cancer cells the tyrosine-phosphorylated PKG-I would be dramatically sensitized to the activation by basal intracellular cGMP levels, resulting in high-level activation (i.e. hyperactivation, estimated to be 90% of maximal

Kact = 1.0 M for cGMP-induced activation

Regulatory Domain

Our laboratory has shown that activation of PKG-I, which occurs tonically in cells that express either eNOS or nNOS, results in stimulation of DNA synthesis/cell proliferation

activity) of PKG-I within the ovarian cancer cells.

and suppression of apoptosis in a variety of mammalian cells, notably human ovarian cancer cells (Fiscus, 2002; Fiscus *et al*, 2002; Fraser *et al*, 2006; Leung *et al*, 2008; Leung *et al*, 2010; Wong & Fiscus, 2010; Wong & Fiscus, 2011). In contrast, activation of both isoforms of PKG-I, following the exposure to higher-level NO in cells expressing both PKG-I isoforms, results in suppression of DNA synthesis/cell proliferation and induction of apoptosis (Wong & Fiscus, 2010). It appears that when PKG-Iis activated by the higher levels of NO, the growth-inhibitory and pro-apoptotic effects of PKG-I predominate over the growthstimulatory and anti-apoptotic effects mediated by PKG-I. Because of these differences in the biological responses mediated by the two isoforms of PKG-I and because most previous studies have used higher levels of NO, there has been confusion in the literature about the role of PKG in regulating cell proliferation and apoptosis.

Figure 2 illustrates the opposite biological effects of PKG-I and PKG-I on cell proliferation and apoptosis, using, as examples, two types of cells that selectively express one isoform of PKG-I or the other. As stated above, our studies have shown that human ovarian cancer cells appear to express predominantly the PKG-I isoform, and that the activation of this kinase by endogenous low-level NO generated by eNOS and nNOS within ovarain cancer cells promotes DNA synthesis/cell proliferation and suppresses apoptosis, contributing to chemoresistance (i.e. interfering with the toxic/cancer-cell-killing effects of cisplatin) (Leung *et al*, 2008; Leung *et al*, 2010), shown in the upper model of Figure 2. The lower model in Figure 2 shows the regulation and downstream target proteins of PKG-I, based in part on published data from the laboratories of I.B. Weinstein and W.J. Thompson. Their laboratories have shown that PKG-I is the predominant isoform of PKG-I expressed in colon cancer cells and that, when activated [following the large increases in intracellular cGMP levels induced by Exisulind, a type-2/type-5 phosphodiesterase (PDE2/PDE5) inhibitor], PKG-I phosphorylates two downstream target proteins, -catenin and MEKK1, resulting in inhibition of cell proliferation and induction of apoptosis (Deguchi *et al*, 2004; Soh *et al*, 2000; Thompson *et al*, 2000). They also showed that the overexpression of PKG-I in colon cancer cells causes a large suppression of cell proliferation and induction of apoptosis. Although their studies further showed that transfection of colon cancer cells with vectors causing overexpression of PKG-I also caused a small inhibition of cell proliferation, it appears likely that this forced overexpression of PKG-I may have resulted in the unnatural exposure of PKG-Ito target proteins that normally would not be phosphorylated by naturally-expressed PKG-I (but rather by PKG-I), leading to biological responses more like those of the PKG-I isoform. Overall, the combined data from our laboratory using human ovarian cancer cells and the data from the laboratories of I.B. Weinstein and W.J. Thompson using colon cancer cells suggest that the two isoforms of PKG-I have opposite effects on cell proliferation and apoptosis. However, further studies will be needed to determine if cell-type differences may have also played a role in the opposite biological responses mediated by the two PKG-I isoforms.

In the model of PKG-I shown in Figure 2, we have also included our current concept about the natural endogenous activator of PKG-I within cancer cells, which we believe likely involves high-level NO generated by iNOS, either within the cancer cells themselves or within invading white blood cells (e.g. monocyte/macrophages), following their exposure to pro-inflammatory cytokines, such as interleukin-1 (IL-1), tumor necrosis factor- (TNF- ) and interferon- (IFN-). These pro-inflammatory cytokines are known to induce the gene expression of iNOS (Chan & Fiscus, 2004; Kleinert *et al*, 2003), which causes high-level

Nitric Oxide/Protein Kinase G-I Promotes c-Src

Activation, Proliferation and Chemoresistance in Ovarian Cancer 325

tested in our laboratory, including breast cancer, colon cancer, lung cancer, melanoma, mesothelioma, ovarian cancer and prostate cancer cells, do indeed express PKG-I isoforms, with PKG-I expressed in all cell lines and PKG-I co-expressed in about half of them. The misunderstanding about whether or not cancer cells actually express PKG-I isoforms had resulted from of the lack of sensitivity of conventional Western blot analysis (and the lack of sensitivity of conventional immunohistochemistry), resulting in the inability to detect the protein expression of the PKG-I isoforms. To avoid this technical problem caused by the inadequate sensitivity of conventional Western blot analysis, our laboratory has begun using a new, state-of-the-art methodology that utilizes the NanoPro100 system, a capillaryelectrophoresis-based immuno-detection instrument, manufactured and marketed by ProteinSimple (previously named Cell Biosciences, Inc.), Santa Clara, CA, USA. The NanoPro100 system allows protein detection with a sensitivity that is >100-times better than conventional Western blot analysis, thus allowing clear identification of lower abundance proteins that have escaped detection by Western blot analysis. Furthermore, the NanoPro100 system is able to cleanly separate the two isoforms of PKG-I, thus making it

Figure 3 shows the dramatic improvement in sensitivity and resolving power of the NanoPro100 system for determining the PKG-I isoform expression profiles in human ovarian cancer cells, compared with conventional Western blot analysis (panel A). Recombinant PKG-I and PKG-I were used as standards in the NanoPro100 system (panel B). The NCI-H2052 mesothelioma cell line, which expresses both isoforms of PKG-I, was used as a positive control, illustrating the correct positions for the two PKG-I isoforms in the NanoPro100 electropherograms following analysis of cell lysates (panel C). Both A2780cp and OV2008 human ovarian cancer cells were found to express exclusively the PKG-I isoform (panels D and E), thus confirming our earlier report using conventional Western blot analysis. Interestingly, because the NanoPro100 system separates proteins based on pI rather than molecular weight as in conventional Western blot analysis, this new state-of-theart technology is able to separate and potentially identify the different phosphorylated forms (phospho-forms) of proteins, illustrated by the additional peaks to the left side of the main peak for PKG-I in panels D and E of Figure 3. The identification of these additional peaks as being phospho-forms of PKG-I is shown by their decrease after treatment of the

much easier to identify which isoforms are expressed in cancer cells.

cell lysates with lambda phosphatase, which removes the phosphate groups.

analysis, cannot directly predict the functional kinase activity of a protein kinase.

Based on the NanoPro100 data of Figure 3, the chemoresistant A2780cp cell line appears to have lower expression levels of PKG-I compared with the chemosensitive OV2008 cell line. On the surface, this seems to be opposite to what would be expected if PKG-I is contributing to chemoresistance in human ovarian cancer cells. However, it should be emphasized that protein expression levels do not indicate the functional activity of protein kinases. Much more important in determining the actual functional activity within cells are the levels of phosphorylation at regulatory sites (e.g. c-Src-mediate phosphorylation of tyrosine residues, in the case of PKG-I) and the intracellular concentrations of allosteric activators (e.g. cGMP, in the case of PKG-I). Another important determinant for functional kinase activity within cells is the subcellular localization, which determines the efficiency of phosphorylation and which of the potential downstream target proteins are actually phosphorylated (as illustrated in Figure 2 and discussed in Section 6.). Thus, the protein expression levels (of the total protein), as measured by Western blot analysis or NanoPro100

production of NO and large increases in the intracellular levels of cGMP. Thus, the role of PKG-I in regulating cell proliferation and apoptosis in cancer cells will depend on whether or not the cells actually express this isoform of PKG and whether or not there are concurrent inflammatory conditions in the tumor that would lead to the induction of iNOS and high levels of NO and cGMP, needed for activating PKG-I.

Figure 2 further shows the difference between PKG-Iand PKG-I in terms of the immediate downstream target proteins that are being phosphorylated by the two protein kinases. Earlier studies had shown that PKG-Iand PKG-I have identical substrate specificities when tested in *in vitro* experiments (Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001), which used freely soluble kinases (either purified kinases or recombinant kinases) dissolved in an aqueous solution. The two isoforms of PKG-I have identical catalytic domains, which results in similar substrate specificity when tested *in vitro*. However, within intact cells, PKG-I and PKG-I have very different subcellular localizations, because of their different localization domains (i.e. the leucine zipper/proteinprotein-interaction domains), which represents the first 100 amino acids at the N-terminal encoded by the different first exons of the two splice variants of PKG-I. This difference in the subcellular localizations results in the exposure of the two PKG-I isoforms to very different sets of downstream target proteins, as illustrated in Figure 2.

Our studies have suggested that there is continuous high-level activation of PKG-I within cancer cells, which results in continuous downstream phosphorylation of four key regulatory proteins: 1) the apoptosis-regulating protein Bad (Johlfs & Fiscus, 2010), 2) the transcription factor CREB [(Fiscus, 2002), further supported by recent data from our laboratory using many types of cancer cells], 3) the oncogenic tyrosine kinase c-Src (Fiscus & Johlfs, 2011; Leung *et al*, 2010), and 4) the actin-filament- and focal-adhesion-associated protein VASP (vasodilator-stimulated phosphoprotein) (Leung *et al*, 2010; Wong & Fiscus, 2010; Wong & Fiscus, 2011). We have proposed that the PKG-I-mediated phosphorylations of Bad, CREB, c-Src and VASP play important roles in promoting chemoresistance, DNA synthesis/cell proliferation, cell attachment and cell migration. Others have shown that PKG-I phosphorylates -catenin and MEKK1 in colon cancer cells, which ultimately leads to increased levels of apoptosis and inhibition of cell proliferation in the colon cancer cells (Deguchi *et al*, 2004; Soh *et al*, 2000; Thompson *et al*, 2000).

#### **7. Identification of PKG-I as the exclusive isoform of PKG-I expressed in A2780cp and OV2008 human ovarian cancer cells using the NanoPro100 system, a new ultrasensitive immuno-detection instrument based on capillary electrophoresis**

Although our previous studies using Western blot analysis had suggested that PKG-I is the predominant isoform of PKG-I expressed in A2780cp and OV2008 human ovarian cancer cells (Leung *et al*, 2010), we had found it difficult to determine the PKG-I isozyme profile with certainty because of the relatively low abundance of the PKG-I isoforms in cancer cells and the difficulty in resolving and identifying the two isoforms of PKG-I using Western blot analysis. Other laboratories, also using Western blot analysis, were unable to detect PKG expression in ovarian cancer cells and various other types of cancer cells, which had lead them to conclude that "PKG expression is lost" in cancer cells (Hou *et al*, 2006; Wong *et al*, 2001). Our studies have now shown that all of the more than 25 different cancer cell lines

production of NO and large increases in the intracellular levels of cGMP. Thus, the role of PKG-I in regulating cell proliferation and apoptosis in cancer cells will depend on whether or not the cells actually express this isoform of PKG and whether or not there are concurrent inflammatory conditions in the tumor that would lead to the induction of iNOS and high

Figure 2 further shows the difference between PKG-Iand PKG-I in terms of the immediate downstream target proteins that are being phosphorylated by the two protein kinases. Earlier studies had shown that PKG-Iand PKG-I have identical substrate specificities when tested in *in vitro* experiments (Francis *et al*, 2010; Hofmann *et al*, 2006; Lincoln *et al*, 2001), which used freely soluble kinases (either purified kinases or recombinant kinases) dissolved in an aqueous solution. The two isoforms of PKG-I have identical catalytic domains, which results in similar substrate specificity when tested *in vitro*. However, within intact cells, PKG-I and PKG-I have very different subcellular localizations, because of their different localization domains (i.e. the leucine zipper/proteinprotein-interaction domains), which represents the first 100 amino acids at the N-terminal encoded by the different first exons of the two splice variants of PKG-I. This difference in the subcellular localizations results in the exposure of the two PKG-I isoforms to very

Our studies have suggested that there is continuous high-level activation of PKG-I within cancer cells, which results in continuous downstream phosphorylation of four key regulatory proteins: 1) the apoptosis-regulating protein Bad (Johlfs & Fiscus, 2010), 2) the transcription factor CREB [(Fiscus, 2002), further supported by recent data from our laboratory using many types of cancer cells], 3) the oncogenic tyrosine kinase c-Src (Fiscus & Johlfs, 2011; Leung *et al*, 2010), and 4) the actin-filament- and focal-adhesion-associated protein VASP (vasodilator-stimulated phosphoprotein) (Leung *et al*, 2010; Wong & Fiscus, 2010; Wong & Fiscus, 2011). We have proposed that the PKG-I-mediated phosphorylations of Bad, CREB, c-Src and VASP play important roles in promoting chemoresistance, DNA synthesis/cell proliferation, cell attachment and cell migration. Others have shown that PKG-I phosphorylates -catenin and MEKK1 in colon cancer cells, which ultimately leads to increased levels of apoptosis and inhibition of cell proliferation in the colon cancer cells

**7. Identification of PKG-I as the exclusive isoform of PKG-I expressed in A2780cp and OV2008 human ovarian cancer cells using the NanoPro100 system, a new ultrasensitive immuno-detection instrument based on** 

Although our previous studies using Western blot analysis had suggested that PKG-I is the predominant isoform of PKG-I expressed in A2780cp and OV2008 human ovarian cancer cells (Leung *et al*, 2010), we had found it difficult to determine the PKG-I isozyme profile with certainty because of the relatively low abundance of the PKG-I isoforms in cancer cells and the difficulty in resolving and identifying the two isoforms of PKG-I using Western blot analysis. Other laboratories, also using Western blot analysis, were unable to detect PKG expression in ovarian cancer cells and various other types of cancer cells, which had lead them to conclude that "PKG expression is lost" in cancer cells (Hou *et al*, 2006; Wong *et al*, 2001). Our studies have now shown that all of the more than 25 different cancer cell lines

levels of NO and cGMP, needed for activating PKG-I.

different sets of downstream target proteins, as illustrated in Figure 2.

(Deguchi *et al*, 2004; Soh *et al*, 2000; Thompson *et al*, 2000).

**capillary electrophoresis** 

tested in our laboratory, including breast cancer, colon cancer, lung cancer, melanoma, mesothelioma, ovarian cancer and prostate cancer cells, do indeed express PKG-I isoforms, with PKG-I expressed in all cell lines and PKG-I co-expressed in about half of them. The misunderstanding about whether or not cancer cells actually express PKG-I isoforms had resulted from of the lack of sensitivity of conventional Western blot analysis (and the lack of sensitivity of conventional immunohistochemistry), resulting in the inability to detect the protein expression of the PKG-I isoforms. To avoid this technical problem caused by the inadequate sensitivity of conventional Western blot analysis, our laboratory has begun using a new, state-of-the-art methodology that utilizes the NanoPro100 system, a capillaryelectrophoresis-based immuno-detection instrument, manufactured and marketed by ProteinSimple (previously named Cell Biosciences, Inc.), Santa Clara, CA, USA. The NanoPro100 system allows protein detection with a sensitivity that is >100-times better than conventional Western blot analysis, thus allowing clear identification of lower abundance proteins that have escaped detection by Western blot analysis. Furthermore, the NanoPro100 system is able to cleanly separate the two isoforms of PKG-I, thus making it much easier to identify which isoforms are expressed in cancer cells.

Figure 3 shows the dramatic improvement in sensitivity and resolving power of the NanoPro100 system for determining the PKG-I isoform expression profiles in human ovarian cancer cells, compared with conventional Western blot analysis (panel A). Recombinant PKG-I and PKG-I were used as standards in the NanoPro100 system (panel B). The NCI-H2052 mesothelioma cell line, which expresses both isoforms of PKG-I, was used as a positive control, illustrating the correct positions for the two PKG-I isoforms in the NanoPro100 electropherograms following analysis of cell lysates (panel C). Both A2780cp and OV2008 human ovarian cancer cells were found to express exclusively the PKG-I isoform (panels D and E), thus confirming our earlier report using conventional Western blot analysis. Interestingly, because the NanoPro100 system separates proteins based on pI rather than molecular weight as in conventional Western blot analysis, this new state-of-theart technology is able to separate and potentially identify the different phosphorylated forms (phospho-forms) of proteins, illustrated by the additional peaks to the left side of the main peak for PKG-I in panels D and E of Figure 3. The identification of these additional peaks as being phospho-forms of PKG-I is shown by their decrease after treatment of the cell lysates with lambda phosphatase, which removes the phosphate groups.

Based on the NanoPro100 data of Figure 3, the chemoresistant A2780cp cell line appears to have lower expression levels of PKG-I compared with the chemosensitive OV2008 cell line. On the surface, this seems to be opposite to what would be expected if PKG-I is contributing to chemoresistance in human ovarian cancer cells. However, it should be emphasized that protein expression levels do not indicate the functional activity of protein kinases. Much more important in determining the actual functional activity within cells are the levels of phosphorylation at regulatory sites (e.g. c-Src-mediate phosphorylation of tyrosine residues, in the case of PKG-I) and the intracellular concentrations of allosteric activators (e.g. cGMP, in the case of PKG-I). Another important determinant for functional kinase activity within cells is the subcellular localization, which determines the efficiency of phosphorylation and which of the potential downstream target proteins are actually phosphorylated (as illustrated in Figure 2 and discussed in Section 6.). Thus, the protein expression levels (of the total protein), as measured by Western blot analysis or NanoPro100 analysis, cannot directly predict the functional kinase activity of a protein kinase.

Nitric Oxide/Protein Kinase G-I Promotes c-Src

test this possibility.

**ovarian cancer** 

Activation, Proliferation and Chemoresistance in Ovarian Cancer 327

In many of our previous studies, there appears to be an inverse relationship between the protein expression levels and the kinase activity of PKG-I. For example, vascular smooth muscle cells have exceptionally high levels of PKG-I protein expression but relatively low levels of PKG-I kinase activation (e.g. 30% - 40% of maximal activity) (Fiscus, 1988; Fiscus & Murad, 1988; Fiscus *et al*, 1983). In contrast, many cancer cells, including human ovarian cancer cells, have relatively low levels of PKG-I protein expression but high levels of PKG-I kinase activation (i.e. hyperactivation, estimated to be 90% of maximum activity) (Johlfs & Fiscus, 2010; Leung *et al*, 2010). We have proposed that the reason for this inverse relationship between protein expression levels and kinase activation levels is because of a negative-feedback mechanism, in which increases in PKG-I kinase activity (as, for example, resulting from c-Src-mediated tyrosine phosphorylation of PKG-I in cancer cells) would result in the negative modulation of PKG-I gene expression and protein expression, ultimately leading to the relatively low protein levels of PKG-I. However, in spite of lower protein levels (as measured in a Western blot analysis), the actual functional activity of PKG-I may remain quite high because of more-targeted subcellular localization, efficiently placing PKG-I in contact with its downstream target proteins. We have proposed that this may be the explanation for the low protein levels and high kinase activity of PKG-I in

cancer cells, especially those overexpressing or having hyperactivated c-Src.

**8. Overall model of the involvement of the NO/cGMP/PKG-I signaling pathway in promoting tumor growth, chemoresistance and angiogenesis in** 

Figure 4 illustrates our overall model showing the involvement of the NO/cGMP/PKG-I pathway in promoting cell proliferation and suppressing apoptosis in human ovarian cancer cells, which would contribute to enhanced tumor growth and chemoresistance. Also shown in the model is the potential role of nearby endothelial cells, which would provide an additional source of endogenous NO within the growing tumor, potentially contributing to the "angiogenic switch", i.e. the increased tumor growth that occurs after the invasion of endothelial cells into the tumor. Many factors are released from the endothelial cells that can stimulate the growth and chemoresistance of the tumor. Because low physiological levels (0.01 – 1 nM) of NO are now recognized to play a key role in promoting cancer cell proliferation and the development of chemoresistance, the NO released from nearby endothelial cells may have an important role in the tumor growth and chemoresistance

Negative feedback regulations of PKG-I expression at both the messenger RNA and protein levels have been shown in studies by Thomas Lincoln's laboratory at the University of South Alabama (Dey *et al*, 2009; Lincoln *et al*, 2001). Exposure of vascular smooth muscle cells to high levels of NO, causing large increases in intracellular cGMP levels, or to cellpermeable cGMP analogs that hyperactivate PKG-I, causes (negative-feedback) downregulation of PKG-I gene expression (Lincoln *et al*, 2001). Furthermore, high-level activation of PKG-I also results in the ubiquitination of PKG-I and its degradation by the proteasome (Dey *et al*, 2009). If such mechanisms are involved in regulating the protein expression levels of PKG-I in cancer cells, then the lower levels of protein expression of PKG-I, as was found in the chemoresistant A2780cp ovarian cancer cells (Figure 3), may actually reflect a higher level of functional PKG-I kinase activity. Future experiments will

Fig. 3. Comparison between traditional Western blot analysis and the new ultrasensitive Nanopro100 system for identifying two PKG-I isoforms expressed in human ovarian cancer cell lysates. A. Western blot analysis of 1.56 g total cellular protein in cell lysates of A2780cp and OV2008 cells. B – E. NanoPro100 analysis using 3.13 ng of total cellular protein in cell lysates. The lower electropherograms in D. & E. represent cell lysates treated for 30 minutes at 37 C with lambda phosphatase to remove phosphate groups from the proteins.

Fig. 3. Comparison between traditional Western blot analysis and the new ultrasensitive Nanopro100 system for identifying two PKG-I isoforms expressed in human ovarian cancer

A2780cp and OV2008 cells. B – E. NanoPro100 analysis using 3.13 ng of total cellular protein in cell lysates. The lower electropherograms in D. & E. represent cell lysates treated for 30 minutes at 37 C with lambda phosphatase to remove phosphate groups from the proteins.

cell lysates. A. Western blot analysis of 1.56 g total cellular protein in cell lysates of

In many of our previous studies, there appears to be an inverse relationship between the protein expression levels and the kinase activity of PKG-I. For example, vascular smooth muscle cells have exceptionally high levels of PKG-I protein expression but relatively low levels of PKG-I kinase activation (e.g. 30% - 40% of maximal activity) (Fiscus, 1988; Fiscus & Murad, 1988; Fiscus *et al*, 1983). In contrast, many cancer cells, including human ovarian cancer cells, have relatively low levels of PKG-I protein expression but high levels of PKG-I kinase activation (i.e. hyperactivation, estimated to be 90% of maximum activity) (Johlfs & Fiscus, 2010; Leung *et al*, 2010). We have proposed that the reason for this inverse relationship between protein expression levels and kinase activation levels is because of a negative-feedback mechanism, in which increases in PKG-I kinase activity (as, for example, resulting from c-Src-mediated tyrosine phosphorylation of PKG-I in cancer cells) would result in the negative modulation of PKG-I gene expression and protein expression, ultimately leading to the relatively low protein levels of PKG-I. However, in spite of lower protein levels (as measured in a Western blot analysis), the actual functional activity of PKG-I may remain quite high because of more-targeted subcellular localization, efficiently placing PKG-I in contact with its downstream target proteins. We have proposed that this may be the explanation for the low protein levels and high kinase activity of PKG-I in cancer cells, especially those overexpressing or having hyperactivated c-Src.

Negative feedback regulations of PKG-I expression at both the messenger RNA and protein levels have been shown in studies by Thomas Lincoln's laboratory at the University of South Alabama (Dey *et al*, 2009; Lincoln *et al*, 2001). Exposure of vascular smooth muscle cells to high levels of NO, causing large increases in intracellular cGMP levels, or to cellpermeable cGMP analogs that hyperactivate PKG-I, causes (negative-feedback) downregulation of PKG-I gene expression (Lincoln *et al*, 2001). Furthermore, high-level activation of PKG-I also results in the ubiquitination of PKG-I and its degradation by the proteasome (Dey *et al*, 2009). If such mechanisms are involved in regulating the protein expression levels of PKG-I in cancer cells, then the lower levels of protein expression of PKG-I, as was found in the chemoresistant A2780cp ovarian cancer cells (Figure 3), may actually reflect a higher level of functional PKG-I kinase activity. Future experiments will test this possibility.

#### **8. Overall model of the involvement of the NO/cGMP/PKG-I signaling pathway in promoting tumor growth, chemoresistance and angiogenesis in ovarian cancer**

Figure 4 illustrates our overall model showing the involvement of the NO/cGMP/PKG-I pathway in promoting cell proliferation and suppressing apoptosis in human ovarian cancer cells, which would contribute to enhanced tumor growth and chemoresistance. Also shown in the model is the potential role of nearby endothelial cells, which would provide an additional source of endogenous NO within the growing tumor, potentially contributing to the "angiogenic switch", i.e. the increased tumor growth that occurs after the invasion of endothelial cells into the tumor. Many factors are released from the endothelial cells that can stimulate the growth and chemoresistance of the tumor. Because low physiological levels (0.01 – 1 nM) of NO are now recognized to play a key role in promoting cancer cell proliferation and the development of chemoresistance, the NO released from nearby endothelial cells may have an important role in the tumor growth and chemoresistance

Nitric Oxide/Protein Kinase G-I Promotes c-Src

reported in our earlier study (Leung *et al*, 2010).

**9. Future research** 

Activation, Proliferation and Chemoresistance in Ovarian Cancer 329

At the time, it was not completely clear how PKG-I was able to promote the activation of c-Src by EGF, but we had hypothesized that PKG-I may be able to catalyze the phosphorylation of a serine or threonine residence of c-Src that was important for enhancing c-Src's tyrosine kinase activity. Upon reviewing the amino acid sequence of c-Src, we recognized that serine-17 could possibly serve as a phosphorylation site for PKG-I, based on the surrounding amino acids that provided a good consensus sequence for PKG-I catalyzed phosphorylation. To test this idea, we have worked with Cell Signaling Technologies (Danvers, MA, USA) over the last three years to develop an antibody that specifically recognizes the phosphorylated-form of serine-17 in c-Src. Using this antibody, we have shown that recombinant human-form PKG-I directly phosphorylates the serine-17 site in recombinant human-form c-Src, resulting in enhanced tyrosine kinase activity of c-Src (Fiscus & Johlfs, 2011). Using intact-cell experiments, involving two mesothelioma cell lines and a non-small cell lung cancer (NSCLC) cell line, we have further shown that gene knockdown of PKG-I expression (using siRNA and shRNA) or pharmacological inhibition of PKG-I activation resulted in dramatically suppressed levels of c-Src phosphorylation at serine-17, which corresponded to the inhibition of cell proliferation, increased levels of apoptosis and decreased attachment of the cells to the extracellular matrix (Fiscus & Johlfs, 2011). These recent studies have shown a clear role of the PKG-I-mediated phosphorylation of c-Src at serine-17 in preventing apoptosis and promoting proliferation, attachment and migration in the mesothelioma and NSCLC cells. It is very likely that a similar PKG-I-catalyzed phosphorylation of c-Src at serine-17 occurs in human ovarian cancer cells, which can explain the dependence of the c-Src activation by EGF on the presence of PKG-I, contributing to the stimulation of ovarian cancer cell proliferation, as

Also shown in the model of Figure 4 is the interaction of PKG-I with two other downstream target proteins, Bad (shown in the model as BAD) and CREB. Previous studies from our laboratory and other laboratories have shown that the nuclear transcription factor CREB can be directly phosphorylated at its serine-133 site by PKG, which results in increased transcriptional activity and downstream regulation of gene expression [reviewed in (Fiscus, 2002; Pilz & Casteel, 2003)]. Also, we have recently shown that PKG-I directly phosphorylates BAD at serine-155, using *in vitro* experiments, and have further shown that a large part of the serine-155 phosphorylation of BAD within neuroblastoma cells is dependent on endogenous PKG-I kinase activity (Johlfs & Fiscus, 2010). Thus, BAD and CREB may be important downstream target proteins mediating the anti-apoptotic and prochemoresistant effects of the NO/cGMP/PKG-I pathway in human ovarian cancer cells.

Future studies will need to determine: 1) if PKG-I is the only isoform of PKG expressed in other human ovarian cancer cell lines as well as in tumor samples of patients with ovarian cancer, as we have shown for the A2780cp and OV2008 cell lines described herein and shown in Figure 3, or if there is a co-expression of the PKG-I isoform in some ovarian cancer cells, like in the NCI-H2052 mesothelioma cell line (Figure 3), 2) what is the subcellular localization (e.g. plasma membrane, mitochondrial, nuclear, and/or cytosolic localization) of PKG-I (and possibly PKG-I in some ovarian cancer cells) and how this determines which downstream target proteins are phosphorylated by the different PKG

commonly found in ovarian cancer. Endothelial cells also play another important role in tumor growth by providing new blood vessels (i.e. angiogenesis) needed for the vascularization and blood perfusion of the growing tumor.

Fig. 4. Cellular model of the involvement of the NO/cGMP/PKG-I signaling pathway in promoting chemoresistance, tumor growth and angiogenesis in ovarian cancer.

Angiogenesis, especially that stimulated by VEGF (vascular endothelial growth factor), is now recognized to involve the stimulation of NO synthesis by eNOS within endothelial cells, which results in PKG activation and PKG-mediated downstream stimulation of MEK and ERK [reviewed in (Pilz & Casteel, 2003)]. This activation of the ERK signaling pathway is thought to result in enhanced proliferation, migration and tube formation of endothelial cells, key components of angiogenesis, all dependent on the NO/cGMP/PKG pathway. Although it has not yet been reported which isoform of PKG is involved in the multiple proangiogenesis responses of endothelial cells, our recent studies suggest that endothelial cells express predominantly the PKG-I isoform (unpublished observations by J.C. Wong and R.R. Fiscus), which likely mediates the stimulation of downstream growth-promoting and pro-angiogenesis pathways in endothelial cells.

Figure 4 illustrates the interaction between PKG-I and c-Src, which results in the reciprocal phosphorylation, i.e. each kinase phosphorylating the other. Our studies have shown that activation of EGF receptors in human ovarian cancer cells causes downstream activation of c-Src, which is completely dependent on the kinase activity of PKG-I (Leung et al., 2010).

commonly found in ovarian cancer. Endothelial cells also play another important role in tumor growth by providing new blood vessels (i.e. angiogenesis) needed for the

Fig. 4. Cellular model of the involvement of the NO/cGMP/PKG-I signaling pathway in

Angiogenesis, especially that stimulated by VEGF (vascular endothelial growth factor), is now recognized to involve the stimulation of NO synthesis by eNOS within endothelial cells, which results in PKG activation and PKG-mediated downstream stimulation of MEK and ERK [reviewed in (Pilz & Casteel, 2003)]. This activation of the ERK signaling pathway is thought to result in enhanced proliferation, migration and tube formation of endothelial cells, key components of angiogenesis, all dependent on the NO/cGMP/PKG pathway. Although it has not yet been reported which isoform of PKG is involved in the multiple proangiogenesis responses of endothelial cells, our recent studies suggest that endothelial cells express predominantly the PKG-I isoform (unpublished observations by J.C. Wong and R.R. Fiscus), which likely mediates the stimulation of downstream growth-promoting and

Figure 4 illustrates the interaction between PKG-I and c-Src, which results in the reciprocal phosphorylation, i.e. each kinase phosphorylating the other. Our studies have shown that activation of EGF receptors in human ovarian cancer cells causes downstream activation of c-Src, which is completely dependent on the kinase activity of PKG-I (Leung et al., 2010).

promoting chemoresistance, tumor growth and angiogenesis in ovarian cancer.

pro-angiogenesis pathways in endothelial cells.

vascularization and blood perfusion of the growing tumor.

At the time, it was not completely clear how PKG-I was able to promote the activation of c-Src by EGF, but we had hypothesized that PKG-I may be able to catalyze the phosphorylation of a serine or threonine residence of c-Src that was important for enhancing c-Src's tyrosine kinase activity. Upon reviewing the amino acid sequence of c-Src, we recognized that serine-17 could possibly serve as a phosphorylation site for PKG-I, based on the surrounding amino acids that provided a good consensus sequence for PKG-I catalyzed phosphorylation. To test this idea, we have worked with Cell Signaling Technologies (Danvers, MA, USA) over the last three years to develop an antibody that specifically recognizes the phosphorylated-form of serine-17 in c-Src. Using this antibody, we have shown that recombinant human-form PKG-I directly phosphorylates the serine-17 site in recombinant human-form c-Src, resulting in enhanced tyrosine kinase activity of c-Src (Fiscus & Johlfs, 2011). Using intact-cell experiments, involving two mesothelioma cell lines and a non-small cell lung cancer (NSCLC) cell line, we have further shown that gene knockdown of PKG-I expression (using siRNA and shRNA) or pharmacological inhibition of PKG-I activation resulted in dramatically suppressed levels of c-Src phosphorylation at serine-17, which corresponded to the inhibition of cell proliferation, increased levels of apoptosis and decreased attachment of the cells to the extracellular matrix (Fiscus & Johlfs, 2011). These recent studies have shown a clear role of the PKG-I-mediated phosphorylation of c-Src at serine-17 in preventing apoptosis and promoting proliferation, attachment and migration in the mesothelioma and NSCLC cells. It is very likely that a similar PKG-I-catalyzed phosphorylation of c-Src at serine-17 occurs in human ovarian cancer cells, which can explain the dependence of the c-Src activation by EGF on the presence of PKG-I, contributing to the stimulation of ovarian cancer cell proliferation, as reported in our earlier study (Leung *et al*, 2010).

Also shown in the model of Figure 4 is the interaction of PKG-I with two other downstream target proteins, Bad (shown in the model as BAD) and CREB. Previous studies from our laboratory and other laboratories have shown that the nuclear transcription factor CREB can be directly phosphorylated at its serine-133 site by PKG, which results in increased transcriptional activity and downstream regulation of gene expression [reviewed in (Fiscus, 2002; Pilz & Casteel, 2003)]. Also, we have recently shown that PKG-I directly phosphorylates BAD at serine-155, using *in vitro* experiments, and have further shown that a large part of the serine-155 phosphorylation of BAD within neuroblastoma cells is dependent on endogenous PKG-I kinase activity (Johlfs & Fiscus, 2010). Thus, BAD and CREB may be important downstream target proteins mediating the anti-apoptotic and prochemoresistant effects of the NO/cGMP/PKG-I pathway in human ovarian cancer cells.

#### **9. Future research**

Future studies will need to determine: 1) if PKG-I is the only isoform of PKG expressed in other human ovarian cancer cell lines as well as in tumor samples of patients with ovarian cancer, as we have shown for the A2780cp and OV2008 cell lines described herein and shown in Figure 3, or if there is a co-expression of the PKG-I isoform in some ovarian cancer cells, like in the NCI-H2052 mesothelioma cell line (Figure 3), 2) what is the subcellular localization (e.g. plasma membrane, mitochondrial, nuclear, and/or cytosolic localization) of PKG-I (and possibly PKG-I in some ovarian cancer cells) and how this determines which downstream target proteins are phosphorylated by the different PKG

Nitric Oxide/Protein Kinase G-I Promotes c-Src

precursor. *J Neurochem* 64(5): 2087-96.

*Acad Sci U S A* 107(51): 22060-5.

*Biol Chem* 277(35): 31801-7.

387-94.

9(12): 775-83.

102(5): 1117-29.

3966-73.

*Physiol* 287(4): R950-60.

**12. References** 

Activation, Proliferation and Chemoresistance in Ovarian Cancer 331

Nevada, USA. Other parts of our research reported herein were conducted at the Nevada Cancer Institute, Las Vegas, Nevada, USA, and were supported by a grant from the U.S. Department of Defense (Grant # W81XWH-07-1-0543) and Start-up Funding from the

Barger SW, Fiscus RR, Ruth P, Hofmann F, Mattson MP (1995) Role of cyclic GMP in the

Batchelor AM, Bartus K, Reynell C, Constantinou S, Halvey EJ, Held KF, Dostmann WR,

Beckman JS, Koppenol WH (1996) Nitric oxide, superoxide, and peroxynitrite: the good, the

Bellamy TC, Griffiths C, Garthwaite J (2002) Differential sensitivity of guanylyl cyclase and

Cantuaria G, Magalhaes A, Angioli R, Mendez L, Mirhashemi R, Wang J, Wang P, Penalver

Chan GH, Fiscus RR (2002) Severe impairment of CGRP-induced hypotension in vivo and

Chan GH, Fiscus RR (2004) Exaggerated production of nitric oxide (NO) and increases in

Chan SL, Fiscus RR (2003) Guanylyl cyclase inhibitors NS2028 and ODQ and protein kinase

Chang S, Hypolite JA, Velez M, Changolkar A, Wein AJ, Chacko S, DiSanto ME (2004)

Cheng Chew SB, Leung PY, Fiscus RR (2003) Preincubation with atrial natriuretic peptide

donor S-nitroso- N-acetylpenicillamine. *Histochem Cell Biol* 120(3): 163-71. Chien JR, Aletti G, Bell DA, Keeney GL, Shridhar V, Hartmann LC (2007) Molecular

Deguchi A, Thompson WJ, Weinstein IB (2004) Activation of protein kinase G is sufficient to

vasorelaxation in vitro in elderly rats. *Eur J Pharmacol* 434(3): 133-9.

regulation of neuronal calcium and survival by secreted forms of beta-amyloid

Vernon J, Garthwaite J (2010) Exquisite sensitivity to subsecond, picomolar nitric oxide transients conferred on cells by guanylyl cyclase-coupled receptors. *Proc Natl* 

mitochondrial respiration to nitric oxide measured using clamped concentrations. *J* 

M, Averette H, Braunschweiger P (2000) Antitumor activity of a novel glyco-nitric

inducible NO-synthase mRNA levels induced by the pro-inflammatory cytokine interleukin-1beta in vascular smooth muscle cells of elderly rats. *Exp Gerontol* 39(3):

G (PKG) inhibitor KT5823 trigger apoptotic DNA fragmentation in immortalized uterine epithelial cells: anti-apoptotic effects of basal cGMP/PKG. *Mol Hum Reprod*

Downregulation of cGMP-dependent protein kinase-1 activity in the corpus cavernosum smooth muscle of diabetic rabbits. *Am J Physiol Regul Integr Comp* 

protects NG108-15 cells against the toxic/proapoptotic effects of the nitric oxide

pathogenesis and therapeutic targets in epithelial ovarian cancer. *J Cell Biochem*

induce apoptosis and inhibit cell migration in colon cancer cells. *Cancer Res* 64(11):

Nevada Cancer Institute, Las Vegas, Nevada, USA, awarded to Dr. Fiscus.

bad, and the ugly. *Am J Physiol* 271(5 Pt 1): C1424-37.

oxide conjugate in ovarian carcinoma. *Cancer* 88(2): 381-8.

isoforms, 3) if the NO/cGMP/PKG-I signaling pathway is involved in promoting cell invasion and metastasis of ovarian cancer cells, 4) if there are other downstream target proteins that contribute to mediating the stimulation of DNA synthesis/cell proliferation, chemoresistance and metastasis of ovarian cancer cells, and 5) if the low-level-NO/cGMP/PKG-I and the higher-level-NO/cGMP/PKG-I signaling pathways are involved in regulating apoptosis, proliferation and differentiation in the subset of ovarian cancer cells known as the ovarian tumor-initiating cells (or ovarian cancer stem cells) that may contribute to the tumorigenesis in ovarian cancer.

Our future studies will utilize the new ultrasensitive NanoPro100 system to determine the expression levels of PKG isoforms in human ovarian cancer cells. Because the NanoPro100 system separates proteins based on pI, rather than molecular weight as in conventional Western blot analysis, the new instrument can cleanly resolve the different phosphorylated forms of proteins, as shown in Figure 3. We will use this capability to identify the different phosphorylated forms of the PKG-I isoforms as well as the downstream phosphorylation of the different target proteins of the PKG isoforms.

#### **10. Conclusions**

Our studies suggest that the NO/cGMP/PKG-I signaling pathway and its interaction with the c-Src tyrosine kinase pathway play an essential role in promoting cell proliferation and chemoresistance in human ovarian cancer cells. The interaction with c-Src involves a novel reciprocal phosphorylation mechanism, which includes c-Src mediating the tyrosinephosphorylation of PKG-I, enhancing PKG-I's serine/threonine kinase activity, and PKG-I mediating the serine-phosphorylation of c-Src (at serine-17), enhancing c-Src's tyrosine kinase activity. We propose that this novel interaction results in an "oncogenic reinforcement" in human ovarian cancer cells, leading to the exaggerated cell proliferation and chemoresistance, illustrated in the model in Figure 4.

This new understanding of the NO/cGMP/PKG-I pathway and its interaction with c-Src in human ovarian cancer cells provides new molecular targets that can be used for developing novel anti-cancer therapeutic agents. However, because NO has multiple cellular and molecular actions, illustrated in Figure 1, and the two PKG-I isoforms mediate very different biological effects, illustrated in Figure 2, future studies will need to recognize these complexities and their importance in development of new therapies for ovarian cancer.

New state-of-the-art instruments, like the NanoPro100 system, which provides >100-times higher sensitivity and much better specificity in identifying and quantifying protein expression and site-specific protein phosphorylation, compared with conventional Western blot analysis, will greatly facilitate our future studies. It is anticipated that the new information that will be learned about the low-level-NO/cGMP/PKG-I signaling pathway and its interaction with the c-Src tyrosine kinase pathway in human ovarian cancer cells will ultimately lead to new therapies that can successfully treat ovarian cancer.

#### **11. Acknowledgements**

Financial support for the research involving the NanoPro100 instrument and the preparation of this book chapter was provided by Start-up Funding from Roseman University of Health Sciences (formerly named University of Southern Nevada), Henderson, Nevada, USA. Other parts of our research reported herein were conducted at the Nevada Cancer Institute, Las Vegas, Nevada, USA, and were supported by a grant from the U.S. Department of Defense (Grant # W81XWH-07-1-0543) and Start-up Funding from the Nevada Cancer Institute, Las Vegas, Nevada, USA, awarded to Dr. Fiscus.

### **12. References**

330 Ovarian Cancer – Basic Science Perspective

isoforms, 3) if the NO/cGMP/PKG-I signaling pathway is involved in promoting cell invasion and metastasis of ovarian cancer cells, 4) if there are other downstream target proteins that contribute to mediating the stimulation of DNA synthesis/cell proliferation, chemoresistance and metastasis of ovarian cancer cells, and 5) if the low-level-NO/cGMP/PKG-I and the higher-level-NO/cGMP/PKG-I signaling pathways are involved in regulating apoptosis, proliferation and differentiation in the subset of ovarian cancer cells known as the ovarian tumor-initiating cells (or ovarian cancer stem cells) that

Our future studies will utilize the new ultrasensitive NanoPro100 system to determine the expression levels of PKG isoforms in human ovarian cancer cells. Because the NanoPro100 system separates proteins based on pI, rather than molecular weight as in conventional Western blot analysis, the new instrument can cleanly resolve the different phosphorylated forms of proteins, as shown in Figure 3. We will use this capability to identify the different phosphorylated forms of the PKG-I isoforms as well as the downstream phosphorylation of

Our studies suggest that the NO/cGMP/PKG-I signaling pathway and its interaction with the c-Src tyrosine kinase pathway play an essential role in promoting cell proliferation and chemoresistance in human ovarian cancer cells. The interaction with c-Src involves a novel reciprocal phosphorylation mechanism, which includes c-Src mediating the tyrosinephosphorylation of PKG-I, enhancing PKG-I's serine/threonine kinase activity, and PKG-I mediating the serine-phosphorylation of c-Src (at serine-17), enhancing c-Src's tyrosine kinase activity. We propose that this novel interaction results in an "oncogenic reinforcement" in human ovarian cancer cells, leading to the exaggerated cell proliferation

This new understanding of the NO/cGMP/PKG-I pathway and its interaction with c-Src in human ovarian cancer cells provides new molecular targets that can be used for developing novel anti-cancer therapeutic agents. However, because NO has multiple cellular and molecular actions, illustrated in Figure 1, and the two PKG-I isoforms mediate very different biological effects, illustrated in Figure 2, future studies will need to recognize these complexities and their importance in development of new therapies for ovarian

New state-of-the-art instruments, like the NanoPro100 system, which provides >100-times higher sensitivity and much better specificity in identifying and quantifying protein expression and site-specific protein phosphorylation, compared with conventional Western blot analysis, will greatly facilitate our future studies. It is anticipated that the new information that will be learned about the low-level-NO/cGMP/PKG-I signaling pathway and its interaction with the c-Src tyrosine kinase pathway in human ovarian cancer cells will

Financial support for the research involving the NanoPro100 instrument and the preparation of this book chapter was provided by Start-up Funding from Roseman University of Health Sciences (formerly named University of Southern Nevada), Henderson,

ultimately lead to new therapies that can successfully treat ovarian cancer.

may contribute to the tumorigenesis in ovarian cancer.

the different target proteins of the PKG isoforms.

and chemoresistance, illustrated in the model in Figure 4.

**10. Conclusions** 

cancer.

**11. Acknowledgements** 


Nitric Oxide/Protein Kinase G-I Promotes c-Src

*Neurochem Int* 56(4): 546-53.

resistance. *Br J Cancer* 98(11): 1803-9.

expression. *J Appl Physiol* 91(3): 1421-30.

*Proc Natl Acad Sci U S A* 105(1): 365-70.

apoptosis. *Gynecol Oncol* 73(2): 257-64.

3065-73.

105.

1034-46.

82(1): 172-6.

*Nitric Oxide* 19(2): 73-6.

Activation, Proliferation and Chemoresistance in Ovarian Cancer 333

Fung E, Fiscus RR, Yim AP, Angelini GD, Arifi AA (2005) The potential use of type-5

Garban HJ, Bonavida B (1999) Nitric oxide sensitizes ovarian tumor cells to Fas-induced

Hofmann F, Feil R, Kleppisch T, Schlossmann J (2006) Function of cGMP-dependent protein

Hou Y, Gupta N, Schoenlein P, Wong E, Martindale R, Ganapathy V, Browning D (2006) An anti-tumor role for cGMP-dependent protein kinase. *Cancer Lett* 240(1): 60-8. Isenberg JS, Ridnour LA, Perruccio EM, Espey MG, Wink DA, Roberts DD (2005)

Johlfs MG, Fiscus RR (2010) Protein kinase G type-Ialpha phosphorylates the apoptosis-

Kleinert H, Schwarz PM, Forstermann U (2003) Regulation of the expression of inducible

Kulie T, Slattengren A, Redmer J, Counts H, Eglash A, Schrager S (2011) Obesity and women's health: an evidence-based review. *J Am Board Fam Med* 24(1): 75-85. LaFevre-Bernt M, Corbin JD, Francis SH, Miller WT (1998) Phosphorylation and activation

Leung EL, Fraser M, Fiscus RR, Tsang BK (2008) Cisplatin alters nitric oxide synthase levels

Leung EL, Wong JC, Johlfs MG, Tsang BK, Fiscus RR (2010) Protein kinase G type Ialpha

Nakamura T, Lipton SA (2010) Redox regulation of mitochondrial fission, protein

Nausch LW, Ledoux J, Bonev AD, Nelson MT, Dostmann WR (2008) Differential patterning

Pilz RB, Casteel DE (2003) Regulation of gene expression by cyclic GMP. *Circ Res* 93(11):

Ridnour LA, Thomas DD, Switzer C, Flores-Santana W, Isenberg JS, Ambs S, Roberts DD,

Rieder J, Jahnke R, Schloesser M, Seibel M, Czechowski M, Marth C, Hoffmann G (2001)

Alzheimer's and Parkinson's diseases. *Apoptosis* 15(11): 1354-63.

kinases as revealed by gene deletion. *Physiol Rev* 86(1): 1-23.

dependent manner. *Proc Natl Acad Sci U S A* 102(37): 13141-6.

nitric oxide synthase. *Biol Chem* 384(10-11): 1343-64.

phosphodiesterase inhibitors in coronary artery bypass graft surgery. *Chest* 128(4):

Thrombospondin-1 inhibits endothelial cell responses to nitric oxide in a cGMP-

regulating protein Bad at serine 155 and protects against apoptosis in N1E-115 cells.

of cGMP-dependent protein kinase by Src. *Biochimica et biophysica acta* 1386(1): 97-

in human ovarian cancer cells: involvement in p53 regulation and cisplatin

activity in human ovarian cancer cells significantly contributes to enhanced Src activation and DNA synthesis/cell proliferation. *Mol Cancer Res* 8(4): 578-91. Lincoln TM, Dey N, Sellak H (2001) Invited review: cGMP-dependent protein kinase

signaling mechanisms in smooth muscle: from the regulation of tone to gene

misfolding, synaptic damage, and neuronal cell death: potential implications for

of cGMP in vascular smooth muscle cells revealed by single GFP-linked biosensors.

Wink DA (2008) Molecular mechanisms for discrete nitric oxide levels in cancer.

Nitric oxide-dependent apoptosis in ovarian carcinoma cell lines. *Gynecol Oncol*


Dey NB, Busch JL, Francis SH, Corbin JD, Lincoln TM (2009) Cyclic GMP specifically

Farias-Eisner R, Sherman MP, Aeberhard E, Chaudhuri G (1994) Nitric oxide is an important mediator for tumoricidal activity in vivo. *Proc Natl Acad Sci U S A* 91(20): 9407-11. Fernhoff NB, Derbyshire ER, Marletta MA (2009) A nitric oxide/cysteine interaction

Fiscus RR (1988) Molecular mechanisms of endothelium-mediated vasodilation. *Semin* 

Fiscus RR (2002) Involvement of cyclic GMP and protein kinase G in the regulation of

Fiscus RR, Chan GHH, Ma ACY (2001) Diabetes mellitus (DM) causes severe impairment of

(eds), pp 973-974. San Diego, California, U.S.A.: American Peptide Society. Fiscus RR, Johlfs MG (2011) Protein kinase G type-I phosphorylates c-Src at serine-17 and

Fiscus RR, Murad F (1988) cGMP-dependent protein kinase activation in intact tissues.

Fiscus RR, Rapoport RM, Murad F (1983) Endothelium-dependent and nitrovasodilator-

Fiscus RR, Torphy TJ, Mayer SE (1984) Cyclic GMP-dependent protein kinase activation in

Fiscus RR, Yuen JP, Chan SL, Kwong JH, Chew SB (2002) Nitric oxide and cyclic GMP as

Francis SH, Busch JL, Corbin JD, Sibley D (2010) cGMP-dependent protein kinases and

Francis SH, Woodford TA, Wolfe L, Corbin JD (1988) Types I alpha and I beta isozymes of

Fraser M, Leung B, Jahani-Asl A, Yan X, Thompson WE, Tsang BK (2003) Chemoresistance

inhibitory domains. *Second Messengers Phosphoproteins* 12(5-6): 301-10. Fraser M, Chan SL, Chan SS, Fiscus RR, Tsang BK (2006) Regulation of p53 and suppression

non-small cell lung cancer cells. *BMC Pharmacology* 11(Suppl. 1): 031. Fiscus RR, Ming SK (2000) Biology of ageing. In *Medicine and Surgery in the Older Person*,

apoptosis and survival in neural cells. *Neurosignals* 11(4): 175-90.

ubiquitination. *Cell Signal* 21(6): 859-66.

*Thromb Hemost* 14 Suppl: 12-22.

*Methods Enzymol* 159: 150-9.

*Biophys Acta* 805(4): 382-92.

*Nucleotide Protein Phosphor Res* 9(6): 415-25.

cancer cells. *Oncogene* 25(15): 2203-12.

pro- and anti-apoptotic agents. *J Card Surg* 17(4): 336-9.

106(51): 21602-7.

Ltd.

525-63.

66.

suppresses Type-Ialpha cGMP-dependent protein kinase expression by

mediates the activation of soluble guanylate cyclase. *Proc Natl Acad Sci U S A*

hypotensive response in vivo and vasorelaxant response in vitro to the neuropeptide CGRP. In *Peptides: The Wave of the Future*, Lebl M, Houghten RA

promotes cell survival, proliferation and attachment in human mesothelioma and

Pang WS, Teoh MK, Ming SK (eds), pp 29-42. Singapore: Armour Publishing Pte

induced activation of cyclic GMP-dependent protein kinase in rat aorta. *J Cyclic* 

canine tracheal smooth muscle by methacholine and sodium nitroprusside. *Biochim* 

cGMP phosphodiesterases in nitric oxide and cGMP action. *Pharmacol Rev* 62(3):

cGMP-dependent protein kinase: alternative mRNA splicing may produce different

of apoptosis by the soluble guanylyl cyclase/cGMP pathway in human ovarian

in human ovarian cancer: the role of apoptotic regulators. *Reprod Biol Endocrinol* 1:


**17** 

*Japan* 

**VEGF Targeting Agents in Ovarian Cancer** 

Angiogenesis, the formation of new blood vessels, is a critical component in the growth and metastasis of cancers and has been recognized as an attractive target for anticancer therapy (Ferrara, 2002). Among the pro-angiogenic factors, vascular endothelial growth factor (VEGF) is recognized as the predominant mediator of angiogenesis in tumor cells (Ferrara & Kerbel, 2005). As VEGF is overexpressed in most ovarian cancers, the VEGF pathway is a

Recent increases in our understanding of the molecular pathways that control tumor angiogenesis have led to the development of novel VEGF-targeting agents for the treatment of ovarian cancer (Burger, 2011). In addition to inhibiting neo-vascularization, antiangiogenic agents are also believed to normalize intratumoral blood vessels. Intratumoral vessels are hyperpermeable, leading to interstitial hypertension and impaired perfusion in tumors. Normalization of the tumor vasculature results in a reduction in interstitial pressure

Many of these agents have been evaluated in clinical trials, and some of them have shown promising clinical activity against ovarian cancer (Burger, 2011). In this article, we review the emerging VEGF-targeting strategies for treating ovarian cancer and provide information about the latest clinical studies of VEGF-targeting agents that have been employed as

Angiogenesis is the process by which new blood vessels grow from the existing vasculature. A tumor is unable to grow beyond 2mm diameter without neoangiogenesis (Carmeliet, 2000), thus, angiogenesis plays an essential role in tumor growth, invasion, and metastasis (Carmeliet, 2000; Kerbel, 1991). Angiogenesis is tightly regulated by balancing pro- and antiangiogenic factors. The transition of a tumor from "avascular phase" to "vascular phase" is termed "angiogenic switch". This switch is believed to be stimulated by an increase in expression of pro-angiogenic factors. A variety of pro-angiogenic factors have been identified and recognized as potential targets of antiangiogenic therapy (Ferrara & Kerbel, 2005). Vascular endothelial cell growth factor (VEGF), one of the key mediators of angiogenesis, promotes the proliferation, survival, and migration of endothelial cells and is essential for blood vessel formation (Ferrara & Kerbel, 2005). VEGF can also affect new vessel formation in tumors by acting as a chemoattractant for bone marrow-derived progenitor cells (Rafii et al., 2002). The major physiological stimulus for VEGF expression is

promising target for anti-angiogenic therapy against ovarian cancer (Burger, 2011).

and the improved delivery of oxygen, nutrients, and cytotoxic agents (Ferrara, 2002).

**1. Introduction** 

treatments for ovarian cancer.

**2. Angiogenesis overview** 

Seiji Mabuchi, Atsuko Wakabayashi and Tadashi Kimura

*Department of Obstetrics and Gynecology, Osaka University* 


## **VEGF Targeting Agents in Ovarian Cancer**

Seiji Mabuchi, Atsuko Wakabayashi and Tadashi Kimura *Department of Obstetrics and Gynecology, Osaka University Japan* 

#### **1. Introduction**

334 Ovarian Cancer – Basic Science Perspective

Sato M, Nakajima T, Goto M, Umezawa Y (2006) Cell-based indicator to visualize picomolar dynamics of nitric oxide release from living cells. *Anal Chem* 78(24): 8175-82. Seth D, Stamler JS (2011) The SNO-proteome: causation and classifications. *Curr Opin Chem* 

Soh JW, Mao Y, Kim MG, Pamukcu R, Li H, Piazza GA, Thompson WJ, Weinstein IB (2000)

Thomas DD, Ridnour LA, Isenberg JS, Flores-Santana W, Switzer CH, Donzelli S, Hussain P,

Thompson WJ, Piazza GA, Li H, Liu L, Fetter J, Zhu B, Sperl G, Ahnen D, Pamukcu R (2000)

Wong AS, Kim SO, Leung PC, Auersperg N, Pelech SL (2001) Profiling of protein kinases in

Wong JC, Fiscus RR (2010) Protein kinase G activity prevents pathological-level nitric oxide-

Wong JC, Fiscus RR (2011) Essential roles of the nitric oxide (NO)/cGMP/protein kinase G

survival of OP9 bone marrow stromal cells. *J Cell Biochem* 112(3): 829-39.

Jun NH2-terminal kinase 1. *Clin Cancer Res* 6(10): 4136-41.

smooth muscle cells. *Cardiovasc Pathol* 19(6): e221-31.

Cyclic GMP mediates apoptosis induced by sulindac derivatives via activation of c-

Vecoli C, Paolocci N, Ambs S, Colton CA, Harris CC, Roberts DD, Wink DA (2008) The chemical biology of nitric oxide: implications in cellular signaling. *Free Radic* 

Exisulind induction of apoptosis involves guanosine 3',5'-cyclic monophosphate phosphodiesterase inhibition, protein kinase G activation, and attenuated beta-

the neoplastic transformation of human ovarian surface epithelium. *Gynecol Oncol*

induced apoptosis and promotes DNA synthesis/cell proliferation in vascular

type-Ialpha (PKG-Ialpha) signaling pathway and the atrial natriuretic peptide (ANP)/cGMP/PKG-Ialpha autocrine loop in promoting proliferation and cell

*Biol* 15(1): 129-36.

*Biol Med* 45(1): 18-31.

82(2): 305-11.

catenin. *Cancer Res* 60(13): 3338-42.

Angiogenesis, the formation of new blood vessels, is a critical component in the growth and metastasis of cancers and has been recognized as an attractive target for anticancer therapy (Ferrara, 2002). Among the pro-angiogenic factors, vascular endothelial growth factor (VEGF) is recognized as the predominant mediator of angiogenesis in tumor cells (Ferrara & Kerbel, 2005). As VEGF is overexpressed in most ovarian cancers, the VEGF pathway is a promising target for anti-angiogenic therapy against ovarian cancer (Burger, 2011).

Recent increases in our understanding of the molecular pathways that control tumor angiogenesis have led to the development of novel VEGF-targeting agents for the treatment of ovarian cancer (Burger, 2011). In addition to inhibiting neo-vascularization, antiangiogenic agents are also believed to normalize intratumoral blood vessels. Intratumoral vessels are hyperpermeable, leading to interstitial hypertension and impaired perfusion in tumors. Normalization of the tumor vasculature results in a reduction in interstitial pressure and the improved delivery of oxygen, nutrients, and cytotoxic agents (Ferrara, 2002).

Many of these agents have been evaluated in clinical trials, and some of them have shown promising clinical activity against ovarian cancer (Burger, 2011). In this article, we review the emerging VEGF-targeting strategies for treating ovarian cancer and provide information about the latest clinical studies of VEGF-targeting agents that have been employed as treatments for ovarian cancer.

#### **2. Angiogenesis overview**

Angiogenesis is the process by which new blood vessels grow from the existing vasculature. A tumor is unable to grow beyond 2mm diameter without neoangiogenesis (Carmeliet, 2000), thus, angiogenesis plays an essential role in tumor growth, invasion, and metastasis (Carmeliet, 2000; Kerbel, 1991). Angiogenesis is tightly regulated by balancing pro- and antiangiogenic factors. The transition of a tumor from "avascular phase" to "vascular phase" is termed "angiogenic switch". This switch is believed to be stimulated by an increase in expression of pro-angiogenic factors. A variety of pro-angiogenic factors have been identified and recognized as potential targets of antiangiogenic therapy (Ferrara & Kerbel, 2005). Vascular endothelial cell growth factor (VEGF), one of the key mediators of angiogenesis, promotes the proliferation, survival, and migration of endothelial cells and is essential for blood vessel formation (Ferrara & Kerbel, 2005). VEGF can also affect new vessel formation in tumors by acting as a chemoattractant for bone marrow-derived progenitor cells (Rafii et al., 2002). The major physiological stimulus for VEGF expression is

VEGF Targeting Agents in Ovarian Cancer 337

subtypes such as serous adenocarcinoma or endometrioid adenocarcinoma were investigated, the expression rate of VEGF was approximately 90% (Wong et al., 2003; Yamamoto et al., 1997; Brustmann, 2004). In a recent report about clear cell carcinoma of the ovary, strong VEGF expression was observed in 86% of cases (Mabuchi et al., 2010b). These results suggest that most epithelial ovarian cancers are dependent on VEGF for tumor

In patients with ovarian cancer, high serum VEGF levels are an independent risk factor for advanced stage and decreased survival (Mabuchi et al., 2010b; Cooper et al., 2002; Li et al., 2004; Hefler et al., 2006). Moreover, previous immunohistochemical analyses have suggested that VEGF expression in ovarian cancer specimens is also associated with poor patient prognosis, advanced stage, and short survival (Mabuchi et al., 2010b; Cooper et al., 2002; Li et al., 2004; Hefler et al., 2006). For example, in a recent investigation of ovarian clear cell carcinomas (Mabuchi et al., 2010b), patients whose tumor showed strong immunoreactivity displayed significantly shorter survival than those with weak immunoreactivity for VEGF (mean: 60 months vs. 40 months, respectively). The association observed between VEGF expression and clinical outcome in ovarian cancer patients makes

The effect of VEGF on vascular permeability is believed to be crucial for malignant ascites formation (Senger et al., 1983; Zhang et al., 2002). In patients with ovarian cancer, high serum VEGF levels was reported to be an independent risk factor for developing ascites formation (Cooper et al., 2002; Li et al., 2004; Hefler et al., 2006). In an in vivo investigation using an intraperitoneal ovarian cancer model, VEGF inhibition resulted in the complete inhibition of ascites formation (Mesiano et al., 1998; Mabuchi et al., 2008). Since patients with advanced or recurrent ovarian cancer frequently suffer from malignant ascites and require paracentesis for symptomatic relief, the ability of VEGF-targeting agents to inhibit ascites formation makes them attractive candidate treatments for ovarian cancer (Numnum et al.,

It has been reported that chemoresistant tumors display greater VEGF expression than chemosensitive tumors (Mabuchi et al., 2010b; Schönau et al., 2007). For example, 5 fluorouracil-resistant colon adenocarcinoma subclones were found to display increased VEGF expression and enhanced pro-angiogenic activity compared to the corresponding primary adenocarcinoma cells (Schönau et al., 2007). Moreover, cisplatin-refractory ovarian cancer cell lines exhibit higher VEGF expression than their parental cisplatin-sensitive cell lines (Mabuchi et al., 2010b). As VEGF increases vascular permeability, which leads to interstitial hypertension and the impaired delivery of cytotoxic agents to tumors; theoretically, increased VEGF production in chemoresistant tumors might further limit the efficacy of chemotherapy (Gerber & Ferrara, 2005). Therefore, VEGF inhibition is a reasonable treatment strategy for overcoming chemoresistance or enhancing the sensitivity

progression, and hence, are candidates for VEGF targeting therapy.

the VEGF pathway an attractive therapeutic target in this patient group.

**4.2 VEGF expression and patient prognosis** 

**4.3 VEGF expression and ascites formation** 

**4.4 VEGF expression and chemoresistance** 

of ovarian cancer to chemotherapeutic agents.

2006).

hypoxia, which commonly develops within tumors when cancer cell proliferation exceeds the rate of blood vessel formation. Hypoxia inducible factor-1 (HIF-1), a transcriptional activator that acts as a central regulator of oxygen homeostasis, regulates the expression of VEGF and promotes angiogenesis, which is essential for fulfilling the metabolic requirements of tumor growth (Forsythe et al., 1996).

### **3. Vascular endothelial growth factor (VEGF): Structure and function**

VEGF (also referred to as VEGF-A) is a dimeric protein that has been shown to stimulate angiogenesis. As it also enhances vascular permeability, VEGF is also recognized as vascular permeability factor (VPF) (Ferrara & Kerbel, 2005).

VEGF is a member of the VEGF/PDGF gene family. Other members of this family include VEGF-B, VEGF-C, VEGF-D, PDGF, and PIGF (Ferrara & Kerbel, 2005). VEGF exerts its biological effects by interacting with the VEGF receptors (VEGFR) present on the cell surface (Figure 1). These transmembrane receptors include VEGFR-1 (also known as Flt-1) and VEGFR-2 (Flk-1), which are predominantly expressed on vascular endothelial cells. A third receptor, VEGFR-3 (Flt-4), is mainly involved in the regulation of lymphatic systems (Karkkainen et al., 2001). The binding of VEGF to its receptor causes the dimerization and phosphorylation of intracellular receptor kinases, which in turn activates a cascade of downstream signals responsible for tumor angiogenesis (Ferrara & Kerbel, 2005).

Fig. 1. VEGF signaling.

#### **4. Role of VEGF in epithelial ovarian cancer: Preclinical findings**

#### **4.1 VEGF expression in epithelial ovarian cancer**

VEGF expression in epithelial ovarian cancer has been intensively examined (Wong et al., 2003; Yamamoto et al., 1997; Brustmann, 2004; Mabuchi et al., 2010b). It has been generally accepted that VEGF expression is greater in ovarian cancers than in normal ovarian tissue or benign ovarian neoplasm. According to previous reports, in which common histological

hypoxia, which commonly develops within tumors when cancer cell proliferation exceeds the rate of blood vessel formation. Hypoxia inducible factor-1 (HIF-1), a transcriptional activator that acts as a central regulator of oxygen homeostasis, regulates the expression of VEGF and promotes angiogenesis, which is essential for fulfilling the metabolic

VEGF (also referred to as VEGF-A) is a dimeric protein that has been shown to stimulate angiogenesis. As it also enhances vascular permeability, VEGF is also recognized as vascular

VEGF is a member of the VEGF/PDGF gene family. Other members of this family include VEGF-B, VEGF-C, VEGF-D, PDGF, and PIGF (Ferrara & Kerbel, 2005). VEGF exerts its biological effects by interacting with the VEGF receptors (VEGFR) present on the cell surface (Figure 1). These transmembrane receptors include VEGFR-1 (also known as Flt-1) and VEGFR-2 (Flk-1), which are predominantly expressed on vascular endothelial cells. A third receptor, VEGFR-3 (Flt-4), is mainly involved in the regulation of lymphatic systems (Karkkainen et al., 2001). The binding of VEGF to its receptor causes the dimerization and phosphorylation of intracellular receptor kinases, which in turn activates a cascade of

**3. Vascular endothelial growth factor (VEGF): Structure and function** 

downstream signals responsible for tumor angiogenesis (Ferrara & Kerbel, 2005).

**4. Role of VEGF in epithelial ovarian cancer: Preclinical findings** 

VEGF expression in epithelial ovarian cancer has been intensively examined (Wong et al., 2003; Yamamoto et al., 1997; Brustmann, 2004; Mabuchi et al., 2010b). It has been generally accepted that VEGF expression is greater in ovarian cancers than in normal ovarian tissue or benign ovarian neoplasm. According to previous reports, in which common histological

**4.1 VEGF expression in epithelial ovarian cancer** 

requirements of tumor growth (Forsythe et al., 1996).

permeability factor (VPF) (Ferrara & Kerbel, 2005).

Fig. 1. VEGF signaling.

subtypes such as serous adenocarcinoma or endometrioid adenocarcinoma were investigated, the expression rate of VEGF was approximately 90% (Wong et al., 2003; Yamamoto et al., 1997; Brustmann, 2004). In a recent report about clear cell carcinoma of the ovary, strong VEGF expression was observed in 86% of cases (Mabuchi et al., 2010b). These results suggest that most epithelial ovarian cancers are dependent on VEGF for tumor progression, and hence, are candidates for VEGF targeting therapy.

#### **4.2 VEGF expression and patient prognosis**

In patients with ovarian cancer, high serum VEGF levels are an independent risk factor for advanced stage and decreased survival (Mabuchi et al., 2010b; Cooper et al., 2002; Li et al., 2004; Hefler et al., 2006). Moreover, previous immunohistochemical analyses have suggested that VEGF expression in ovarian cancer specimens is also associated with poor patient prognosis, advanced stage, and short survival (Mabuchi et al., 2010b; Cooper et al., 2002; Li et al., 2004; Hefler et al., 2006). For example, in a recent investigation of ovarian clear cell carcinomas (Mabuchi et al., 2010b), patients whose tumor showed strong immunoreactivity displayed significantly shorter survival than those with weak immunoreactivity for VEGF (mean: 60 months vs. 40 months, respectively). The association observed between VEGF expression and clinical outcome in ovarian cancer patients makes the VEGF pathway an attractive therapeutic target in this patient group.

#### **4.3 VEGF expression and ascites formation**

The effect of VEGF on vascular permeability is believed to be crucial for malignant ascites formation (Senger et al., 1983; Zhang et al., 2002). In patients with ovarian cancer, high serum VEGF levels was reported to be an independent risk factor for developing ascites formation (Cooper et al., 2002; Li et al., 2004; Hefler et al., 2006). In an in vivo investigation using an intraperitoneal ovarian cancer model, VEGF inhibition resulted in the complete inhibition of ascites formation (Mesiano et al., 1998; Mabuchi et al., 2008). Since patients with advanced or recurrent ovarian cancer frequently suffer from malignant ascites and require paracentesis for symptomatic relief, the ability of VEGF-targeting agents to inhibit ascites formation makes them attractive candidate treatments for ovarian cancer (Numnum et al., 2006).

#### **4.4 VEGF expression and chemoresistance**

It has been reported that chemoresistant tumors display greater VEGF expression than chemosensitive tumors (Mabuchi et al., 2010b; Schönau et al., 2007). For example, 5 fluorouracil-resistant colon adenocarcinoma subclones were found to display increased VEGF expression and enhanced pro-angiogenic activity compared to the corresponding primary adenocarcinoma cells (Schönau et al., 2007). Moreover, cisplatin-refractory ovarian cancer cell lines exhibit higher VEGF expression than their parental cisplatin-sensitive cell lines (Mabuchi et al., 2010b). As VEGF increases vascular permeability, which leads to interstitial hypertension and the impaired delivery of cytotoxic agents to tumors; theoretically, increased VEGF production in chemoresistant tumors might further limit the efficacy of chemotherapy (Gerber & Ferrara, 2005). Therefore, VEGF inhibition is a reasonable treatment strategy for overcoming chemoresistance or enhancing the sensitivity of ovarian cancer to chemotherapeutic agents.

VEGF Targeting Agents in Ovarian Cancer 339

Among the VEGF-targeting agents including bevacizumab, aflibercept, and TKI, bevacizumab has been used most commonly for the treatment of ovarian cancer in the clinical setting. Thus, we herein provide information regarding the preclinical activity of

Preclinical in vivo studies have demonstrated that inhibiting VEGF activity using a murine monoclonal antibody to human VEGF (A4.6.1) significantly inhibited the growth of subcutaneously inoculated ovarian tumors. Moreover, in mice carrying intraperitoneal tumors, treatment with A4.6.1 completely inhibited ascites production (Mesiano et al., 1998). Bevacizumab is the humanized form of A4.6.1, which was developed in 1997 (Presta et al., 1997). In a preclinical investigation, treatment with bevacizumab significantly inhibited the growth of intraperitoneally inoculated serous ovarian cancer. In addition, bevacizumab treatment significantly inhibited ascites production and prolonged survival of the mice

The single agent activity of bevacizumab on chemoresistant ovarian cancer has also been evaluated preclinically. According to a recent report, the growth inhibitory effect of bevaciumab on cisplatin-resistant ovarian cancer was similar to the effect of bevacizumab on cisplatin-sensitive ovarian cancer (Mabuchi et al., 2010b). Collectively, these results suggest that both platinum-sensitive and platinum-resistant ovarian cancer are the candidates for

Not only to inhibit neo-vascularization, VEGF-targeted agents are believed to normalize intra-tumoral blood vessels and improved delivery of oxygen, nutrients, and cytotoxic agents (Gerber & Ferrara, 2005; Jain, 2005). Thus, theoretically, VEGF-targeting agents

In a mouse model of ovarian cancer, treatment with bevacizumab in combination with paclitaxel significantly reduced tumor growth compared with paclitaxel alone (83.3% reduction in the combination arm versus 58.5% reduction in the paclitaxel alone arm) and resulted in the complete inhibition of ascites formation (Hu et al., 2002). Similarly, treatment with bevacizumab in combination with cisplatin significantly reduced tumor growth and ascites formation compared with cisplatin therapy alone (Mabuchi et al.,

There is a strong rationale for using angiogenesis targeted agents in the maintenance therapy setting. Since it has been reported that tumors require a vascular blood supply to grow beyond 2 mm (Gimbrone et al., 1972), any subclinical ovarian tumors that are present after a complete clinical response to first-line chemotherapy should require angiogenesis for their continued proliferation, invasion, and metastasis. Thus, VEGF-targeting maintenance therapy after standard primary treatment might be beneficial for patients with ovarian

**6. Therapeutic potential of VEGF targeted agents in ovarian cancer:** 

**Preclinical findings** 

**6.1 Monotherapy** 

(Mabuchi et al., 2008).

VEGF-targeting therapy.

**6.2 Combination therapy** 

**6.3 Maintenance therapy** 

2008).

cancer.

should be efficacious in combination with chemotherapy.

bevacizumab in ovarian cancer.

#### **4.5 VEGF receptor expression in epithelial ovarian cancer**

VEGFR are predominantly expressed on vascular endothelial cells. However, recent reports have suggested that VEGFR are also expressed by some tumor cells including ovarian cancer cells (Sood et al., 2001; Spannuth et al., 2009). In a study by Spannuth et al, in situ hybridization revealed that VEGFR-1 and VEGFR-2 expression was observed in 85% and 15% of human ovarian cancer specimens, respectively. Moreover, using the ovarian cancer cell lines, the authors showed that functionally active VEGFR is present on most ovarian cancer cells (Spannuth et al., 2009). Although the biological role of the VEGFR expressed on tumor cells remains unclear, they might represent an additional target of ovarian cancer therapy.

### **5. VEGF targeting agents**

It is generally accepted that, unlike normal vasculature, the intratumoral vessels produced by VEGF-mediated angiogenesis are hyperpermeable, leading to increased interstitial fluid pressure and the impaired perfusion of oxygen and cytotoxic agents into tumors. The resultant hypoxic conditions in tumor cells further increase the expression of VEGF and limit the efficacy of chemotherapy and radiotherapy (Gerber & Ferrara, 2005). Theoretically, the inhibition of VEGF by VEGF-targeting agents should inhibit neo-vascularization and "normalize" poorly formed, leaky intratumoral blood vessels. This could lead to the improved delivery of cytotoxic agents and oxygen to tumors (Gerber & Ferrara, 2005).

There are two major strategies used to inhibit the VEGF pathway in cancer therapy (Spannuth et al., 2008). One is the inhibition of the VEGF ligand with antibodies or soluble receptors, and the other is the inhibition of the VEGF receptor with tyrosine kinase inhibitors (TKI) or receptor antibodies (Figure 2). Various VEGF-targeting agents have been identified and are currently being evaluated clinically or preclinically. Of these, bevacizumab, aflibercept, and several TKI are currently being evaluated in phase III clinical trials for the treatment of epithelial ovarian cancer (Table 1-2).

Fig. 2. Strategy to inhibit VEGF signaling

#### **6. Therapeutic potential of VEGF targeted agents in ovarian cancer: Preclinical findings**

Among the VEGF-targeting agents including bevacizumab, aflibercept, and TKI, bevacizumab has been used most commonly for the treatment of ovarian cancer in the clinical setting. Thus, we herein provide information regarding the preclinical activity of bevacizumab in ovarian cancer.

#### **6.1 Monotherapy**

338 Ovarian Cancer – Basic Science Perspective

VEGFR are predominantly expressed on vascular endothelial cells. However, recent reports have suggested that VEGFR are also expressed by some tumor cells including ovarian cancer cells (Sood et al., 2001; Spannuth et al., 2009). In a study by Spannuth et al, in situ hybridization revealed that VEGFR-1 and VEGFR-2 expression was observed in 85% and 15% of human ovarian cancer specimens, respectively. Moreover, using the ovarian cancer cell lines, the authors showed that functionally active VEGFR is present on most ovarian cancer cells (Spannuth et al., 2009). Although the biological role of the VEGFR expressed on tumor cells

It is generally accepted that, unlike normal vasculature, the intratumoral vessels produced by VEGF-mediated angiogenesis are hyperpermeable, leading to increased interstitial fluid pressure and the impaired perfusion of oxygen and cytotoxic agents into tumors. The resultant hypoxic conditions in tumor cells further increase the expression of VEGF and limit the efficacy of chemotherapy and radiotherapy (Gerber & Ferrara, 2005). Theoretically, the inhibition of VEGF by VEGF-targeting agents should inhibit neo-vascularization and "normalize" poorly formed, leaky intratumoral blood vessels. This could lead to the improved delivery of cytotoxic agents and oxygen to tumors (Gerber & Ferrara, 2005). There are two major strategies used to inhibit the VEGF pathway in cancer therapy (Spannuth et al., 2008). One is the inhibition of the VEGF ligand with antibodies or soluble receptors, and the other is the inhibition of the VEGF receptor with tyrosine kinase inhibitors (TKI) or receptor antibodies (Figure 2). Various VEGF-targeting agents have been identified and are currently being evaluated clinically or preclinically. Of these, bevacizumab, aflibercept, and several TKI are currently being evaluated in phase III clinical

remains unclear, they might represent an additional target of ovarian cancer therapy.

**4.5 VEGF receptor expression in epithelial ovarian cancer** 

trials for the treatment of epithelial ovarian cancer (Table 1-2).

Fig. 2. Strategy to inhibit VEGF signaling

**5. VEGF targeting agents** 

Preclinical in vivo studies have demonstrated that inhibiting VEGF activity using a murine monoclonal antibody to human VEGF (A4.6.1) significantly inhibited the growth of subcutaneously inoculated ovarian tumors. Moreover, in mice carrying intraperitoneal tumors, treatment with A4.6.1 completely inhibited ascites production (Mesiano et al., 1998). Bevacizumab is the humanized form of A4.6.1, which was developed in 1997 (Presta et al., 1997). In a preclinical investigation, treatment with bevacizumab significantly inhibited the growth of intraperitoneally inoculated serous ovarian cancer. In addition, bevacizumab treatment significantly inhibited ascites production and prolonged survival of the mice (Mabuchi et al., 2008).

The single agent activity of bevacizumab on chemoresistant ovarian cancer has also been evaluated preclinically. According to a recent report, the growth inhibitory effect of bevaciumab on cisplatin-resistant ovarian cancer was similar to the effect of bevacizumab on cisplatin-sensitive ovarian cancer (Mabuchi et al., 2010b). Collectively, these results suggest that both platinum-sensitive and platinum-resistant ovarian cancer are the candidates for VEGF-targeting therapy.

#### **6.2 Combination therapy**

Not only to inhibit neo-vascularization, VEGF-targeted agents are believed to normalize intra-tumoral blood vessels and improved delivery of oxygen, nutrients, and cytotoxic agents (Gerber & Ferrara, 2005; Jain, 2005). Thus, theoretically, VEGF-targeting agents should be efficacious in combination with chemotherapy.

In a mouse model of ovarian cancer, treatment with bevacizumab in combination with paclitaxel significantly reduced tumor growth compared with paclitaxel alone (83.3% reduction in the combination arm versus 58.5% reduction in the paclitaxel alone arm) and resulted in the complete inhibition of ascites formation (Hu et al., 2002). Similarly, treatment with bevacizumab in combination with cisplatin significantly reduced tumor growth and ascites formation compared with cisplatin therapy alone (Mabuchi et al., 2008).

#### **6.3 Maintenance therapy**

There is a strong rationale for using angiogenesis targeted agents in the maintenance therapy setting. Since it has been reported that tumors require a vascular blood supply to grow beyond 2 mm (Gimbrone et al., 1972), any subclinical ovarian tumors that are present after a complete clinical response to first-line chemotherapy should require angiogenesis for their continued proliferation, invasion, and metastasis. Thus, VEGF-targeting maintenance therapy after standard primary treatment might be beneficial for patients with ovarian cancer.

VEGF Targeting Agents in Ovarian Cancer 341

ovarian, primary peritoneal, or fallopian tube cancer (Figure 3). After a median follow-up period of 17.4 months, the preliminary results from 1873 patients were presented at the 2010 meeting of the American Society of Clinical Oncology (ASCO) (Burger et al., 2010). The patients who were treated with concurrent or maintenance bevacizumab showed significantly longer PFS than the standard chemotherapy arm (14.1 months versus 10.3 months; hazard ratio, 0.717; 95% confidence interval, 0.625-0.824; p<0.0001). No significant difference in PFS was observed between the patients treated with concurrent bevacizumab and those treated with standard chemotherapy (11.2 months versus 10.3 months; HR, 0.908; 95% CI, 0.759-1.040; p=0.008). The OS data were not mature at the time of the presentation

Study Setting Trial design Status Outcome

Completed accrual

Completed accrual

Open to accrual

Open to accrual

Completed accrual

Open to accrual

Favoring Arm 3; HR 0.717; p<0.0001.

Favoring arm 2; HR 0.81; p<0.0041.

Results not

yet available

Arm 3: CT+Bev+maintenance Bev

Arm 2: CT+Bev+maintenance Bev

Arm 1: PTX (iv,d1, 8, 15)+ CBDCA (iv,d1)+ Bev+ maintenance Bev

Arm 2: PTX (iv, d1, 8, 15)+ CBDCA (ip, d1)+Bev+ maintenance Bev

 CDDP (ip, d2)+ Bev+maintenance Bev

Arm 3: PTX (iv,d1)+ PTX (ip, d8)+

Arm 2: CT+Bev+maintenance Bev

Arm 2: CG+Bev+maintenance Bev

Arm 2: PTX, Topo, or PLD+Bev

Table 1. Summary of randomized controlled trials evaluating bevacizumab in ovarian cancer. CT, carboplatin plus paclitaxel; CG, carboplatin plus gemcitabine, Bev, bevacizumab, PTX, paclitaxel; CBDCA, carboplatin; CDDP, cisplatin; Topo, topotecan; PLD, liposomal

due to the cohort suffering a mortality rate of 24%.

Arm 1: CT

Arm 1: CT

Arm 2: CT+Bev

GOG 218 First-line

ICON-7 First-line

GOG 252 First-line

adjuvant

adjuvant

adjuvant

GOG 213 Recurrent Arm 1: CT

OCEANS Recurrent Arm 1: CG

doxorubicin; HR, hazard ratio.

AURELIA Recurrent Arm 1: PTX, Topo, or PLD

The effect of VEGF-targeting agent as a maintenance therapy has been investigated in an *in vivo* ovarian cancer model (Mabuchi et al., 2008). In this investigation, athymic mice were intraperitoneally inoculated with serous ovarian cancer cells. When bevacizumab was used as a maintenance treatment after a complete clinical response to front-line chemotherapy had been obtained, bevacizumab significantly inhibited the recurrence of ovarian cancer and prolonged the survival of the mice (Mabuchi et al., 2008). This is the only preclinical report in which a survival benefit was derived from the use of a VEGF-targeting agent in the setting of maintenance therapy.

#### **7. VEGF targeting agents in ovarian cancer: A clinical trial review**

#### **7.1 Bevacizumab**

To date, bevacizumab, a recombinant human monoclonal antibody to the VEGF ligand, is the most studied VEGF-targeting agent in patients with ovarian cancer (Eskander & Randall, 2011).

#### **7.1.1 Bevacizumab in the setting of front-line, maintenance, or salvage therapy**

The clinical activity of bevacizumab as a single agent has been prospectively examined in two phase II trials involving patients with recurrent ovarian cancer (Cannistra et al., 2007; Burger et al., 2007). The gynecologic oncology group (GOG) evaluated the efficacy of bevacizumab in a phase II clinical trial involving 62 patients with recurrent ovarian cancer (GOG 170-D). Of the patients enrolled, 3 patients showed a complete response, and 4 patients demonstrated a partial response, giving an overall response rate of 18% and a median response duration of 10.25 months. Importantly, an additional 34 patients (55%) showed disease stabilization (Burger et al., 2007). Canninstra et al. also investigated the activity of single agent bevacizumab in 44 patients with platinum-resistant, heavily pretreated ovarian cancer. Of the 44 patients, according to the RECIST guidelines, there were no complete responders, but 7 partial responders were observed. The median duration of the response was 4.3 months (Cannistra et al., 2007).

The effect of bevacizumab has also been examined in the setting of combination therapy in two phase II studies. Garcia et al. evaluated the activity of a combination of bevacizumab and oral cyclophosphamide in patients with recurrent ovarian cancer. Of the 70 patients enrolled, 17 patients (24%) demonstrated a partial response, and 44 patients (63%) showed disease stabilization (Garcia et al., 2008). Penson et al. recently reported on the efficacy of bevacizumab in combination with carboplatin and paclitaxel as a first-line chemotherapy for patients with advanced Mullerian tumors, most of which (73%) were ovarian cancers. Of a total of 28 patients with measurable disease, 11 patients (39%) demonstrated a complete response, and 10 patients (36%) showed a partial response (Penson et al., 2010).

The evidence of clinical activity found in these phase II studies has led to the development of phase III trials examining the use of bevacizumab for the treatment of ovarian cancer (Teoh & Secord, 2011; Burger, 2011) (Table 1).

The efficacy of Bevacizumab in combination with carboplatin and paclitaxel as a primary treatment is being evaluated in three phase III trials. The first is a three-arm placebocontrolled trial, GOG 218 (NCT00262847): a 3-armed trial designed to investigate the clinical benefit of adding bevacizumab to front-line carboplatin-paclitaxel chemotherapy, as well as the benefit of bevacizumab maintenance therapy in patients with advanced stage epithelial

The effect of VEGF-targeting agent as a maintenance therapy has been investigated in an *in vivo* ovarian cancer model (Mabuchi et al., 2008). In this investigation, athymic mice were intraperitoneally inoculated with serous ovarian cancer cells. When bevacizumab was used as a maintenance treatment after a complete clinical response to front-line chemotherapy had been obtained, bevacizumab significantly inhibited the recurrence of ovarian cancer and prolonged the survival of the mice (Mabuchi et al., 2008). This is the only preclinical report in which a survival benefit was derived from the use of a VEGF-targeting agent in the

To date, bevacizumab, a recombinant human monoclonal antibody to the VEGF ligand, is the most studied VEGF-targeting agent in patients with ovarian cancer (Eskander &

The clinical activity of bevacizumab as a single agent has been prospectively examined in two phase II trials involving patients with recurrent ovarian cancer (Cannistra et al., 2007; Burger et al., 2007). The gynecologic oncology group (GOG) evaluated the efficacy of bevacizumab in a phase II clinical trial involving 62 patients with recurrent ovarian cancer (GOG 170-D). Of the patients enrolled, 3 patients showed a complete response, and 4 patients demonstrated a partial response, giving an overall response rate of 18% and a median response duration of 10.25 months. Importantly, an additional 34 patients (55%) showed disease stabilization (Burger et al., 2007). Canninstra et al. also investigated the activity of single agent bevacizumab in 44 patients with platinum-resistant, heavily pretreated ovarian cancer. Of the 44 patients, according to the RECIST guidelines, there were no complete responders, but 7 partial responders were observed. The median duration

The effect of bevacizumab has also been examined in the setting of combination therapy in two phase II studies. Garcia et al. evaluated the activity of a combination of bevacizumab and oral cyclophosphamide in patients with recurrent ovarian cancer. Of the 70 patients enrolled, 17 patients (24%) demonstrated a partial response, and 44 patients (63%) showed disease stabilization (Garcia et al., 2008). Penson et al. recently reported on the efficacy of bevacizumab in combination with carboplatin and paclitaxel as a first-line chemotherapy for patients with advanced Mullerian tumors, most of which (73%) were ovarian cancers. Of a total of 28 patients with measurable disease, 11 patients (39%) demonstrated a complete

The evidence of clinical activity found in these phase II studies has led to the development of phase III trials examining the use of bevacizumab for the treatment of ovarian cancer

The efficacy of Bevacizumab in combination with carboplatin and paclitaxel as a primary treatment is being evaluated in three phase III trials. The first is a three-arm placebocontrolled trial, GOG 218 (NCT00262847): a 3-armed trial designed to investigate the clinical benefit of adding bevacizumab to front-line carboplatin-paclitaxel chemotherapy, as well as the benefit of bevacizumab maintenance therapy in patients with advanced stage epithelial

response, and 10 patients (36%) showed a partial response (Penson et al., 2010).

**7.1.1 Bevacizumab in the setting of front-line, maintenance, or salvage therapy** 

of the response was 4.3 months (Cannistra et al., 2007).

(Teoh & Secord, 2011; Burger, 2011) (Table 1).

**7. VEGF targeting agents in ovarian cancer: A clinical trial review** 

setting of maintenance therapy.

**7.1 Bevacizumab** 

Randall, 2011).

ovarian, primary peritoneal, or fallopian tube cancer (Figure 3). After a median follow-up period of 17.4 months, the preliminary results from 1873 patients were presented at the 2010 meeting of the American Society of Clinical Oncology (ASCO) (Burger et al., 2010). The patients who were treated with concurrent or maintenance bevacizumab showed significantly longer PFS than the standard chemotherapy arm (14.1 months versus 10.3 months; hazard ratio, 0.717; 95% confidence interval, 0.625-0.824; p<0.0001). No significant difference in PFS was observed between the patients treated with concurrent bevacizumab and those treated with standard chemotherapy (11.2 months versus 10.3 months; HR, 0.908; 95% CI, 0.759-1.040; p=0.008). The OS data were not mature at the time of the presentation due to the cohort suffering a mortality rate of 24%.


Table 1. Summary of randomized controlled trials evaluating bevacizumab in ovarian cancer. CT, carboplatin plus paclitaxel; CG, carboplatin plus gemcitabine, Bev, bevacizumab, PTX, paclitaxel; CBDCA, carboplatin; CDDP, cisplatin; Topo, topotecan; PLD, liposomal doxorubicin; HR, hazard ratio.

VEGF Targeting Agents in Ovarian Cancer 343

The activity of bevacizumab in patients with recurrent ovarian cancer is currently being evaluated in several phase III trials. GOG 213 (NCT00565851) was designed to evaluate the roles of secondary cytoreductive surgery and bevacizumab in combination with carboplatin-

The Ovarian Cancer Evaluation of Avastin and Safety-AVF4095g trial (OCEANS) is a phase III study designed to compare gemcitabine and carboplatin with or without bevacizumab in patients with platinum-sensitive recurrent ovarian, primary peritoneal, or fallopian tube cancer (NCT00434642). After 6 cycles of combination chemotherapy with carboplatingemcitabine, the patients will continue to receive bevacizumab or placebo consolidation therapy for up to 51 weeks (Figure 7). After a median follow-up period of 24 months, preliminary results from 484 patients were presented at the 2011 meeting of the ASCO

paclitaxel in patients with platinum-sensitive recurrent ovarian cancer (Figure 6).

Fig. 5. GOG 252 schema.

Fig. 6. GOG 213 schema.

Fig. 3. GOG 218 schema.

The second study is a non-placebo-controlled trial organized by the International Collaborative Ovarian Neoplasm trial, ICON7 (NCT00483782). This study is 2-armed trial designed to compare carboplatin-paclitaxel with bevacizumab plus carboplatin-paclitaxel followed by maintenance bevacizumab therapy in patients with stage IIB-IV or early-stage high-risk ovarian (stage I-IIa with grade 3 or clear cell histology), primary peritoneal, or fallopian tube cancer (Figure 4). Data from the 1528 patients enrolled in this trial were presented at European Society of Medical Oncology meeting (ESMO) in 2010 (Perren et al 2010). The patients that were treated with concurrent and maintenance bevacizumab had a median PFS of 19 months, which was longer than the 17.3 months observed in the patients treated with chemotherapy alone (HR, 0.81; 95% CI, 0.70-0.94; p<0.0041). The result of interim analysis presented at the 2011 meeting of the ASCO also showed the superiority of the concurrent and maintenance bevacizumab compared to the standard chemotherapy (median PFS of 19.8 months versus 17.4 months; HR, 0.87; 95% CI, 0.77-0.99; p=0.039). Survival data will not be mature until 2013 (Kristensen et al., 2011).

Fig. 4. ICON7 schema.

The last trial is GOG252 (NCT00951496), which will not determine the efficacy of additional bevacizumab, but rather is designed to compare intravenous versus intraperitoneal carboplatin, and intraperitoneal carboplatin versus intraperitoneal cisplatin plus paclitaxel in the setting of front-line therapy. All participants received bevacizumab in the front-line setting as well as bevacizumab consolidation chemotherapy (Figure 5).

The second study is a non-placebo-controlled trial organized by the International Collaborative Ovarian Neoplasm trial, ICON7 (NCT00483782). This study is 2-armed trial designed to compare carboplatin-paclitaxel with bevacizumab plus carboplatin-paclitaxel followed by maintenance bevacizumab therapy in patients with stage IIB-IV or early-stage high-risk ovarian (stage I-IIa with grade 3 or clear cell histology), primary peritoneal, or fallopian tube cancer (Figure 4). Data from the 1528 patients enrolled in this trial were presented at European Society of Medical Oncology meeting (ESMO) in 2010 (Perren et al 2010). The patients that were treated with concurrent and maintenance bevacizumab had a median PFS of 19 months, which was longer than the 17.3 months observed in the patients treated with chemotherapy alone (HR, 0.81; 95% CI, 0.70-0.94; p<0.0041). The result of interim analysis presented at the 2011 meeting of the ASCO also showed the superiority of the concurrent and maintenance bevacizumab compared to the standard chemotherapy (median PFS of 19.8 months versus 17.4 months; HR, 0.87; 95% CI, 0.77-0.99; p=0.039).

The last trial is GOG252 (NCT00951496), which will not determine the efficacy of additional bevacizumab, but rather is designed to compare intravenous versus intraperitoneal carboplatin, and intraperitoneal carboplatin versus intraperitoneal cisplatin plus paclitaxel in the setting of front-line therapy. All participants received bevacizumab in the front-line

Survival data will not be mature until 2013 (Kristensen et al., 2011).

setting as well as bevacizumab consolidation chemotherapy (Figure 5).

Fig. 3. GOG 218 schema.

Fig. 4. ICON7 schema.

Fig. 5. GOG 252 schema.

The activity of bevacizumab in patients with recurrent ovarian cancer is currently being evaluated in several phase III trials. GOG 213 (NCT00565851) was designed to evaluate the roles of secondary cytoreductive surgery and bevacizumab in combination with carboplatinpaclitaxel in patients with platinum-sensitive recurrent ovarian cancer (Figure 6).

Fig. 6. GOG 213 schema.

The Ovarian Cancer Evaluation of Avastin and Safety-AVF4095g trial (OCEANS) is a phase III study designed to compare gemcitabine and carboplatin with or without bevacizumab in patients with platinum-sensitive recurrent ovarian, primary peritoneal, or fallopian tube cancer (NCT00434642). After 6 cycles of combination chemotherapy with carboplatingemcitabine, the patients will continue to receive bevacizumab or placebo consolidation therapy for up to 51 weeks (Figure 7). After a median follow-up period of 24 months, preliminary results from 484 patients were presented at the 2011 meeting of the ASCO

VEGF Targeting Agents in Ovarian Cancer 345

patients with recurrent ovarian cancer with symptomatic ascites resulted in relief of symptoms associated with ascites formation. Moreover, objectively, no patient required a paracentesis after the initiation of bevacizumab therapy (Numnum et al., 2006). These results suggest that it is reasonable to consider the use of bevacizumab as a palliative agent

Aflibercept (AVE0005/VEGF trap) is a fusion protein that inactivates VEGF by acting as a decoy receptor for VEGF, preventing VEGF binding to VEGFR. Preclinical in vivo studies have demonstrated that treatment with aflibercept resulted in a decreased tumor burden and reduced ascites formation as a result of tumor angiogenesis inhibition (Byrne et al.,

The clinical activity of aflibercept in ovarian cancer has been evaluated in several phase I/II studies (Moroney et al., 2009). In a randomized, double-blind, phase II study of patients with recurrent ovarian cancer, aflibercept produced a significant tumor response (according to the RECIST criteria) or CA-125 response (defined as a 50% reduction in the CA-125 protein level) was observed in 18 (13.8%) out of 130 evaluable patients. In addition, of the 40 patients who had evaluable ascites at the baseline, 77.5% demonstrated the complete

On the basis of the promising results from these preclinical and clinical investigations, aflibercept has been evaluated in a phase III trial (Table 2). In this trial, the effect of aflibercept on the necessity of repeated paracentesis for symptomatic ascites in patients with

Another strategy is the inhibition of the VEGF receptor using tyrosine kinase inhibitors (Teoh & Secord, 2011). In contrast to the specific inhibition of the VEGF pathway using bevacizumab or aflibercept, most TKI target multiple receptors responsible for tumorangiogenesis. Theoretically, these multi-targeting anti-angiogenic agents inhibit tumor angiogenesis more completely than agents that specifically target VEGF, and thus, might have a greater therapeutic benefit. Several TKI have demonstrated clinical activity in phase I/II trials involving patients with ovarian cancer and are currently being evaluated in phase

in patients with end stage ovarian cancer with symptomatic ascites.

disappearance or stabilization of their ascites (Tew et al., 2007).

advanced ovarian cancer has been evaluated (NCT00327444) (Figure 9).

**7.2 Aflibercept (AVE0005/VEGF trap)** 

Fig. 9. NCT00327444 schema.

III trials (Table 2).

**7.3 Tyrosine kinase inhibitors** 

2003).

(Aghajanian et al., 2011). Patients treated with concurrent and maintenance bevacizumab showed significantly longer PFS compared to the standard chemotherapy arm (12.4 months versus 8.4 months; hazard ratio, 0.484; 95% Cl, 0.388-0.605; p<0.0001). The survival data are not mature.

#### Fig. 7. OCEANS schema.

The AURELIA study (NCT00976911) is a phase III study designed to evaluate the benefit of adding bevacizumab to standard chemotherapy in patients with recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer who displayed disease progression within 6 months of platinum therapy. The patients will receive paclitaxel, topotecan, or liposomal doxorubicin with or without concomitant bevacizumab (Figure 8).

Fig. 8. AURELIA schema.

#### **7.1.2 Bevacizumab for palliative treatment**

The ability of bevacizumab as a palliative agent for symptomatic ascites has also been evaluated in several studies. In a report by Numnum et al, treatment with bevacizumab for

(Aghajanian et al., 2011). Patients treated with concurrent and maintenance bevacizumab showed significantly longer PFS compared to the standard chemotherapy arm (12.4 months versus 8.4 months; hazard ratio, 0.484; 95% Cl, 0.388-0.605; p<0.0001). The survival data are

The AURELIA study (NCT00976911) is a phase III study designed to evaluate the benefit of adding bevacizumab to standard chemotherapy in patients with recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer who displayed disease progression within 6 months of platinum therapy. The patients will receive paclitaxel, topotecan, or

The ability of bevacizumab as a palliative agent for symptomatic ascites has also been evaluated in several studies. In a report by Numnum et al, treatment with bevacizumab for

liposomal doxorubicin with or without concomitant bevacizumab (Figure 8).

not mature.

Fig. 7. OCEANS schema.

Fig. 8. AURELIA schema.

**7.1.2 Bevacizumab for palliative treatment** 

patients with recurrent ovarian cancer with symptomatic ascites resulted in relief of symptoms associated with ascites formation. Moreover, objectively, no patient required a paracentesis after the initiation of bevacizumab therapy (Numnum et al., 2006). These results suggest that it is reasonable to consider the use of bevacizumab as a palliative agent in patients with end stage ovarian cancer with symptomatic ascites.

#### **7.2 Aflibercept (AVE0005/VEGF trap)**

Aflibercept (AVE0005/VEGF trap) is a fusion protein that inactivates VEGF by acting as a decoy receptor for VEGF, preventing VEGF binding to VEGFR. Preclinical in vivo studies have demonstrated that treatment with aflibercept resulted in a decreased tumor burden and reduced ascites formation as a result of tumor angiogenesis inhibition (Byrne et al., 2003).

The clinical activity of aflibercept in ovarian cancer has been evaluated in several phase I/II studies (Moroney et al., 2009). In a randomized, double-blind, phase II study of patients with recurrent ovarian cancer, aflibercept produced a significant tumor response (according to the RECIST criteria) or CA-125 response (defined as a 50% reduction in the CA-125 protein level) was observed in 18 (13.8%) out of 130 evaluable patients. In addition, of the 40 patients who had evaluable ascites at the baseline, 77.5% demonstrated the complete disappearance or stabilization of their ascites (Tew et al., 2007).

On the basis of the promising results from these preclinical and clinical investigations, aflibercept has been evaluated in a phase III trial (Table 2). In this trial, the effect of aflibercept on the necessity of repeated paracentesis for symptomatic ascites in patients with advanced ovarian cancer has been evaluated (NCT00327444) (Figure 9).

Fig. 9. NCT00327444 schema.

#### **7.3 Tyrosine kinase inhibitors**

Another strategy is the inhibition of the VEGF receptor using tyrosine kinase inhibitors (Teoh & Secord, 2011). In contrast to the specific inhibition of the VEGF pathway using bevacizumab or aflibercept, most TKI target multiple receptors responsible for tumorangiogenesis. Theoretically, these multi-targeting anti-angiogenic agents inhibit tumor angiogenesis more completely than agents that specifically target VEGF, and thus, might have a greater therapeutic benefit. Several TKI have demonstrated clinical activity in phase I/II trials involving patients with ovarian cancer and are currently being evaluated in phase III trials (Table 2).

VEGF Targeting Agents in Ovarian Cancer 347

Pazopanib, an oral TKI that targets all three VEGFR, PDGFR, and c-kit, was approved by the United States Food and Drug Administration in 2009 for the treatment of patients with advanced renal cell carcinoma. The clinical activity of pazopanib in ovarian cancer has been evaluated in several phase I/II trials (Monk et al., 2010; Friedlander et al., 2010). In a phase II trial in which the CA-125 response (>50% decrease from baseline) was the primary endpoint, pazopanib demonstrated a CA-125 response rate of 31% (11 out of 36 patients). In 17 patients with measurable disease, the overall response rate (according to the RECIST

On the basis of these results, the activity of pazopanib is currently being evaluated in a phase III study, which was designed to compare the efficacy of pazopanib versus placebo in women whose disease had not progressed after first-line chemotherapy for epithelial

BIBF 1120 is an orally available TKI that binds and inhibits VEGFR, PDGFR, and FGFR tyrosine kinases. On the basis of a promising randomized controlled phase II trial highlighting the benefit of maintenance treatment with BIBF 1120 after salvage chemotherapy in patients with recurrent ovarian cancer (Ledermann et al., 2009), the

ovarian, fallopian tube, or primary peritoneal cancer (NCT00866697) (Figure 11).

Fig. 10. NCT00532194 schema.

Fig. 11. NCT00866697 schema.

**7.3.3 BIBF 1120** 

**7.3.2 Pazopanib** 

criteria) was 18%.


Table 2. Summary of the randomized controlled trials evaluating the efficacy of tyrosine kinase inhibitors against ovarian cancer. Ced, Cediranib; PFS, progression free survival; OS, overall survival.

\* Maintenance treatment for patients whose cancer had not progressed during first line chemotherapy.

#### **7.3.1 Cediranib**

Cediranib is an oral TKI that targets all three VEGFR, PDGFR, and c-kit. In the setting of a phase II study of patients with recurrent epithelial ovarian cancer or fallopian tube cancer, cediranib demonstrated significant clinical activity. In this trial, single agent cediranib showed a clinical benefit rate (complete response, partial response, stable disease, or CA125 non-progression) of 30% (Matulonis et al., 2009).

Cediranib is currently being evaluated in a phase III trial (ICON6), which was designed to investigate the clinical benefit of adding cediranib to carboplatin-paclitaxel, as well as the benefit of continuing cediranib as a maintenance therapy in patients with platinumsensitive, recurrent ovarian cancer (NCT00532194) (Figure 10).

Arm 2: Aflibercept

Arm 2: CT+Ced

Arm 3: CT+Ced+ maintenance Ced

Arm 2: Pazopanib

Arm 1: CT

1120

Table 2. Summary of the randomized controlled trials evaluating the efficacy of tyrosine kinase inhibitors against ovarian cancer. Ced, Cediranib; PFS, progression free survival; OS,

\* Maintenance treatment for patients whose cancer had not progressed during first line

Cediranib is an oral TKI that targets all three VEGFR, PDGFR, and c-kit. In the setting of a phase II study of patients with recurrent epithelial ovarian cancer or fallopian tube cancer, cediranib demonstrated significant clinical activity. In this trial, single agent cediranib showed a clinical benefit rate (complete response, partial response, stable disease, or CA125

Cediranib is currently being evaluated in a phase III trial (ICON6), which was designed to investigate the clinical benefit of adding cediranib to carboplatin-paclitaxel, as well as the benefit of continuing cediranib as a maintenance therapy in patients with platinum-

Arm 2: CT+BIBF

Time to repeat paracentesis

PFS and OS

Completed Results not yet available

Open to accrual

accrual

accrual

PFS Completed

PFS Open to

Compound Target Setting Patients Endopoint Status

Recurrent Arm 1: CT

Maintenance\* Arm 1: Placebo

Aflibercept VEGF Recurrent Arm 1: Placebo

Front-line adjuvant

Cediranib VEGFR

Pazopanib VEGFR

BIBF 1120 VEGFR

overall survival.

chemotherapy.

**7.3.1 Cediranib** 




non-progression) of 30% (Matulonis et al., 2009).

sensitive, recurrent ovarian cancer (NCT00532194) (Figure 10).

Fig. 10. NCT00532194 schema.

#### **7.3.2 Pazopanib**

Pazopanib, an oral TKI that targets all three VEGFR, PDGFR, and c-kit, was approved by the United States Food and Drug Administration in 2009 for the treatment of patients with advanced renal cell carcinoma. The clinical activity of pazopanib in ovarian cancer has been evaluated in several phase I/II trials (Monk et al., 2010; Friedlander et al., 2010). In a phase II trial in which the CA-125 response (>50% decrease from baseline) was the primary endpoint, pazopanib demonstrated a CA-125 response rate of 31% (11 out of 36 patients). In 17 patients with measurable disease, the overall response rate (according to the RECIST criteria) was 18%.

On the basis of these results, the activity of pazopanib is currently being evaluated in a phase III study, which was designed to compare the efficacy of pazopanib versus placebo in women whose disease had not progressed after first-line chemotherapy for epithelial ovarian, fallopian tube, or primary peritoneal cancer (NCT00866697) (Figure 11).

#### **7.3.3 BIBF 1120**

BIBF 1120 is an orally available TKI that binds and inhibits VEGFR, PDGFR, and FGFR tyrosine kinases. On the basis of a promising randomized controlled phase II trial highlighting the benefit of maintenance treatment with BIBF 1120 after salvage chemotherapy in patients with recurrent ovarian cancer (Ledermann et al., 2009), the

VEGF Targeting Agents in Ovarian Cancer 349

therapy. In a recent analysis of 1745 patients with colorectal, breast, or non-small-cell lung cancer from five randomized trials, the addition of bevacizumab to chemotherapy was associated with an increased risk of ATE (incidence of 3.8% in the combination therapy group vs 1.7% in the chemotherapy alone group) (Scappaticci et al., 2007). The precise incidence of ATE in patients with ovarian cancer who have been treated with VEGFtargeting agents is unknown; however, so far, 30 ATE events have been reported. ATE events are rarely reported in patients who have been treated with aflibercept or TKI (Stone

The most serious complication of VEGF-targeting agents is bowel perforation. In a recent review of 9 clinical studies examining the use of bevacizumab in ovarian cancer patients, the overall frequency of bowel perforation was 5.4%, which was higher than the 2.4% observed in patients with colorectal cancer (Han & Monk, 2007). Bowel perforation was also observed in patients that had been treated with aflibercept (Colombo et al., 2008), however, there have been no reported cases of bowel perforation involving patients that were treated with TKI monotherapy (Stone et al., 2010). The management of bowel perforation is difficult because of the increased likelihood of surgical and postoperative complications, such as thrombosis or compromised wound healing caused by bevacizumab treatment, suggesting the importance of preventing this serious complication. The precise mechanisms of, and risk factors for, bowel perforation are largely unknown. However, Simpkins et al. suggested from their experience that bowel perforation can be avoided by carefully selecting patients without clinical symptoms of bowel obstruction, evidence of rectosigmoid involvement on a pelvic examination, or evidence of bowel involvement on a computed tomography (CT)

At this point, no standard recommendations exist for the management of the adverse effects induced by VEGF-targeting agents. Further investigations are needed to solve this issue.

Recent increases in our understanding of cancer biology and the molecular pathways that control tumor angiogenesis have led to the identification of novel VEGF-targeting agents that can be used to treat ovarian cancer. Although VEGF-targeting agents have yielded promising results in ovarian cancer in the settings of front-line treatment and salvage treatment, several important clinical issues remain unanswered. The optimal methods for evaluating the efficacy of VEGF-targeting agents have yet to be clarified. As VEGFtargeting agents have cytostatic rather than cytotoxic effects, the traditional criteria applied to cytotoxic agents, such as the RECIST criteria, might be less applicable. The identification of surrogate biomarkers that can be used to guide drug choice or optimal dosing or to predict the tumor response or drug resistance is of paramount importance. In addition, increased understanding of the mechanisms underlying the unique toxic effects of VEFG-targeting agents, as well as the development of evidence-based management strategies for these adverse effects, are also necessary. Choosing the optimal VEGFtargeting agent for each patient will extend patient survival without reducing quality of

et al., 2010).

scan (Simpkins et al., 2007).

life in the near future.

**10. Acknowledgement** 

We thank Remina Emoto for providing secretarial assistance.

**9. Conclusions and future directions** 

Arbeitsgemeinschaft Gynakologische Oncologie (AGO) is currently conducting a Phase III trial (AGO-OVAR-12). This trial was designed to evaluate the efficacy of BIBF 1120 in combination with carboplatin-paclitaxel in patients with advanced epithelial ovarian cancer in the setting of front-line treatment (NCT01015118) (Figure 12).

Fig. 12. NCT01015118 schema.

#### **8. Toxic effects of VEGF-targeting agents**

As VEGF mediates physiologically important processes in healthy tissues, VEGF-targeting agents are associated with unique and potentially problematic side effects (Stone et al., 2010).

Hypertension is the most common side effect of VEGF-targeting agents. It typically occurs within 3 weeks of the beginning of therapy. The pathogenesis of hypertension associated with VEGF-targeting agents is not fully understood. However, it is speculated that the suppression of nitric oxide production by VEGF antagonism leads to vasoconstriction and decreased sodium ion renal excretion, which results in elevated blood pressure (Izzedine et al., 2009). A recent review of the adverse effects of anti-angiogenic therapies suggested that the incidence of grade 3/4 hypertension in patients that had been treated with bevacizumab, aflibercept, or TKI was 8-26%, 9-32%, and 5-48%, respectively (Stone et al., 2010).

Proteinuria is another common side effect observed in patients treated with bevacizumab or aflibercept. In clinical trials of bevacizumab or aflibercept, grade 3/4 proteinuria occurred in approximately 1-4% of patients (Izzedine et al., 2010). Proteinuria is rare in patients treated with TKI. Such proteinuria is reported to occur as a result of the disruption of VEGFdependent glomerular endothelial integrity (Ostendorf et al., 1999).

Wound healing complications is another serious side effect that is reported to occur in patients treated with VEGF-targeting agents. As the wound-healing process is dependent on angiogenesis, VEGF-targeting agents have the potential to delay the wound-healing process in patients who undergo surgery. On the basis of a previous report suggesting that patients who undergo surgery within 28-60 days of bevacizumab therapy are at an increased risk of wound-healing complications, physicians recommend avoiding major surgery at least 30 days after the completion of bevacizumab treatment (Shord et al., 2009).

The increased risk of atrial thromboembolic events (ATE), usually myocardial or cerebrovascular events, is another serious adverse effect associated with bevacizumab

Arbeitsgemeinschaft Gynakologische Oncologie (AGO) is currently conducting a Phase III trial (AGO-OVAR-12). This trial was designed to evaluate the efficacy of BIBF 1120 in combination with carboplatin-paclitaxel in patients with advanced epithelial ovarian cancer

As VEGF mediates physiologically important processes in healthy tissues, VEGF-targeting agents are associated with unique and potentially problematic side effects (Stone et al.,

Hypertension is the most common side effect of VEGF-targeting agents. It typically occurs within 3 weeks of the beginning of therapy. The pathogenesis of hypertension associated with VEGF-targeting agents is not fully understood. However, it is speculated that the suppression of nitric oxide production by VEGF antagonism leads to vasoconstriction and decreased sodium ion renal excretion, which results in elevated blood pressure (Izzedine et al., 2009). A recent review of the adverse effects of anti-angiogenic therapies suggested that the incidence of grade 3/4 hypertension in patients that had been treated with bevacizumab, aflibercept, or TKI was 8-26%, 9-32%, and 5-48%, respectively (Stone et al.,

Proteinuria is another common side effect observed in patients treated with bevacizumab or aflibercept. In clinical trials of bevacizumab or aflibercept, grade 3/4 proteinuria occurred in approximately 1-4% of patients (Izzedine et al., 2010). Proteinuria is rare in patients treated with TKI. Such proteinuria is reported to occur as a result of the disruption of VEGF-

Wound healing complications is another serious side effect that is reported to occur in patients treated with VEGF-targeting agents. As the wound-healing process is dependent on angiogenesis, VEGF-targeting agents have the potential to delay the wound-healing process in patients who undergo surgery. On the basis of a previous report suggesting that patients who undergo surgery within 28-60 days of bevacizumab therapy are at an increased risk of wound-healing complications, physicians recommend avoiding major surgery at least 30

The increased risk of atrial thromboembolic events (ATE), usually myocardial or cerebrovascular events, is another serious adverse effect associated with bevacizumab

dependent glomerular endothelial integrity (Ostendorf et al., 1999).

days after the completion of bevacizumab treatment (Shord et al., 2009).

in the setting of front-line treatment (NCT01015118) (Figure 12).

Fig. 12. NCT01015118 schema.

2010).

2010).

**8. Toxic effects of VEGF-targeting agents** 

therapy. In a recent analysis of 1745 patients with colorectal, breast, or non-small-cell lung cancer from five randomized trials, the addition of bevacizumab to chemotherapy was associated with an increased risk of ATE (incidence of 3.8% in the combination therapy group vs 1.7% in the chemotherapy alone group) (Scappaticci et al., 2007). The precise incidence of ATE in patients with ovarian cancer who have been treated with VEGFtargeting agents is unknown; however, so far, 30 ATE events have been reported. ATE events are rarely reported in patients who have been treated with aflibercept or TKI (Stone et al., 2010).

The most serious complication of VEGF-targeting agents is bowel perforation. In a recent review of 9 clinical studies examining the use of bevacizumab in ovarian cancer patients, the overall frequency of bowel perforation was 5.4%, which was higher than the 2.4% observed in patients with colorectal cancer (Han & Monk, 2007). Bowel perforation was also observed in patients that had been treated with aflibercept (Colombo et al., 2008), however, there have been no reported cases of bowel perforation involving patients that were treated with TKI monotherapy (Stone et al., 2010). The management of bowel perforation is difficult because of the increased likelihood of surgical and postoperative complications, such as thrombosis or compromised wound healing caused by bevacizumab treatment, suggesting the importance of preventing this serious complication. The precise mechanisms of, and risk factors for, bowel perforation are largely unknown. However, Simpkins et al. suggested from their experience that bowel perforation can be avoided by carefully selecting patients without clinical symptoms of bowel obstruction, evidence of rectosigmoid involvement on a pelvic examination, or evidence of bowel involvement on a computed tomography (CT) scan (Simpkins et al., 2007).

At this point, no standard recommendations exist for the management of the adverse effects induced by VEGF-targeting agents. Further investigations are needed to solve this issue.

#### **9. Conclusions and future directions**

Recent increases in our understanding of cancer biology and the molecular pathways that control tumor angiogenesis have led to the identification of novel VEGF-targeting agents that can be used to treat ovarian cancer. Although VEGF-targeting agents have yielded promising results in ovarian cancer in the settings of front-line treatment and salvage treatment, several important clinical issues remain unanswered. The optimal methods for evaluating the efficacy of VEGF-targeting agents have yet to be clarified. As VEGFtargeting agents have cytostatic rather than cytotoxic effects, the traditional criteria applied to cytotoxic agents, such as the RECIST criteria, might be less applicable. The identification of surrogate biomarkers that can be used to guide drug choice or optimal dosing or to predict the tumor response or drug resistance is of paramount importance. In addition, increased understanding of the mechanisms underlying the unique toxic effects of VEFG-targeting agents, as well as the development of evidence-based management strategies for these adverse effects, are also necessary. Choosing the optimal VEGFtargeting agent for each patient will extend patient survival without reducing quality of life in the near future.

#### **10. Acknowledgement**

We thank Remina Emoto for providing secretarial assistance.

VEGF Targeting Agents in Ovarian Cancer 351

Friedlander, M., Hancock, KC., Rischin, D., Messing, MJ., Stringer, CA., Matthys, GM., Ma,

Gerber, HP., & Ferrara, N. Pharmacology and pharmacodynamics of bevacizumab as

Gimbrone, MA. Jr., Leapman, SB., Cotran, RS., & Folkman, J. Tumor dormancy in vivo by

Han, ES., Monk, & BJ. What is the risk of bowel perforation associated with bevacizumab

Hefler, LA., Zeillinger, R., Grimm, C., Sood, AK., Cheng, WF., Gadducci, A., Tempfer, CB.,

prognostic parameter in ovarian cancer. Gynecol Oncol. 2006;103:512-517. Hu, L., Hofmann, J., Zaloudek, C., Ferrara, N., Hamilton, T., & Jaffe, RB. Vascular

Izzedine, H., Ederhy, S., Goldwasser, F., Soria, JC., Milano, G., Cohen, A., Khayat, D., &

Izzedine, H., Massard, C., Spano, JP., Goldwasser, F., Khayat, D., & Soria, JC. VEGF

Jain, RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic

Karkkainen, MJ., Saaristo, A., Jussila, L., Karila, KA., Lawrence, EC., Pajusola, K., Bueler, H.,

Kerbel, RS. Inhibition of tumor angiogenesis as a strategy to circumvent acquired resistance

Kristensen, G., Perren, T., Qian, W. et al., Result of interim analysis of overall survival in the

Ledermann, JA., Rustin, GJ., Hackshaw, A. et al., A randomised phase II placebo-controlled

lymphedema. Proc Natl Acad Sci U S A. 2001;98:12677-12682.

to anti-cancer therapeutic agents. Bioessays. 1991;13:31-36.

Society of Clinical Oncology; Chicago, IL, USA (June, 2011)

prevention of neovascularization. J Exp Med 1972;136:261-276.

therapy in ovarian cancer? Gynecol Oncol. 2007;105:3-6.

patients with recurrent ovarian cancer. Gynecol Oncol. 2010;119:32-37. Garcia, AA., Hirte, H., Fleming, G., Yang, D., Tsao-Wei, DD., Roman, L., Groshen, S.,

26:76-82.

Cancer Res. 2005;65:671-680.

2002;161:1917-1924.

Ann Oncol. 2009;20:807-815.

therapy. Science 2005;307:58-62.

Belgrade, Serbia (October, 2009).

management. Eur J Cancer. 2010;46:439-448.

B., Hodge, JP., & Lager, JJ. A Phase II, open-label study evaluating pazopanib in

Swenson, S., Markland, F., Gandara, D., Scudder, S., Morgan, R., Chen, H., Lenz, HJ., & Oza, AM. Phase II clinical trial of bevacizumab and low-dose metronomic oral cyclophosphamide in recurrent ovarian cancer: a trial of the California, Chicago, and Princess Margaret Hospital phase II consortia. J Clin Oncol 2008;

monotherapy or in combination with cytotoxic therapy in preclinical studies.

& Reinthaller, A. Preoperative serum vascular endothelial growth factor as a

endothelial growth factor immunoneutralization plus Paclitaxel markedly reduces tumor burden and ascites in athymic mouse model of ovarian cancer. Am J Pathol.

Spano, JP. Management of hypertension in angiogenesis inhibitor-treated patients.

signalling inhibition-induced proteinuria: Mechanisms, significance and

Eichmann, A., Kauppinen, R., Kettunen, MI., Yla-Herttuala, S., Finegold, DN., Ferrell, RE., & Alitalo K. A model for gene therapy of human hereditary

GCIG ICON7 phase III randomized trial of bevacizumab in women with newly diagnosed ovarian cancer, Presented at: the 47th Annual Meeting of the American

trial using maintenance therapy to evaluate the vascular targeting agent BIBF 1120 following treatment of relapsed ovarian cancer, Presented at: the 16th Biennial International Meeting of the European Society of Gynaecological Oncology;

#### **11. References**


Aghajanian, C., Finkler, NJ., Rutherford, T. et al., OCEANS: A randomized, double-blinded,

Brustmann, H. Vascular endothelial growth factor expression in serous ovarian carcinoma:

Burger, RA., Sill, MW., Monk, BJ., Greer, BE., & Sorosk, JI. Phase II trial of bevacizumab in

Burger, RA. Overview of anti-angiogenic agents in development for ovarian cancer. Gynecol

Byrne, AT., Ross, L., Holash, J., Nakanishi, M., Hu, L., Hofmann, JI., Yancopoulos, GD., &

Cannistra, SA., Matulonis, UA., Penson, RT., Hambleton, J., Dupont, J., Mackey, H.,

Carmeliet, P. Mechanisms of angiogenesis and arteriogenesis. Nat Med. 2000;6:389-395. Colombo, N., Mangili, G., Mammoliti, S. et al., Aflibercept (VEGF Trap) for advanced

American Society of Clinical Oncology; Chicago, IL, USA (June, 2008). Cooper, BC., Ritchie, JM., Broghammer, CL., Coffin, J., Sorosky, JI., Buller, RE., Hendrix, MJ.,

Eskander, RN., & Randall, LM. Bevacizumab in the treatment of ovarian cancer. Biologics.

Ferrara, N. VEGF and the quest for tumour angiogenesis factors. Nat Rev Cancer 2002;2:795-

Ferrara, N., & Kerbel RS. Angiogenesis as a therapeutic target. Nature. 2005;438:967-974. Forsythe, JA., Jiang, BH., Iyer, NV., Agani, F., Leung, SW., Koos, RD., & Semenza, GL.

inducible factor 1. Mol Cell Biol. 1996;16:4604-4613.

significance in ovarian cancer. Clin Cancer Res. 2002;8:3193-3197.

peritoneal serous cancer. J Clin Oncol 2007;25:5180-5186.

Gynecologic Oncology Group Study. J Clin Oncol 2007; 25:5165-5171. Burger, RA., Brady, MF. Bookman, MA. et al., Phase III trial of bevacizumab (BEV) in the

Oncology; Chicago, IL, USA (June, 2010).

Oncol. 2011;121:230-238.

2011;5:1-5.

803.

Cancer Res. 2003;9:5721-5728.

placebo-controlled phase III trial of chemotherapy with or without bevacizumab (BEV) in patients with platinum-sensitive recurrent epithelial ovarian (EOC), primary peritoneal (PPC), or fallopian tube cancer (FTC), Presented at: the 47th Annual Meeting of the American Society of Clinical Oncology; Chicago, IL, USA

relationship with topoisomerase II alpha and prognosis. Gynecol Oncol 2004;95:16-

persistent or recurrent epithelial ovarian cancer or primary peritoneal cancer: a

primary treatment of advanced epithelial ovarian cancer (EOC), primary peritoneal cancer (PPC), or fallopian tube cancer (FTC): a gynecologic oncology group study, Presented at: the 46th Annual Meeting of the American Society of Clinical

Jaffe, RB. Vascular endothelial growth factor-trap decreases tumor burden, inhibits ascites, and causes dramatic vascular remodeling in an ovarian cancer model. Clin

Douglas, J., Burger, RA., Armstrong, D., Wenham, R., & McGuire, W. Phase II study of bevacizumab in patients with platinum-resistant ovarian cancer or

epithelial ovarian cancer (EOC) patients (pts) with symptomatic malignant ascites: Preliminary results of a pilot study, Presented at: the 44th Annual Meeting of the

& Sood, AK.Preoperative serum vascular endothelial growth factor levels:

Activation of vascular endothelial growth factor gene transcription by hypoxia-

**11. References** 

(June, 2011).

22.


VEGF Targeting Agents in Ovarian Cancer 353

Presta, LG., Chen, H., O'Connor, SJ., Chisholm, V., Meng, YG., Krummen, L., Winkler, M., &

Rafii, S., Lyden, D., Benezra, R., Hattori, K., & Heissig, B. Vascular and haematopoietic

Schönau, KK., Steger, GG., & Mader, RM. Angiogenic effect of naive and 5-fluorouracil resistant colon carcinoma on endothelial cells in vitro. Cancer Lett 2007;257:73-78. Scappaticci, FA., Skillings, JR., Holden, SN., Gerber, HP., Miller, K., Kabbinavar, F.,

chemotherapy and bevacizumab. J Natl Cancer Inst. 2007;99:1232-1239. Senger, DR., Galli, SJ., Dvorak, AM., Perruzzi, CA., Harvey, VS., & Dvorak, HF. Tumor cells

Shord, SS., Bressler, LR., Tierney, LA., Cuellar, S., & George, A. Understanding and

Simpkins, F., Belinson, JL., Rose, & PG. Avoiding bevacizumab related gastrointestinal

Sood, AK., Seftor, EA., Fletcher, MS., Gardner, LM., Heidger, PM., Buller, RE., Seftor, RE., &

Spannuth, WA., Sood, AK., & Coleman, RL. Angiogenesis as a strategic target for ovarian

Spannuth, WA., Nick, AM., Jennings, NB., Armaiz-Pena, GN., Mangala, LS., Danes, CG.,

Stone, RL., Sood, AK., & Coleman, RL. Collateral damage: toxic effects of targeted antiangiogenic therapies in ovarian cancer. Lancet Oncol. 2010;11:465-475. Teoh, DG., & Secord, AA. Antiangiogenic therapies in epithelial ovarian cancer. Cancer

Tew, WP., Colombo, N., Ray-Coquard, I. et al., VEGF-Trap for patients (pts) with

Wong, C., Wellman, TL., & Lounsbury, KM. VEGF and HIF-1alpha expression are

cancer therapy. Nat Clin Pract Oncol. 2008;5:194-204.

cells. Int J Cancer. 2009;124:1045-1053.

(October, 2010).

1997;57:4593-4599.

2002;2:826-835.

Science 1983;219: 983-985.

2007;107:118-123.

2001;158:1279-1288.

Control. 2011;18:31-43.

2007).

513-517.

Syst Pharm. 2009;66:999-1013.

Presented at: the 35th European Society of Medical Oncology meeting; Milan, Italy

Ferrara, N. Humanization of an anti-vascular endothelial growth factor monoclonal antibody for the therapy of solid tumors and other disorders. Cancer Res.

stem cells: novel targets for anti-angiogenesis therapy? Nat Rev Cancer.

Bergsland, E., Ngai, J., Holmgren, E., Wang, J., & Hurwitz, H. Arterial thromboembolic events in patients with metastatic carcinoma treated with

secrete a vascular permeability factor that promotes accumulation of ascites fluid.

managing the possible adverse effects associated with bevacizumab. Am J Health

toxicity for recurrent ovarian cancer by careful patient screening. Gynecol Oncol.

Hendrix, MJ. Molecular determinants of ovarian cancer plasticity. Am J Pathol.

Lin, YG., Merritt, WM., Thaker, PH., Kamat, AA., Han, LY., Tonra, JR., Coleman, RL., Ellis, LM., & Sood AK. Functional significance of VEGFR-2 on ovarian cancer

recurrent platinum-resistant epithelial ovarian cancer (EOC): preliminary results of a randomized, multicenter phase II study, Presented at: the 43th Annual Meeting of the American Society of Clinical Oncology; Chicago, IL, USA (June,

increased in advanced stages of epithelial ovarian cancer. Gynecol Oncol 2003;91:


Li, L., Wang, L., Zhang, W., Tang, B., Zhang, J., Song, H., Yao, D., Tang, Y., Chen, X., Yang,

Mabuchi, S., Terai, Y., Morishige, K., Tanabe-Kimura, A., Sasaki, H., Kanemura, M.,

Mabuchi, S., Kawase, C., Altomare, DA., Morishige, K., Hayashi, M., Sawada, K., Ito, K.,

Mabuchi, S., Morishige, K., & Kimura, T. Use of monoclonal antibodies in the treatment of

Matulonis, UA., Berlin, S., Ivy, P., Tyburski, K., Krasner, C., Zarwan, C., Berkenblit, A.,

Mesiano, S., Ferrara, N., & Jaffe, RB. Role of vascular endothelial growth factor in ovarian

Monk, BJ., Mas Lopez, L., Zarba, JJ., Oaknin, A., Tarpin, C., Termrungruanglert, W., Alber,

Moroney, JW., Sood, AK., & Coleman, RL. Aflibercept in epithelial ovarian carcinoma.

Numnum, TM., Rocconi, RP., Whitworth, J., & Barnes, MN. The use of bevacizumab to

Ostendorf, T., Kunter, U., Eitner, F., Loos, A., Regele, H., Kerjaschki, D., Henninger, DD.,

Penson, RT., Dizon, DS., Cannistra, SA., Roche, MR., Krasner, CN., Berlin, ST., Horowitz,

Perren, T. et al., ICON7: A phase III randomized gynecologic cancer intergroup trial of

survival in ovarian cancer. Anticancer Res. 2004;24:1973-1979.

cancer model. Clin Cancer Res 2008;14:7781-7789.

ovarian cancer. Curr Opin Obstet Gynecol. 2010b;22:3-8.

2422.

Oncol. 2009;27:5601-5606.

1998;153:1249-1256.

2010;28:3562-3569.

Future Oncol. 2009;5:591-600.

Oncol 2006;102:425-428.

Invest. 1999;104:913-923.

2010;28:154-159.

Z., Wang, G., Li, X., Zhao, J., Ding, H., Reed, E., & Li, QQ. Correlation of serum VEGF levels with clinical stage, therapy efficacy, tumor metastasis and patient

Tsunetoh, S., Tanaka, Y., Sakata, M., Burger, RA., Kimura, T., & Ohmichi, M. Maintenance treatment with bevacizumab prolongs survival in an in vivo ovarian

Terai, Y., Nishio, Y., Klein-Szanto, AJ., Burger, RA., Ohmichi, M., Testa, JR., & Kimura, T. Vascular endothelial growth factor is a promising therapeutic target for the treatment of clear cell carcinoma of the ovary. Mol Cancer Ther. 2010a;9:2411-

Campos, S., Horowitz, N., Cannistra, SA., Lee, H., Lee, J., Roche, M., Hill, M., Whalen, C., Sullivan, L., Tran, C., Humphreys, BD., & Penson, RT. Cediranib, an oral inhibitor of vascular endothelial growth factor receptor kinases, is an active drug in recurrent epithelial ovarian, fallopian tube, and peritoneal cancer. J Clin

cancer: inhibition of ascites formation by immunoneutralization. Am J Pathol.

JA., Ding, J., Stutts, MW., & Pandite, LN. Phase II, open-label study of pazopanib or lapatinib monotherapy compared with pazopanib plus lapatinib combination therapy in patients with advanced and recurrent cervical cancer. J Clin Oncol.

palliate symptomatic ascites in patients with refractory ovarian carcinoma. Gynecol

Janjic, N., & Floege, J. VEGF(165) mediates glomerular endothelial repair. J Clin

NS., Disilvestro, PA., Matulonis, UA., Lee, H., King, MA., & Campos, SM. Phase II study of carboplatin, paclitaxel, and bevacizumab with maintenance bevacizumab as first-line chemotherapy for advanced mullerian tumors. J Clin Oncol

concurrent bevacizumab and chemotherapy followed by maintenance bevacizumab, versus chemotherapy alone in women with newly diagnosed epithelial ovarian (EOC), primary peritoneal (PPC), or fallopian tube cancer (FTC), Presented at: the 35th European Society of Medical Oncology meeting; Milan, Italy (October, 2010).


**18** 

*1CompchemRes 2Keele University* 

*UK* 

**Autotaxin – A Target for the Treatment** 

It has been known for a number of years that many patients with ovarian cancer suffer an accumulation of ascites fluid that contains a factor which supports the intraperitoneal growth of ovarian cancer cells (Mills et al. 1990). Following from the identification of lysophosphatidic acid (LPA) as a major growth factor in serum (van Corven et al. 1989), LPA was identified as the major "ovarian cancer activating factor" in ascites fluid (Xu et al. 1995). LPA was shown to accumulate to high concentrations (up to 80 µM) in ascites fluid. Since then, numerous publications have demonstrated the role of LPA in several biological processes relevant to cancer including cell migration and invasion, inhibition of apoptosis and senescence, angiogenesis and chemoresistance. Increases in plasma LPA are also being considered as a diagnostic biomarker of ovarian cancer (e.g. (Bese et al. 2010)). It is perhaps surprising then, that compounds interfering with this pathway have made slow progress to the clinic. Part of the reason for this likely reflects the complexity of the LPA signalling pathway. However, recent work has delineated many of the enzymes and receptors involved in regulating the LPA signalling pathways, revealing complexity in different LPA species, in the pathways involved in the metabolism of LPA, in LPA receptors and finally in the (patho)physiological responses to LPA. An understanding of how these pathways are deregulated in ovarian cancer has begun to suggest potential targets for the development of therapeutic drugs. One such target is autotaxin, an enzyme involved in the synthesis of LPA. Recently, several crystal structures of autotaxin have been solved, and these provide powerful tools to aid the development of autotaxin inhibitors. However, to fully appreciate

the potential of autotaxin as a drug target, we first review LPA signalling pathways.

LPA (Fig. 1) itself provides a first example of complexity in this pathway, as it comprises a family of molecules. In general, LPA consists of a glycerol moiety linked as an ester to phosphate and fatty acid moieties. However, LPA molecules may differ in the length and the degree of unsaturation of the fatty acid, and the fatty acid may be attached to the *sn*1 or

**1. Introduction** 

**2. The LPA signalling pathway** 

**2.1 Complexity in LPA** 

**of Drug-Resistant Ovarian Cancer?** 

John King-Underwood1, Steven M. Allin2, Charles W. Redman3 and Alan Richardson2,\*

*3University Hospital of North Staffordshire* 


### **Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer?**

John King-Underwood1, Steven M. Allin2, Charles W. Redman3 and Alan Richardson2,\* *1CompchemRes 2Keele University 3University Hospital of North Staffordshire UK* 

#### **1. Introduction**

354 Ovarian Cancer – Basic Science Perspective

Yamamoto, S., Konishi, I., Mandai, M., Kuroda, H., Komatsu, T., Nanbu, K., Sakahara, H., &

Zhang, L., Yang, N., Garcia, JR., Mohamed, A., Benencia, F., Rubin, SC., Allman, D., &

analysis of serum VEGF levels. Br J Cancer 1997;76:1221-1227.

2002;161:2295-309.

Mori, T. Expression of vascular endothelial growth factor (VEGF) in epithelial ovarian neoplasms: correlation with clinicopathology and patient survival, and

Coukos, G. Generation of a syngeneic mouse model to study the effects of vascular endothelial growth factor in ovarian carcinoma. Am J Pathol

> It has been known for a number of years that many patients with ovarian cancer suffer an accumulation of ascites fluid that contains a factor which supports the intraperitoneal growth of ovarian cancer cells (Mills et al. 1990). Following from the identification of lysophosphatidic acid (LPA) as a major growth factor in serum (van Corven et al. 1989), LPA was identified as the major "ovarian cancer activating factor" in ascites fluid (Xu et al. 1995). LPA was shown to accumulate to high concentrations (up to 80 µM) in ascites fluid. Since then, numerous publications have demonstrated the role of LPA in several biological processes relevant to cancer including cell migration and invasion, inhibition of apoptosis and senescence, angiogenesis and chemoresistance. Increases in plasma LPA are also being considered as a diagnostic biomarker of ovarian cancer (e.g. (Bese et al. 2010)). It is perhaps surprising then, that compounds interfering with this pathway have made slow progress to the clinic. Part of the reason for this likely reflects the complexity of the LPA signalling pathway. However, recent work has delineated many of the enzymes and receptors involved in regulating the LPA signalling pathways, revealing complexity in different LPA species, in the pathways involved in the metabolism of LPA, in LPA receptors and finally in the (patho)physiological responses to LPA. An understanding of how these pathways are deregulated in ovarian cancer has begun to suggest potential targets for the development of therapeutic drugs. One such target is autotaxin, an enzyme involved in the synthesis of LPA. Recently, several crystal structures of autotaxin have been solved, and these provide powerful tools to aid the development of autotaxin inhibitors. However, to fully appreciate the potential of autotaxin as a drug target, we first review LPA signalling pathways.

#### **2. The LPA signalling pathway**

#### **2.1 Complexity in LPA**

LPA (Fig. 1) itself provides a first example of complexity in this pathway, as it comprises a family of molecules. In general, LPA consists of a glycerol moiety linked as an ester to phosphate and fatty acid moieties. However, LPA molecules may differ in the length and the degree of unsaturation of the fatty acid, and the fatty acid may be attached to the *sn*1 or

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 357

it remains a possibility that autotaxin is not the enzyme responsible for the accumulation of LPA observed in ovarian cancer. In addition to autotaxin, extracellular LPA could potentially also be derived from the hydrolysis of phosphatidic acid by secreted

A number of intracellular enzymes including glycerol 3-phosphate fatty acid transferase (GPFAT) and phospholipase D (PLD) and phospholipase A2 may also contribute to the production of LPA at the cell membrane. It is possible that intracellular enzymes contribute to the accumulation of extracellular LPA. Although GPFAT has not received much attention in ovarian cancer, PLD2 has recently been shown to contribute to EGF-induced LPA production (Snider et al. 2010). Other potential sources of LPA include intracellular isoforms of phospholipase A2 which use phosphatidic acid as a substrate (Fig. 1). cPLA2 is a calcium-dependant phospholipase implicated in cell migration whereas iPLA2 is a calciumindependent phospholipase. Which cells are the potential sources of LPA? Platelets have previously been shown to be an important source of LPA in serum (Boucharaba et al. 2004), and Xu and co-workers have clearly shown in a murine model of ovarian cancer that both host and tumor cells contribute to the formation of LPA in ascites and this is catalysed by iPLA2 (Li et al. 2010). Peritoneal mesothelial cells are one potential source of extracellular

If both host and tumor cells contribute to the accumulation of peritoneal LPA, and there are several pathways capable of contributing to the formation of LPA, it seems reasonable to ask whether animal models accurately reflect clinical reality. In xenograft studies it is common to implant a human tumor cell into a murine host. Is the relative contribution of different LPA biosynthetic pathways in xenograft studies quantitatively similar to that observed in ovarian cancer? This is important because the relative contribution of, e.g., autotaxin and iPLA2 to the generation of LPA will likely influence the success of inhibitors of these individual enzymes when used in patients. Thus, we consider that although preclincial experiments may continue to shed light on validity of the different LPA biosynthetic pathways as drug targets, a definitive answer will only be provided by clinical studies. The concentration of LPA in ascites fluid is controlled by its rate of elimination as well as its rate of synthesis. It is important, therefore, to consider also pathways of LPA catabolism. Two lipid phosphatases, LPP1 and LPP3, have been implicated in the hydrolysis of LPA. Importantly, LPP1 shows reduced expression in ovarian cancer cells, suggesting that this might contribute to increased levels of LPA (Tanyi et al. 2003; Tanyi et al. 2003). Correspondingly, expression of these genes has been shown to inhibit several of the responses ascribed to LPA, for example colony formation and cell migration. Understanding the pathways that regulate the expression of these phosphatases is important, as it might provide targets which can be used to increase the expression of LPP1 or LPP3 and so

It is also worth considering how the expression of LPA anabolic and catabolic enzymes might vary between patients as this may influence the design of clinical trials. Although we await further data to address this, it is worth considering the potential impact on the clinical use of drugs regulating the LPA pathway. It seems that it will be appropriate to select patients most likely to benefit from a particular enzyme inhibitor taking into account which biosynthetic pathways are deregulated. For example, PLD2 has been implicated in EGFdriven LPA production (Snider et al. 2010), so patients whose tumors are driven by the EGF pathway may be more dependent on PLD2 than other LPA producing enzymes. Similarly, VEGF regulates autotaxin production (see below), so tumors in which VEGF production is

phospholipase A2 (sPLA2).

LPA produced by PLA2 (Ren et al. 2006).

develop drugs to increase LPA catabolism.

*sn*2 positions on the glycerol. In some cases, the fatty acid is replaced by an alkyl chain attached via an ether linkage. The phosphate may be attached to both the second and third glycerol hydroxyl groups, forming a cyclophosphate.

Fig. 1. **A** Lysophosphatidic acid is comprised of fatty acid, glycerol and phosphate moieties. **B**. Principle routes to the biosynthesis of LPA. ATX, autotaxin; PLA2, phospholipase A2. Note that autotaxin behaves as a phosopholipase D that uses *lyso* substrates, i.e. those lacking one fatty acid attached to the glycerol.

#### **2.2 Complexity in the synthesis of LPA**

There are a number of enzymes which can catalyze the synthesis of LPA in ovarian cancer and it is becoming more clear which of these contribute to the accumulation of LPA in ovarian cancer. LPA is synthesized both intracellularly and extracellularly, by ovarian cancer cells as well as mesothelial cells. Autotaxin is a secreted phospholipase that catalyses the hydrolysis of lysophosphatidyl choline to produce LPA and choline (fig. 2). Increased expression of autotaxin is observed in several cancers including renal (Stassar et al. 2001), thyroid (Kehlen et al. 2004), glioblastoma (Hoelzinger et al. 2005; Kishi et al. 2006), follicular lymphoma (Masuda et al. 2008), hepatic (Wu et al. 2010), prostate (Nouh et al. 2009) and pancreatic cancer (Nakai et al. 2011). Autotaxin expression is also increased in chemoresistant ovarian cancer compared to chemosensitive disease (Jazaeri et al. 2005). Ectopic expression of autotaxin in mammary epithelium is sufficient to cause high frequency breast cancer (Liu et al. 2009). Together these observations point to role for autotaxin in several cancer types. The elevated levels of LPA observed in ascites obtained from patients with ovarian cancer suggests a role in ovarian cancer. The increase in LPA levels are accompanied by elevated LPC, the substrate of autotaxin (Liu et al. 2009). Autotaxin itself is also present in ascites fluid (Tokumura et al. 2007). Although transgenic mice lacking autotaxin die as embryos, heterozygotes with one functional allele encoding autotaxin show a 50% reduction in plasma LPA (Tanaka et al. 2006), suggesting that autotaxin is the enzyme primarily responsible for the synthesis of LPA in plasma. However,

*sn*2 positions on the glycerol. In some cases, the fatty acid is replaced by an alkyl chain attached via an ether linkage. The phosphate may be attached to both the second and third

Fig. 1. **A** Lysophosphatidic acid is comprised of fatty acid, glycerol and phosphate moieties. **B**. Principle routes to the biosynthesis of LPA. ATX, autotaxin; PLA2, phospholipase A2. Note that autotaxin behaves as a phosopholipase D that uses *lyso* substrates, i.e. those

There are a number of enzymes which can catalyze the synthesis of LPA in ovarian cancer and it is becoming more clear which of these contribute to the accumulation of LPA in ovarian cancer. LPA is synthesized both intracellularly and extracellularly, by ovarian cancer cells as well as mesothelial cells. Autotaxin is a secreted phospholipase that catalyses the hydrolysis of lysophosphatidyl choline to produce LPA and choline (fig. 2). Increased expression of autotaxin is observed in several cancers including renal (Stassar et al. 2001), thyroid (Kehlen et al. 2004), glioblastoma (Hoelzinger et al. 2005; Kishi et al. 2006), follicular lymphoma (Masuda et al. 2008), hepatic (Wu et al. 2010), prostate (Nouh et al. 2009) and pancreatic cancer (Nakai et al. 2011). Autotaxin expression is also increased in chemoresistant ovarian cancer compared to chemosensitive disease (Jazaeri et al. 2005). Ectopic expression of autotaxin in mammary epithelium is sufficient to cause high frequency breast cancer (Liu et al. 2009). Together these observations point to role for autotaxin in several cancer types. The elevated levels of LPA observed in ascites obtained from patients with ovarian cancer suggests a role in ovarian cancer. The increase in LPA levels are accompanied by elevated LPC, the substrate of autotaxin (Liu et al. 2009). Autotaxin itself is also present in ascites fluid (Tokumura et al. 2007). Although transgenic mice lacking autotaxin die as embryos, heterozygotes with one functional allele encoding autotaxin show a 50% reduction in plasma LPA (Tanaka et al. 2006), suggesting that autotaxin is the enzyme primarily responsible for the synthesis of LPA in plasma. However,

glycerol hydroxyl groups, forming a cyclophosphate.

lacking one fatty acid attached to the glycerol.

**2.2 Complexity in the synthesis of LPA** 

it remains a possibility that autotaxin is not the enzyme responsible for the accumulation of LPA observed in ovarian cancer. In addition to autotaxin, extracellular LPA could potentially also be derived from the hydrolysis of phosphatidic acid by secreted phospholipase A2 (sPLA2).

A number of intracellular enzymes including glycerol 3-phosphate fatty acid transferase (GPFAT) and phospholipase D (PLD) and phospholipase A2 may also contribute to the production of LPA at the cell membrane. It is possible that intracellular enzymes contribute to the accumulation of extracellular LPA. Although GPFAT has not received much attention in ovarian cancer, PLD2 has recently been shown to contribute to EGF-induced LPA production (Snider et al. 2010). Other potential sources of LPA include intracellular isoforms of phospholipase A2 which use phosphatidic acid as a substrate (Fig. 1). cPLA2 is a calcium-dependant phospholipase implicated in cell migration whereas iPLA2 is a calciumindependent phospholipase. Which cells are the potential sources of LPA? Platelets have previously been shown to be an important source of LPA in serum (Boucharaba et al. 2004), and Xu and co-workers have clearly shown in a murine model of ovarian cancer that both host and tumor cells contribute to the formation of LPA in ascites and this is catalysed by iPLA2 (Li et al. 2010). Peritoneal mesothelial cells are one potential source of extracellular LPA produced by PLA2 (Ren et al. 2006).

If both host and tumor cells contribute to the accumulation of peritoneal LPA, and there are several pathways capable of contributing to the formation of LPA, it seems reasonable to ask whether animal models accurately reflect clinical reality. In xenograft studies it is common to implant a human tumor cell into a murine host. Is the relative contribution of different LPA biosynthetic pathways in xenograft studies quantitatively similar to that observed in ovarian cancer? This is important because the relative contribution of, e.g., autotaxin and iPLA2 to the generation of LPA will likely influence the success of inhibitors of these individual enzymes when used in patients. Thus, we consider that although preclincial experiments may continue to shed light on validity of the different LPA biosynthetic pathways as drug targets, a definitive answer will only be provided by clinical studies.

The concentration of LPA in ascites fluid is controlled by its rate of elimination as well as its rate of synthesis. It is important, therefore, to consider also pathways of LPA catabolism. Two lipid phosphatases, LPP1 and LPP3, have been implicated in the hydrolysis of LPA. Importantly, LPP1 shows reduced expression in ovarian cancer cells, suggesting that this might contribute to increased levels of LPA (Tanyi et al. 2003; Tanyi et al. 2003). Correspondingly, expression of these genes has been shown to inhibit several of the responses ascribed to LPA, for example colony formation and cell migration. Understanding the pathways that regulate the expression of these phosphatases is important, as it might provide targets which can be used to increase the expression of LPP1 or LPP3 and so develop drugs to increase LPA catabolism.

It is also worth considering how the expression of LPA anabolic and catabolic enzymes might vary between patients as this may influence the design of clinical trials. Although we await further data to address this, it is worth considering the potential impact on the clinical use of drugs regulating the LPA pathway. It seems that it will be appropriate to select patients most likely to benefit from a particular enzyme inhibitor taking into account which biosynthetic pathways are deregulated. For example, PLD2 has been implicated in EGFdriven LPA production (Snider et al. 2010), so patients whose tumors are driven by the EGF pathway may be more dependent on PLD2 than other LPA producing enzymes. Similarly, VEGF regulates autotaxin production (see below), so tumors in which VEGF production is

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 359

LPA has several well characterized effects upon ovarian cancer cell migration and invasion. Firstly, activation of Src kinase by LPA leads to the breakdown of cell-cell junctions, promoting cell scattering (Huang et al. 2008). The breakdown of cell junctions is facilitated by activation uPA (urokinase plasminogen activator) by LPA, which leads to proteolysis of E-cadherin. Secondly, LPA triggers cytoskeletal reorganization (Do et al. 2007; Kim et al. 2011) and reorganization of cell contacts with the extracellular matrix which promotes cell motility (Sawada et al. 2002; Bian et al. 2004; Bian et al. 2006). Thirdly, LPA induces the expression of several proteases including uPA (Pustilnik et al. 1999; Li et al. 2005), MMP1 (Wang et al. 2011), MMP2 (Fishman et al. 2001) MMP7 and MMP9 (Park et al. 2011) which contribute to the breakdown of extracellular matrix allowing invasion through basement membrane. In addition, LPA decreases expression of TIMP metalloprotease inhibitors (Sengupta et al. 2007), thereby potentiating the effect of

Although the role of autotaxin in migration and invasion has not yet been studied in ovarian cancer to the same level of detail as in other cancers, the role of autotaxin in these processes is well founded. Indeed, autotaxin was first identified through its activity as an autocrine motility factor (Stracke et al. 1992) and integrin α6β4, which is associated with and invasive phenotype, can increase the expression of autotaxin (Chen and O'Connor 2005). Autotaxin and LPA promote the expression of the extracellular matrix protein osteopontin which promotes migration (Zhang et al. 2011). Autotaxin activates the small G-proteins cdc42 and Rac (Jung et al. 2002; Hoelzinger et al. 2008; Harper et al. 2010) and focal adhesion kinase (Jung et al. 2002), proteins which are key regulators of cell motility. More direct evidence comes from the observation that knockdown of autotaxin inhibits cell migration in several cancer types (Kishi et al. 2006; Gaetano et al. 2009; Harper et al. 2010) and over-expression of autotaxin increases motility (Kishi et al. 2006; Harper et al. 2010). Autotaxin regulates the formation of invadopodia (Harper et al. 2010) and induces the expression of uPA (Lee et al. 2006) and MMP3 (Haga et al. 2009). Correspondingly, knockdown of autotaxin inhibits invasion (Hoelzinger et al. 2008) while over-expression promotes invasion (Nam et al. 2000; Yang et al. 2002). Finally, autotaxin promotes osteolytic bone metastases derived from breast cancer cells (David et al. 2010). Taken together, these observations suggest that LPA and autotaxin are likely to promote an invasive phenotype in ovarian cancer cells. We discuss

LPA contributes to providing a microenvironment that is conducive to tumor growth. It does this in part by supressing apoptosis and senescence. LPA is itself a growth factor (van Corven et al. 1989) for several cell types. It stimulates the growth of cultures of ovarian cancer cells (Xu et al. 1995; Hu et al. 2003) by several pathways (Hurst and Hooks 2009). It also induces the expression of the growth factor Groα (Lee et al. 2006). Finally, iPLA2, one of the enzymes involved in the synthesis of LPA, can promote cell cycle progression in the

LPA induces the production of the major angiogenic factor VEGF by ovarian cancer cells (Hu et al. 2001) and mesenchymal stem cells (Jeon et al. 2010). LPA also increases VEGF receptor expression on endothelial cells. The effect of LPA is apparently amplified by VEGF-

**3. Physiological and pathophysiological functions of LPA** 

**3.1 LPA/autotaxin and migration and invasion** 

below the therapeutic implications of these observations.

**3.2 LPA/autotaxin and a supportive microenvironment** 

absence of exogenous growth factors (Song et al. 2007).

activation of proteases.

substantially elevated may be more dependent on autotaxin for LPA production. The expression level of LPP1 or LPP3 may also influence the response to drugs inhibiting the production of LPA. We speculate that inter-patient variability in the enzymes catalysing LPA catabolism may lead to different response to drugs which inhibit LPA synthesis and evaluating the extent of any clinical variation may prove to be important.

#### **2.3 Complexity in LPA receptors**

Two classes of cell surface receptors for LPA have been described, all of which are G-protein coupled receptors (Tigyi 2010). The first of these classes comprise the receptors LPA1, LPA2 and LPA3. These are closely related and form part of the EDG (endothelial differentiation gene) family of receptors which also includes receptors for the bioactive lipid sphingosine 1 phosphate (S1P). At high (µM) concentrations, LPA may also bind to S1P receptors. A second set of LPA receptors are more closely related to purinergic receptors including LPA4 (also known as P2Y9), LPA5 (GPR95). There are several additional receptors that are also reported to respond to LPA including GPR35, GPR87, P2Y5 and P2Y10 and further characterization of these is on-going. Clearly, it is important to consider which of these receptors should be exploited as drug targets in ovarian cancer.

LPA2 and LPA3 appear to promote ovarian tumorigenesis. The expression of LPA2 and LPA3 is increased in ovarian cancer, (Fang et al. 2002; Wang et al. 2007; Murph et al. 2008) and over- expression of these receptors in Sk-Ov-3 cells promotes growth of primary tumors and metastasis (Yu et al. 2008). In clinical samples, expression of LPA2 and LPA3 correlates with tumor stage (Wang et al. 2007). In contrast, the expression of LPA1 is decreased in ovarian cancer and expression of LPA1 promotes apoptosis (Furui et al. 1999). These observations are important from a therapeutic perspective, because it suggest that ovarian cancer patients might benefit from a drug which is an LPA2, LPA3 antagonist but it may be preferable that such a drug does not bind with high affinity to LPA1. However, it should be noted that the growth inhibitory properties of LPA1 were found to be independent of LPA (Furui et al. 1999) and encouraging results have already been obtained with a pan-LPA receptor antagonist in xenograft studies (Zhang et al. 2009). We already have substantial experience developing (non-oncological) drugs using G-protein coupled receptors as drug targets, suggesting this may be a fruitful avenue for therapeutic research.

As well as binding to cell surface receptors, LPA has been proposed to activate the nuclear hormone receptor PPARγ. These intracellular receptors function as transcription factors and drive the expression of genes involved in diverse physiological responses including glucose and lipid metabolism, inflammatory response and apoptosis. PPARγ is over-expressed in ovarian cancer (Zhang et al. 2005) and its expression is associated with a poor response to chemotherapy and shortened survival (Davidson et al. 2009). Although this might lead to the hypothesis that activation of PPARγ by LPA is tumorigenic, confusingly synthetic PPAR agonists (the "glitazones") inhibit the proliferation of ovarian cancer cells and induce apoptosis (Yang et al. 2007). Glitazones also display synergistic activity with platinum chemotherapy through down-regulation of metallothionines involved in the detoxification of platinum (Girnun et al. 2007). Thus, the contribution of the activation of PPARγ by LPA to ovarian tumorigenesis remains to be further clarified.

Finally, it has also been pointed out that LPA binds to a number of intracellular cytoskeletal proteins (Tigyi 2010) possibly reflecting an intracellular role for LPA in regulating cell migration.

#### **3. Physiological and pathophysiological functions of LPA**

#### **3.1 LPA/autotaxin and migration and invasion**

358 Ovarian Cancer – Basic Science Perspective

substantially elevated may be more dependent on autotaxin for LPA production. The expression level of LPP1 or LPP3 may also influence the response to drugs inhibiting the production of LPA. We speculate that inter-patient variability in the enzymes catalysing LPA catabolism may lead to different response to drugs which inhibit LPA synthesis and

Two classes of cell surface receptors for LPA have been described, all of which are G-protein coupled receptors (Tigyi 2010). The first of these classes comprise the receptors LPA1, LPA2 and LPA3. These are closely related and form part of the EDG (endothelial differentiation gene) family of receptors which also includes receptors for the bioactive lipid sphingosine 1 phosphate (S1P). At high (µM) concentrations, LPA may also bind to S1P receptors. A second set of LPA receptors are more closely related to purinergic receptors including LPA4 (also known as P2Y9), LPA5 (GPR95). There are several additional receptors that are also reported to respond to LPA including GPR35, GPR87, P2Y5 and P2Y10 and further characterization of these is on-going. Clearly, it is important to consider which of these

LPA2 and LPA3 appear to promote ovarian tumorigenesis. The expression of LPA2 and LPA3 is increased in ovarian cancer, (Fang et al. 2002; Wang et al. 2007; Murph et al. 2008) and over- expression of these receptors in Sk-Ov-3 cells promotes growth of primary tumors and metastasis (Yu et al. 2008). In clinical samples, expression of LPA2 and LPA3 correlates with tumor stage (Wang et al. 2007). In contrast, the expression of LPA1 is decreased in ovarian cancer and expression of LPA1 promotes apoptosis (Furui et al. 1999). These observations are important from a therapeutic perspective, because it suggest that ovarian cancer patients might benefit from a drug which is an LPA2, LPA3 antagonist but it may be preferable that such a drug does not bind with high affinity to LPA1. However, it should be noted that the growth inhibitory properties of LPA1 were found to be independent of LPA (Furui et al. 1999) and encouraging results have already been obtained with a pan-LPA receptor antagonist in xenograft studies (Zhang et al. 2009). We already have substantial experience developing (non-oncological) drugs using G-protein coupled receptors as drug targets,

As well as binding to cell surface receptors, LPA has been proposed to activate the nuclear hormone receptor PPARγ. These intracellular receptors function as transcription factors and drive the expression of genes involved in diverse physiological responses including glucose and lipid metabolism, inflammatory response and apoptosis. PPARγ is over-expressed in ovarian cancer (Zhang et al. 2005) and its expression is associated with a poor response to chemotherapy and shortened survival (Davidson et al. 2009). Although this might lead to the hypothesis that activation of PPARγ by LPA is tumorigenic, confusingly synthetic PPAR agonists (the "glitazones") inhibit the proliferation of ovarian cancer cells and induce apoptosis (Yang et al. 2007). Glitazones also display synergistic activity with platinum chemotherapy through down-regulation of metallothionines involved in the detoxification of platinum (Girnun et al. 2007). Thus, the contribution of the activation of PPARγ by LPA

Finally, it has also been pointed out that LPA binds to a number of intracellular cytoskeletal proteins (Tigyi 2010) possibly reflecting an intracellular role for LPA in

evaluating the extent of any clinical variation may prove to be important.

receptors should be exploited as drug targets in ovarian cancer.

suggesting this may be a fruitful avenue for therapeutic research.

to ovarian tumorigenesis remains to be further clarified.

regulating cell migration.

**2.3 Complexity in LPA receptors** 

LPA has several well characterized effects upon ovarian cancer cell migration and invasion. Firstly, activation of Src kinase by LPA leads to the breakdown of cell-cell junctions, promoting cell scattering (Huang et al. 2008). The breakdown of cell junctions is facilitated by activation uPA (urokinase plasminogen activator) by LPA, which leads to proteolysis of E-cadherin. Secondly, LPA triggers cytoskeletal reorganization (Do et al. 2007; Kim et al. 2011) and reorganization of cell contacts with the extracellular matrix which promotes cell motility (Sawada et al. 2002; Bian et al. 2004; Bian et al. 2006). Thirdly, LPA induces the expression of several proteases including uPA (Pustilnik et al. 1999; Li et al. 2005), MMP1 (Wang et al. 2011), MMP2 (Fishman et al. 2001) MMP7 and MMP9 (Park et al. 2011) which contribute to the breakdown of extracellular matrix allowing invasion through basement membrane. In addition, LPA decreases expression of TIMP metalloprotease inhibitors (Sengupta et al. 2007), thereby potentiating the effect of activation of proteases.

Although the role of autotaxin in migration and invasion has not yet been studied in ovarian cancer to the same level of detail as in other cancers, the role of autotaxin in these processes is well founded. Indeed, autotaxin was first identified through its activity as an autocrine motility factor (Stracke et al. 1992) and integrin α6β4, which is associated with and invasive phenotype, can increase the expression of autotaxin (Chen and O'Connor 2005). Autotaxin and LPA promote the expression of the extracellular matrix protein osteopontin which promotes migration (Zhang et al. 2011). Autotaxin activates the small G-proteins cdc42 and Rac (Jung et al. 2002; Hoelzinger et al. 2008; Harper et al. 2010) and focal adhesion kinase (Jung et al. 2002), proteins which are key regulators of cell motility. More direct evidence comes from the observation that knockdown of autotaxin inhibits cell migration in several cancer types (Kishi et al. 2006; Gaetano et al. 2009; Harper et al. 2010) and over-expression of autotaxin increases motility (Kishi et al. 2006; Harper et al. 2010). Autotaxin regulates the formation of invadopodia (Harper et al. 2010) and induces the expression of uPA (Lee et al. 2006) and MMP3 (Haga et al. 2009). Correspondingly, knockdown of autotaxin inhibits invasion (Hoelzinger et al. 2008) while over-expression promotes invasion (Nam et al. 2000; Yang et al. 2002). Finally, autotaxin promotes osteolytic bone metastases derived from breast cancer cells (David et al. 2010). Taken together, these observations suggest that LPA and autotaxin are likely to promote an invasive phenotype in ovarian cancer cells. We discuss below the therapeutic implications of these observations.

#### **3.2 LPA/autotaxin and a supportive microenvironment**

LPA contributes to providing a microenvironment that is conducive to tumor growth. It does this in part by supressing apoptosis and senescence. LPA is itself a growth factor (van Corven et al. 1989) for several cell types. It stimulates the growth of cultures of ovarian cancer cells (Xu et al. 1995; Hu et al. 2003) by several pathways (Hurst and Hooks 2009). It also induces the expression of the growth factor Groα (Lee et al. 2006). Finally, iPLA2, one of the enzymes involved in the synthesis of LPA, can promote cell cycle progression in the absence of exogenous growth factors (Song et al. 2007).

LPA induces the production of the major angiogenic factor VEGF by ovarian cancer cells (Hu et al. 2001) and mesenchymal stem cells (Jeon et al. 2010). LPA also increases VEGF receptor expression on endothelial cells. The effect of LPA is apparently amplified by VEGF-

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 361

apoptosis contributes to resistance to paclitaxel and carboplatin. As these drugs are the cornerstone of ovarian cancer chemotherapy, the potential of the LPA pathway as a therapeutic target is again underlined. Early work demonstrated that LPA confers resistance to cisplatin (Frankel and Mills 1996) and this has also been observed in colon cancer cells (Sun et al. 2009). We conducted a screen to identify genes that confer resistance to carboplatin, and one of the hits identified in that screen was autotaxin. Expression of autotaxin delayed apoptosis induced by carboplatin, while apoptosis was accelerated after inhibition of autotaxin by either siRNA or with a small molecule inhibitor (Vidot et al. 2010). More recently, LPA and autotaxin have been shown to confer resistance of breast and melanoma cancer cells to paclitaxel (Samadi et al. 2009). Resistance to paclitaxel depends on PI 3-kinase, presumably reflecting the role of PI 3-kinase downstream of LPA in survival signalling that was noted above. Remarkably, resistance to paclitaxel conferred by LPA by restores normal spindle function in cells exposed to paclitaxel and the cells escape M-phase arrest (Samadi et al. 2011). The LPA2 receptor is one candidate for mediating chemoresistance, because LPA2-/- mice exhibit increased radiation-induced apoptosis (Deng et al. 2007). Thus, there is direct evidence linking autotaxin to resistance to both

Other proteins in the LPA pathway may also contribute to chemoresistance. RGS proteins (Regulator of G-protein signalling) attenuate signalling by LPA receptors by increasing the GTPase activity of G-proteins that are activated by LPA receptors (Hurst et al. 2008). Expression of several RGS proteins is decreased in ovarian cancer cell lines that are resistant to cisplatin (Hooks et al. 2010). Knockdown of expression of two RGS protein, RGS10 and RGS17, causes a 2-3 fold reduction in the potency but a striking 6-fold reduction in cisplatin potency is observed when the expression of both RGS proteins is inhibited. This suggests that loss of expression of RGS proteins, leading to increased activity of LPA receptor

In addition to inhibiting apoptosis through G-protein signalling, the LPA2 receptor also regulates the pro-apoptotic protein Siva-1. Activation of p53 following DNA damage increases the expression of pro-apoptotic Siva-1 and this contributes to cisplatin-induced apoptosis (Barkinge et al. 2009), as well apoptosis induced by uvltraviolet light (Chu et al. 2004). LPA causes ubiquitination and turnover of Siva-1 and this contributes to suppression of apoptosis by LPA (Lin et al. 2007). This may be mediated by the LPA2 receptor. LPA2 is distinct from other LPA receptors in containing zinc finger and a Cterminal PDZ binding motifs. These motifs serve to recruit NHERF2 and TRIP6, which form a ternary complex with Siva-1. Both NHERF2 and TRIP6 are required for LPA to confer resistance to cisplatin (E et al. 2009). But how does Siva-1 induce apoptosis? In part, this probably reflects inhibition of the cell survival driven by the transcription factor NFB. Intriguingly, Siva-1 can also inhibit Bcl-XL, a member of the anti-apoptotic Bcl-2 family proteins that suppress activation of Bak and Bax in the intrinsic apoptosis pathway (Xue et al. 2002). We have shown previously that inhibition of Bcl-XL increases sensitivity to carboplatin (Witham et al. 2007). Together with our observation that autotaxin confers resistance to carboplatin (Vidot et al. 2010), these data suggest that autotaxin may confer resistance to carboplatin by suppressing the intrinsic apoptosis pathway (fig. 2). As we discuss below, this predicts that autotaxin inhibitors may be useful in the treatment of

signalling through G-proteins, may contribute to resistance to chemotherapy.

chemotherapeutic agents used to treat ovarian cancer.

drug-resistant ovarian cancer.

induced expression of autotaxin by ovarian cancer cells (Ptaszynska et al. 2008) and endothelial cells (Ptaszynska et al. 2010) thereby potentiating LPA production. LPA also promotes the expression of other pro-angiogenic factors including IL-8 by tumor cells and SDF-1 by mesenchymal stem cells (Jeon et al. 2010). These observations suggest a key role for autotaxin and LPA in ovarian cancer driven angiogenesis and have led to the suggestion that autotaxin may also be a therapeutic target for inhibiting angiogenesis (Ptaszynska et al. 2010). In addition to its role in angiogenesis VEGF has also been implicated in LPA induced invasion (So et al. 2005; Wang et al. 2009; Wang et al. 2011).

#### **3.3 LPA/autotaxin and inhibition of apoptosis and chemoresistance**

The potential contribution of LPA to resistance to chemotherapy is of considerable therapeutic significance. Patients with ovarian cancer often receive chemotherapy comprising a taxane and a platinum-based compound, often paclitaxel and carboplatin. Although these drugs are initially effective, many patients eventually relapse with a disease that has become resistant to chemotherapy. Thus, a key reason that approximately 30% of patients diagnosed with ovarian cancer survive only 5-years post-diagnosis is the development of drug resistance. Understanding the molecular basis of drug resistance and developing drugs which restore drug sensitivity is one strategy to improve the treatment of ovarian cancer.

LPA causes the translocation of the pro-apoptotic receptor Fas from the cell surface, making tumor cells less responsive to stimuli that activate the extrinsic apoptosis pathway (Meng et al. 2005). Fas activates an intracellular caspase protease cascade to drive apoptosis. cFLIP is an inhibitor of caspase-8 activation, and the increased expression of cFLIP that is induced by LPA further contributes to suppression of apoptosis by LPA (Kang et al. 2004). At the same time, LPA induces the expression of Fas ligand (FasL) on tumor cells, and this promotes apoptosis of lymphocytes (Meng et al. 2004; Meng et al. 2005) presumably allowing tumor cells to avoid immune surveillance. LPA also increases the expression of the survival factor GEP (Kamrava et al. 2005). LPA inhibits the intrinsic apoptosis pathway by promoting phosphorylation of the pro-apoptotic protein BAD (Kang et al. 2004), which prevents BAD from promoting apoptosis through activation of Bak and Bax and permeabilization of the mitochondrial outer membrane. These observations suggest that LPA can regulate both the intrinsic and extrinsic apoptosis pathways, underlining the importance of this pathway as a therapeutic target.

In addition to LPA, there is evidence directly linking autotaxin to cell survival. Expression of autotaxin suppresses apoptosis in response to serum starvation (Song et al. 2005). LPA has been shown to activate the PI 3-kinase/Akt pathway in several cell types, including in ovarian cancer cells (Baudhuin et al. 2002). This pathway is a well described cell survival pathway and contributes to LPA suppressing both the extrinsic and the intrinsic apoptosis pathways (Kang et al. 2004). Similarly, inhibition of apoptosis by autotaxin is dependent on the PI 3-kinase pathway (Song et al. 2005).

As well as inhibition of apoptosis, one of the hallmarks of cancer is the avoidance of senescence. LPA suppresses p53-dependant replicative senescence (Kortlever et al. 2008), at least in part through induction of telomerase (Bermudez et al. 2007; Yang et al. 2008).

Along with many other chemotherapeutic agents, carboplatin and paclitaxel induce apoptosis. It seems reasonable to presume that the ability of autotaxin and LPA to suppress

induced expression of autotaxin by ovarian cancer cells (Ptaszynska et al. 2008) and endothelial cells (Ptaszynska et al. 2010) thereby potentiating LPA production. LPA also promotes the expression of other pro-angiogenic factors including IL-8 by tumor cells and SDF-1 by mesenchymal stem cells (Jeon et al. 2010). These observations suggest a key role for autotaxin and LPA in ovarian cancer driven angiogenesis and have led to the suggestion that autotaxin may also be a therapeutic target for inhibiting angiogenesis (Ptaszynska et al. 2010). In addition to its role in angiogenesis VEGF has also been implicated in LPA induced

The potential contribution of LPA to resistance to chemotherapy is of considerable therapeutic significance. Patients with ovarian cancer often receive chemotherapy comprising a taxane and a platinum-based compound, often paclitaxel and carboplatin. Although these drugs are initially effective, many patients eventually relapse with a disease that has become resistant to chemotherapy. Thus, a key reason that approximately 30% of patients diagnosed with ovarian cancer survive only 5-years post-diagnosis is the development of drug resistance. Understanding the molecular basis of drug resistance and developing drugs which restore drug sensitivity is one strategy to improve the treatment of

LPA causes the translocation of the pro-apoptotic receptor Fas from the cell surface, making tumor cells less responsive to stimuli that activate the extrinsic apoptosis pathway (Meng et al. 2005). Fas activates an intracellular caspase protease cascade to drive apoptosis. cFLIP is an inhibitor of caspase-8 activation, and the increased expression of cFLIP that is induced by LPA further contributes to suppression of apoptosis by LPA (Kang et al. 2004). At the same time, LPA induces the expression of Fas ligand (FasL) on tumor cells, and this promotes apoptosis of lymphocytes (Meng et al. 2004; Meng et al. 2005) presumably allowing tumor cells to avoid immune surveillance. LPA also increases the expression of the survival factor GEP (Kamrava et al. 2005). LPA inhibits the intrinsic apoptosis pathway by promoting phosphorylation of the pro-apoptotic protein BAD (Kang et al. 2004), which prevents BAD from promoting apoptosis through activation of Bak and Bax and permeabilization of the mitochondrial outer membrane. These observations suggest that LPA can regulate both the intrinsic and extrinsic apoptosis pathways, underlining the importance of this pathway as a

In addition to LPA, there is evidence directly linking autotaxin to cell survival. Expression of autotaxin suppresses apoptosis in response to serum starvation (Song et al. 2005). LPA has been shown to activate the PI 3-kinase/Akt pathway in several cell types, including in ovarian cancer cells (Baudhuin et al. 2002). This pathway is a well described cell survival pathway and contributes to LPA suppressing both the extrinsic and the intrinsic apoptosis pathways (Kang et al. 2004). Similarly, inhibition of apoptosis by autotaxin is dependent on

As well as inhibition of apoptosis, one of the hallmarks of cancer is the avoidance of senescence. LPA suppresses p53-dependant replicative senescence (Kortlever et al. 2008), at

Along with many other chemotherapeutic agents, carboplatin and paclitaxel induce apoptosis. It seems reasonable to presume that the ability of autotaxin and LPA to suppress

least in part through induction of telomerase (Bermudez et al. 2007; Yang et al. 2008).

invasion (So et al. 2005; Wang et al. 2009; Wang et al. 2011).

ovarian cancer.

therapeutic target.

the PI 3-kinase pathway (Song et al. 2005).

**3.3 LPA/autotaxin and inhibition of apoptosis and chemoresistance** 

apoptosis contributes to resistance to paclitaxel and carboplatin. As these drugs are the cornerstone of ovarian cancer chemotherapy, the potential of the LPA pathway as a therapeutic target is again underlined. Early work demonstrated that LPA confers resistance to cisplatin (Frankel and Mills 1996) and this has also been observed in colon cancer cells (Sun et al. 2009). We conducted a screen to identify genes that confer resistance to carboplatin, and one of the hits identified in that screen was autotaxin. Expression of autotaxin delayed apoptosis induced by carboplatin, while apoptosis was accelerated after inhibition of autotaxin by either siRNA or with a small molecule inhibitor (Vidot et al. 2010). More recently, LPA and autotaxin have been shown to confer resistance of breast and melanoma cancer cells to paclitaxel (Samadi et al. 2009). Resistance to paclitaxel depends on PI 3-kinase, presumably reflecting the role of PI 3-kinase downstream of LPA in survival signalling that was noted above. Remarkably, resistance to paclitaxel conferred by LPA by restores normal spindle function in cells exposed to paclitaxel and the cells escape M-phase arrest (Samadi et al. 2011). The LPA2 receptor is one candidate for mediating chemoresistance, because LPA2-/- mice exhibit increased radiation-induced apoptosis (Deng et al. 2007). Thus, there is direct evidence linking autotaxin to resistance to both chemotherapeutic agents used to treat ovarian cancer.

Other proteins in the LPA pathway may also contribute to chemoresistance. RGS proteins (Regulator of G-protein signalling) attenuate signalling by LPA receptors by increasing the GTPase activity of G-proteins that are activated by LPA receptors (Hurst et al. 2008). Expression of several RGS proteins is decreased in ovarian cancer cell lines that are resistant to cisplatin (Hooks et al. 2010). Knockdown of expression of two RGS protein, RGS10 and RGS17, causes a 2-3 fold reduction in the potency but a striking 6-fold reduction in cisplatin potency is observed when the expression of both RGS proteins is inhibited. This suggests that loss of expression of RGS proteins, leading to increased activity of LPA receptor signalling through G-proteins, may contribute to resistance to chemotherapy.

In addition to inhibiting apoptosis through G-protein signalling, the LPA2 receptor also regulates the pro-apoptotic protein Siva-1. Activation of p53 following DNA damage increases the expression of pro-apoptotic Siva-1 and this contributes to cisplatin-induced apoptosis (Barkinge et al. 2009), as well apoptosis induced by uvltraviolet light (Chu et al. 2004). LPA causes ubiquitination and turnover of Siva-1 and this contributes to suppression of apoptosis by LPA (Lin et al. 2007). This may be mediated by the LPA2 receptor. LPA2 is distinct from other LPA receptors in containing zinc finger and a Cterminal PDZ binding motifs. These motifs serve to recruit NHERF2 and TRIP6, which form a ternary complex with Siva-1. Both NHERF2 and TRIP6 are required for LPA to confer resistance to cisplatin (E et al. 2009). But how does Siva-1 induce apoptosis? In part, this probably reflects inhibition of the cell survival driven by the transcription factor NFB. Intriguingly, Siva-1 can also inhibit Bcl-XL, a member of the anti-apoptotic Bcl-2 family proteins that suppress activation of Bak and Bax in the intrinsic apoptosis pathway (Xue et al. 2002). We have shown previously that inhibition of Bcl-XL increases sensitivity to carboplatin (Witham et al. 2007). Together with our observation that autotaxin confers resistance to carboplatin (Vidot et al. 2010), these data suggest that autotaxin may confer resistance to carboplatin by suppressing the intrinsic apoptosis pathway (fig. 2). As we discuss below, this predicts that autotaxin inhibitors may be useful in the treatment of drug-resistant ovarian cancer.

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 363

signalling pathway which might provide drug targets to treat ovarian cancer. Drugs could be developed which: inhibit the synthesis of LPA; increase the catabolism of LPA; upregulate LPA binding proteins to sequester LPA; inhibit LPA binding to its receptors or inhibit LPA receptor expression; inhibit downstream signalling. (Note that strategies to modulate the tumor environment are already being explored as inhibitors of the VEGF pathway, e.g. bevacizumab, are currently in clinical trials in ovarian cancer and encouraging

Although several of these approaches are feasible, in several cases we consider that there is currently insufficient data to identify a drug target as well validated in ovarian cancer. For example, there are multiple signalling pathways activated by LPA receptors. Although experimental data is accumulating, several potential drug targets in these signalling pathways activated by LPA receptors require validation in additional cell lines and evaluation in clinical samples. Until such data is forthcoming, we consider that developing drugs which inhibit the synthesis of LPA or which inhibit LPA receptors are currently the most promising avenues. As we have discussed, there are difficulties with these approaches too. The complexity of the LPA pathway suggests to us that it may be difficult to gather robust target validation data with preclinical studies alone, and that well designed clinical research with inhibitors of autotaxin, iPLA2 or LPA receptors will be necessary to confirm the best approach(s). Thus, for the remainder of this review we will focus on autotaxin as

To date a number of metal chelators, lipid analogues and non-lipid small molecules have been discovered to be inhibitors of autotaxin. In this section we have concentrated on recent

Cui and Macdonald have developed a series of tyrosine-derived β-hydroxyphosphonates as analogues of LPA that display activity as inhibitors of autotaxin (Cui et al. 2007; Cui et al. 2008). The synthesis of this series of compounds is highlighted in Figure 3. The sodium borohydride reduction step gave rise to a mixture of two diasteroisomeric products that were separated and isolated by column chromatography. In the initial publications (Cui et al. 2007; Cui et al. 2008) the relative stereochemistry at the new chiral centre had not been determined, but later work from this group on a more advanced series of inhibitors gave insight to the relationship between stereochemistry and activity in the lead compounds (East et al. 2010). From an initial series of targets prepared (R1 = C15H31, variation of R2), the most active compound to be identified was compound **1a**, derived from *S*-tyrosine and later confirmed to have the relative stereochemistry shown (Fig. 3), which was able to inhibit 73% of autotaxin activity when tested at a concentration of 1 µM. The *syn* isomer, **1b**, was less active achieving 37% inhibition at the same concentration. Interestingly, the corresponding isomers of compound **1** prepared from the enantiomer *R*-tyrosine did *not* show potent inhibition of autotaxin even though they contained the same pyridyl subunit. Structural modification based around varying the length of, or incorporation of unsaturation into, the lipophilic side chains (R1, Fig. 3) of compounds **1a** and **1b** did not result in an increase in

In a follow-up study (East et al. 2010) the SAR of the pyridyl region was further explored and important structural features were determined to be: the nitrogen heteroatom, the presence of the methoxy substituent, the presence of methyl groups. Extending the alkyl

results have been obtained.)

one potential target to inhibiting the LPA pathway.

**5. Current status of autotaxin inhibitors** 

reports of small molecule inhibitors of autotaxin.

activity from that originally seen with **1a**.

Fig. 2. Regulation of chemoresistance by LPA and autotaxin. CTX denotes chemotherapy, PI-3K, PI 3-kinse.

The observation that RGS and Siva pathways both contribute to chemoresistance in different cell lines highlights the point that there are multiple mechanisms that can cause drug resistance. Thus, if the signalling pathways that are activated by LPA receptors are used as therapeutic targets to restore chemosensitivity, it may be necessary to develop several different therapeutic agents and use them in accordance with the particular pathway that is driving chemoresistance in a individual patient's tumor. If multiple pathways promote resistance, several drugs may be necessary. Alternatively, it may be more straight forwards to develop drugs which either inhibit the LPA receptor(s) or prevent the production of LPA itself.

As well as contributing to resistance to chemotherapy, autotaxin also confer resistance to histone deacetylase inhibitors. HDAC3 and HDAC7 repress the expression of autotaxin. Consequently, exposure to the HDAC inhibitor trichostatin (TSA) increases the expression of autotaxin and the subsequent production of LPA inhibits apoptosis induced by TSA. This suggests that autotaxin confers resistance to HDAC inhibitors (Li et al. 2011). One clinical use of autotaxin inhibitors may be in combination with HDAC inhibitors.

#### **4. Is autotaxin a valid target in ovarian cancer?**

A starting point for drug discovery is "target validation" – a process in which data is amassed to give confidence that inhibiting a particular drug target will afford the desired therapeutic outcome. The foregoing discussion highlights several points in the LPA

Fig. 2. Regulation of chemoresistance by LPA and autotaxin. CTX denotes chemotherapy,

drugs which either inhibit the LPA receptor(s) or prevent the production of LPA itself.

use of autotaxin inhibitors may be in combination with HDAC inhibitors.

**4. Is autotaxin a valid target in ovarian cancer?** 

The observation that RGS and Siva pathways both contribute to chemoresistance in different cell lines highlights the point that there are multiple mechanisms that can cause drug resistance. Thus, if the signalling pathways that are activated by LPA receptors are used as therapeutic targets to restore chemosensitivity, it may be necessary to develop several different therapeutic agents and use them in accordance with the particular pathway that is driving chemoresistance in a individual patient's tumor. If multiple pathways promote resistance, several drugs may be necessary. Alternatively, it may be more straight forwards to develop

As well as contributing to resistance to chemotherapy, autotaxin also confer resistance to histone deacetylase inhibitors. HDAC3 and HDAC7 repress the expression of autotaxin. Consequently, exposure to the HDAC inhibitor trichostatin (TSA) increases the expression of autotaxin and the subsequent production of LPA inhibits apoptosis induced by TSA. This suggests that autotaxin confers resistance to HDAC inhibitors (Li et al. 2011). One clinical

A starting point for drug discovery is "target validation" – a process in which data is amassed to give confidence that inhibiting a particular drug target will afford the desired therapeutic outcome. The foregoing discussion highlights several points in the LPA

PI-3K, PI 3-kinse.

signalling pathway which might provide drug targets to treat ovarian cancer. Drugs could be developed which: inhibit the synthesis of LPA; increase the catabolism of LPA; upregulate LPA binding proteins to sequester LPA; inhibit LPA binding to its receptors or inhibit LPA receptor expression; inhibit downstream signalling. (Note that strategies to modulate the tumor environment are already being explored as inhibitors of the VEGF pathway, e.g. bevacizumab, are currently in clinical trials in ovarian cancer and encouraging results have been obtained.)

Although several of these approaches are feasible, in several cases we consider that there is currently insufficient data to identify a drug target as well validated in ovarian cancer. For example, there are multiple signalling pathways activated by LPA receptors. Although experimental data is accumulating, several potential drug targets in these signalling pathways activated by LPA receptors require validation in additional cell lines and evaluation in clinical samples. Until such data is forthcoming, we consider that developing drugs which inhibit the synthesis of LPA or which inhibit LPA receptors are currently the most promising avenues. As we have discussed, there are difficulties with these approaches too. The complexity of the LPA pathway suggests to us that it may be difficult to gather robust target validation data with preclinical studies alone, and that well designed clinical research with inhibitors of autotaxin, iPLA2 or LPA receptors will be necessary to confirm the best approach(s). Thus, for the remainder of this review we will focus on autotaxin as one potential target to inhibiting the LPA pathway.

#### **5. Current status of autotaxin inhibitors**

To date a number of metal chelators, lipid analogues and non-lipid small molecules have been discovered to be inhibitors of autotaxin. In this section we have concentrated on recent reports of small molecule inhibitors of autotaxin.

Cui and Macdonald have developed a series of tyrosine-derived β-hydroxyphosphonates as analogues of LPA that display activity as inhibitors of autotaxin (Cui et al. 2007; Cui et al. 2008). The synthesis of this series of compounds is highlighted in Figure 3. The sodium borohydride reduction step gave rise to a mixture of two diasteroisomeric products that were separated and isolated by column chromatography. In the initial publications (Cui et al. 2007; Cui et al. 2008) the relative stereochemistry at the new chiral centre had not been determined, but later work from this group on a more advanced series of inhibitors gave insight to the relationship between stereochemistry and activity in the lead compounds (East et al. 2010). From an initial series of targets prepared (R1 = C15H31, variation of R2), the most active compound to be identified was compound **1a**, derived from *S*-tyrosine and later confirmed to have the relative stereochemistry shown (Fig. 3), which was able to inhibit 73% of autotaxin activity when tested at a concentration of 1 µM. The *syn* isomer, **1b**, was less active achieving 37% inhibition at the same concentration. Interestingly, the corresponding isomers of compound **1** prepared from the enantiomer *R*-tyrosine did *not* show potent inhibition of autotaxin even though they contained the same pyridyl subunit. Structural modification based around varying the length of, or incorporation of unsaturation into, the lipophilic side chains (R1, Fig. 3) of compounds **1a** and **1b** did not result in an increase in activity from that originally seen with **1a**.

In a follow-up study (East et al. 2010) the SAR of the pyridyl region was further explored and important structural features were determined to be: the nitrogen heteroatom, the presence of the methoxy substituent, the presence of methyl groups. Extending the alkyl

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 365

Fig. 4. (i) KOH, DMSO, appropriate benzyl bromide (as methyl ester), rt, 30 min; (ii) NaOH, DMSO/H2O, reflux, 4 h, 91% yield; (iii) NaH, DMF, rt, appropriate benzyl chloride, 22 h,

Replacing this group with a boronic acid moiety gave compound **3** that was found to be a potent inhibitor of autotaxin both *in vitro* and *in vivo* (IC50 = 6 nM). These results were rationalized on the basis that since the carboxylic acid group in **2** was expected to bind close to the active site threonine (Thr210) in autotaxin, a boronic acid moiety might be expected to do the same. There was a precedent for this since the proteasome inhibitor bortezomib binds to a threonine oxygen nucleophile at the active site through a boronic acid group (Groll et al. 2006). The boronic acid-based thiazolidinediones showed greater affinity for autotaxin and are expected to show improved selectivity over other hydrolytic enzymes. The boronic acids such as **3** are expected to have the same binding site as the original lead **2**, but they show mixed-type inhibition rather than the competitive inhibition displayed by **2**. Ovaa has recently extended this work and has reported that the imidazolidine analogues such as **3b**

Virtual screening techniques have been used by Parrill, Baker and co-workers (Parrill et al. 2008; Hoeglund et al. 2010; Hoeglund et al. 2010; North et al. 2010). This has led to series of autotaxin inhibitors with pipemidic acid or pthalimide cores and related compounds (fig. 5). Of the pipemidic acid-based inhibitors (eg **4**, **5**, fig. 5), compound **4** (IC50 = 1.6 µM) was used as a lead to investigate the activity of a range of analogues with varying substitution on the pendent benzene ring. The synthetic approach was straightforward (fig. 6), starting with commercially available pipemidic acid and a range of substituted phenyl isothiocyanates to produce 30 compounds for evaluation. Themes to emerge were that *meta*

74% yield; (iv) piperidine, EtOH, reflux, 20 h, 63% yield.

show a similar level of activity to **3a** (Albers et al. 2011).

chain of the alkyloxy substituent to ethyl or propyl led to a fall in activity. Activity was retained on removing the heteroatom as long as the methoxy group and methyl substitution were also retained. In all cases the *anti*-isomer was more active than the *syn* isomer.

Docking studies were carried out on compound **1a** and suggested the proximity of the phosphonate to the two zinc centres at the active site and that the lipophilic side chain was able to fill a large lipophilic pocket, thought to bind the lipid tail of LPC. An aromaticguanidine binding interaction was also suggested between the benzyl substituent and Arg456 and a weak H-bonding interaction between the methoxy substituent and Lys209 within the hydrophilic leaving group pocket of autotaxin. The degree of interaction was dependent upon the electron density of the aryl ring, with more electron rich substituents on the pyridyl ring favouring the interaction. Interestingly the aromatic ring of the tyrosine unit appeared to act solely as an appropriate spacer unit between the more important pharmacophore groups.

Fig. 3. Tyrosine based inhibitors of autotaxin. (i) Acid chloride, Et3N, DCM, 0 oC, 3 h; (ii) Appropriate mesylate, K2CO3, 18-crown-6, acetone, reflux, overnight; (iii) *n*-BuLi, dimethyl methylphosphonate, -78 oC, 3 h; (iv) NaBH4, THF, EtOH, 0 oC, 2 h; (v) TMSBr, pyridine, DCM, rt, 4 h, then H2O/MeOH overnight.

A study by Ovaa and co-workers (Albers et al. 2010; Albers et al. 2010) on a collection of *ca.*  40,000 compounds has allowed the identification of a group of thiazolidinediones as autotaxin inhibitors. The general class of compound was prepared as outlined in Figure 4. From the initial screen, compound **2** was found to be the most active (IC50 = 56nM) and was selected for further optimization. Although structural variation at the benzylidene and benzyl groups did not lead to an increase in activity, the opportunity was taken to investigate pharmacophoric variation of the carboxylic acid substituent.

chain of the alkyloxy substituent to ethyl or propyl led to a fall in activity. Activity was retained on removing the heteroatom as long as the methoxy group and methyl substitution

Docking studies were carried out on compound **1a** and suggested the proximity of the phosphonate to the two zinc centres at the active site and that the lipophilic side chain was able to fill a large lipophilic pocket, thought to bind the lipid tail of LPC. An aromaticguanidine binding interaction was also suggested between the benzyl substituent and Arg456 and a weak H-bonding interaction between the methoxy substituent and Lys209 within the hydrophilic leaving group pocket of autotaxin. The degree of interaction was dependent upon the electron density of the aryl ring, with more electron rich substituents on the pyridyl ring favouring the interaction. Interestingly the aromatic ring of the tyrosine unit appeared to act solely as an appropriate spacer unit between the more important pharmacophore groups.

Fig. 3. Tyrosine based inhibitors of autotaxin. (i) Acid chloride, Et3N, DCM, 0 oC, 3 h; (ii) Appropriate mesylate, K2CO3, 18-crown-6, acetone, reflux, overnight; (iii) *n*-BuLi, dimethyl methylphosphonate, -78 oC, 3 h; (iv) NaBH4, THF, EtOH, 0 oC, 2 h; (v) TMSBr, pyridine,

A study by Ovaa and co-workers (Albers et al. 2010; Albers et al. 2010) on a collection of *ca.*  40,000 compounds has allowed the identification of a group of thiazolidinediones as autotaxin inhibitors. The general class of compound was prepared as outlined in Figure 4. From the initial screen, compound **2** was found to be the most active (IC50 = 56nM) and was selected for further optimization. Although structural variation at the benzylidene and benzyl groups did not lead to an increase in activity, the opportunity was taken to

investigate pharmacophoric variation of the carboxylic acid substituent.

DCM, rt, 4 h, then H2O/MeOH overnight.

were also retained. In all cases the *anti*-isomer was more active than the *syn* isomer.

Fig. 4. (i) KOH, DMSO, appropriate benzyl bromide (as methyl ester), rt, 30 min; (ii) NaOH, DMSO/H2O, reflux, 4 h, 91% yield; (iii) NaH, DMF, rt, appropriate benzyl chloride, 22 h, 74% yield; (iv) piperidine, EtOH, reflux, 20 h, 63% yield.

Replacing this group with a boronic acid moiety gave compound **3** that was found to be a potent inhibitor of autotaxin both *in vitro* and *in vivo* (IC50 = 6 nM). These results were rationalized on the basis that since the carboxylic acid group in **2** was expected to bind close to the active site threonine (Thr210) in autotaxin, a boronic acid moiety might be expected to do the same. There was a precedent for this since the proteasome inhibitor bortezomib binds to a threonine oxygen nucleophile at the active site through a boronic acid group (Groll et al. 2006). The boronic acid-based thiazolidinediones showed greater affinity for autotaxin and are expected to show improved selectivity over other hydrolytic enzymes. The boronic acids such as **3** are expected to have the same binding site as the original lead **2**, but they show mixed-type inhibition rather than the competitive inhibition displayed by **2**. Ovaa has recently extended this work and has reported that the imidazolidine analogues such as **3b** show a similar level of activity to **3a** (Albers et al. 2011).

Virtual screening techniques have been used by Parrill, Baker and co-workers (Parrill et al. 2008; Hoeglund et al. 2010; Hoeglund et al. 2010; North et al. 2010). This has led to series of autotaxin inhibitors with pipemidic acid or pthalimide cores and related compounds (fig. 5). Of the pipemidic acid-based inhibitors (eg **4**, **5**, fig. 5), compound **4** (IC50 = 1.6 µM) was used as a lead to investigate the activity of a range of analogues with varying substitution on the pendent benzene ring. The synthetic approach was straightforward (fig. 6), starting with commercially available pipemidic acid and a range of substituted phenyl isothiocyanates to produce 30 compounds for evaluation. Themes to emerge were that *meta*

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 367

The phthalimide-derived small molecule lead inhibitors that were identified in the virtual screen by the Parrill and co-workers were also further evaluated including some dimeric examples, (Fig. 7) (Hoeglund et al. 2010). It is noticeable that these compounds contain terminal functionality that would be expected to contribute towards binding to zinc at the active site. Compounds **8** and **10** showed mixed-mode inhibition, whereas compound **9** 

Fig. 7. Phthalimide-derived autotaxin inhibitors, all of which showed moderate potency

coupling of the benzoic acid derivative to introduce the long alkyl side chain.

Compound **14** is currently undergoing further preclinical study.

A recent report by Miller and Tigyi (Gupte et al. 2011) has built upon work carried out by Ferry and co-workers reporting that compound **11** (known as S32826, fig. 8; (Ferry et al. 2008)) possessed nanomolar activity as an autotaxin inhibitor. S32826 was inactive when evaluated using *in vivo* systems, and it has been presumed by Miller and Tigyi (Gupte et al. 2011) that this is due to the propensity for hydrolysis of the amide bond, making S32826 relatively unstable. In their own work Miller and Tigyi report a series of benzyl and naphthalene methyl phosphonic acid-derived compounds, of which **12** and **13** are most active, as inhibitors of autotaxin and that possess anti-invasive and anti-metastatic activity (Gupte et al. 2011). The synthetic approach to compound **12** (fig. 8) begins with a Heck

Compound **12** shows 94.8% inhibition of autotaxin and has an IC50 of 0.17 µM, with a Ki of 0.27 µM and displays a mixed mode of inhibition. In addition to inhibiting the invasion of MM1 hepatoma cells *in vitro* in a dose-dependent fashion, compound **13** significantly decreases lung metastasis of B16-F10 syngeneic mouse melanoma. Compound **12** has an average terminal half-life of 10 5 hours and causes a long-lasting decrease in plasma LPA

Prestwich has recently reported the synthesis of a hydroxylated S32826 analogue, (fig. 8, **14)**, that retains acceptable levels of solubility (4mg/ml) whilst maintaining its potency as an inhibitor of autotaxin (Ki = 24.2 nM) and has potential for *in vivo* utility (Jiang et al. 2011).

showed competitive inhibition of autotaxin.

(IC50 = 5 - 10µM) against autotaxin.

levels.

substitution is preferred regardless of substituent, suggesting steric or conformational preferences rather than electronic effects are playing a role here. Within the *meta* class of compounds, inhibition was improved in the order: OMe<F<Cl<I,<CF3, reflecting neither size nor electronic trends. A single *meta*-trifluoromethyl group was preferred over two, and the singly substituted compound showed three times greater affinity for the enzyme than the original lead compound **4**. Of the compounds screened, compound **7** emerged as the most potent analogue in this study (IC50 = 0.9 µM; Ki = 0.7 µM), and showed competitive inhibition.

Fig. 5. Hits identified by virtual screening by Parrill and co-workers all of which inhibited autotaxin with IC50 ~ 2µM. Compounds **4** and **5** are pipemidic acid derivatives.

Fig. 6. Synthesis of autotaxin inhibitors basded on pipemidic acid. Compound 7 was the most potent analog reported (IC50 ~ 1 µM).

substitution is preferred regardless of substituent, suggesting steric or conformational preferences rather than electronic effects are playing a role here. Within the *meta* class of compounds, inhibition was improved in the order: OMe<F<Cl<I,<CF3, reflecting neither size nor electronic trends. A single *meta*-trifluoromethyl group was preferred over two, and the singly substituted compound showed three times greater affinity for the enzyme than the original lead compound **4**. Of the compounds screened, compound **7** emerged as the most potent analogue in this study (IC50 = 0.9 µM; Ki = 0.7 µM), and showed competitive

Fig. 5. Hits identified by virtual screening by Parrill and co-workers all of which inhibited

Fig. 6. Synthesis of autotaxin inhibitors basded on pipemidic acid. Compound 7 was the

most potent analog reported (IC50 ~ 1 µM).

autotaxin with IC50 ~ 2µM. Compounds **4** and **5** are pipemidic acid derivatives.

inhibition.

The phthalimide-derived small molecule lead inhibitors that were identified in the virtual screen by the Parrill and co-workers were also further evaluated including some dimeric examples, (Fig. 7) (Hoeglund et al. 2010). It is noticeable that these compounds contain terminal functionality that would be expected to contribute towards binding to zinc at the active site. Compounds **8** and **10** showed mixed-mode inhibition, whereas compound **9**  showed competitive inhibition of autotaxin.

Fig. 7. Phthalimide-derived autotaxin inhibitors, all of which showed moderate potency (IC50 = 5 - 10µM) against autotaxin.

A recent report by Miller and Tigyi (Gupte et al. 2011) has built upon work carried out by Ferry and co-workers reporting that compound **11** (known as S32826, fig. 8; (Ferry et al. 2008)) possessed nanomolar activity as an autotaxin inhibitor. S32826 was inactive when evaluated using *in vivo* systems, and it has been presumed by Miller and Tigyi (Gupte et al. 2011) that this is due to the propensity for hydrolysis of the amide bond, making S32826 relatively unstable. In their own work Miller and Tigyi report a series of benzyl and naphthalene methyl phosphonic acid-derived compounds, of which **12** and **13** are most active, as inhibitors of autotaxin and that possess anti-invasive and anti-metastatic activity (Gupte et al. 2011). The synthetic approach to compound **12** (fig. 8) begins with a Heck coupling of the benzoic acid derivative to introduce the long alkyl side chain.

Compound **12** shows 94.8% inhibition of autotaxin and has an IC50 of 0.17 µM, with a Ki of 0.27 µM and displays a mixed mode of inhibition. In addition to inhibiting the invasion of MM1 hepatoma cells *in vitro* in a dose-dependent fashion, compound **13** significantly decreases lung metastasis of B16-F10 syngeneic mouse melanoma. Compound **12** has an average terminal half-life of 10 5 hours and causes a long-lasting decrease in plasma LPA levels.

Prestwich has recently reported the synthesis of a hydroxylated S32826 analogue, (fig. 8, **14)**, that retains acceptable levels of solubility (4mg/ml) whilst maintaining its potency as an inhibitor of autotaxin (Ki = 24.2 nM) and has potential for *in vivo* utility (Jiang et al. 2011). Compound **14** is currently undergoing further preclinical study.

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 369

lasso loop (residues 539-590) and finally a nuclease domain (residues 539-862). It has also been suggested that a glycan chain located between the phosphodiesterase and nuclease domains is essential for correct folding of the protein. Strong bonding and electrostatic interactions are observed between the phosphodiesterase and the C-terminal region of the NUC domains including a disulphide cysteine bridge between residues 413 and 805 along with seven hydrogen bonds and nine salt bridges. This, combined with the extension of the Lasso loop from the PDE domain around the NUC domain, makes for a tight and well-ordered protein.

**PDB CODE Description Reference**  2XRG.pdb Rat ATX with ligand Hausmann et al 2XRN.pdb Rat ATX no ligand Hausmann et al 3NKM.pdb Mouse ATX Nishimasu et al 3NKN.pdb Mouse ATX 14:0 LPA Nishimasu et al 3NKO.pdb Mouse ATX 16:0 LPA Nishimasu et al 3NKP.pdb Mouse ATX 18:1 LPA Nishimasu et al 3NKQ.pdb Mouse ATX 18:3 LPA Nishimasu et al 3NKR.pdb Mouse ATX 22:6 LPA Nishimasu et al Table 1. Structures of human autotaxin available in the Protein database. (www.pdb.org)

Fig. 9. The domain structure of autotaxin. The figure shows the location of the somatomedin domain (SMB), the phosphodiesterase domain (PDE) containing the catalytic site, the lasso

The SMB domains are required for integrin binding and so may play a role in recruiting autotaxin to the cell surface where LPA receptors are located. A tunnel between one of the SMB domains and the ligand binding pocket has been suggested to facilitate delivery of LPA to cell surface receptors (Tabchy et al. 2011). The location of the tunnel compared to the catalytic site is shown in figure 10. Many lipophilic molecules are transported bound to protein carrier, such as albumin, so it seems reasonable that the tunnel fulfils a carrier role.

loop and the nuclease domain (NUC).

Metabolically stable analogues of LPA, **15** and **16**, were designed by Prestwich and coworkers (Jiang et al. 2007). Compound **15** showed selective agonist activity for LPA2, whereas **16** is a selective antagonist of LPA4 and indeed is the first antagonist of this receptor to be reported. Compound **15** was found to be as effective as natural LPA as an inhibitor of autotaxin. Arguably the most interesting compound though, **16**, showed pan-antagonism of LPA GPCR's and was also active as an autotaxin inhibitor, thus having potential in anticancer and anti-metastasis models in cancer therapy (Zhang et al. 2009).

Fig. 8. (i) Pd(OAc)2, Et3N, DMF, reflux, 16 h; (ii) LAH, THF, 0 oC-rt, 4 h; (iii) H2, Pd/C, MeOH, rt, 2 h; (iv) PBr3, Et2O, rt, 30 min; (v) P(OMe)3, reflux, 18 h; (vi) TMSBr, CH3CN, reflux, 1 h; (vii) MeOH, rt, 30 min.

#### **6. Structure of autotaxin and molecular modelling to aid drug design**

#### **6.1 Autotaxin structure**

The availability of data describing the structure of autotaxin is a superb tool to support drug discovery. Of the 8 protein crystal structures of autotaxin (*ENPP2*) that have thus far been described (Hausmann et al. 2011; Nishimasu et al. 2011) only one (2XRG.pdb; Table 1) has a small drug-like molecule bound in the active site. There are two unliganded structures. The others contain a range of phospholipids and LPA analogues with a variety of fatty chains and LPA analogues. The overall architecture of autotaxin is shown in figure 9. The N-terminus begins with a pair of somatomedin-B like (SMB) domains (residues 56-96 and 96-140) which lead into a phosphodiesterase domain (160-539) which contains a catalytic zinc binding site, a

Metabolically stable analogues of LPA, **15** and **16**, were designed by Prestwich and coworkers (Jiang et al. 2007). Compound **15** showed selective agonist activity for LPA2, whereas **16** is a selective antagonist of LPA4 and indeed is the first antagonist of this receptor to be reported. Compound **15** was found to be as effective as natural LPA as an inhibitor of autotaxin. Arguably the most interesting compound though, **16**, showed pan-antagonism of LPA GPCR's and was also active as an autotaxin inhibitor, thus having potential in

anticancer and anti-metastasis models in cancer therapy (Zhang et al. 2009).

Fig. 8. (i) Pd(OAc)2, Et3N, DMF, reflux, 16 h; (ii) LAH, THF, 0 oC-rt, 4 h; (iii) H2, Pd/C, MeOH, rt, 2 h; (iv) PBr3, Et2O, rt, 30 min; (v) P(OMe)3, reflux, 18 h; (vi) TMSBr, CH3CN,

**6. Structure of autotaxin and molecular modelling to aid drug design** 

The availability of data describing the structure of autotaxin is a superb tool to support drug discovery. Of the 8 protein crystal structures of autotaxin (*ENPP2*) that have thus far been described (Hausmann et al. 2011; Nishimasu et al. 2011) only one (2XRG.pdb; Table 1) has a small drug-like molecule bound in the active site. There are two unliganded structures. The others contain a range of phospholipids and LPA analogues with a variety of fatty chains and LPA analogues. The overall architecture of autotaxin is shown in figure 9. The N-terminus begins with a pair of somatomedin-B like (SMB) domains (residues 56-96 and 96-140) which lead into a phosphodiesterase domain (160-539) which contains a catalytic zinc binding site, a

reflux, 1 h; (vii) MeOH, rt, 30 min.

**6.1 Autotaxin structure** 

lasso loop (residues 539-590) and finally a nuclease domain (residues 539-862). It has also been suggested that a glycan chain located between the phosphodiesterase and nuclease domains is essential for correct folding of the protein. Strong bonding and electrostatic interactions are observed between the phosphodiesterase and the C-terminal region of the NUC domains including a disulphide cysteine bridge between residues 413 and 805 along with seven hydrogen bonds and nine salt bridges. This, combined with the extension of the Lasso loop from the PDE domain around the NUC domain, makes for a tight and well-ordered protein.


Table 1. Structures of human autotaxin available in the Protein database. (www.pdb.org)

Fig. 9. The domain structure of autotaxin. The figure shows the location of the somatomedin domain (SMB), the phosphodiesterase domain (PDE) containing the catalytic site, the lasso loop and the nuclease domain (NUC).

The SMB domains are required for integrin binding and so may play a role in recruiting autotaxin to the cell surface where LPA receptors are located. A tunnel between one of the SMB domains and the ligand binding pocket has been suggested to facilitate delivery of LPA to cell surface receptors (Tabchy et al. 2011). The location of the tunnel compared to the catalytic site is shown in figure 10. Many lipophilic molecules are transported bound to protein carrier, such as albumin, so it seems reasonable that the tunnel fulfils a carrier role.

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 371

The main features of the catalytic site are shown in figure 11. There are two di-cationic zinc atoms (fig 10 and fig. 11). The first is fully co-ordinated with a histidine, and threonine and two aspartate residues. The second zinc ion is co-ordinated by an aspartate and two histidine residues, leaving it available for co-ordination by a further negatively charged

Asn231 and Thr210 have been shown to be crucial for catalysis. These are located close to one zinc ions and provide a hydrogen bonding environment. Asn231 gives a preference for hydrogen bond acceptors while Thr210 provides an opportunity for irreversible (covalently bonded) ligands such as boronic acids to react. The conserved water that is held by hydrogen bonds between Asp311 and Glu308 can also play a role in control of ligand

The remainder of the site consists of a large hydrophobic pocket which has recognition features for the lipophilic chain of LPA. Within this pocket most of the side chains appear to be less mobile, although there are however three notable exceptions, Glu308, Phe274 and Arg244, some of which may help determine the potency with which ligands bind to autotaxin. Movement of Glu308 seems to displace a water molecule. Phe274 is mobile and unresolved in some structures but is shown to occupy various positions (fig. 12) in the hydrophobic pocket in others crystal structures in some cases moving to accommodate the ligand. We speculate that movement of this residue allows LPA to move from the active site into the hydrophobic tunnel. Arg244 is also mobile but its location adjacent to the solvent

Fig. 12. This diagram shows the movement of side-chain Phe274 (grey, in centre of diagram)

We have used the published crystal structures in an effort to understand the activity of several of the compounds described in section 5. In particular we evaluated whether the reported ligands were involved in zinc binding or whether they could occupy the hydrophobic pocket. Pharmacophoric overlay of autotaxin ligands has previously been reported to give a reasonable explanation of their relative binding modes by assuming that

within the ligand binding site. The green shows the bound boronic acid inhibitor.

**6.3 Docking methods** 

species such as phosphate or carboxylate.

orientation and enantio-selectivity of ligand recognition.

suggests that it may not be as important for ligand recognition.

Fig. 10. **A**. A cut away diagram of the hydrophobic channel (right) adjacent to the ligand binding pocket. The main features of the binding site are highlighted with ovals. **B.** A closeup view of the local environment of catalytic zincs. The residues are coloured: grey, carbon; blue, nitrogen; red oxygen, light blue, zinc.

#### **6.2 Ligand binding site**

The overlay of several published X-ray structures of autotaxin shows a high conservation of ligand binding sites with only a few mobile residues. It is important to consider these as they may change the shape of the pocket or the electronic environment presented to the ligand and consequently affect ligand binding (fig 11).

Fig. 11. **A**. The overlay of the active sites of 8 published protein structures of autotaxin. Residues whose positions have moved significantly between the structures are shown emboldened. Carbon atoms in LPA are shown in green whereas autotaxin carbon atoms are coloured grey; other atoms are coloured purple (phosphate), blue (nitrogen), red (oxygen) and light blue (zinc). **B.** An annotated view of the main features of the ligand binding site

Fig. 10. **A**. A cut away diagram of the hydrophobic channel (right) adjacent to the ligand binding pocket. The main features of the binding site are highlighted with ovals. **B.** A closeup view of the local environment of catalytic zincs. The residues are coloured: grey, carbon;

The overlay of several published X-ray structures of autotaxin shows a high conservation of ligand binding sites with only a few mobile residues. It is important to consider these as they may change the shape of the pocket or the electronic environment presented to the

Fig. 11. **A**. The overlay of the active sites of 8 published protein structures of autotaxin. Residues whose positions have moved significantly between the structures are shown emboldened. Carbon atoms in LPA are shown in green whereas autotaxin carbon atoms are coloured grey; other atoms are coloured purple (phosphate), blue (nitrogen), red (oxygen) and light blue (zinc). **B.** An annotated view of the main features of the ligand binding site

blue, nitrogen; red oxygen, light blue, zinc.

ligand and consequently affect ligand binding (fig 11).

**6.2 Ligand binding site** 

The main features of the catalytic site are shown in figure 11. There are two di-cationic zinc atoms (fig 10 and fig. 11). The first is fully co-ordinated with a histidine, and threonine and two aspartate residues. The second zinc ion is co-ordinated by an aspartate and two histidine residues, leaving it available for co-ordination by a further negatively charged species such as phosphate or carboxylate.

Asn231 and Thr210 have been shown to be crucial for catalysis. These are located close to one zinc ions and provide a hydrogen bonding environment. Asn231 gives a preference for hydrogen bond acceptors while Thr210 provides an opportunity for irreversible (covalently bonded) ligands such as boronic acids to react. The conserved water that is held by hydrogen bonds between Asp311 and Glu308 can also play a role in control of ligand orientation and enantio-selectivity of ligand recognition.

The remainder of the site consists of a large hydrophobic pocket which has recognition features for the lipophilic chain of LPA. Within this pocket most of the side chains appear to be less mobile, although there are however three notable exceptions, Glu308, Phe274 and Arg244, some of which may help determine the potency with which ligands bind to autotaxin. Movement of Glu308 seems to displace a water molecule. Phe274 is mobile and unresolved in some structures but is shown to occupy various positions (fig. 12) in the hydrophobic pocket in others crystal structures in some cases moving to accommodate the ligand. We speculate that movement of this residue allows LPA to move from the active site into the hydrophobic tunnel. Arg244 is also mobile but its location adjacent to the solvent suggests that it may not be as important for ligand recognition.

Fig. 12. This diagram shows the movement of side-chain Phe274 (grey, in centre of diagram) within the ligand binding site. The green shows the bound boronic acid inhibitor.

#### **6.3 Docking methods**

We have used the published crystal structures in an effort to understand the activity of several of the compounds described in section 5. In particular we evaluated whether the reported ligands were involved in zinc binding or whether they could occupy the hydrophobic pocket. Pharmacophoric overlay of autotaxin ligands has previously been reported to give a reasonable explanation of their relative binding modes by assuming that

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 373

Ligands which bind with high affinity interacted with both the zinc ion and also occupied the hydrophobic pocket e.g. with a benzyl group. In contrast, ligands which bind with low affinity failed to bridge the zinc ion and the hydrophobic portion of the ligand binding site. This leads us to conclude that for ligands to bind with high affinity, they should preferably bind both these sites. Docking studies also suggest that the hydrophobic tunnel provides an alternative location for ligands but these ligands identified so far fail to achieve better than micromolar

Once inhibitors of autotaxin complete preclinical evaluation, how could these be evaluated in clinical trials? As we have discussed above, autotaxin may not be the only enzyme that contributes to the formation of LPA in ovarian cancer and so it is possible that inhibition of autotaxin may not elicit the desired therapeutic effect. It will be important, therefore, to include in early clinical trials a measurement of the change in LPA in ascites following treatment with an autotaxin inhibitor and to establish biomarkers (e.g. measurement of

The data we have reviewed also suggests a number of different settings in which autotaxin inhibitors could be used. Autotaxin inhibitors may be useful to inhibit the growth of primary tumors or to inhibit tumor cell migration, invasion and metastases. Clinical trials to evaluate these may differ somewhat, for example using different surrogate endpoints (tumor shrinkage versus decreased metastasis). Different schedules of drug administration may also be appropriate. To cause tumor cell death, relatively short term treatment with the drug may suffice, but suppression of metastasis may require prolonged treatment. This highlights the importance of considering the therapeutic goal that is being evaluated with an

As we have discussed, there is a large body of evidence indicating a role for LPA in cancer cell migration and invasion. In addition, several studies demonstrate in animal models of metastasis that inhibitors of autotaxin reduce colonization of the lung by tumors cells (Baker et al. 2006; Gupte et al. 2010; Gupte et al. 2011). Thus, one potential clinical use of autotaxin inhibitors is to inhibit metastasis. Unfortunately, many ovarian cancer patients present with advanced disease, and significant dissemination of the tumor within the peritoneal cavity has already taken place by the time of diagnosis. Although it may be beneficial to prevent further metastasis and progression to later stage disease, there may also be micro-metastases that are not evident on examination. It is not clear, then, whether inhibiting further metastasis would be helpful. Evaluation of autotaxin inhibitors as anti-metastatic agents in patients with early stage disease will also be challenging. Relatively few patients are diagnosed with early stage disease and these patients generally have a good prognosis, with 90% of patients surviving more than 5 years. A large cohort of patients may also be required to ensure sufficient patients are evaluated who lack pre-existing micro-metastases but who will progress to more advanced disease. The cost of such a large and long trial may be

We have also reviewed the substantial evidence linking LPA and autotaxin to cell survival. Autotaxin inhibitors may have an indirect cytotoxic effect or inhibit the growth of primary (and secondary) tumors. This might reflect deprivation of LPA directly causing apoptosis of the tumor cells, or it might reflect a less supportive

activities. These observations may be used to design improved autotaxin inhibitors.

autotaxin in ascites fluid) to stratify the patients which are likely to respond.

**7. Potential clinical uses of autotaxin inhibitors** 

autotaxin inhibitor at the outset.

prohibitive unless an alternative is found.

the ligands all make the same interactions within the autotaxin pocket (North et al. 2010). Here, however, we have used docking models of the catalytic zinc pocket to identify and compare the interactions of reported inhibitors. Ligands were placed within the binding pocket by aligning them with the pharmacophore map that had been overlaid onto the inhibitor bound in structure 2XRG. The steric requirements of the pocket were then used to refine our understanding of the activities of the ligands.

The pharmacophore map of the active site was constructed based on the binding of the boronic acid inhibitor HA155 (fig. 13). Docking to this map was performed using MOE software from the Chemical Computing Group. The placements of ligands were constrained to pharmacophoric points within the active site and refinement was performed with the MMFF94x force field (Halgren 1996).

The docking model faithfully reproduced the position of the ligand from 2xrg.pdb and its close analogues. This gives us confidence in the docking models used. While the pharmacophore map suggests a rich hydrogen bonding network around the ligand, most of the "linker" region of the ligands fail to interact with the pocket other than with a few hydrophobic contacts. The conserved nature of the ligands' hydrogen bonding motif, as can be seen from previous pharmacophore modelling efforts (North et al. 2010), suggests that there may be unresolved water atoms which are located in the site (large blue spheres in fig. 13).

Fig. 13. **A** Pharmacophoric map and shape of ligand from 2XRG.pdb which takes into account the steric and electronic requirements of the pocket when placing the ligand. (Blue Mesh= hydrogen bond acceptor projected points, Green Mesh=Hydrophobic areas, Brown/yellow Mesh=Aromatic centers) B. The zinc binding portion of molecule **5** after docking to the pharmacophore map in the binding site.

The best fit to the zinc pocket was obtained for the carboxylic acid group from molecules **4** and **5** which appear to satisfy all the steric and electronic requirements of the local pocket (Fig below) When the hydrophobic tail of the docked molecules is prevented from occupying the ligand binding pocket because of a lack of ligand flexibility, it is often placed by the docking software in the tunnel region of the protein. This may be influenced by Phe274 which may act as a switch closing one or the other of these sites. Stabilization of this residue may contribute to ligand affinity.

the ligands all make the same interactions within the autotaxin pocket (North et al. 2010). Here, however, we have used docking models of the catalytic zinc pocket to identify and compare the interactions of reported inhibitors. Ligands were placed within the binding pocket by aligning them with the pharmacophore map that had been overlaid onto the inhibitor bound in structure 2XRG. The steric requirements of the pocket were then used to

The pharmacophore map of the active site was constructed based on the binding of the boronic acid inhibitor HA155 (fig. 13). Docking to this map was performed using MOE software from the Chemical Computing Group. The placements of ligands were constrained to pharmacophoric points within the active site and refinement was performed with the

The docking model faithfully reproduced the position of the ligand from 2xrg.pdb and its close analogues. This gives us confidence in the docking models used. While the pharmacophore map suggests a rich hydrogen bonding network around the ligand, most of the "linker" region of the ligands fail to interact with the pocket other than with a few hydrophobic contacts. The conserved nature of the ligands' hydrogen bonding motif, as can be seen from previous pharmacophore modelling efforts (North et al. 2010), suggests that there may be unresolved

Fig. 13. **A** Pharmacophoric map and shape of ligand from 2XRG.pdb which takes into account the steric and electronic requirements of the pocket when placing the ligand. (Blue Mesh= hydrogen bond acceptor projected points, Green Mesh=Hydrophobic areas, Brown/yellow Mesh=Aromatic centers) B. The zinc binding portion of molecule **5** after

The best fit to the zinc pocket was obtained for the carboxylic acid group from molecules **4** and **5** which appear to satisfy all the steric and electronic requirements of the local pocket (Fig below) When the hydrophobic tail of the docked molecules is prevented from occupying the ligand binding pocket because of a lack of ligand flexibility, it is often placed by the docking software in the tunnel region of the protein. This may be influenced by Phe274 which may act as a switch closing one or the other of these sites. Stabilization of this

docking to the pharmacophore map in the binding site.

residue may contribute to ligand affinity.

refine our understanding of the activities of the ligands.

water atoms which are located in the site (large blue spheres in fig. 13).

MMFF94x force field (Halgren 1996).

Ligands which bind with high affinity interacted with both the zinc ion and also occupied the hydrophobic pocket e.g. with a benzyl group. In contrast, ligands which bind with low affinity failed to bridge the zinc ion and the hydrophobic portion of the ligand binding site. This leads us to conclude that for ligands to bind with high affinity, they should preferably bind both these sites. Docking studies also suggest that the hydrophobic tunnel provides an alternative location for ligands but these ligands identified so far fail to achieve better than micromolar activities. These observations may be used to design improved autotaxin inhibitors.

#### **7. Potential clinical uses of autotaxin inhibitors**

Once inhibitors of autotaxin complete preclinical evaluation, how could these be evaluated in clinical trials? As we have discussed above, autotaxin may not be the only enzyme that contributes to the formation of LPA in ovarian cancer and so it is possible that inhibition of autotaxin may not elicit the desired therapeutic effect. It will be important, therefore, to include in early clinical trials a measurement of the change in LPA in ascites following treatment with an autotaxin inhibitor and to establish biomarkers (e.g. measurement of autotaxin in ascites fluid) to stratify the patients which are likely to respond.

The data we have reviewed also suggests a number of different settings in which autotaxin inhibitors could be used. Autotaxin inhibitors may be useful to inhibit the growth of primary tumors or to inhibit tumor cell migration, invasion and metastases. Clinical trials to evaluate these may differ somewhat, for example using different surrogate endpoints (tumor shrinkage versus decreased metastasis). Different schedules of drug administration may also be appropriate. To cause tumor cell death, relatively short term treatment with the drug may suffice, but suppression of metastasis may require prolonged treatment. This highlights the importance of considering the therapeutic goal that is being evaluated with an autotaxin inhibitor at the outset.

As we have discussed, there is a large body of evidence indicating a role for LPA in cancer cell migration and invasion. In addition, several studies demonstrate in animal models of metastasis that inhibitors of autotaxin reduce colonization of the lung by tumors cells (Baker et al. 2006; Gupte et al. 2010; Gupte et al. 2011). Thus, one potential clinical use of autotaxin inhibitors is to inhibit metastasis. Unfortunately, many ovarian cancer patients present with advanced disease, and significant dissemination of the tumor within the peritoneal cavity has already taken place by the time of diagnosis. Although it may be beneficial to prevent further metastasis and progression to later stage disease, there may also be micro-metastases that are not evident on examination. It is not clear, then, whether inhibiting further metastasis would be helpful. Evaluation of autotaxin inhibitors as anti-metastatic agents in patients with early stage disease will also be challenging. Relatively few patients are diagnosed with early stage disease and these patients generally have a good prognosis, with 90% of patients surviving more than 5 years. A large cohort of patients may also be required to ensure sufficient patients are evaluated who lack pre-existing micro-metastases but who will progress to more advanced disease. The cost of such a large and long trial may be prohibitive unless an alternative is found.

We have also reviewed the substantial evidence linking LPA and autotaxin to cell survival. Autotaxin inhibitors may have an indirect cytotoxic effect or inhibit the growth of primary (and secondary) tumors. This might reflect deprivation of LPA directly causing apoptosis of the tumor cells, or it might reflect a less supportive

Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 375

currently undergoing preclinical discovery, and it cannot be long before the first of these enter clinic trials. In this review, we have focused on the role of autotaxin in ovarian cancer, but it also plays a role in other cancer types as well as other pathophysiological conditions such as neuropathic pain. It seems plausible that autotaxin inhibitors will serve as new medicines and perhaps none of these applications is as exciting as the potential to treat drug-resistant ovarian cancer, a disease for which therapeutic options are currently

Albers, H. M., A. Dong, L. A. van Meeteren, D. A. Egan, M. Sunkara, E. W. van Tilburg, K.

LPA in the circulation. *Proc Natl Acad Sci U S A* vol. 107(16) pp. 7257-7262 Albers, H. M., L. J. Hendrickx, R. J. van Tol, J. Hausmann, A. Perrakis & H. Ovaa (2011).

Albers, H. M., L. A. van Meeteren, D. A. Egan, E. W. van Tilburg, W. H. Moolenaar & H.

Baker, D. L., Y. Fujiwara, K. R. Pigg, R. Tsukahara, S. Kobayashi, H. Murofushi, A.

Barkinge, J. L., R. Gudi, H. Sarah, F. Chu, A. Borthakur, B. S. Prabhakar & K. V. Prasad

Baudhuin, L. M., K. L. Cristina, J. Lu & Y. Xu (2002). Akt activation induced by

Bermudez, Y., H. Yang, B. O. Saunders, J. Q. Cheng, S. V. Nicosia & P. A. Kruk (2007).

Bese, T., M. Barbaros, E. Baykara, O. Guralp, S. Cengiz, F. Demirkiran, C. Sanioglu & M.

Bian, D., C. Mahanivong, J. Yu, S. M. Frisch, Z. K. Pan, R. D. Ye & S. Huang (2006). The

Bian, D., S. Su, C. Mahanivong, R. K. Cheng, Q. Han, Z. K. Pan, P. Sun & S. Huang (2004).

Schuurman, O. van Tellingen, A. J. Morris, S. S. Smyth, W. H. Moolenaar & H. Ovaa (2010). Boronic acid-based inhibitor of autotaxin reveals rapid turnover of

Structure-based design of novel boronic Acid-based inhibitors of autotaxin. *J Med* 

Ovaa (2010). Discovery and optimization of boronic acid based inhibitors of

Uchiyama, K. Murakami-Murofushi, E. Koh, R. W. Bandle, H. S. Byun, R. Bittman, D. Fan, M. Murph, G. B. Mills & G. Tigyi (2006). Carba analogs of cyclic phosphatidic acid are selective inhibitors of autotaxin and cancer cell invasion and

(2009). The p53-induced Siva-1 plays a significant role in cisplatin-mediated

lysophosphatidic acid and sphingosine-1-phosphate requires both mitogenactivated protein kinase kinase and p38 mitogen-activated protein kinase and is

VEGF- and LPA-induced telomerase in human ovarian cancer cells is Sp1-

Arvas (2010). Comparison of total plasma lysophosphatidic acid and serum CA-125 as a tumor marker in the diagnosis and follow-up of patients with epithelial

G12/13-RhoA signaling pathway contributes to efficient lysophosphatidic acid-

Lysophosphatidic Acid Stimulates Ovarian Cancer Cell Migration via a Ras-MEK

limited.

**9. References** 

*Chem* vol. 54(13) pp. 4619-4626

apoptosis. *J Carcinog* vol. 8 pp. 2

autotaxin. *J Med Chem* vol. 53(13) pp. 4958-4967

metastasis. *J Biol Chem* vol. 281(32) pp. 22786-22793

cell-line specific. *Mol Pharmacol* vol. 62(3) pp. 660-671

ovarian cancer. *J Gynecol Oncol* vol. 21(4) pp. 248-254

Kinase 1 Pathway. *Cancer Res* vol. 64(12) pp. 4209-4217

stimulated cell migration. *Oncogene* vol. 25(15) pp. 2234-2244

dependent. *Gynecol Oncol* vol. 106(3) pp. 526-537

microenvironment. In support of this approach, BrP-LPA (Bromophosfonolysophosphatidic acid; **16**, fig. 8) causes regression of breast tumor cells both in 3D *in vitro* models (Xu and Prestwich 2010) and as a xenograft (Zhang et al. 2009), although as the authors clearly state, this drug is also a pan LPA receptor antagonist and its activity cannot be ascribed to inhibition of autotaxin alone. Also, over-expression of autotaxin is sufficient to induce breast cancer, suggesting that selective inhibition of autotaxin may be sufficient to cause regression of comparable tumor types. An alternative to using an autotaxin inhibitor as a single agent is to use it in combination with chemotherapy and this is supported by the data implicating autotaxin and LPA in chemoresistance that we have discussed. Clearly this strategy has potential in patients who have developed chemoresistant disease, but it may also be useful to increase the response in patients whose tumors are sensitive to chemotherapy.

A pragmatic solution may be to evaluate autotaxin inhibitors first for their ability to inhibit tumor growth (either as a single agent or in combination with chemotherapy) and if this is successful evaluate their use as anti-metastatic agents after drug receives marketing approval. This may mitigate some of the risk associated with following a purely antimetastatic approach to drug development.

Fig. 14. The hydrophobic "tail" in ligands may occupy the hydrophobic pocket or the hydrophobic tunnel and Phe274 divides the two hydrophobic ligand binding sites.

#### **8. Conclusion**

Our understanding of autotaxin, in terms of it biological function, its structure and its potential as a drug target in ovarian cancer is rapidly evolving. Several compounds are currently undergoing preclinical discovery, and it cannot be long before the first of these enter clinic trials. In this review, we have focused on the role of autotaxin in ovarian cancer, but it also plays a role in other cancer types as well as other pathophysiological conditions such as neuropathic pain. It seems plausible that autotaxin inhibitors will serve as new medicines and perhaps none of these applications is as exciting as the potential to treat drug-resistant ovarian cancer, a disease for which therapeutic options are currently limited.

#### **9. References**

374 Ovarian Cancer – Basic Science Perspective

microenvironment. In support of this approach, BrP-LPA (Bromophosfonolysophosphatidic acid; **16**, fig. 8) causes regression of breast tumor cells both in 3D *in vitro* models (Xu and Prestwich 2010) and as a xenograft (Zhang et al. 2009), although as the authors clearly state, this drug is also a pan LPA receptor antagonist and its activity cannot be ascribed to inhibition of autotaxin alone. Also, over-expression of autotaxin is sufficient to induce breast cancer, suggesting that selective inhibition of autotaxin may be sufficient to cause regression of comparable tumor types. An alternative to using an autotaxin inhibitor as a single agent is to use it in combination with chemotherapy and this is supported by the data implicating autotaxin and LPA in chemoresistance that we have discussed. Clearly this strategy has potential in patients who have developed chemoresistant disease, but it may also be useful to increase the response in patients

A pragmatic solution may be to evaluate autotaxin inhibitors first for their ability to inhibit tumor growth (either as a single agent or in combination with chemotherapy) and if this is successful evaluate their use as anti-metastatic agents after drug receives marketing approval. This may mitigate some of the risk associated with following a purely anti-

Fig. 14. The hydrophobic "tail" in ligands may occupy the hydrophobic pocket or the hydrophobic tunnel and Phe274 divides the two hydrophobic ligand binding sites.

Our understanding of autotaxin, in terms of it biological function, its structure and its potential as a drug target in ovarian cancer is rapidly evolving. Several compounds are

whose tumors are sensitive to chemotherapy.

metastatic approach to drug development.

**8. Conclusion** 


Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 377

Ferry, G., N. Moulharat, J. P. Pradere, P. Desos, A. Try, A. Genton, A. Giganti, M. Beucher-

Fishman, D. A., Y. Liu, S. M. Ellerbroek & M. S. Stack (2001). Lysophosphatidic acid

Frankel, A. & G. B. Mills (1996). Peptide and lipid growth factors decrease cis-

Furui, T., R. LaPushin, M. Mao, H. Khan, S. R. Watt, M. A. Watt, Y. Lu, X. Fang, S. Tsutsui,

Gaetano, C. G., N. Samadi, J. L. Tomsig, T. L. Macdonald, K. R. Lynch & D. N. Brindley

Girnun, G. D., E. Naseri, S. B. Vafai, L. Qu, J. D. Szwaya, R. Bronson, J. A. Alberta & B. M.

Groll, M., C. R. Berkers, H. L. Ploegh & H. Ovaa (2006). Crystal structure of the boronic acid-

Gupte, R., R. Patil, J. Liu, Y. Wang, S. C. Lee, Y. Fujiwara, J. Fells, A. L. Bolen, K. Emmons-

Gupte, R., A. Siddam, Y. Lu, W. Li, Y. Fujiwara, N. Panupinthu, T. C. Pham, D. L. Baker, A.

Halgren, T. A. (1996). Merck molecular force field .1. Basis, form, scope, parameterization,

Harper, K., D. Arsenault, S. Boulay-Jean, A. Lauzier, F. Lucien & C. M. Dubois (2010).

invasion in ovarian cancer cells. *Cancer Res* vol. 61(7) pp. 3194-3199

independent manner. *Clin Cancer Res* vol. 5(12) pp. 4308-4318

819

*Cancer Res* vol. 2(8) pp. 1307-1313

cells. *Mol Carcinog* vol. 48(9) pp. 801-809

*Structure* vol. 14(3) pp. 451-456

490-519

drugs in cancer. *Cancer Cell* vol. 11(5) pp. 395-406

metastatic activity. *ChemMedChem* vol. 6(5) pp. 922-935

HT-1080 cells. *Cancer Invest* vol. 27(4) pp. 384-390

formation. *Cancer Res* vol. 70(11) pp. 4634-4643

Gaudin, M. Lonchampt, M. Bertrand, J. S. Saulnier-Blache, G. C. Tucker, A. Cordi & J. A. Boutin (2008). S32826, a nanomolar inhibitor of autotaxin: discovery, synthesis and applications as a pharmacological tool. *J Pharmacol Exp Ther* vol. 327(3) pp. 809-

promotes matrix metalloproteinase (MMP) activation and MMP-dependent

diamminedichloroplatinum-induced cell death in human ovarian cancer cells. *Clin* 

Z. H. Siddik, R. C. Bast & G. B. Mills (1999). Overexpression of edg-2/vzg-1 induces apoptosis and anoikis in ovarian cancer cells in a lysophosphatidic acid-

(2009). Inhibition of autotaxin production or activity blocks lysophosphatidylcholine-induced migration of human breast cancer and melanoma

Spiegelman (2007). Synergy between PPARgamma ligands and platinum-based

based proteasome inhibitor bortezomib in complex with the yeast 20S proteasome.

Thompson, C. R. Yates, A. Siddam, N. Panupinthu, T. C. Pham, D. L. Baker, A. L. Parrill, G. B. Mills, G. Tigyi & D. D. Miller (2011). Benzyl and naphthalene methylphosphonic acid inhibitors of autotaxin with anti-invasive and anti-

L. Parrill, M. Gotoh, K. Murakami-Murofushi, S. Kobayashi, G. B. Mills, G. Tigyi & D. D. Miller (2010). Synthesis and pharmacological evaluation of the stereoisomers of 3-carba cyclic-phosphatidic acid. *Bioorg Med Chem Lett* vol. 20(24) pp. 7525-7528 Haga, A., H. Nagai & Y. Deyashiki (2009). Autotaxin promotes the expression of matrix

metalloproteinase-3 via activation of the MAPK cascade in human fibrosarcoma

and performance of MMFF94. *Journal of Computational Chemistry* vol. 17(5-6) pp.

Autotaxin promotes cancer invasion via the lysophosphatidic acid receptor 4: participation of the cyclic AMP/EPAC/Rac1 signaling pathway in invadopodia


Boucharaba, A., C. M. Serre, S. Gres, J. S. Saulnier-Blache, J. C. Bordet, J. Guglielmi, P.

Chen, M. & K. L. O'Connor (2005). Integrin alpha6beta4 promotes expression of

Chu, F., A. Borthakur, X. Sun, J. Barkinge, R. Gudi, S. Hawkins & K. V. Prasad (2004). The

Cui, P., J. L. Tomsig, W. F. McCalmont, S. Lee, C. J. Becker, K. R. Lynch & T. L. Macdonald

David, M., E. Wannecq, F. Descotes, S. Jansen, B. Deux, J. Ribeiro, C. M. Serre, S. Gres, N.

Davidson, B., R. Hadar, H. T. Stavnes, C. G. Trope & R. Reich (2009). Expression of the

Deng, W., E. Shuyu, R. Tsukahara, W. J. Valentine, G. Durgam, V. Gududuru, L. Balazs, V.

Do, T. V., J. C. Symowicz, D. M. Berman, L. A. Liotta, E. F. Petricoin, 3rd, M. S. Stack & D. A.

E, S., Y. J. Lai, R. Tsukahara, C. S. Chen, Y. Fujiwara, J. Yue, J. H. Yu, H. Guo, A. Kihara, G.

East, J. E., A. J. Kennedy, J. L. Tomsig, A. R. De Leon, K. R. Lynch & T. L. Macdonald (2010).

Fang, X., M. Schummer, M. Mao, S. Yu, F. H. Tabassam, R. Swaby, Y. Hasegawa, J. L. Tanyi,

autotaxin (ATX). *Bioorg Med Chem Lett* vol. 20(23) pp. 7132-7136

(ATX) inhibitors. *Bioorg Med Chem Lett* vol. 17(6) pp. 1634-1640

114(12) pp. 1714-1725

24(32) pp. 5125-5130

*Chem* vol. 16(5) pp. 2212-2225

osteoclasts. *PLoS One* vol. 5(3) pp. e9741

*Hum Pathol* vol. 40(5) pp. 705-713

*Cancer Res* vol. 5(2) pp. 121-131

vol. 284(21) pp. 14558-14571

pp. 1834-1851

pp. 257-264

Clezardin & O. Peyruchaud (2004). Platelet-derived lysophosphatidic acid supports the progression of osteolytic bone metastases in breast cancer. *J Clin Invest* vol.

autotaxin/ENPP2 autocrine motility factor in breast carcinoma cells. *Oncogene* vol.

Siva-1 putative amphipathic helical region (SAH) is sufficient to bind to BCL-XL and sensitize cells to UV radiation induced apoptosis. *Apoptosis* vol. 9(1) pp. 83-95 Cui, P., W. F. McCalmont, J. L. Tomsig, K. R. Lynch & T. L. Macdonald (2008). alpha- and

beta-substituted phosphonate analogs of LPA as autotaxin inhibitors. *Bioorg Med* 

(2007). Synthesis and biological evaluation of phosphonate derivatives as autotaxin

Bendriss-Vermare, M. Bollen, S. Saez, J. Aoki, J. S. Saulnier-Blache, P. Clezardin & O. Peyruchaud (2010). Cancer cell expression of autotaxin controls bone metastasis formation in mouse through lysophosphatidic acid-dependent activation of

peroxisome proliferator-activated receptors-alpha, -beta, and -gamma in ovarian carcinoma effusions is associated with poor chemoresponse and shorter survival.

Manickam, M. Arsura, L. VanMiddlesworth, L. R. Johnson, A. L. Parrill, D. D. Miller & G. Tigyi (2007). The lysophosphatidic acid type 2 receptor is required for protection against radiation-induced intestinal injury. *Gastroenterology* vol. 132(5)

Fishman (2007). Lysophosphatidic acid down-regulates stress fibers and upregulates pro-matrix metalloproteinase-2 activation in ovarian cancer cells. *Mol* 

Tigyi & F. T. Lin (2009). Lysophosphatidic acid 2 receptor-mediated supramolecular complex formation regulates its antiapoptotic effect. *J Biol Chem*

Synthesis and structure-activity relationships of tyrosine-based inhibitors of

R. LaPushin, A. Eder, R. Jaffe, J. Erickson & G. B. Mills (2002). Lysophosphatidic acid is a bioactive mediator in ovarian cancer. *Biochim Biophys Acta* vol. 1582(1-3)


Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 379

Jiang, G., D. Madan & G. D. Prestwich (2011). Aromatic phosphonates inhibit the

Jiang, G., Y. Xu, Y. Fujiwara, T. Tsukahara, R. Tsukahara, J. Gajewiak, G. Tigyi & G. D.

Jung, I. D., J. Lee, S. Y. Yun, C. G. Park, W. S. Choi, H. W. Lee, O. H. Choi, J. W. Han & H. Y.

Kamrava, M., F. Simpkins, E. Alejandro, C. Michener, E. Meltzer & E. C. Kohn (2005).

prosurvival factor for ovarian cancer. *Oncogene* vol. 24(47) pp. 7084-7093 Kang, Y. C., K. M. Kim, K. S. Lee, S. Namkoong, S. J. Lee, J. A. Han, D. Jeoung, K. S. Ha, Y.

Kehlen, A., N. Englert, A. Seifert, T. Klonisch, H. Dralle, J. Langner & C. Hoang-Vu (2004).

Kim, E. K., J. M. Park, S. Lim, J. W. Choi, H. S. Kim, H. Seok, J. K. Seo, K. Oh, D. S. Lee, K. T.

Kishi, Y., S. Okudaira, M. Tanaka, K. Hama, D. Shida, J. Kitayama, T. Yamori, J. Aoki, T.

Kortlever, R. M., T. R. Brummelkamp, L. A. van Meeteren, W. H. Moolenaar & R. Bernards

Lee, Z., R. F. Swaby, Y. Liang, S. Yu, S. Liu, K. H. Lu, R. C. Bast, Jr., G. B. Mills & X. Fang

Li, H., X. Ye, C. Mahanivong, D. Bian, J. Chun & S. Huang (2005). Signaling mechanisms

expression in ovarian cancer cells. *J Biol Chem* vol. 280(11) pp. 10564-10571 Li, H., Z. Zhao, G. Wei, L. Yan, D. Wang, H. Zhang, G. E. Sandusky, J. Turk & Y. Xu (2010).

lysophosphatidic acid signaling. *Mol Cancer Res* vol. 6(9) pp. 1452-1460 Lee, J., I. Duk Jung, C. Gyo Park, J. W. Han & H. Young Lee (2006). Autotaxin stimulates

Prestwich (2007). Alpha-substituted phosphonate analogues of lysophosphatidic acid (LPA) selectively inhibit production and action of LPA. *ChemMedChem* vol.

Lee (2002). Cdc42 and Rac1 are necessary for autotaxin-induced tumor cell motility

Lysophosphatidic acid and endothelin-induced proliferation of ovarian cancer cell lines is mitigated by neutralization of granulin-epithelin precursor (GEP), a

G. Kwon & Y. M. Kim (2004). Serum bioactive lysophospholipids prevent TRAILinduced apoptosis via PI3K/Akt-dependent cFLIP expression and Bad

Expression, regulation and function of autotaxin in thyroid carcinomas. *Int J Cancer*

Kim, S. H. Ryu & P. G. Suh (2011). Activation of AMP-activated Protein Kinase Is Essential for Lysophosphatidic Acid-induced Cell Migration in Ovarian Cancer

Fujimaki & H. Arai (2006). Autotaxin is overexpressed in glioblastoma multiforme and contributes to cell motility of glioblastoma by converting lysophosphatidylcholine to lysophosphatidic acid. *J Biol Chem* vol. 281(25) pp.

(2008). Suppression of the p53-dependent replicative senescence response by

urokinase-type plasminogen activator expression through phosphoinositide 3 kinase-Akt-nuclear [corrected] factor kappa B signaling cascade in human

(2006). Lysophosphatidic acid is a major regulator of growth-regulated oncogene

responsible for lysophosphatidic acid-induced urokinase plasminogen activator

Group VIA phospholipase A2 in both host and tumor cells is involved in ovarian

lysophospholipase D activity of autotaxin. *Bioorg Med Chem Lett*

in A2058 melanoma cells. *FEBS Lett* vol. 532(3) pp. 351-356

phosphorylation. *Cell Death Differ* vol. 11(12) pp. 1287-1298

Cells. *J Biol Chem* vol. 286(27) pp. 24036-24045

melanoma cells. *Melanoma Res* vol. 16(5) pp. 445-452

alpha in ovarian cancer. *Cancer Res* vol. 66(5) pp. 2740-2748

cancer development. *FASEB J* vol. 24(10) pp. 4103-4116

2(5) pp. 679-690

vol. 109(6) pp. 833-838

17492-17500


Hausmann, J., S. Kamtekar, E. Christodoulou, J. E. Day, T. Wu, Z. Fulkerson, H. M. Albers,

Hoeglund, A. B., H. E. Bostic, A. L. Howard, I. W. Wanjala, M. D. Best, D. L. Baker & A. L.

Hoeglund, A. B., A. L. Howard, I. W. Wanjala, T. C. Pham, A. L. Parrill & D. L. Baker (2010).

Hoelzinger, D. B., L. Mariani, J. Weis, T. Woyke, T. J. Berens, W. S. McDonough, A. Sloan, S.

Hoelzinger, D. B., M. Nakada, T. Demuth, T. Rosensteel, L. B. Reavie & M. E. Berens (2008).

Hooks, S. B., P. Callihan, M. K. Altman, J. H. Hurst, M. W. Ali & M. M. Murph (2010).

Hu, Y. L., C. Albanese, R. G. Pestell & R. B. Jaffe (2003). Dual mechanisms for

Hu, Y. L., M. K. Tee, E. J. Goetzl, N. Auersperg, G. B. Mills, N. Ferrara & R. B. Jaffe (2001).

Hurst, J. H., P. A. Henkel, A. L. Brown & S. B. Hooks (2008). Endogenous RGS proteins

Hurst, J. H. & S. B. Hooks (2009). Lysophosphatidic acid stimulates cell growth by different

Jazaeri, A. A., C. S. Awtrey, G. V. Chandramouli, Y. E. Chuang, J. Khan, C. Sotiriou, O.

Jeon, E. S., S. C. Heo, I. H. Lee, Y. J. Choi, J. H. Park, K. U. Choi, Y. Park do, D. S. Suh, M. S.

Rho-dependent pathways. *Pharmacology* vol. 83(6) pp. 333-347

mesenchymal stem cells. *Exp Mol Med* vol. 42(4) pp. 280-293

in human ovarian cancer cells. *J Natl Cancer Inst* vol. 93(10) pp. 762-768 Huang, R. Y., S. M. Wang, C. Y. Hsieh & J. C. Wu (2008). Lysophosphatidic acid induces

18(2) pp. 198-204

53(3) pp. 1056-1066

769-776

7(1) pp. 7-16

*Neurooncol* vol. 86(3) pp. 297-309

*Inst* vol. 95(10) pp. 733-740

*Int J Cancer* vol. 123(4) pp. 801-809

cancer cells. *Cell Signal* vol. 20(2) pp. 381-389

cancers. *Clin Cancer Res* vol. 11(17) pp. 6300-6310

ovarian cancer cells. *Mol Cancer* vol. 9 pp. 289

L. A. van Meeteren, A. J. Houben, L. van Zeijl, S. Jansen, M. Andries, T. Hall, L. E. Pegg, T. E. Benson, M. Kasiem, K. Harlos, C. W. Kooi, S. S. Smyth, H. Ovaa, M. Bollen, A. J. Morris, W. H. Moolenaar & A. Perrakis (2011). Structural basis of substrate discrimination and integrin binding by autotaxin. *Nat Struct Mol Biol* vol.

Parrill (2010). Optimization of a pipemidic acid autotaxin inhibitor. *J Med Chem* vol.

Characterization of non-lipid autotaxin inhibitors. *Bioorg Med Chem* vol. 18(2) pp.

W. Coons & M. E. Berens (2005). Gene expression profile of glioblastoma multiforme invasive phenotype points to new therapeutic targets. *Neoplasia* vol.

Autotaxin: a secreted autocrine/paracrine factor that promotes glioma invasion. *J* 

Regulators of G-Protein signaling RGS10 and RGS17 regulate chemoresistance in

lysophosphatidic acid stimulation of human ovarian carcinoma cells. *J Natl Cancer* 

Lysophosphatidic acid induction of vascular endothelial growth factor expression

ovarian cancer cell dispersal by activating Fyn kinase associated with p120-catenin.

attenuate Galpha(i)-mediated lysophosphatidic acid signaling pathways in ovarian

mechanisms in SKOV-3 and Caov-3 ovarian cancer cells: distinct roles for Gi- and

Aprelikova, C. J. Yee, K. K. Zorn, M. J. Birrer, J. C. Barrett & J. Boyd (2005). Gene expression profiles associated with response to chemotherapy in epithelial ovarian

Yoon & J. H. Kim (2010). Ovarian cancer-derived lysophosphatidic acid stimulates secretion of VEGF and stromal cell-derived factor-1 alpha from human


Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 381

Nouh, M. A., X. X. Wu, H. Okazoe, H. Tsunemori, R. Haba, A. M. Abou-Zeid, M. D. Saleem,

Park, S. Y., K. J. Jeong, N. Panupinthu, S. Yu, J. Lee, J. W. Han, J. M. Kim, J. S. Lee, J. Kang, C.

Parrill, A. L., U. Echols, T. Nguyen, T. C. Pham, A. Hoeglund & D. L. Baker (2008). Virtual

Ptaszynska, M. M., M. L. Pendrak, R. W. Bandle, M. L. Stracke & D. D. Roberts (2008).

Ptaszynska, M. M., M. L. Pendrak, M. L. Stracke & D. D. Roberts (2010). Autotaxin signaling

Ren, J., Y. J. Xiao, L. S. Singh, X. Zhao, Z. Zhao, L. Feng, T. M. Rose, G. D. Prestwich & Y. Xu

Samadi, N., R. T. Bekele, I. S. Goping, L. M. Schang & D. N. Brindley (2011).

Samadi, N., C. Gaetano, I. S. Goping & D. N. Brindley (2009). Autotaxin protects MCF-7

Sawada, K., K. Morishige, M. Tahara, R. Kawagishi, Y. Ikebuchi, K. Tasaka & Y. Murata

Sengupta, S., K. S. Kim, M. P. Berk, R. Oates, P. Escobar, J. Belinson, W. Li, D. J. Lindner, B.

Snider, A. J., Z. Zhang, Y. Xie & K. E. Meier (2010). Epidermal growth factor increases

So, J., F. Q. Wang, J. Navari, J. Schreher & D. A. Fishman (2005). LPA-induced epithelial

progression. *Cancer Sci* vol. 100(9) pp. 1631-1638

expression. *Oncogene* vol. 30(11) pp. 1351-1359

cells. *Clin Cancer Res* vol. 5(11) pp. 3704-3710

cells. *Cancer Res* vol. 66(6) pp. 3006-3014

*Oncogene* vol. 28(7) pp. 1028-1039

6015-6020

pp. C163-170

ovarian cancer cells. *Mol Cancer Res* vol. 6(3) pp. 352-363

taxol-induced mitotic arrest. *PLoS One* vol. 6(5) pp. e20608

induced cell invasion. *Oncogene* vol. 26(20) pp. 2894-2901

receptor-2 (VEGF-R2). *Gynecol Oncol* vol. 97(3) pp. 870-878

*Med Chem* vol. 16(4) pp. 1784-1795

M. Inui, M. Sugimoto, J. Aoki & Y. Kakehi (2009). Expression of autotaxin and acylglycerol kinase in prostate cancer: association with cancer development and

G. Park, G. B. Mills & H. Y. Lee (2011). Lysophosphatidic acid augments human hepatocellular carcinoma cell invasion through LPA1 receptor and MMP-9

screening approaches for the identification of non-lipid autotaxin inhibitors. *Bioorg* 

Positive feedback between vascular endothelial growth factor-A and autotaxin in

via lysophosphatidic acid receptors contributes to vascular endothelial growth factor-induced endothelial cell migration. *Mol Cancer Res* vol. 8(3) pp. 309-321 Pustilnik, T. B., V. Estrella, J. R. Wiener, M. Mao, A. Eder, M. A. Watt, R. C. Bast, Jr. & G. B.

Mills (1999). Lysophosphatidic acid induces urokinase secretion by ovarian cancer

(2006). Lysophosphatidic acid is constitutively produced by human peritoneal mesothelial cells and enhances adhesion, migration, and invasion of ovarian cancer

Lysophosphatidate induces chemo-resistance by releasing breast cancer cells from

breast cancer and MDA-MB-435 melanoma cells against Taxol-induced apoptosis.

(2002). Alendronate inhibits lysophosphatidic acid-induced migration of human ovarian cancer cells by attenuating the activation of rho. *Cancer Res* vol. 62(21) pp.

Williams & Y. Xu (2007). Lysophosphatidic acid downregulates tissue inhibitor of metalloproteinases, which are negatively involved in lysophosphatidic acid-

lysophosphatidic acid production in human ovarian cancer cells: roles for phospholipase D2 and receptor transactivation. *Am J Physiol Cell Physiol* vol. 298(1)

ovarian cancer (EOC) in vitro invasion and migration are mediated by VEGF


Li, S., B. Wang, Y. Xu & J. Zhang (2011). Autotaxin is induced by TSA through HDAC3 and

Lin, F. T., Y. J. Lai, N. Makarova, G. Tigyi & W. C. Lin (2007). The lysophosphatidic acid 2

Liu, S., M. Umezu-Goto, M. Murph, Y. Lu, W. Liu, F. Zhang, S. Yu, L. C. Stephens, X. Cui, G.

Masuda, A., K. Nakamura, K. Izutsu, K. Igarashi, R. Ohkawa, M. Jona, K. Higashi, H.

Meng, Y., L. Graves, T. V. Do, J. So & D. A. Fishman (2004). Upregulation of FasL by LPA on

Meng, Y., S. Kang & D. A. Fishman (2005). Lysophosphatidic acid stimulates fas ligand

Meng, Y., S. Kang, J. So, S. Reierstad & D. A. Fishman (2005). Translocation of Fas by LPA

Mills, G. B., C. May, M. Hill, S. Campbell, P. Shaw & A. Marks (1990). Ascitic fluid from

Murph, M. M., G. H. Nguyen, H. Radhakrishna & G. B. Mills (2008). Sharpening the edges

and mechanisms of regulation. *Biochim Biophys Acta* vol. 1781(9) pp. 547-557 Nakai, Y., H. Ikeda, K. Nakamura, Y. Kume, M. Fujishiro, N. Sasahira, K. Hirano, H.

Nam, S. W., T. Clair, C. K. Campo, H. Y. Lee, L. A. Liotta & M. L. Stracke (2000). Autotaxin

Nishimasu, H., S. Okudaira, K. Hama, E. Mihara, N. Dohmae, A. Inoue, R. Ishitani, J.

small-molecule autotaxin inhibitors. *J Med Chem* vol. 53(8) pp. 3095-3105

pancreatic cancer. *Clin Biochem* vol. 44(8-9) pp. 576-581

transformed cells. *Oncogene* vol. 19(2) pp. 241-247

10 pp. 18

pp. 539-550

vol. 282(52) pp. 37759-37769

*Haematol* vol. 143(1) pp. 60-70

*Oncol* vol. 95(3) pp. 488-495

54(8) pp. 807-814

96(2) pp. 462-469

86(3) pp. 851-855

HDAC7 inhibition and antagonizes the TSA-induced cell apoptosis. *Mol Cancer* vol.

receptor mediates down-regulation of Siva-1 to promote cell survival. *J Biol Chem*

Murrow, K. Coombes, W. Muller, M. C. Hung, C. M. Perou, A. V. Lee, X. Fang & G. B. Mills (2009). Expression of autotaxin and lysophosphatidic acid receptors increases mammary tumorigenesis, invasion, and metastases. *Cancer Cell* vol. 15(6)

Yokota, S. Okudaira, T. Kishimoto, T. Watanabe, Y. Koike, H. Ikeda, Y. Kozai, M. Kurokawa, J. Aoki & Y. Yatomi (2008). Serum autotaxin measurement in haematological malignancies: a promising marker for follicular lymphoma. *Br J* 

ovarian cancer cell surface leads to apoptosis of activated lymphocytes. *Gynecol* 

microvesicle release from ovarian cancer cells. *Cancer Immunol Immunother* vol.

prevents ovarian cancer cells from anti-Fas-induced apoptosis. *Gynecol Oncol* vol.

human ovarian cancer patients contains growth factors necessary for intraperitoneal growth of human ovarian adenocarcinoma cells. *J Clin Invest* vol.

of understanding the structure/function of the LPA1 receptor: expression in cancer

Isayama, M. Tada, T. Kawabe, Y. Komatsu, M. Omata, J. Aoki, K. Koike & Y. Yatomi (2011). Specific increase in serum autotaxin activity in patients with

(ATX), a potent tumor motogen, augments invasive and metastatic potential of ras-

Takagi, J. Aoki & O. Nureki (2011). Crystal structure of autotaxin and insight into GPCR activation by lipid mediators. *Nat Struct Mol Biol* vol. 18(2) pp. 205-212 North, E. J., A. L. Howard, I. W. Wanjala, T. C. Pham, D. L. Baker & A. L. Parrill (2010).

Pharmacophore development and application toward the identification of novel,


Autotaxin – A Target for the Treatment of Drug-Resistant Ovarian Cancer? 383

Vidot, S., J. Witham, R. Agarwal, S. Greenhough, H. S. Bamrah, G. J. Tigyi, S. B. Kaye & A.

Wang, F. Q., E. Barfield, S. Dutta, T. Pua & D. A. Fishman (2009). VEGFR-2 silencing by

Wang, F. Q., J. Fisher & D. A. Fishman (2011). MMP-1-PAR1 axis mediates LPA-induced epithelial ovarian cancer (EOC) invasion. *Gynecol Oncol* vol. 120(2) pp. 247-255 Wang, P., X. Wu, W. Chen, J. Liu & X. Wang (2007). The lysophosphatidic acid (LPA)

Witham, J., M. R. Valenti, A. K. De-Haven-Brandon, S. Vidot, S. A. Eccles, S. B. Kaye & A.

Wu, J. M., Y. Xu, N. J. Skill, H. Sheng, Z. Zhao, M. Yu, R. Saxena & M. A. Maluccio (2010).

Xu, X. & G. D. Prestwich (2010). Inhibition of tumor growth and angiogenesis by a

Xu, Y., D. C. Gaudette, J. D. Boynton, A. Frankel, X. J. Fang, A. Sharma, J. Hurteau, G. Casey,

Xue, L., F. Chu, Y. Cheng, X. Sun, A. Borthakur, M. Ramarao, P. Pandey, M. Wu, S. F.

Yang, K., D. Zheng, X. Deng, L. Bai, Y. Xu & Y. S. Cong (2008). Lysophosphatidic acid

invasiveness of breast cancer cells. *Clin Exp Metastasis* vol. 19(7) pp. 603-608 Yang, Y. C., Y. P. Tsao, T. C. Ho & I. P. Choung (2007). Peroxisome proliferator-activated

Yu, S., M. M. Murph, Y. Lu, S. Liu, H. S. Hall, J. Liu, C. Stephens, X. Fang & G. B. Mills

Zhang, H., X. Xu, J. Gajewiak, R. Tsukahara, Y. Fujiwara, J. Liu, J. I. Fells, D. Perygin, A. L.

1alpha and the PI3K pathway. *J Cell Biochem* vol. 105(5) pp. 1194-1201 Yang, S. Y., J. Lee, C. G. Park, S. Kim, S. Hong, H. C. Chung, S. K. Min, J. W. Han, H. W. Lee

carcinoma cell lines. *Int J Gynecol Cancer* vol. 17(2) pp. 418-425

epithelial ovarian carcinoma. *Br J Cancer* vol. 92(1) pp. 113-119

cancer cells to carboplatin. *Clin Cancer Res* vol. 13(23) pp. 7191-7198

human hepatocellular carcinoma. *Mol Cancer* vol. 9 pp. 71

xenograft model. *Cancer* vol. 116(7) pp. 1739-1750

cancer cells. *Cell Signal* vol. 22(6) pp. 926-935

*Oncol* vol. 104(3) pp. 714-720

pp. 1223-1232

99(10) pp. 6925-6930

(EOC) invasion. *Gynecol Oncol* vol. 115(3) pp. 414-423

Richardson (2010). Autotaxin delays apoptosis induced by carboplatin in ovarian

small interference RNA (siRNA) suppresses LPA-induced epithelial ovarian cancer

receptors their expression and significance in epithelial ovarian neoplasms. *Gynecol* 

Richardson (2007). The Bcl-2/Bcl-XL family inhibitor ABT-737 sensitizes ovarian

Autotaxin expression and its connection with the TNF-alpha-NF-kappaB axis in

lysophosphatidic acid antagonist in an engineered three-dimensional lung cancer

A. Goodbody, A. Mellors & et al. (1995). Characterization of an ovarian cancer activating factor in ascites from ovarian cancer patients. *Clin Cancer Res* vol. 1(10)

Schlossman & K. V. Prasad (2002). Siva-1 binds to and inhibits BCL-X(L)-mediated protection against UV radiation-induced apoptosis. *Proc Natl Acad Sci U S A* vol.

activates telomerase in ovarian cancer cells through hypoxia-inducible factor-

& H. Y. Lee (2002). Expression of autotaxin (NPP-2) is closely linked to

receptor-gamma agonists cause growth arrest and apoptosis in human ovarian

(2008). Lysophosphatidic acid receptors determine tumorigenicity and aggressiveness of ovarian cancer cells. *J Natl Cancer Inst* vol. 100(22) pp. 1630-1642 Zhang, G. Y., N. Ahmed, C. Riley, K. Oliva, G. Barker, M. A. Quinn & G. E. Rice (2005).

Enhanced expression of peroxisome proliferator-activated receptor gamma in

Parrill, G. Tigyi & G. D. Prestwich (2009). Dual activity lysophosphatidic acid


Song, J., T. Clair, J. H. Noh, J. W. Eun, S. Y. Ryu, S. N. Lee, Y. M. Ahn, S. Y. Kim, S. H. Lee,

Song, Y., P. Wilkins, W. Hu, K. S. Murthy, J. Chen, Z. Lee, R. Oyesanya, J. Wu, S. E. Barbour

Stassar, M. J., G. Devitt, M. Brosius, L. Rinnab, J. Prang, T. Schradin, J. Simon, S. Petersen, A.

Stracke, M. L., H. C. Krutzsch, E. J. Unsworth, A. Arestad, V. Cioce, E. Schiffmann & L. A.

a novel motility-stimulating protein. *J Biol Chem* vol. 267(4) pp. 2524-2529 Sun, H., J. Ren, Q. Zhu, F. Z. Kong, L. Wu & B. R. Pan (2009). Effects of lysophosphatidic

Tabchy, A., G. Tigyi & G. B. Mills (2011). Location, location, location: a crystal-clear view of autotaxin saturating LPA receptors. *Nat Struct Mol Biol* vol. 18(2) pp. 117-118 Tanaka, M., S. Okudaira, Y. Kishi, R. Ohkawa, S. Iseki, M. Ota, S. Noji, Y. Yatomi, J. Aoki &

Tanyi, J. L., Y. Hasegawa, R. Lapushin, A. J. Morris, J. K. Wolf, A. Berchuck, K. Lu, D. I.

Tanyi, J. L., A. J. Morris, J. K. Wolf, X. Fang, Y. Hasegawa, R. Lapushin, N. Auersperg, Y. J.

a target for therapy in ovarian cancer. *Cancer Res* vol. 63(5) pp. 1073-1082 Tigyi, G. (2010). Aiming drug discovery at lysophosphatidic acid targets. *Br J Pharmacol* vol.

Tokumura, A., T. Kume, K. Fukuzawa, M. Tahara, K. Tasaka, J. Aoki, H. Arai, K. Yasuda &

van Corven, E. J., A. Groenink, K. Jalink, T. Eichholtz & W. H. Moolenaar (1989).

signaling pathways mediated by G proteins. *Cell* vol. 59(1) pp. 45-54

*Commun* vol. 337(3) pp. 967-975

pp. 427-436

1372-1382

25830

vol. 15(36) pp. 4547-4555

Pt 1) pp. 3534-3545

161(2) pp. 241-270

pp. 1641-1649

W. S. Park, N. J. Yoo, J. Y. Lee & S. W. Nam (2005). Autotaxin (lysoPLD/NPP2) protects fibroblasts from apoptosis through its enzymatic product, lysophosphatidic acid, utilizing albumin-bound substrate. *Biochem Biophys Res* 

& X. Fang (2007). Inhibition of calcium-independent phospholipase A2 suppresses proliferation and tumorigenicity of ovarian carcinoma cells. *Biochem J* vol. 406(3)

Kopp-Schneider & M. Zoller (2001). Identification of human renal cell carcinoma associated genes by suppression subtractive hybridization. *Br J Cancer* vol. 85(9) pp.

Liotta (1992). Identification, purification, and partial sequence analysis of autotaxin,

acid on human colon cancer cells and its mechanisms of action. *World J Gastroenterol*

H. Arai (2006). Autotaxin stabilizes blood vessels and is required for embryonic vasculature by producing lysophosphatidic acid. *J Biol Chem* vol. 281(35) pp. 25822-

Smith, K. Kalli, L. C. Hartmann, K. McCune, D. Fishman, R. Broaddus, K. W. Cheng, E. N. Atkinson, J. M. Yamal, R. C. Bast, E. A. Felix, R. A. Newman & G. B. Mills (2003). Role of decreased levels of lipid phosphate phosphatase-1 in accumulation of lysophosphatidic acid in ovarian cancer. *Clin Cancer Res* vol. 9(10

Sigal, R. A. Newman, E. A. Felix, E. N. Atkinson & G. B. Mills (2003). The human lipid phosphate phosphatase-3 decreases the growth, survival, and tumorigenesis of ovarian cancer cells: validation of the lysophosphatidic acid signaling cascade as

H. Kanzaki (2007). Peritoneal fluids from patients with certain gynecologic tumor contain elevated levels of bioactive lysophospholipase D activity. *Life Sci* vol. 80(18)

Lysophosphatidate-induced cell proliferation: identification and dissection of


**19** 

*Canada* 

**Potential Monoclonal Antibody** 

Gregory Lee, Mingang Zhu and Bixia Ge

*The University of British Columbia Vancouver* 

*UBC Center for Reproductive Health,* 

**Therapy for the Treatment of Ovarian Cancer** 

Although ovarian cancer is the fifth most common cancer among women, it causes more death than any other type of female reproductive cancer (Mørch et al., 2009). Besides difficulties in early detection, limited options for the treatment of ovarian cancer at late stages have been the major cause of high mortality rate (Jemal et al., 2003). About 76% of women with ovarian cancer survive 1 year after diagnosis, but only about 45% will live 5 years after diagnosis (Choi et al., 2008). Therefore, it may be desirable to look for alternative means of treating this type of cancer rather than the conventional ones including chemo- or

During the last two decades, target-oriented antibody-based anti-cancer drugs have become the main stream choices for cancer treatments in humans. Although the efficacy of cancer treatments varies greatly with individual cases, overall improvements of patients' care and survival are significant, when compared to those of the conventional ones. Besides those approved by the FDA of the United States of America for the clinical treatments of cancer, numerous antibody-drug candidates are still at various stages of clinical trials and pending

Generally speaking, the majority of antibody-based anti-cancer drugs are target-oriented and the adverse side effects upon infusion of the antibody drugs are relatively mild as compared to those of the traditional ones. Therefore, selections of suitable targets against the tumor cells have become an essential step for the long term antibody drug development. In general, the ideal tumor target for the antibody drugs can be selected based on its accessibility, high abundance and surface homogeneity. Moreover, it should not be highly expressed on normal cells or tissues, especially the vital organs in humans (McGuire et al.,

Recently, two monoclonal antibodies were identified and selected based on these criteria for ovarian cancer. One is RP215 which recognizes a carbohydrate-associated epitope found preferentially in cancer cell-expressed immunoglobulin superfamily proteins, designated in general as CA215. The other is GHR106 which was shown to react with the extracellular domain of human GnRH receptor. Both CA215 and GnRH receptor are widely expressed among cancer cells of different tissue origins, especially those of the human ovary with positive rates ranging from 60-80% (Lee et al., 2008, 2009; Lee & Ge,

for the final approval by the FDA (Waldmann, 2003).

**1. Introduction** 

radiotherapy.

1996).

receptor pan-antagonist/autotaxin inhibitor reduces breast cancer cell migration in vitro and causes tumor regression in vivo. *Cancer Res* vol. 69(13) pp. 5441-5449

Zhang, R., J. Wang, S. Ma, Z. Huang & G. Zhang (2011). Requirement of Osteopontin in the migration and protection against Taxol-induced apoptosis via the ATX-LPA axis in SGC7901 cells. *BMC Cell Biol* vol. 12 pp. 11

## **Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer**

Gregory Lee, Mingang Zhu and Bixia Ge *UBC Center for Reproductive Health, The University of British Columbia Vancouver Canada* 

#### **1. Introduction**

384 Ovarian Cancer – Basic Science Perspective

SGC7901 cells. *BMC Cell Biol* vol. 12 pp. 11

receptor pan-antagonist/autotaxin inhibitor reduces breast cancer cell migration in vitro and causes tumor regression in vivo. *Cancer Res* vol. 69(13) pp. 5441-5449 Zhang, R., J. Wang, S. Ma, Z. Huang & G. Zhang (2011). Requirement of Osteopontin in the

migration and protection against Taxol-induced apoptosis via the ATX-LPA axis in

Although ovarian cancer is the fifth most common cancer among women, it causes more death than any other type of female reproductive cancer (Mørch et al., 2009). Besides difficulties in early detection, limited options for the treatment of ovarian cancer at late stages have been the major cause of high mortality rate (Jemal et al., 2003). About 76% of women with ovarian cancer survive 1 year after diagnosis, but only about 45% will live 5 years after diagnosis (Choi et al., 2008). Therefore, it may be desirable to look for alternative means of treating this type of cancer rather than the conventional ones including chemo- or radiotherapy.

During the last two decades, target-oriented antibody-based anti-cancer drugs have become the main stream choices for cancer treatments in humans. Although the efficacy of cancer treatments varies greatly with individual cases, overall improvements of patients' care and survival are significant, when compared to those of the conventional ones. Besides those approved by the FDA of the United States of America for the clinical treatments of cancer, numerous antibody-drug candidates are still at various stages of clinical trials and pending for the final approval by the FDA (Waldmann, 2003).

Generally speaking, the majority of antibody-based anti-cancer drugs are target-oriented and the adverse side effects upon infusion of the antibody drugs are relatively mild as compared to those of the traditional ones. Therefore, selections of suitable targets against the tumor cells have become an essential step for the long term antibody drug development. In general, the ideal tumor target for the antibody drugs can be selected based on its accessibility, high abundance and surface homogeneity. Moreover, it should not be highly expressed on normal cells or tissues, especially the vital organs in humans (McGuire et al., 1996).

Recently, two monoclonal antibodies were identified and selected based on these criteria for ovarian cancer. One is RP215 which recognizes a carbohydrate-associated epitope found preferentially in cancer cell-expressed immunoglobulin superfamily proteins, designated in general as CA215. The other is GHR106 which was shown to react with the extracellular domain of human GnRH receptor. Both CA215 and GnRH receptor are widely expressed among cancer cells of different tissue origins, especially those of the human ovary with positive rates ranging from 60-80% (Lee et al., 2008, 2009; Lee & Ge,

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 387

lines (OC-3-VGH, OVCAR-3 and SKOV-3). When GHR106 was used as the probe for the same assay, the molecular weight of GnRH receptor of these cancer cells was found to be 60 kDa. The results were consistent with those reported previously for human GnRH receptor

(A)

(B) (C)

number of specimens.

Fig. 1. (A) Immunohistochemical study to reveal staining of three different ovarian cancer cell lines, OC-3-VGH, OVCAR-3 and SKOV-3 with RP215, GHR106 and normal mouse IgG (NMIgG, negative control); (B) Immunohistochemical study to reveal staining of selected ovarian cancer tissue sections (P-128, P-162, P107 and 525) with RP215. The areas of staining are highlighted with the arrows; (C) Results of immunohistochemical staining of GnRH receptor of ovarian tissue sections at different stages (I to IV) of ovarian cancer. n = total

n=8

0

30

60

Positive Stainging (%)

90

120

n=8

n=20

I II III IV Normal

Stage of Ovarian Cancer

n=4

n=13

(Lee & Ge, 2010a). The results of such analysis are presented in Figure 2.

2010a). The binding of either of these two monoclonal antibodies was found to inhibit the growth of ovarian cancer cells *in vitro* and *in vivo* through studies of induced apoptosis and complement-dependent cytotoxicity. Therefore, additional preclinical studies were performed to elucidate the mechanisms of action of these monoclonal antibodies as anticancer drugs for the treatment of ovarian cancer. These studies should represent our efforts to demonstrate the potential use of these monoclonal antibodies as the anti-ovarian cancer drugs in the future.

#### **2. Results and discussion**

In this study, two monoclonal antibodies, RP215 and GHR106, were evaluated to see if they are suitable drug candidates for the potential treatment of ovarian cancer. Several *in vitro* and *in vivo* experiments were performed including (1) immunohistochemical staining and Western blot assay to reveal the relative abundance of target antigens, CA215 and GnRH receptor, respectively, each of which appears on the surface of cancer cells of the ovary, (2) apoptosis and complement-dependent cytotoxicity assays to demonstrate the respective anti-cancer efficacy of these two monoclonal antibodies, (3) elucidation of respective molecular mechanisms of action of RP215 and GHR106 as anti-cancer drugs through gene expression/regulation studies, (4) nude mouse experiments to reveal the dose-dependent inhibition of the growth of tumor cells and (5) construction of chimeric forms of RP215 and GHR106 monoclonal antibodies for future preclinical and clinical studies, (6) clinical evaluations of CA215 levels from serum specimens of cancer patients by using the established enzyme immunoassay kits, and (7) glycoanalysis to elucidate the proposed structure of carbohydrate-associated epitope(s) recognized by RP215 in CA215.

#### **2.1 Immunohistochemical studies and Western blot assay**

RP215 and GHR106 monoclonal antibodies were found to react with antigens localized on the surface of a variety of cancer cells including that of the ovary. By indirect immunohistochemical studies, it can be shown that three of the selected ovarian cancer cell lines, including OC-3-VGH, OVCAR-3, and SKOV-3, were strongly stained with RP215 and GHR106, respectively, whereas normal mouse IgG as the negative control gave little or no colour staining. The results of such an analysis are presented in Figure 1A (Lee et al., 1992, 2008, 2009; Lee & Ge, 2010a). The immunohistochemical studies of selected cancerous tissue sections of the human ovary were also performed with RP215 as the primary antibody probe. The results are presented in Figure 1B for comparisons. In a separate study, GHR106 was used as a probe to carry out immunohistochemical staining of cancerous tissue sections of the ovary from patients at four various stages of ovarian cancer (Chien et al., 2004). From the results of this analysis, it was generally concluded that the positive staining rates of ovarian cancer tissue sections increase with the stages of this disease (37.5% at stage I to 100% at stage IV) (Chien et al., 2004). In contrast, the immunohistochemical staining of tissue sections of the normal ovary showed negative staining results. The results of such analysis are summarized in Figure 1C.

The antigens recognized by RP215 and GHR106 in the cell extract of ovarian cancer cells were also determined by Western blot assay (Lee et al., 2008, 2009; Lee & Ge, 2010a). In the case of RP215, the corresponding antigen, CA215, was found to have a molecular size ranging from 50 to 75 kDa for the cell extract from either of the three ovarian cancer cell

2010a). The binding of either of these two monoclonal antibodies was found to inhibit the growth of ovarian cancer cells *in vitro* and *in vivo* through studies of induced apoptosis and complement-dependent cytotoxicity. Therefore, additional preclinical studies were performed to elucidate the mechanisms of action of these monoclonal antibodies as anticancer drugs for the treatment of ovarian cancer. These studies should represent our efforts to demonstrate the potential use of these monoclonal antibodies as the anti-ovarian

In this study, two monoclonal antibodies, RP215 and GHR106, were evaluated to see if they are suitable drug candidates for the potential treatment of ovarian cancer. Several *in vitro* and *in vivo* experiments were performed including (1) immunohistochemical staining and Western blot assay to reveal the relative abundance of target antigens, CA215 and GnRH receptor, respectively, each of which appears on the surface of cancer cells of the ovary, (2) apoptosis and complement-dependent cytotoxicity assays to demonstrate the respective anti-cancer efficacy of these two monoclonal antibodies, (3) elucidation of respective molecular mechanisms of action of RP215 and GHR106 as anti-cancer drugs through gene expression/regulation studies, (4) nude mouse experiments to reveal the dose-dependent inhibition of the growth of tumor cells and (5) construction of chimeric forms of RP215 and GHR106 monoclonal antibodies for future preclinical and clinical studies, (6) clinical evaluations of CA215 levels from serum specimens of cancer patients by using the established enzyme immunoassay kits, and (7) glycoanalysis to elucidate the proposed

structure of carbohydrate-associated epitope(s) recognized by RP215 in CA215.

RP215 and GHR106 monoclonal antibodies were found to react with antigens localized on the surface of a variety of cancer cells including that of the ovary. By indirect immunohistochemical studies, it can be shown that three of the selected ovarian cancer cell lines, including OC-3-VGH, OVCAR-3, and SKOV-3, were strongly stained with RP215 and GHR106, respectively, whereas normal mouse IgG as the negative control gave little or no colour staining. The results of such an analysis are presented in Figure 1A (Lee et al., 1992, 2008, 2009; Lee & Ge, 2010a). The immunohistochemical studies of selected cancerous tissue sections of the human ovary were also performed with RP215 as the primary antibody probe. The results are presented in Figure 1B for comparisons. In a separate study, GHR106 was used as a probe to carry out immunohistochemical staining of cancerous tissue sections of the ovary from patients at four various stages of ovarian cancer (Chien et al., 2004). From the results of this analysis, it was generally concluded that the positive staining rates of ovarian cancer tissue sections increase with the stages of this disease (37.5% at stage I to 100% at stage IV) (Chien et al., 2004). In contrast, the immunohistochemical staining of tissue sections of the normal ovary showed negative staining results. The results of such analysis

The antigens recognized by RP215 and GHR106 in the cell extract of ovarian cancer cells were also determined by Western blot assay (Lee et al., 2008, 2009; Lee & Ge, 2010a). In the case of RP215, the corresponding antigen, CA215, was found to have a molecular size ranging from 50 to 75 kDa for the cell extract from either of the three ovarian cancer cell

**2.1 Immunohistochemical studies and Western blot assay** 

cancer drugs in the future.

**2. Results and discussion** 

are summarized in Figure 1C.

lines (OC-3-VGH, OVCAR-3 and SKOV-3). When GHR106 was used as the probe for the same assay, the molecular weight of GnRH receptor of these cancer cells was found to be 60 kDa. The results were consistent with those reported previously for human GnRH receptor (Lee & Ge, 2010a). The results of such analysis are presented in Figure 2.

(A)

Fig. 1. (A) Immunohistochemical study to reveal staining of three different ovarian cancer cell lines, OC-3-VGH, OVCAR-3 and SKOV-3 with RP215, GHR106 and normal mouse IgG (NMIgG, negative control); (B) Immunohistochemical study to reveal staining of selected ovarian cancer tissue sections (P-128, P-162, P107 and 525) with RP215. The areas of staining are highlighted with the arrows; (C) Results of immunohistochemical staining of GnRH receptor of ovarian tissue sections at different stages (I to IV) of ovarian cancer. n = total number of specimens.

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 389

\*\* \*\* \*\*

\* \* \*\*

123 456 789

\*\*\*

1234567

\*\*\*

1234567

\*

\* \*

Fig. 3. (A) The TUNEL assay to demonstrate the increase in apoptosis of cancer cells in response to the treatment of cultured OC-3-VGH (Lanes 1-3), OVCAR-3 (Lanes 4-6) and SKOV-3 (Lanes 7-9) cells for 48 h with either of the following monoclonal antibodies (dose: 10 μg/mL each): normal mouse IgG (Lanes 1, 4, 7), RP215 (Lanes 2, 5, 8) and GHR106 (Lanes 3, 6,9), respectively; (B) The TUNEL assay to demonstrate the increase in apoptosis of OC-3- VGH cells in response to the treatment by chimeric as well as murine monoclonal antibodies (dose: 10 μg/mL each) for 48 h: Lane 1: non treatment; Lane 2: normal mouse IgG; Lane 3: ChRP215; Lane 4: ChGHR106; Lane 5: RP215; Lane 6: GHR106; and Lane 7: 0.1 μg/mL Antide; (C) The TUNEL assay to demonstrate the increase in apoptosis of cancer cells in response to the incubation of OC-3-VGH cancer cells for 48 h with the following antibodies (1 μg/mL each): Lanes 1-7 correspond to that of normal mouse IgG, RCA10, RCA100, RCA104, RCA110, RCA111, and RP215, respectively (to be described in section 2.11). All data presented are statistically significant at \* P < 0.05, \*\* P < 0.01, and \*\*\*P<0.001.

Apoptosis (%increase)

Apoptosis (% increase)

Apoptosis (% increase)

\*

\* \*\*\*

A

\*

\*\* \*\*

C

B

Fig. 2. Western blot assay to reveal protein bands from the extract of three ovarian cancer cell lines, OC-3-VGH (1 and 4), SKOV-3 (2 and 5) and OVCAR-3 (3 and 6), respectively, when probed with RP215 (lanes 1, 2 and 3) as well as GHR106 (lanes 4, 5 and 6). The molecular weight markers of 50 kDa, 60 kDa and 75 kDa are indicated by arrows.

#### **2.2 Effects of RP215 and GHR106 monoclonal antibodies on the induction of apoptosis to ovarian cancer cells**

By using terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assay, the induced apoptosis of cancer cells upon incubation with RP215 and GHR106 could be clearly demonstrated. As shown in Figure 3A, all of the three ovarian cancer cell lines were found to undergo significant cellular apoptosis, when incubated with 10 μg/mL of either RP215 or GHR106 (24 - 48 h). A significant apoptosis of cancer cells was also induced upon treatments with 0.1 μg/mL of a GnRH antagonist, Antide or GnRH. Under the same assay conditions, the chimeric form of RP215, ChRP215, was found to induce apoptosis of the OC-3-VGH cancer cell line, similar to that observed for murine RP215. The results of such a comparative analysis are also summarized in Figure 3B. Similarly, chimeric form of GHR106, ChGHR106, was found to have the same effects as that of GHR106 in inducing apoptosis of the ovarian cancer cells (Figure 3B).

In addition to RP215, the induction of apoptosis to OC-3-VGH ovarian cancer cell has also been confirmed with other RP215-related monoclonal antibodies (RCA10, RCA100, RCA104, RCA110 and RCA111, see section 2.11), as demonstrated in Figure 3C.

#### **2.3 Effects of RP215 and GHR106 monoclonal antibodies on complement-dependent cytotoxicity of ovarian cancer cells**

The complement-dependent cytotoxicity assay was also employed to study the induction of complement-dependent cell lysis to OC-3-VGH ovarian cancer cells in the presence of complement and RP215 or GHR106 (10 μg/mL for 2 h incubation). The chimeric forms of monoclonal antibodies, ChRP215 or ChGHR106, also demonstrated a similar degree of complement-dependent cytotoxicity reaction to cultured ovarian cancer cells under the same conditions of incubation. In contrast, complement alone or complement with GnRH antagonist, Antide (0.1 μg/mL), revealed no effect on complement-dependent cytotoxicity to ovarian cancer cells (data not shown). Results of this complement-dependent cytotoxicity assay with different monoclonal antibodies plus complement are summarized in Figure 4 for comparisons.

Fig. 2. Western blot assay to reveal protein bands from the extract of three ovarian cancer cell lines, OC-3-VGH (1 and 4), SKOV-3 (2 and 5) and OVCAR-3 (3 and 6), respectively, when probed with RP215 (lanes 1, 2 and 3) as well as GHR106 (lanes 4, 5 and 6). The molecular weight markers of 50 kDa, 60 kDa and 75 kDa are indicated by arrows.

By using terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assay, the induced apoptosis of cancer cells upon incubation with RP215 and GHR106 could be clearly demonstrated. As shown in Figure 3A, all of the three ovarian cancer cell lines were found to undergo significant cellular apoptosis, when incubated with 10 μg/mL of either RP215 or GHR106 (24 - 48 h). A significant apoptosis of cancer cells was also induced upon treatments with 0.1 μg/mL of a GnRH antagonist, Antide or GnRH. Under the same assay conditions, the chimeric form of RP215, ChRP215, was found to induce apoptosis of the OC-3-VGH cancer cell line, similar to that observed for murine RP215. The results of such a comparative analysis are also summarized in Figure 3B. Similarly, chimeric form of GHR106, ChGHR106, was found to have the same effects as that of GHR106 in inducing apoptosis of the ovarian

In addition to RP215, the induction of apoptosis to OC-3-VGH ovarian cancer cell has also been confirmed with other RP215-related monoclonal antibodies (RCA10, RCA100, RCA104,

**2.3 Effects of RP215 and GHR106 monoclonal antibodies on complement-dependent** 

The complement-dependent cytotoxicity assay was also employed to study the induction of complement-dependent cell lysis to OC-3-VGH ovarian cancer cells in the presence of complement and RP215 or GHR106 (10 μg/mL for 2 h incubation). The chimeric forms of monoclonal antibodies, ChRP215 or ChGHR106, also demonstrated a similar degree of complement-dependent cytotoxicity reaction to cultured ovarian cancer cells under the same conditions of incubation. In contrast, complement alone or complement with GnRH antagonist, Antide (0.1 μg/mL), revealed no effect on complement-dependent cytotoxicity to ovarian cancer cells (data not shown). Results of this complement-dependent cytotoxicity assay with different monoclonal antibodies plus complement are summarized in Figure 4

RCA110 and RCA111, see section 2.11), as demonstrated in Figure 3C.

**2.2 Effects of RP215 and GHR106 monoclonal antibodies on the induction of** 

**apoptosis to ovarian cancer cells** 

cancer cells (Figure 3B).

for comparisons.

**cytotoxicity of ovarian cancer cells** 

Fig. 3. (A) The TUNEL assay to demonstrate the increase in apoptosis of cancer cells in response to the treatment of cultured OC-3-VGH (Lanes 1-3), OVCAR-3 (Lanes 4-6) and SKOV-3 (Lanes 7-9) cells for 48 h with either of the following monoclonal antibodies (dose: 10 μg/mL each): normal mouse IgG (Lanes 1, 4, 7), RP215 (Lanes 2, 5, 8) and GHR106 (Lanes 3, 6,9), respectively; (B) The TUNEL assay to demonstrate the increase in apoptosis of OC-3- VGH cells in response to the treatment by chimeric as well as murine monoclonal antibodies (dose: 10 μg/mL each) for 48 h: Lane 1: non treatment; Lane 2: normal mouse IgG; Lane 3: ChRP215; Lane 4: ChGHR106; Lane 5: RP215; Lane 6: GHR106; and Lane 7: 0.1 μg/mL Antide; (C) The TUNEL assay to demonstrate the increase in apoptosis of cancer cells in response to the incubation of OC-3-VGH cancer cells for 48 h with the following antibodies (1 μg/mL each): Lanes 1-7 correspond to that of normal mouse IgG, RCA10, RCA100, RCA104, RCA110, RCA111, and RP215, respectively (to be described in section 2.11). All data presented are statistically significant at \* P < 0.05, \*\* P < 0.01, and \*\*\*P<0.001.

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 391

cells are presented in Table 1. From this gene regulation analysis, it can be shown that the molecular mechanisms of action by which the apoptosis of cancer cells is induced with

**Gene RP215 (10 µg/ml ) GHR106 (10 µg/ml) Antide (0.1 µg/ml)** GnRH N.Ta ↑ ↑ GnRH receptor N.T N.C N.C IgG ↑<sup>c</sup> N.T N.T NFKB1b ↑ N.T N.T P0 ↓<sup>c</sup> ↓ ↓ P1 ↓↑ ↑ P2 ↓ N.C N.C EGF N.C<sup>a</sup> ↓ ↓ c-fos ↓ N.C N.C

bAbbrevations used: NFKB1: nuclear factor of kappa light polypeptide gene enhancer in B cells 1; P0, P1 and P2 are genes expressed by selected ribosomal proteins; c-fos: cellular oncogene proteins; EGF:

<sup>c</sup>↑ and ↓ indicate significant up and down gene regulations, respectively when compared to that of

Table 1. Gene regulation studies in cultured OC-3-VGH ovarian cancer cells using RP215,

**2.5 Expressions of immunoglobulins of different classes from ovarian cancer cells** 

OC-3-VGH ovarian cancer cells which were derived from a single clone were found to express IgG, IgA and IgM, simultaneously. This is in contrast with those of B cells, each of which expresses only a single class of immunoglobulins. (Qiu et.al., 2003; Huang et al.,2008; Zhang et al., 2010 ). This observation strongly suggests that immunoglobulins may be expressed by cancer cells through different mechanisms as compared to those of normal

Our previous studies have demonstrated that CA215 and GHR106 receptor are expressed on the surface of many human cancer cells including those of the ovary. Through *in vitro*  studies with induced apoptosis and complement-dependent cytotoxicity reactions, it was clearly demonstrated that CA215 and GnRH receptor on the surface of cancer cells may be suitable targets by the respective monoclonal antibodies (Lee et al., 2009; Lee & Ge, 2010a). Therefore, nude mouse experiments were performed with OC-3-VGH ovarian cancer cell

Following a single injection of three different doses of RP215 (1-10 mg/kg doses) at the time of tumor implantation, a significant inhibition of tumor growth was observed in a dose dependent manner. Fifteen days after the tumor implant and antibody treatments, the tumor volumes were compared. As shown in the histogram of Figure 5, the dose-dependent inhibition of tumor growth by RP215 was statistically significant as compared to that of the

line as the model to evaluate the anti-tumor efficacy of RP215 monoclonal antibody.

RP215 are quite different from those of GHR106 or GnRH antagonists, Antide.

aN.T: not tested; N.C: not changed

GAPDH (Glyceraldehyde 3-phosphate dehydrogenase).

epidermal growth factor.

GHR106 and Antide.

**derived from a single clone** 

human B cells (Zheng et al., 2009).

**2.6 Nude mouse experiments** 

Fig. 4. Complement-dependent cytotoxicity assay to demonstrate the respective effects of normal mouse IgG (Lane 3), ChRP215 (Lane 4), ChGHR106 (Lane 5), RP215 (Lane 6), GHR106 (Lane 7), and goat anti-human IgG (Lane 8) on the complement-dependent lysis of OC-3-VGH ovarian cells. White, monoclonal antibody (10 μg/mL) alone; Grey, monoclonal antibody (10 μg/ml) plus complement; Black, non-treatment (Lane 1) or 3 μL freshly prepared rabbit baby complement (Lane 2) as control. \* and \*\* indicate statistical significance of P<0.05 and P<0.01, respectively.

#### **2.4 Effects of RP215 or GHR106 monoclonal antibodies on the expression of selected genes in ovarian cancer cells**

Molecular mechanisms of action by which RP215 or GHR106 inhibit the growth of OC-3- VGH ovarian cancer cells were elucidated through the studies of gene regulation by qualitative and semi-quantitative RT-PCR (Lee & Ge, 2010a; Kang et al., 2003; Leung et al., 2003; Gründker et.al., 2000). A number of genes related to the growth of cancer cells were examined for their expressions in response to the treatment of cultured ovarian cancer cells with either of these two monoclonal antibodies for 48 h. The results of this qualitative analysis with a number of selected genes are summarized in Table 1.

Parallel to this gene expression study, the effects of GnRH antagonist, Antide, on the expression of selected genes in ovarian cancer cells are also presented for comparisons with those of GHR106. Based on the results of this comparative analysis, it was clearly demonstrated that both GHR106 and Antide induce the same patterns of gene regulations to ovarian cancer cells upon their respective ligand binding.

Generally speaking, the incubation of RP215 with OC-3-VGH ovarian cancer cells was found to increase the gene expression of IgG and NFKB1 (Li & Verma, 2002), but genes of ribosomal proteins, P0, P1 and P2 were significantly down-regulated (Lee & Ge, 2010b). RP215 incubation to cultured cancer cells were found to have no effect on EGF (epidermal growth factor) (Gründker et al., 2000), but caused a significant decrease in the gene expression of *c-fos* (Dragunow & Faull, 1989). On the other hand, under the same incubation conditions, both GHR106 monoclonal antibody and GnRH antagonist, Antide, were shown to up-regulate the expression of GnRH gene, but not that of GnRH receptor (Lee & Ge, 2010b). Gene regulations of ribosomal proteins by GHR106 or Antide were also different from those by RP215. While P0 gene was found to be down-regulated, P1 gene was upregulated instead (Lee & Ge, 2010b). Although no changes in *c-fos* gene regulation were observed, EGF was found to be down-regulated significantly with the treatment of GHR106 or Antide. The results of such a comprehensive analysis with OC-3-VGH ovarian cancer


cells are presented in Table 1. From this gene regulation analysis, it can be shown that the molecular mechanisms of action by which the apoptosis of cancer cells is induced with RP215 are quite different from those of GHR106 or GnRH antagonists, Antide.

aN.T: not tested; N.C: not changed

390 Ovarian Cancer – Basic Science Perspective

significance of P<0.05 and P<0.01, respectively.

**genes in ovarian cancer cells** 

% Specific Lysis

\* \*\*

123 4 5 6 7 8

Fig. 4. Complement-dependent cytotoxicity assay to demonstrate the respective effects of normal mouse IgG (Lane 3), ChRP215 (Lane 4), ChGHR106 (Lane 5), RP215 (Lane 6), GHR106 (Lane 7), and goat anti-human IgG (Lane 8) on the complement-dependent lysis of OC-3-VGH ovarian cells. White, monoclonal antibody (10 μg/mL) alone; Grey, monoclonal antibody (10 μg/ml) plus complement; Black, non-treatment (Lane 1) or 3 μL freshly prepared rabbit baby complement (Lane 2) as control. \* and \*\* indicate statistical

**2.4 Effects of RP215 or GHR106 monoclonal antibodies on the expression of selected** 

Molecular mechanisms of action by which RP215 or GHR106 inhibit the growth of OC-3- VGH ovarian cancer cells were elucidated through the studies of gene regulation by qualitative and semi-quantitative RT-PCR (Lee & Ge, 2010a; Kang et al., 2003; Leung et al., 2003; Gründker et.al., 2000). A number of genes related to the growth of cancer cells were examined for their expressions in response to the treatment of cultured ovarian cancer cells with either of these two monoclonal antibodies for 48 h. The results of this qualitative

Parallel to this gene expression study, the effects of GnRH antagonist, Antide, on the expression of selected genes in ovarian cancer cells are also presented for comparisons with those of GHR106. Based on the results of this comparative analysis, it was clearly demonstrated that both GHR106 and Antide induce the same patterns of gene regulations to

Generally speaking, the incubation of RP215 with OC-3-VGH ovarian cancer cells was found to increase the gene expression of IgG and NFKB1 (Li & Verma, 2002), but genes of ribosomal proteins, P0, P1 and P2 were significantly down-regulated (Lee & Ge, 2010b). RP215 incubation to cultured cancer cells were found to have no effect on EGF (epidermal growth factor) (Gründker et al., 2000), but caused a significant decrease in the gene expression of *c-fos* (Dragunow & Faull, 1989). On the other hand, under the same incubation conditions, both GHR106 monoclonal antibody and GnRH antagonist, Antide, were shown to up-regulate the expression of GnRH gene, but not that of GnRH receptor (Lee & Ge, 2010b). Gene regulations of ribosomal proteins by GHR106 or Antide were also different from those by RP215. While P0 gene was found to be down-regulated, P1 gene was upregulated instead (Lee & Ge, 2010b). Although no changes in *c-fos* gene regulation were observed, EGF was found to be down-regulated significantly with the treatment of GHR106 or Antide. The results of such a comprehensive analysis with OC-3-VGH ovarian cancer

analysis with a number of selected genes are summarized in Table 1.

ovarian cancer cells upon their respective ligand binding.

\*

\*\*

\*

bAbbrevations used: NFKB1: nuclear factor of kappa light polypeptide gene enhancer in B cells 1; P0, P1 and P2 are genes expressed by selected ribosomal proteins; c-fos: cellular oncogene proteins; EGF: epidermal growth factor.

<sup>c</sup>↑ and ↓ indicate significant up and down gene regulations, respectively when compared to that of GAPDH (Glyceraldehyde 3-phosphate dehydrogenase).

Table 1. Gene regulation studies in cultured OC-3-VGH ovarian cancer cells using RP215, GHR106 and Antide.

#### **2.5 Expressions of immunoglobulins of different classes from ovarian cancer cells derived from a single clone**

OC-3-VGH ovarian cancer cells which were derived from a single clone were found to express IgG, IgA and IgM, simultaneously. This is in contrast with those of B cells, each of which expresses only a single class of immunoglobulins. (Qiu et.al., 2003; Huang et al.,2008; Zhang et al., 2010 ). This observation strongly suggests that immunoglobulins may be expressed by cancer cells through different mechanisms as compared to those of normal human B cells (Zheng et al., 2009).

#### **2.6 Nude mouse experiments**

Our previous studies have demonstrated that CA215 and GHR106 receptor are expressed on the surface of many human cancer cells including those of the ovary. Through *in vitro*  studies with induced apoptosis and complement-dependent cytotoxicity reactions, it was clearly demonstrated that CA215 and GnRH receptor on the surface of cancer cells may be suitable targets by the respective monoclonal antibodies (Lee et al., 2009; Lee & Ge, 2010a). Therefore, nude mouse experiments were performed with OC-3-VGH ovarian cancer cell line as the model to evaluate the anti-tumor efficacy of RP215 monoclonal antibody.

Following a single injection of three different doses of RP215 (1-10 mg/kg doses) at the time of tumor implantation, a significant inhibition of tumor growth was observed in a dose dependent manner. Fifteen days after the tumor implant and antibody treatments, the tumor volumes were compared. As shown in the histogram of Figure 5, the dose-dependent inhibition of tumor growth by RP215 was statistically significant as compared to that of the

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 393

RP215 monoclonal antibody was shown to react with carbohydrate-associated epitope preferentially expressed by cancer cells in a mixture of glycoproteins designated as CA215. To reveal the molecular identity of CA215, MALDI-TOF MS (matrix assisted laser desorption ionization time-of-flight mass spectrometry) analysis was performed for more than one hundred tryptic peptides of CA215 affinity-purified from the shed medium of cultured OC-3-VGH ovarian cancer cells. It was generally concluded that CA215 consists mainly of immunoglobulin superfamily proteins (61%) including immunoglobulins (42%), T cell receptors (6%) and cell adhesion molecules (8%) as well as mucins (7%) and other

> **Number of Peptides Matched Total = 124 (Percentage)**

Total with homologya

: 75/124 (60.5%)

**2.8 Molecular identity of CA215 and expression of immunoglobulin superfamily** 

unrelated ones. Results of this analysis are summarized in Table 2.

**Molecular Function/Category**

 Antibodies and immunoglobulins 52 (42.0%) T cell receptor chains 7 (5.7%)

 MHC I and MHC II 6 (4.9%) III. Adhesion molecules 10 (8.1%) IV.Cytokine and growth factors 8 (6.5%) V. Receptor tyrosine kinase/phosphatise 7 (5.7%)

Immunoglobulin superfamily related (e.g. titin) 12 (9.7%)

Immunoglobulin superfamily unrelated (e.g. mucin) 9 (7.3%)

Table 2. MALDI-TOF MS analysis of tryptic peptides derived from affinity-purified CA215. By using semi-quantitative RT-PCR of the cell extract of over 20 cancer cell lines, it was observed that as many as 80% of these cancer cells express T cell receptors (α and β chains) at significantly high levels including OC-3-VGH, OVCAR-3 and SKOV-3 cell lines. Results of such an analysis are summarized in Table 3. In contrast with the exception of Raji (lymphoma) and Jurkat (T cell leukemia) cell lines, none of these cancer cell lines express CD3, CD4 and CD8 co-receptors and/or co-stimulator genes, indicating the non-functional nature of most of the cancer cell-expressed T cell receptors. In addition, numerous immunoglobulin superfamily protein-like cell adhesion molecules such as CD47, CD54, CD58 and CD147 are also highly expressed among all the cancer cell lines. Widespread T cell receptor expressions among cancer cells may have implications on cancer immunotherapy via T cell activation as well as the induced tolerance of T cells caused by

**proteins by cancer cells** 

I. Antigen receptors

VI. Others

II. Antigen presenting molecules

a Excluding overlapping matched peptides

tumor-associated antigens in humans (Lee et al., 2011a).

negative control. In addition, I131-labelled RP215 (12 μCi/mg) was found to be more effective in the suppression of tumor growth than the unlabelled antibody.

Several nude mouse experiments were also performed with models of several other cancer cell lines. These included: C33A (cervix) and SK-MES-1 (lung) for RP215 as well as Hep2G (liver) for GHR106. In each case, significant tumor reduction or inhibition of tumor growth was observed 14-17 days after tumor implantation and subsequent injection of antibody drugs (RP215 or GHR106: dose 10 mg/kg) according to similar protocols for nude mouse experiments as described above (data not presented).

Fig. 5. Tumor volumes at Day 15 were compared for nude mice treated by RP215 in unlabelled and I131-labelled form (specific activity 12 μCi/mg): Lane 1: negative control (no treatment, open column); Lane 2: positive control (black column, 60 mg/kg cyclophosphamide ); Lane 3: AH (antibody high dose: 10 mg/kg); Lane 4: AM (antibody medium dose: 5 mg/kg); Lane 5: I-AH (I131-labeled antibody high dose: 10 mg/kg); Lane 6: I-AM (I131-labeled antibody median dose: 5 mg/kg); Lane 7: I-AL (I131-labeled antibody low dose: 2 mg/kg). Data are statistically significant at \*P<0.05, \*\*P<0.01.

#### **2.7 Anti-idiotypic monoclonal antibodies as anti-cancer vaccines**

RP215 monoclonal antibody was shown to recognize carbohydrate-associated epitope which is preferentially expressed in a mixture of different glycoproteins designated as CA215 identified in many cancer cells in humans. Polyclonal and rat monoclonal antibodies against the Fab-idiotypic domains of RP215 were generated and characterized (Lee et al., 2010a and 2010b; Lee & Ge, 2010a and 2010b). The anti-idiotypic (aid) antibodies were found to bear the internal images of the RP215-specific carbohydrate-associated epitope. Following immunizations in mice with rat anti-idiotypic monoclonal antibody, the resulting anti-aid or Ab3 sera were found to behave like RP215 in biochemical and immunological properties (Lee et al., 2010a and 2010b; Lee & Ge, 2010a and 2010b).

By Western blot assay, Ab3 antisera were found to react with protein bands identical to those recognized by RP215. By immunohistochemical staining study, the majority of cancer cells from different tissue origins were positively stained by both Ab3 antisera and RP215. Ab3 antisera were shown to induce apoptosis, similar to that observed for RP215. Based on these experimental observations, it was therefore hypothesized that rat anti-idiotypic monoclonal antibodies or its Fab fragments bear the internal images of RP215-specific epitope (Lee et al., 2010a and 2010b; Lee & Ge, 2010a and 2010b) and may be suitable as anti-cancer vaccines to induce Ab3 responses in humans for cancer treatments or preventions (Lee et al., 2010b).

negative control. In addition, I131-labelled RP215 (12 μCi/mg) was found to be more

Several nude mouse experiments were also performed with models of several other cancer cell lines. These included: C33A (cervix) and SK-MES-1 (lung) for RP215 as well as Hep2G (liver) for GHR106. In each case, significant tumor reduction or inhibition of tumor growth was observed 14-17 days after tumor implantation and subsequent injection of antibody drugs (RP215 or GHR106: dose 10 mg/kg) according to similar protocols for nude mouse

\* \*\*

Fig. 5. Tumor volumes at Day 15 were compared for nude mice treated by RP215 in unlabelled and I131-labelled form (specific activity 12 μCi/mg): Lane 1: negative control (no

cyclophosphamide ); Lane 3: AH (antibody high dose: 10 mg/kg); Lane 4: AM (antibody medium dose: 5 mg/kg); Lane 5: I-AH (I131-labeled antibody high dose: 10 mg/kg); Lane 6: I-AM (I131-labeled antibody median dose: 5 mg/kg); Lane 7: I-AL (I131-labeled antibody low

RP215 monoclonal antibody was shown to recognize carbohydrate-associated epitope which is preferentially expressed in a mixture of different glycoproteins designated as CA215 identified in many cancer cells in humans. Polyclonal and rat monoclonal antibodies against the Fab-idiotypic domains of RP215 were generated and characterized (Lee et al., 2010a and 2010b; Lee & Ge, 2010a and 2010b). The anti-idiotypic (aid) antibodies were found to bear the internal images of the RP215-specific carbohydrate-associated epitope. Following immunizations in mice with rat anti-idiotypic monoclonal antibody, the resulting anti-aid or Ab3 sera were found to behave like RP215 in biochemical and immunological properties

By Western blot assay, Ab3 antisera were found to react with protein bands identical to those recognized by RP215. By immunohistochemical staining study, the majority of cancer cells from different tissue origins were positively stained by both Ab3 antisera and RP215. Ab3 antisera were shown to induce apoptosis, similar to that observed for RP215. Based on these experimental observations, it was therefore hypothesized that rat anti-idiotypic monoclonal antibodies or its Fab fragments bear the internal images of RP215-specific epitope (Lee et al., 2010a and 2010b; Lee & Ge, 2010a and 2010b) and may be suitable as anti-cancer vaccines to induce Ab3 responses in humans for cancer treatments or preventions (Lee et al., 2010b).

treatment, open column); Lane 2: positive control (black column, 60 mg/kg

dose: 2 mg/kg). Data are statistically significant at \*P<0.05, \*\*P<0.01.

**2.7 Anti-idiotypic monoclonal antibodies as anti-cancer vaccines** 

(Lee et al., 2010a and 2010b; Lee & Ge, 2010a and 2010b).

\*

1234567

\*\*

\*\*

effective in the suppression of tumor growth than the unlabelled antibody.

experiments as described above (data not presented).

0

50

100

Tumor volume (mm3)

150

200

#### **2.8 Molecular identity of CA215 and expression of immunoglobulin superfamily proteins by cancer cells**

RP215 monoclonal antibody was shown to react with carbohydrate-associated epitope preferentially expressed by cancer cells in a mixture of glycoproteins designated as CA215. To reveal the molecular identity of CA215, MALDI-TOF MS (matrix assisted laser desorption ionization time-of-flight mass spectrometry) analysis was performed for more than one hundred tryptic peptides of CA215 affinity-purified from the shed medium of cultured OC-3-VGH ovarian cancer cells. It was generally concluded that CA215 consists mainly of immunoglobulin superfamily proteins (61%) including immunoglobulins (42%), T cell receptors (6%) and cell adhesion molecules (8%) as well as mucins (7%) and other unrelated ones. Results of this analysis are summarized in Table 2.


a Excluding overlapping matched peptides

Table 2. MALDI-TOF MS analysis of tryptic peptides derived from affinity-purified CA215.

By using semi-quantitative RT-PCR of the cell extract of over 20 cancer cell lines, it was observed that as many as 80% of these cancer cells express T cell receptors (α and β chains) at significantly high levels including OC-3-VGH, OVCAR-3 and SKOV-3 cell lines. Results of such an analysis are summarized in Table 3. In contrast with the exception of Raji (lymphoma) and Jurkat (T cell leukemia) cell lines, none of these cancer cell lines express CD3, CD4 and CD8 co-receptors and/or co-stimulator genes, indicating the non-functional nature of most of the cancer cell-expressed T cell receptors. In addition, numerous immunoglobulin superfamily protein-like cell adhesion molecules such as CD47, CD54, CD58 and CD147 are also highly expressed among all the cancer cell lines. Widespread T cell receptor expressions among cancer cells may have implications on cancer immunotherapy via T cell activation as well as the induced tolerance of T cells caused by tumor-associated antigens in humans (Lee et al., 2011a).

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 395

**Accession Number Peptide Detecteda Peptide Sequence Homology of Proteins (%)**

CAC12842.1 EEQF**N**STFR<sup>a</sup> Immunoglobulin heavy chain (Fc) (100%)<sup>b</sup> CAA04843.1 EEQF**N**STYR Immunoglobulin heavy chain (Fc) (100%)

AAD38158.1 D**T**LMI**S**R Immunoglobulin heavy chain (Fc) (100%) AAC39746.2 GYLPEPV**T**V**T**WN**S**G**T**L**T**NGVR Immunoglobulin heavy chain (Fab) (90%)<sup>c</sup> AAN76042.1 **S**V**S**L**T**CMINGFYP**S**DI**S**VEWEK Immunoglobulin heavy chain (Fc) (90%) CAJ75462.1 Q**SS**GLY**S**L**SS**VVSVT**SSS**QPVTCNV Immunoglobulin heavy chain (Fab and Fc) (100%)

Hemicentin (100%) Titin (100%) Palladin isoform 4 (92%) LRN4 (78%) Immunoglobulin superfamily proteins

Immunoglobulin heavy chain (Fc) (98%) IgA variable region (89%) IgM (98%) Zinc finger protein 414 isoform I (100%) Forkhead box protein C2 (100%) Immunoglobulin heavy chain variable region (83%)

AAB60643.2 L**S**VP**TS**EWQR Cathepsin S (100%

Table 4. Results of N-linked and O-linked glycosylation site mappings of CA215.

RP215 was found to recognize carbohydrate-associated epitope(s) detected preferentially in the cancer cell-expressed CA215, but rarely found in normal cells. This monoclonal antibody alone can be used in sandwich immunoassays for the determination of soluble CA215, if multi-RP215-specific epitope exists in a given CA215 glycoprotein molecule. Therefore, serum levels of CA215 can be determined quantitatively by using RP215 for both capturing and signal detection in a typical sandwich enzyme immunoassay. This enzyme immunoassay has been used in the diagnostics and monitoring of ovarian cancer and cervical cancers (Lee, 2009). CA215 levels in serum specimens of cancer patients at different disease stages can be determined by this enzyme immunoassay kit. Typical results of this analysis from a group of ovarian cancer patients at different stages are

**2.10 Clinical diagnostic applications of RP215-based immunoassays** 

ABY48864.2 VY**T**MGPPREEL**SS**R

AAK68690.1 F**T**CLA**T**NDAGD**S**SK

NP\_001139647.1 **T**FP**S**VR

aBold letters indicate glycosylation sites bFc: constant region of immunoglobulins cFab: variable region of immunoglobulins

presented in Figure 6.


a Abbreviations used: TCR, T cell receptor (α chain or β chain)

b Signal intensities follow the order of +++ (strongest), ++, +, ±, - (neg), GAPDH was used as the internal standards.

Table 3. Gene expressions of immunoglobulin superfamily proteins by RT-PCR.

#### **2.9 Structural elucidations of carbohydrate-associated epitope recognized by RP215 monoclonal antibody**

In collaboration with Complex Carbohydrate Research Center at University of Georgia, efforts have been made to elucidate the carbohydrate-associated epitope(s) recognized by RP215 in CA215. (Lee & Ge, 2009). By using CA215 affinity-purified from OC-3-VGH ovarian and C33A cervical cancer cell lines, profiles of N-linked and O-linked glycans were analyzed and compared with those of other known glycoproteins from normal and cancerous tissues. In the case of N-linked glycans, high mannose and complex bisecting structures with terminal N-glycolylneuraminic acid were detected in CA215. In the case of O-linked glycans, several oligosaccharides were detected in CA215 with structures similar to those of mucins, but with terminal N-glycolylneuraminic acid. N-linked and O-linked glycosylation site mappings of CA215 were performed. A total of two N-linked and eight Olinked glycopeptides were detected. Protein BLAST search of peptide sequence homology revealed that two N-linked and six O-linked glycopeptides were almost 100% matched to human immunoglobulin heavy chains. One of the remaining O-linked ones was matched to immunoglobulin superfamily proteins such as titin and hemicentin. Results of N-linked and O-linked glycosylation site mappings are summarized in Table 4. Based on the results of this extensive glyconanalysis, it can be suggested that both N-linked and O-linked glycans with unique terminal N-glycolylneuraminic acid in CA215 are structurally related to those of mucins. However, N-glycolylneuraminic acid might not to be directly involved in the RP215 epitope recognition as no loss of CA215 activity was found when OC-3-VGH cells were cultured in medium containing human serum instead of fetal calf serum as culture medium supplement (Lee & Azadi, 2011).

**Designation cell line SKOV-3 OVCAR-3 OC-3-VGH Raji Jurkat** ATCC NO. HTB-77 CCL-86 TIB-152

TCR (α)

standards.

**monoclonal antibody** 

supplement (Lee & Azadi, 2011).

a Abbreviations used: TCR, T cell receptor (α chain or β chain)

**Origin Human Ovarian Lymphoma T-cell**

TCR (β) + ± + +++ +++ IgG (Fc) + ++ + ++ + CD3 - - - + + CD4 - - - - - CD8 - - - - - CD47 ++ ++ ++ ++ ++ CD54 ++ ++ + + ++ CD58 ++ ++ ++ ++ ++ CD147 ++ ++ ++ ++ ++

b Signal intensities follow the order of +++ (strongest), ++, +, ±, - (neg), GAPDH was used as the internal

**2.9 Structural elucidations of carbohydrate-associated epitope recognized by RP215** 

In collaboration with Complex Carbohydrate Research Center at University of Georgia, efforts have been made to elucidate the carbohydrate-associated epitope(s) recognized by RP215 in CA215. (Lee & Ge, 2009). By using CA215 affinity-purified from OC-3-VGH ovarian and C33A cervical cancer cell lines, profiles of N-linked and O-linked glycans were analyzed and compared with those of other known glycoproteins from normal and cancerous tissues. In the case of N-linked glycans, high mannose and complex bisecting structures with terminal N-glycolylneuraminic acid were detected in CA215. In the case of O-linked glycans, several oligosaccharides were detected in CA215 with structures similar to those of mucins, but with terminal N-glycolylneuraminic acid. N-linked and O-linked glycosylation site mappings of CA215 were performed. A total of two N-linked and eight Olinked glycopeptides were detected. Protein BLAST search of peptide sequence homology revealed that two N-linked and six O-linked glycopeptides were almost 100% matched to human immunoglobulin heavy chains. One of the remaining O-linked ones was matched to immunoglobulin superfamily proteins such as titin and hemicentin. Results of N-linked and O-linked glycosylation site mappings are summarized in Table 4. Based on the results of this extensive glyconanalysis, it can be suggested that both N-linked and O-linked glycans with unique terminal N-glycolylneuraminic acid in CA215 are structurally related to those of mucins. However, N-glycolylneuraminic acid might not to be directly involved in the RP215 epitope recognition as no loss of CA215 activity was found when OC-3-VGH cells were cultured in medium containing human serum instead of fetal calf serum as culture medium

Table 3. Gene expressions of immunoglobulin superfamily proteins by RT-PCR.

<sup>a</sup> +++<sup>b</sup> + ++ +++ +++

 **leukemia**


aBold letters indicate glycosylation sites bFc: constant region of immunoglobulins

cFab: variable region of immunoglobulins

Table 4. Results of N-linked and O-linked glycosylation site mappings of CA215.

#### **2.10 Clinical diagnostic applications of RP215-based immunoassays**

RP215 was found to recognize carbohydrate-associated epitope(s) detected preferentially in the cancer cell-expressed CA215, but rarely found in normal cells. This monoclonal antibody alone can be used in sandwich immunoassays for the determination of soluble CA215, if multi-RP215-specific epitope exists in a given CA215 glycoprotein molecule. Therefore, serum levels of CA215 can be determined quantitatively by using RP215 for both capturing and signal detection in a typical sandwich enzyme immunoassay. This enzyme immunoassay has been used in the diagnostics and monitoring of ovarian cancer and cervical cancers (Lee, 2009). CA215 levels in serum specimens of cancer patients at different disease stages can be determined by this enzyme immunoassay kit. Typical results of this analysis from a group of ovarian cancer patients at different stages are presented in Figure 6.

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 397

Serum CA215 levels were also found to be correlated with the conditions of clinical treatments. It is interesting to note that the mean serum CA215 levels remained at relatively high levels including those at pre-operative stages and within 7 days following surgical operations. In contrast, serum CA215 levels decreased significantly when determined 7 days

By employing the same enzyme immunoassay kit, the clinical utility of CA215 as a pan cancer biomarker was evaluated with clinically defined serum specimens from over 500 cancer patients and compared with results obtained by nine other established cancer biomarkers including AFP, CEA, CA215, CA19-9, CA15-3, Cyfra21, etc. (Lee et al., 2010b). A combination of CA215 with other tissue-associated cancer markers generally resulted in much higher cancer detection rates. By Western blot assay with RP215, it was revealed that cancer cell-expressed IgG's with RP215-specific epitope were detected in patients' serum specimens which were shown to have high levels of CA215 by enzyme immunoassay. In contrast, normal human serum specimens revealed the absence of cancer cell-expressed

In conclusion, RP215-based immunoassay kit may be suitable for the monitoring of cancer in patients including that of the ovary in terms of disease conditions as well as clinical

**2.11 Immunodominance of carbohydrate-associated epitope(s) recognized by RP215** 

During our investigation of the carbohydrate-associated epitope(s) recognized by RP215, it was observed that the immunodominance of this unique epitope(s) in CA215 exists in mice. When mice were immunized with affinity-purified CA215 derived from the shed medium of OC-3-VGH ovarian cancer cells, only five monoclonal antibodies (RCA10, RCA100, RCA104, RCA110 and RCA111) were recovered from about 1000 hybridomas generated by cell fusion between NS-1 myeloma cells and the spleen cells from mice immunized with purified CA215. Unexpectedly, all five were shown to react with carbohydrate-associated epitope(s) which were similar to that of RP215. Judging from comparisons of the primary structures of these five monoclonal antibodies and RP215, three distinct groups of monoclonal antibodies were categorized. Group I including RP215, RCA10 and RCA100 were shown to have identical amino acid sequences and react with the linear epitope of this unique carbohydrate-associated epitope. Group II (RCA104 and RCA111) and Group III (RCA110) monoclonal antibodies were found to recognize only the conformational structure(s) of this carbohydrate-associated epitope and lost their respective CA215 binding upon treatment with methanol or SDS. However, all the monoclonal antibodies were shown to exhibit common characteristics including (1) a decrease in CA215 binding upon the periodate treatment, (2) mutual pairing among monoclonal antibodies for sandwich immunoassays, and (3) loss of CA215 binding by a given monoclonal antibody in the presence of excess of any other monoclonal antibodies (Lee et al., 2011). Furthermore, all of these monoclonal antibodies were shown to induce apoptosis to cancer cells upon incubation with 1 μg/mL of any of these monoclonal

The reason for the existence of immunodominant carbohydrate-associated epitope(s) recognized by RP215 and other CA215-derived monoclonal antibodies in mice is currently

CA215 as determined by the same enzyme immunoassay (Lee et al., 2010b).

after the surgical operations. (Lee, 2009).

antibodies as clearly demonstrated in Figure 3C.

unknown (Lee et al., 2011b).

treatments.

**in CA215** 

Fig. 6. Serum levels of CA215 pan cancer marker (expressed in AU/mL) for normal healthy individuals (NS) and for patients with ovarian cancer (OC) defined by respective stages (I, II, and III) of disease conditions and those with endometriosis (EM) and pelvic benign tumor (PET). Solid lines indicate the mean (M) serum CA215 levels in each category and dotted lines represent the mean values plus one standard deviation (SD). The number of cases in each category is indicated on the top of the scattered gram.

Statistics:


Compared to the known biomarker for ovarian cancer, such as CA125, enzyme immunoassay for CA215 revealed a similar degree of sensitivity and specificity to those of the known biomarker, CA125. When both biomarkers are combined for clinical diagnosis, the detection sensitivity was found to increase significantly (from 68% to 87%, n=31) (Lee, 2009).

(n=40)

(n=10) (n=23)

NS I II III EM PBT

OC

Fig. 6. Serum levels of CA215 pan cancer marker (expressed in AU/mL) for normal healthy individuals (NS) and for patients with ovarian cancer (OC) defined by respective stages (I, II, and III) of disease conditions and those with endometriosis (EM) and pelvic benign tumor (PET). Solid lines indicate the mean (M) serum CA215 levels in each category and dotted lines represent the mean values plus one standard deviation (SD). The number of cases in

1. Normal control vs. ovarian carcinoma stage I (P < 0.001); stage II (P < 0.001); stage III (P

Compared to the known biomarker for ovarian cancer, such as CA125, enzyme immunoassay for CA215 revealed a similar degree of sensitivity and specificity to those of the known biomarker, CA125. When both biomarkers are combined for clinical diagnosis, the detection sensitivity was found to increase significantly (from 68% to 87%, n=31) (Lee,

< 0.001); stage I vs. stage III (P < 0.05); stage II vs. stage III (P > 0.05).

each category is indicated on the top of the scattered gram.

2. Normal control vs. endometriosis (P > 0.05). 3. Normal control vs. pelvic benign tumors (P = 0.05).

(n=24) (n=7)


Statistics:

2009).

0

20

40

60

(n=59)

80

CA215 (AU/mL)

100

120

140

160

Serum CA215 levels were also found to be correlated with the conditions of clinical treatments. It is interesting to note that the mean serum CA215 levels remained at relatively high levels including those at pre-operative stages and within 7 days following surgical operations. In contrast, serum CA215 levels decreased significantly when determined 7 days after the surgical operations. (Lee, 2009).

By employing the same enzyme immunoassay kit, the clinical utility of CA215 as a pan cancer biomarker was evaluated with clinically defined serum specimens from over 500 cancer patients and compared with results obtained by nine other established cancer biomarkers including AFP, CEA, CA215, CA19-9, CA15-3, Cyfra21, etc. (Lee et al., 2010b). A combination of CA215 with other tissue-associated cancer markers generally resulted in much higher cancer detection rates. By Western blot assay with RP215, it was revealed that cancer cell-expressed IgG's with RP215-specific epitope were detected in patients' serum specimens which were shown to have high levels of CA215 by enzyme immunoassay. In contrast, normal human serum specimens revealed the absence of cancer cell-expressed CA215 as determined by the same enzyme immunoassay (Lee et al., 2010b).

In conclusion, RP215-based immunoassay kit may be suitable for the monitoring of cancer in patients including that of the ovary in terms of disease conditions as well as clinical treatments.

#### **2.11 Immunodominance of carbohydrate-associated epitope(s) recognized by RP215 in CA215**

During our investigation of the carbohydrate-associated epitope(s) recognized by RP215, it was observed that the immunodominance of this unique epitope(s) in CA215 exists in mice. When mice were immunized with affinity-purified CA215 derived from the shed medium of OC-3-VGH ovarian cancer cells, only five monoclonal antibodies (RCA10, RCA100, RCA104, RCA110 and RCA111) were recovered from about 1000 hybridomas generated by cell fusion between NS-1 myeloma cells and the spleen cells from mice immunized with purified CA215. Unexpectedly, all five were shown to react with carbohydrate-associated epitope(s) which were similar to that of RP215. Judging from comparisons of the primary structures of these five monoclonal antibodies and RP215, three distinct groups of monoclonal antibodies were categorized. Group I including RP215, RCA10 and RCA100 were shown to have identical amino acid sequences and react with the linear epitope of this unique carbohydrate-associated epitope. Group II (RCA104 and RCA111) and Group III (RCA110) monoclonal antibodies were found to recognize only the conformational structure(s) of this carbohydrate-associated epitope and lost their respective CA215 binding upon treatment with methanol or SDS. However, all the monoclonal antibodies were shown to exhibit common characteristics including (1) a decrease in CA215 binding upon the periodate treatment, (2) mutual pairing among monoclonal antibodies for sandwich immunoassays, and (3) loss of CA215 binding by a given monoclonal antibody in the presence of excess of any other monoclonal antibodies (Lee et al., 2011). Furthermore, all of these monoclonal antibodies were shown to induce apoptosis to cancer cells upon incubation with 1 μg/mL of any of these monoclonal antibodies as clearly demonstrated in Figure 3C.

The reason for the existence of immunodominant carbohydrate-associated epitope(s) recognized by RP215 and other CA215-derived monoclonal antibodies in mice is currently unknown (Lee et al., 2011b).

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 399

VGH, OVCAR-3 or SKOV-3 ovarian cancer cells were incubated with various primary antibodies (RP215 and GHR106) for 2 h at 37 °C. Normal mouse IgG of the same concentration served as the parallel negative control. The incubation with labeled secondary antibodies as well as the colour staining was performed at room temperature for 1 h by

TUNEL assay was performed to study the apoptotic effects of different monoclonal antibodies including RP215, ChRP215, GHR106 and ChGHR106 on cultured prostate cancer cells. In Situ Cell Death Detection Kit, POD (Roche, Canada) was employed for detection and quantitation of apoptosis at cellular levels. Briefly, OC-3-VGH, OVCAR-3 and SKOV-3 ovarian cancer cells were cultured in RPMI 1640 medium at 37 °C in a CO2 (5%) incubator for 24 h until all cancer cells became attached to the microwells. Selected antibodies of known concentrations were added separately and co-incubated for 48 h. As the negative control, normal mouse IgG of the same concentration (10 μg/mL) was used for the same incubation period. At the end of the incubation, the attached cells were removed from the tissue culture wells. Apoptosis of treated cancer cells were determined quantitatively by TUNEL assay with the instruction provided by the supplier (Gorczyca et

Complement-dependent cytotoxicity assay was performed according to the standard protocol described previously (Lee et al., 2011a). Briefly, 1 x 105 cultured OC-3-VGH cells in 1 mL of appropriate culture medium (RPMI 1640 with 10% FCS) were plated in 24-well plates for 2 h before treatment. RP215, ChRP215, GHR106, and ChGHR106 were added separately to give a final concentration of 10 μg/mL and incubated for 15 min at room temperature, respectively. Three μL of freshly prepared rabbit baby complement (CL3441; Cedarlane labs, Burlington, NC, USA) was added to each well followed by incubation at 37°C for 2 h. After incubation at room temperature, the cells were recovered by centrifugation. Trypan blue (0.4%) (SV30084.01; Thermo Scientific, Waltham, MA, USA) was added and mixed gently. The percentages of cells stained with trypan blue were determined by cell counting under the regular microscope. Normal mouse IgG of the same concentration was used as the negative control. Incubation with the antibody or the complement alone served as the respective negative control for parallel comparisons in this experiment. Statistical analysis was performed to determine the significance of the assay by

Total RNA was extracted from OC-3-VGH, OVCAR-3, SKOV-3, Raji, and Jurkat cell lines (105-107 cells) by using QAIGEN RNeasy mini kit (Mississauga, ON, Canada) according to the manufacturer's manual. Total RNA integrity and quality were checked before the reverse transcription. RNase-free DNase set was performed to avoid genomic gene interference. Reverse transcription of total RNA (0.5 μg - 5 μg/20 μL) to cDNA was performed by using oligo (dT)15 primer and EasyScript first strand synthesis kit from Applied Biological Materials Inc. (ABM) (Richmond, BC, Canada) following the

following the protocols and instructions provided in the ABC Kits.

**3.5 TUNEL assay for assessment of cellular apoptosis** 

**3.6 Complement-dependent cytotoxicity assay** 

trypan blue method (Griffioen et al., 2009; Zhao et al., 2010).

**3.7 Total RNA extraction and cDNA synthesis** 

al.,1993).

#### **2.12 Chimerization of RP215 and GHR106 monoclonal antibodies**

In order to proceed further with preclinical and clinical studies, efforts were made to generate human/mouse chimeric forms of RP215 and GHR106 monoclonal antibodies (Lee et al., 2009; Lee & Ge, 2010a). Details of this genetic engineering process including the elucidation of primary structures of the Fab regions of these two monoclonal antibodies were performed through contract research services (Avantgen, San Diego). Generally speaking, both the murine and chimeric forms of RP215 and GHR106 were found to have almost identical binding affinity to their respective antigens, CA215 and GnRH receptor, on the cancer cells. Successful constructions of chimeric forms of these two monoclonal antibodies are essential to generate humanized forms as effective anti-cancer drugs for treatments of human cancers including that of the ovary.

### **3. Experimental procedure**

#### **3.1 Chemicals**

All the chemicals were purchased from Sigma Chemicals Co (St Louis, MD) unless otherwise specified.

#### **3.2 Cell lines**

Human ovarian cancer cell lines, including OVCAR-3 and SKOV-3, as well as other cancer cell lines, including Raji and Jurkat were obtained from the American Type Culture Collection (ATCC), Rockville, MD, USA. All cell lines were cultured at 37 °C in a humidified atmosphere consisting of 5% CO2 and 95% air and maintained by subculturing cells twice a week according to supplier's instructions. OC-3-VGH ovarian cancer cell lines were established in 1986 by Dept. OBS/GYN, Veterans General Hospital, Taipei, Taiwan. This is the cell line of serus origin and has been used to serve as a model for the discovery and studies of RP215 in our laboratory (Lee et al., 1992). This cancer cell line was maintained in RPMI1640 containing 10% fetal calf serum indefinitely.

#### **3.3 Western blot assays**

Western blot assay of cell extract from three ovarian cancer cell lines was performed according to the reported procedure (Lee et al., 1992; Liu et al., 1992). RP215 and GHR106 monoclonal antibodies were used as the respective probes to identify the molecular sites of CA215 as well as GnRH receptor in selected cancer cell extract. Briefly, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed with cancer cell extract in the presence of reducing agent (β-mercaptoethanol). Indirect Western blot assays were carried out separately by using 10 μg/mL each of RP215 or GHR106 as the respective primary antibody probe for 1 h incubation at room temperature. Subsequently, alkaline phosphatase (ALP) labeled goat anti-mouse IgG was used as the secondary antibody for an additional 1 h incubation. This was followed by the color development with the substrate, pnitrophenylphosphate purchased from Bio-Rad Laboratories (Mississauga, ON, Canada). Appropriate positive and negative controls as well as the molecular weight standards (BioRad laboratories, Mississauga, ON, Canada) were included in the routine Western blot assay.

#### **3.4 Immunohistochemical studies**

The immunohistochemical staining experiments were performed by using the avidin/biotin complex (ABC) method from Vector Laboratories (Burlingame, CA). Methanol fixed OC-3VGH, OVCAR-3 or SKOV-3 ovarian cancer cells were incubated with various primary antibodies (RP215 and GHR106) for 2 h at 37 °C. Normal mouse IgG of the same concentration served as the parallel negative control. The incubation with labeled secondary antibodies as well as the colour staining was performed at room temperature for 1 h by following the protocols and instructions provided in the ABC Kits.

#### **3.5 TUNEL assay for assessment of cellular apoptosis**

398 Ovarian Cancer – Basic Science Perspective

In order to proceed further with preclinical and clinical studies, efforts were made to generate human/mouse chimeric forms of RP215 and GHR106 monoclonal antibodies (Lee et al., 2009; Lee & Ge, 2010a). Details of this genetic engineering process including the elucidation of primary structures of the Fab regions of these two monoclonal antibodies were performed through contract research services (Avantgen, San Diego). Generally speaking, both the murine and chimeric forms of RP215 and GHR106 were found to have almost identical binding affinity to their respective antigens, CA215 and GnRH receptor, on the cancer cells. Successful constructions of chimeric forms of these two monoclonal antibodies are essential to generate humanized forms as effective anti-cancer drugs for

All the chemicals were purchased from Sigma Chemicals Co (St Louis, MD) unless

Human ovarian cancer cell lines, including OVCAR-3 and SKOV-3, as well as other cancer cell lines, including Raji and Jurkat were obtained from the American Type Culture Collection (ATCC), Rockville, MD, USA. All cell lines were cultured at 37 °C in a humidified atmosphere consisting of 5% CO2 and 95% air and maintained by subculturing cells twice a week according to supplier's instructions. OC-3-VGH ovarian cancer cell lines were established in 1986 by Dept. OBS/GYN, Veterans General Hospital, Taipei, Taiwan. This is the cell line of serus origin and has been used to serve as a model for the discovery and studies of RP215 in our laboratory (Lee et al., 1992). This cancer cell line was maintained in

Western blot assay of cell extract from three ovarian cancer cell lines was performed according to the reported procedure (Lee et al., 1992; Liu et al., 1992). RP215 and GHR106 monoclonal antibodies were used as the respective probes to identify the molecular sites of CA215 as well as GnRH receptor in selected cancer cell extract. Briefly, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed with cancer cell extract in the presence of reducing agent (β-mercaptoethanol). Indirect Western blot assays were carried out separately by using 10 μg/mL each of RP215 or GHR106 as the respective primary antibody probe for 1 h incubation at room temperature. Subsequently, alkaline phosphatase (ALP) labeled goat anti-mouse IgG was used as the secondary antibody for an additional 1 h incubation. This was followed by the color development with the substrate, pnitrophenylphosphate purchased from Bio-Rad Laboratories (Mississauga, ON, Canada). Appropriate positive and negative controls as well as the molecular weight standards (BioRad laboratories, Mississauga, ON, Canada) were included in the routine Western blot assay.

The immunohistochemical staining experiments were performed by using the avidin/biotin complex (ABC) method from Vector Laboratories (Burlingame, CA). Methanol fixed OC-3-

**2.12 Chimerization of RP215 and GHR106 monoclonal antibodies** 

treatments of human cancers including that of the ovary.

RPMI1640 containing 10% fetal calf serum indefinitely.

**3. Experimental procedure** 

**3.1 Chemicals** 

**3.2 Cell lines** 

otherwise specified.

**3.3 Western blot assays** 

**3.4 Immunohistochemical studies** 

TUNEL assay was performed to study the apoptotic effects of different monoclonal antibodies including RP215, ChRP215, GHR106 and ChGHR106 on cultured prostate cancer cells. In Situ Cell Death Detection Kit, POD (Roche, Canada) was employed for detection and quantitation of apoptosis at cellular levels. Briefly, OC-3-VGH, OVCAR-3 and SKOV-3 ovarian cancer cells were cultured in RPMI 1640 medium at 37 °C in a CO2 (5%) incubator for 24 h until all cancer cells became attached to the microwells. Selected antibodies of known concentrations were added separately and co-incubated for 48 h. As the negative control, normal mouse IgG of the same concentration (10 μg/mL) was used for the same incubation period. At the end of the incubation, the attached cells were removed from the tissue culture wells. Apoptosis of treated cancer cells were determined quantitatively by TUNEL assay with the instruction provided by the supplier (Gorczyca et al.,1993).

#### **3.6 Complement-dependent cytotoxicity assay**

Complement-dependent cytotoxicity assay was performed according to the standard protocol described previously (Lee et al., 2011a). Briefly, 1 x 105 cultured OC-3-VGH cells in 1 mL of appropriate culture medium (RPMI 1640 with 10% FCS) were plated in 24-well plates for 2 h before treatment. RP215, ChRP215, GHR106, and ChGHR106 were added separately to give a final concentration of 10 μg/mL and incubated for 15 min at room temperature, respectively. Three μL of freshly prepared rabbit baby complement (CL3441; Cedarlane labs, Burlington, NC, USA) was added to each well followed by incubation at 37°C for 2 h. After incubation at room temperature, the cells were recovered by centrifugation. Trypan blue (0.4%) (SV30084.01; Thermo Scientific, Waltham, MA, USA) was added and mixed gently. The percentages of cells stained with trypan blue were determined by cell counting under the regular microscope. Normal mouse IgG of the same concentration was used as the negative control. Incubation with the antibody or the complement alone served as the respective negative control for parallel comparisons in this experiment. Statistical analysis was performed to determine the significance of the assay by trypan blue method (Griffioen et al., 2009; Zhao et al., 2010).

#### **3.7 Total RNA extraction and cDNA synthesis**

Total RNA was extracted from OC-3-VGH, OVCAR-3, SKOV-3, Raji, and Jurkat cell lines (105-107 cells) by using QAIGEN RNeasy mini kit (Mississauga, ON, Canada) according to the manufacturer's manual. Total RNA integrity and quality were checked before the reverse transcription. RNase-free DNase set was performed to avoid genomic gene interference. Reverse transcription of total RNA (0.5 μg - 5 μg/20 μL) to cDNA was performed by using oligo (dT)15 primer and EasyScript first strand synthesis kit from Applied Biological Materials Inc. (ABM) (Richmond, BC, Canada) following the

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 401

In this chapter, two monclonal antibodies, RP215 and GHR106, were identified, characterized and assessed to see if they are suitable for development of antibody-based anti-cancer drugs. The target antigens recognized by these two monoclonal antibodies, CA215 and GnRH receptor, were found to be expressed on the surface of cancer cells of many human tissue origins including that of the ovary (Lee et al., 2008). Initially, we have shown by immunohistochemical studies and Western blot assays (Lee et al., 2009) that these two monoclonal antibodies react specifically with antigens of three cancer cell lines, OC-3- VGH, OVCAR-3 and SKOV-3. Immunohistochemical studies with tissue sections of 87 ovarian cancer specimens revealed that the positive staining rates with RP215 can be as high as 64.4% (56/87). These studies have become the basis for further preclinical studies to establish if these monoclonal antibodies are suitable as anti-cancer drugs for the treatment of

By *in vitro* apoptosis assay as well as complement-dependent cytotoxicity assay, we have been able to show that these two monoclonal antibodies or their chimeric forms could induce apoptosis to ovarian cancer cells and complement-dependent cytotoxicity reactions upon incubation with cultured cancer cells. Furthermore, *in vivo* nude mouse experiments with implanted OC-3-VGH ovarian cancer cells revealed significant dose-dependent inhibition of the growth of tumor cells by RP215 (Lee et al., 2009). By using MALDI-TOF MS for analysis of tryptic peptides derived from affinity-purified CA215, it can be shown that affinity-purified CA215 consist mainly of glycoproteins including immunoglobulin superfamily proteins and mucins. Therefore, some of the cancer cell-expressed CA215 with unique carbohydrate-associated epitope recognized by RP215 seems to be critical to the

Efforts have been made to elucidate the primary carbohydrate structures of RP215-specific epitope(s) in CA215 through extensive N-linked and O-linked glycoanalysis and glycosylation site mappings. It was generally concluded that oligosaccharides with terminal NeuGc with structures related to those in mucins might not be crucial in the epitope(s)

The results of our studies have clearly demonstrated that GHR106, which reacts with the cancer cell-expressed GnRH receptor, can behave like a long acting GnRH analog in its biological actions except with a much longer half life (15-21 days) than the former (Lee & Ge, 2010a). Similar to those of GnRH analogs, apoptosis of many human cancer cells can be induced for effective cancer treatments (Leung et al., 2003). In addition, complementdependent cytotoxicity and antibody-dependent cell-mediated cytotoxicity reactions could possibly be induced only with antibody-based anti-cancer drugs such as GHR106. We believe that the results presented in this study should provide a strong basis for these two monoclonal antibodies to be developed further in humans as anti-cancer drugs which might

It was generally postulated that certain tumor-associated antigens on the surface of cancer cells are overexpressed when compared with those of the normal cells (Rosenberg, 1995; Boon & van der Bruggen, 1996; Topalian, 1994; Lee, 2009). Their specific antibodies can be induced or generated to inhibit the growth of cancer cells through mechanisms of cellular

growth of cancer cells, *in vitro* or *in vivo* (Lee et al., 2011a).

potentially target ovarian cancer as well as others in humans.

**4.2 Implications to the immunology of cancer cells** 

recognition by RP215 (Lee & Azadi, 2011).

**4. Conclusion** 

ovarian cancers.

**4.1 General conclusion** 

manufacturer's protocol. Reaction mixtures with RNA template but without reversetranscriptase were used as the negative controls for cDNA synthesis.

#### **3.8 Semi-quantitative analysis of mRNA expressions of selected genes by RT-PCR**

All primers required for PCR amplification were obtained from Integrated DNA Technologies (San Diego, CA) and listed as follows:

IgG: sense: 5'-ACGGCGTGGAGGTGCATAATG -3'; antisense: 5'-

CGGGAGGCGTGGTCTTGTAGTT-3'; T cell receptor α chain constant region: sense: 5'-

GTGCTAGACATGAGGTCTATGGAC-3' and antisense: 5'-

GGATTCGGAAGGGAATCACTGACAGG-3'; T cell receptor β chain constant region: sense: 5'-TCTCGGCCACCTTCTGGC-3'; antisense: 5'-CATCAGCACGAGGGCACTGA-3'; CD3:

sense: 5'-CTCTCTGGCCTGGTACTGGC-3' and antisense: 5'-

GGCTGATAGACCTGGTCATTCCTCA-3'; CD4: sense: 5'

ACTAAAGGTCCATCCAAGCTGA-3' and antisense: 5'-

GCAGTCAATCCGAACACTAGCA-3'; CD8: sense: 5'-TTCGAGCCAAGCAGCGTCCT-3' and antisense: 5' CGGCACGAAGTGGCTGAAGTA-3'; CD47: sense: 5'-

GAGTGATGCTGTCTCACACAC-3' and antisense: 5'-CTCATCCATACCACCGGATCT-3';

CD54: sense: 5'-CGGCACGAAGTGGCTGAAGTA-3' and antisense: 5'-

CGAGGTGTTCTCAAACAGCTCCAG-3'; CD58: sense: 5'-

AGAGCATTACAACAGCCATCG-3' and antisense: 5'-CGCTGCTTGGGATACAGGTT-3';

CD147: sense: 5'-CGAGGTGTTCTCAAACAGCTCCAG-3' and antisense: 5'-

CTTCCGGCGCTTCTCGTAGATG-3';

GnRH: sense: 5'-AACCTCTTCACCTTCTGCTGCCT-3' and antisense: 5'-

GATTTCTTCCCAGACCCTCTTACGAG-3'; GnRH receptor: sense: 5'-

TGACACGGGTCCTTCATCAG-3' and antisense: 5'-AAGTGGATCAAAGCATGGGTTT-3'; NFκB: sense: 5'-CACTAAGCAGGAAGATGTGGTGGAG-3' and antisense: 5'-

CATGGCAGGCTATTGCTCATCATGG-3;' P0: sense: 5'-TTGTGTTCACCAAGGAGG-3' and antisense: 5'-GTAGCCAATCTGCAGACAG-3'; P1: sense: 5'-

CAAGGTGCTCGGTCCTTC-3' and antisense: 5'-GAACATGTTATAAAAGAGG-3'; P2: sense: 5'-TCCGCCGCAGACGCCGC-3' and antisense: 5'-TGCAGGGAGCAGGAATT-3'; EGF: sense: 5'-AAGGAAATCCTCGATGAAGCCT-3' and antisense: 5'-

TGTCTTTGTGTTCCCGGACATA -3'; c-fos: sense: 5'-GAGATTGCCAACCTGCTGAA-3' and antisense: 5'-AGACGAAGGAAGACGTGTAA-3'.

A house-keeping gene, glyceraldehyde-3-phosphate dehydrogenase was amplified and used as an internal control in the experiments. (Sense: 5'-GAAATCCCATCACCATCTTCC-3' and antisense: 5' - CCAGGGGTCTTACTCCTTGG-3').

PCR was performed by using 2×PCR MasterMix kit (ABM, Richmond, BC, Canada) according to the manufacturer's protocols. After denaturing at 94 °C for 4 min, 20-35 cycles were performed under the following conditions: denaturing at 94 °C for 40 s; annealing at 58 °C for 60 s and extension at 72 °C for 60 s. A final complete extension was then executed at 72 °C for 7 min. At the end, the PCR product was checked by 1.5% agarose gel electrophoresis. The negative control from cDNA synthesis was further used in a PCR reaction and served as the negative control.

#### **3.9 Statistical analysis**

All experiments were performed in triplicate. All the results were presented as mean ± S.D. Student t-test was performed to estimate the statistical significance.

### **4. Conclusion**

400 Ovarian Cancer – Basic Science Perspective

manufacturer's protocol. Reaction mixtures with RNA template but without reverse-

**3.8 Semi-quantitative analysis of mRNA expressions of selected genes by RT-PCR**  All primers required for PCR amplification were obtained from Integrated DNA

CGGGAGGCGTGGTCTTGTAGTT-3'; T cell receptor α chain constant region: sense: 5'-

GGATTCGGAAGGGAATCACTGACAGG-3'; T cell receptor β chain constant region: sense: 5'-TCTCGGCCACCTTCTGGC-3'; antisense: 5'-CATCAGCACGAGGGCACTGA-3'; CD3:

GCAGTCAATCCGAACACTAGCA-3'; CD8: sense: 5'-TTCGAGCCAAGCAGCGTCCT-3'

GAGTGATGCTGTCTCACACAC-3' and antisense: 5'-CTCATCCATACCACCGGATCT-3';

AGAGCATTACAACAGCCATCG-3' and antisense: 5'-CGCTGCTTGGGATACAGGTT-3';

TGACACGGGTCCTTCATCAG-3' and antisense: 5'-AAGTGGATCAAAGCATGGGTTT-

CATGGCAGGCTATTGCTCATCATGG-3;' P0: sense: 5'-TTGTGTTCACCAAGGAGG-3'

CAAGGTGCTCGGTCCTTC-3' and antisense: 5'-GAACATGTTATAAAAGAGG-3'; P2: sense: 5'-TCCGCCGCAGACGCCGC-3' and antisense: 5'-TGCAGGGAGCAGGAATT-3';

TGTCTTTGTGTTCCCGGACATA -3'; c-fos: sense: 5'-GAGATTGCCAACCTGCTGAA-3'

PCR was performed by using 2×PCR MasterMix kit (ABM, Richmond, BC, Canada) according to the manufacturer's protocols. After denaturing at 94 °C for 4 min, 20-35 cycles were performed under the following conditions: denaturing at 94 °C for 40 s; annealing at 58 °C for 60 s and extension at 72 °C for 60 s. A final complete extension was then executed at

72 °C for 7 min. At the end, the PCR product was checked by 1.5% agarose gel electrophoresis. The negative control from cDNA synthesis was further used in a PCR

Student t-test was performed to estimate the statistical significance.

A house-keeping gene, glyceraldehyde-3-phosphate dehydrogenase was amplified and used as an internal control in the experiments. (Sense: 5'-GAAATCCCATCACCATCTTCC-3' and

All experiments were performed in triplicate. All the results were presented as mean ± S.D.

transcriptase were used as the negative controls for cDNA synthesis.

IgG: sense: 5'-ACGGCGTGGAGGTGCATAATG -3'; antisense: 5'-

GTGCTAGACATGAGGTCTATGGAC-3' and antisense: 5'-

sense: 5'-CTCTCTGGCCTGGTACTGGC-3' and antisense: 5'- GGCTGATAGACCTGGTCATTCCTCA-3'; CD4: sense: 5' ACTAAAGGTCCATCCAAGCTGA-3' and antisense: 5'-

CGAGGTGTTCTCAAACAGCTCCAG-3'; CD58: sense: 5'-

CTTCCGGCGCTTCTCGTAGATG-3';

and antisense: 5' CGGCACGAAGTGGCTGAAGTA-3'; CD47: sense: 5'-

CD54: sense: 5'-CGGCACGAAGTGGCTGAAGTA-3' and antisense: 5'-

CD147: sense: 5'-CGAGGTGTTCTCAAACAGCTCCAG-3' and antisense: 5'-

GnRH: sense: 5'-AACCTCTTCACCTTCTGCTGCCT-3' and antisense: 5'- GATTTCTTCCCAGACCCTCTTACGAG-3'; GnRH receptor: sense: 5'-

EGF: sense: 5'-AAGGAAATCCTCGATGAAGCCT-3' and antisense: 5'-

and antisense: 5'-GTAGCCAATCTGCAGACAG-3'; P1: sense: 5'-

and antisense: 5'-AGACGAAGGAAGACGTGTAA-3'.

antisense: 5' - CCAGGGGTCTTACTCCTTGG-3').

reaction and served as the negative control.

**3.9 Statistical analysis** 

3'; NFκB: sense: 5'-CACTAAGCAGGAAGATGTGGTGGAG-3' and antisense: 5'-

Technologies (San Diego, CA) and listed as follows:

#### **4.1 General conclusion**

In this chapter, two monclonal antibodies, RP215 and GHR106, were identified, characterized and assessed to see if they are suitable for development of antibody-based anti-cancer drugs. The target antigens recognized by these two monoclonal antibodies, CA215 and GnRH receptor, were found to be expressed on the surface of cancer cells of many human tissue origins including that of the ovary (Lee et al., 2008). Initially, we have shown by immunohistochemical studies and Western blot assays (Lee et al., 2009) that these two monoclonal antibodies react specifically with antigens of three cancer cell lines, OC-3- VGH, OVCAR-3 and SKOV-3. Immunohistochemical studies with tissue sections of 87 ovarian cancer specimens revealed that the positive staining rates with RP215 can be as high as 64.4% (56/87). These studies have become the basis for further preclinical studies to establish if these monoclonal antibodies are suitable as anti-cancer drugs for the treatment of ovarian cancers.

By *in vitro* apoptosis assay as well as complement-dependent cytotoxicity assay, we have been able to show that these two monoclonal antibodies or their chimeric forms could induce apoptosis to ovarian cancer cells and complement-dependent cytotoxicity reactions upon incubation with cultured cancer cells. Furthermore, *in vivo* nude mouse experiments with implanted OC-3-VGH ovarian cancer cells revealed significant dose-dependent inhibition of the growth of tumor cells by RP215 (Lee et al., 2009). By using MALDI-TOF MS for analysis of tryptic peptides derived from affinity-purified CA215, it can be shown that affinity-purified CA215 consist mainly of glycoproteins including immunoglobulin superfamily proteins and mucins. Therefore, some of the cancer cell-expressed CA215 with unique carbohydrate-associated epitope recognized by RP215 seems to be critical to the growth of cancer cells, *in vitro* or *in vivo* (Lee et al., 2011a).

Efforts have been made to elucidate the primary carbohydrate structures of RP215-specific epitope(s) in CA215 through extensive N-linked and O-linked glycoanalysis and glycosylation site mappings. It was generally concluded that oligosaccharides with terminal NeuGc with structures related to those in mucins might not be crucial in the epitope(s) recognition by RP215 (Lee & Azadi, 2011).

The results of our studies have clearly demonstrated that GHR106, which reacts with the cancer cell-expressed GnRH receptor, can behave like a long acting GnRH analog in its biological actions except with a much longer half life (15-21 days) than the former (Lee & Ge, 2010a). Similar to those of GnRH analogs, apoptosis of many human cancer cells can be induced for effective cancer treatments (Leung et al., 2003). In addition, complementdependent cytotoxicity and antibody-dependent cell-mediated cytotoxicity reactions could possibly be induced only with antibody-based anti-cancer drugs such as GHR106. We believe that the results presented in this study should provide a strong basis for these two monoclonal antibodies to be developed further in humans as anti-cancer drugs which might potentially target ovarian cancer as well as others in humans.

#### **4.2 Implications to the immunology of cancer cells**

It was generally postulated that certain tumor-associated antigens on the surface of cancer cells are overexpressed when compared with those of the normal cells (Rosenberg, 1995; Boon & van der Bruggen, 1996; Topalian, 1994; Lee, 2009). Their specific antibodies can be induced or generated to inhibit the growth of cancer cells through mechanisms of cellular

Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 403

In this study, two potential antibody-based anti-cancer drugs were introduced and preclinical studies were performed. In view of the high and widespread expressions of the target antigens, namely, CA215 and GnRH receptor among ovarian cancer cells, we are optimistic about their potential in treating ovarian cancers following appropriate research,

This project was supported in parts by Vancouver Biotech Ltd, the NSERC and IRAP

Bast, R.C. Jr.; Klug, T.L.; Schaetzl, E.; Lavin, P.; Niloff, J.M.; Greber, T.F.; Zurawski, V.R. Jr.

Boon, T. & van der Bruggen, P. (1996). Human tumor antigens recognized by T

Choi, M.; Fuller, C.D.; Thomas, C. R. & Wang, S. (2008). Conditional survival in ovarian cancer: results from the SEER dataset 1988-2001. *Gynecol Oncol.* 109(2): 203-9. Dragunow, M. & Faull, R. (1989). The use of c-fos as a metabolic marker in neuronal

Gorczyca, W.; Bigman, K.; Mittelman, A.; Ahmed, T.; Gong, J.; Melamed, M.R. &

Griffioen, M.; van Egmond, E.H.; Kester, M.G.; Willemze, R.; Falkenburg, J.H. & Heemskerk,

Gründker, C.; Völker, P.; Schulz, K.D. & Emons, G. (2000). Luteinizing hormone-releasing

expression in human gynecological cancers. *Gynecol Oncol.* 78(2): 194-202. Horna, P. & Sotomayor, E.M. (2007). Cellular and molecular mechanisms of tumor-induced

apoptosis during treatment of leukemias. Leukemia. 7(5): 659-70.

Brockhausen, I.(2000). O-linked chain glycosyltransferases. *Methods Mol Biol*. 125:273-93. Chien, C.H.; Chen, C.H.; Lee, C.Y.; Chang, T.C.; Chen, R.J. & Chow, S.N. (2004). Detection of

epithelial ovarian cancers. *Int J Gynecol Cancer*. 14(3):451-8.

pathway tracing. *J. Neuroscience Methods* 29(3): 261-265. Finn, O. (2008). Cancer Immunology. *N Engl J Med*. 358:2704-2715

T-cell tolerance. *Curr Cancer Drug Targets.* 7(1):41-53.

cell therapy. *Haematologica.* 94: 1316-1320.

& Knapp, R.C. (1984). Monitoring human ovarian carcinoma with a combination of CA 125, CA 19-9, and carcinoembryonic antigen. *Am J Obstet Gynecol*.

gonadotropin-releasing hormone receptor and its mRNA in primary human

Darzynkiewicz, Z. (1993). Induction of DNA strand breaks associated with

M. H. (2009). Retroviral transfer of human CD20 as a suicide gene for adoptive T-

hormone agonist triptorelin and antagonist cetrorelix inhibit EGF-induced c-fos

development and clinical trials in the near future.

Suzanne Potzold is a co-op student from Vancouver Biotech Ltd.

**5. Acknowledgement** 

**6. Conflict of interest statement** 

149(5):553-9.

GL is co-founder of Vancouver Biotech Ltd.

lymphocytes. *J Exp Med.* 183(3):725-9.

(#743918) kindly.

**7. References** 

apoptosis, complement-dependent cytotoxicity and/or antibody-dependent cell-mediated cytotoxicity reactions (Oldham & Dillman, 2008; Sanz et al., 2004). These have become the mechanistic basis of almost all the antibody-based anti-cancer drugs currently utilized for clinical treatments of human cancers (Finn, 2010; Wang & Rosenberg, 1999). On the other hand, cancer cells can be neutralized through the mechanisms of T cell activations *in vitro* or *in vivo* (Wang & Rosenberg, 1999) following appropriate processes (Zou, 2006). Based on this principle, the first anti-prostate cancer vaccine, Provenge (Sipuleucel-T) was developed and approved by the US-FDA for use clinically (Kantoff et al., 2010).

Similarly, in this study, we have explored the potential applications of RP215 and GHR106 monoclonal antibodies as antibody-based anti-cancer drugs. In the case of RP215, the molecular nature of CA215 and epitope-specific oligosaccharides were elucidated. Although our knowledge about the immunology of cancer cells has been advanced significantly, more questions arising from this study needs to be addressed here.

During the extensive molecular analysis of cancer cell-expressed CA215 with the unique carbohydrate-associated epitope recognized by RP215 monoclonal antibody, many glycoproteins involved in the normal immune system were identified and found to be highly expressed among many types of cancer cells, but rarely found in non-immune cells in humans. Among these are immunoglobulin superfamily proteins including all immunoglobulins, T cell receptors (α and β) and many cell adhesion CD molecules. The functional significance for the expression of these molecules in cancer cells remains to be explored (Lee et al., 2011a). First of all, the expression of immunoglobulins by cancer cells may be implicated with the existence of innate immunity in these cell types (Lee, 2009; Lee & Ge, 2009). Secondly, the widespread expressions of T cell receptors in cancer cells may also have implications in immune activations of T cells as well as the immune tolerance by T cells on cancer cells (Lee et al., 2011a). Thirdly, the high expression of CDrelated cell adhesion molecules may facilitate the metastasis of cancer cells (Lee et al., 2011a). Therefore, more research work needs to be performed to resolve some of these puzzles.

We believe that for cancer cells to survive under the environment of the human immune system, a special protective mechanism should exist among cancer cells. Therefore, much more effort will be required to analyze and study the "immune system" of cancer cells, before any effective immuno-therapy of cancer cells can be achieved (Zou, 2006; Horna & Sotomayor, 2006).

Another question which needs to be addressed is the preferential attachment of RP215 specific carbohydrate-associated epitope(s) in cancer cell-expressed CA215. In the case of immunoglobulin superfamily glycoproteins, the common, but special glycosylations sites might be generated from the conserved and common domain structures of these types of molecules as reported previously (Lee & Azadi, 2011). Mucin proteins, on the other hand, are known to consist of glycosylation sites which are favorable for the unique epitope attachments. However, it is equally difficult to explain the preferential absence of the RP215 recognition sites among the same proteins in normal cells. We believe that the unique RP215-specific epitope(s) is the result of aberrant expressions of certain glycosyltransferases (Yang et al., 1994; Brockhausen, 2000), which are common to all cancer cells. This question can be answered only when the carbohydrate moieties of the epitope(s) structures are completely elucidated in the future (Lee and Azadi, 2011).

In this study, two potential antibody-based anti-cancer drugs were introduced and preclinical studies were performed. In view of the high and widespread expressions of the target antigens, namely, CA215 and GnRH receptor among ovarian cancer cells, we are optimistic about their potential in treating ovarian cancers following appropriate research, development and clinical trials in the near future.

#### **5. Acknowledgement**

402 Ovarian Cancer – Basic Science Perspective

apoptosis, complement-dependent cytotoxicity and/or antibody-dependent cell-mediated cytotoxicity reactions (Oldham & Dillman, 2008; Sanz et al., 2004). These have become the mechanistic basis of almost all the antibody-based anti-cancer drugs currently utilized for clinical treatments of human cancers (Finn, 2010; Wang & Rosenberg, 1999). On the other hand, cancer cells can be neutralized through the mechanisms of T cell activations *in vitro* or *in vivo* (Wang & Rosenberg, 1999) following appropriate processes (Zou, 2006). Based on this principle, the first anti-prostate cancer vaccine, Provenge (Sipuleucel-T) was developed and

Similarly, in this study, we have explored the potential applications of RP215 and GHR106 monoclonal antibodies as antibody-based anti-cancer drugs. In the case of RP215, the molecular nature of CA215 and epitope-specific oligosaccharides were elucidated. Although our knowledge about the immunology of cancer cells has been advanced significantly, more

During the extensive molecular analysis of cancer cell-expressed CA215 with the unique carbohydrate-associated epitope recognized by RP215 monoclonal antibody, many glycoproteins involved in the normal immune system were identified and found to be highly expressed among many types of cancer cells, but rarely found in non-immune cells in humans. Among these are immunoglobulin superfamily proteins including all immunoglobulins, T cell receptors (α and β) and many cell adhesion CD molecules. The functional significance for the expression of these molecules in cancer cells remains to be explored (Lee et al., 2011a). First of all, the expression of immunoglobulins by cancer cells may be implicated with the existence of innate immunity in these cell types (Lee, 2009; Lee & Ge, 2009). Secondly, the widespread expressions of T cell receptors in cancer cells may also have implications in immune activations of T cells as well as the immune tolerance by T cells on cancer cells (Lee et al., 2011a). Thirdly, the high expression of CDrelated cell adhesion molecules may facilitate the metastasis of cancer cells (Lee et al., 2011a). Therefore, more research work needs to be performed to resolve some of these

We believe that for cancer cells to survive under the environment of the human immune system, a special protective mechanism should exist among cancer cells. Therefore, much more effort will be required to analyze and study the "immune system" of cancer cells, before any effective immuno-therapy of cancer cells can be achieved (Zou, 2006; Horna &

Another question which needs to be addressed is the preferential attachment of RP215 specific carbohydrate-associated epitope(s) in cancer cell-expressed CA215. In the case of immunoglobulin superfamily glycoproteins, the common, but special glycosylations sites might be generated from the conserved and common domain structures of these types of molecules as reported previously (Lee & Azadi, 2011). Mucin proteins, on the other hand, are known to consist of glycosylation sites which are favorable for the unique epitope attachments. However, it is equally difficult to explain the preferential absence of the RP215 recognition sites among the same proteins in normal cells. We believe that the unique RP215-specific epitope(s) is the result of aberrant expressions of certain glycosyltransferases (Yang et al., 1994; Brockhausen, 2000), which are common to all cancer cells. This question can be answered only when the carbohydrate moieties of the epitope(s) structures are

approved by the US-FDA for use clinically (Kantoff et al., 2010).

questions arising from this study needs to be addressed here.

completely elucidated in the future (Lee and Azadi, 2011).

puzzles.

Sotomayor, 2006).

This project was supported in parts by Vancouver Biotech Ltd, the NSERC and IRAP (#743918) kindly.

Suzanne Potzold is a co-op student from Vancouver Biotech Ltd.

#### **6. Conflict of interest statement**

GL is co-founder of Vancouver Biotech Ltd.

#### **7. References**


Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer 405

Lee, G.; Zhu, M.; Ge, B.; Cheung, A. P,; Chien,C.; Chow,S.; Ding. Y. & Yao,H. (2011b).

Leung, P.C.; Cheng, C.K. & Zhu, X.M. (2003). Multi-factorial role of GnRH-I and GnRH-II in

Li, Q. & Verma, I.M. (2002). NF-kappaB regulation in the immune system. *Nat. Rev.* 

Liu, M.S.; Aebersold, R.; Fann, C.H. & Lee, C.Y. (1992). Molecular and developmental

McGuire, W.P.; Hoskins, W.J.; Brady, M.F.; Kucera, P.R.; Partridge, E.E.; Look, K.Y.; Clarke-

Mørch, L.S.; Løkkegaard, E.; Andreasen, A.H.; Krüger-Kjaer, S. & Lidegaard, O. (2009).

Oldham, R.K. & Dillman, R.O. (2008). Monoclonal antibodies in cancer therapy: 25 years of

Qiu, X.; Zhu, X.; Zhang, L.; Mao, Y.; Zhang, J.; Hao, P.; Li, G.; Lv, P.; Li, X.; Sun, X.; Wu, L.;

Tan, A.; De La Peña, H. & Seifalian, A.M. (2010). The application of exosomes as a nanoscale

Topalian, S.L. (1994). MHC class II restricted tumor antigens and the role of CD4+ T cells in

Zhang, S.; Mao, Y.; Huang, J.; Ma, T.; Zhang, L.; Zhu, X.; Zheng, J.; Wu, L.; Yin, C.C. & Qiu

Yang, J.M.; Byrd, J.C.; Siddiki, B.B.; Chung, Y.S.; Okuno, M.; Sowa, M.; Kim, Y.S.; Matta, K.L.

Zhao, X.; Singh, S.; Pardoux, C.; Zhao, J.; Hsi, E.D.; Abo, A. & Korver, W. (2010). Targeting

Zheng, J.; Huang, J.; Mao, Y.; Liu, S.; Sun, X.; Zhu, X.; Ma, T.; Zhang, L.; Ji, J.; Zhang, Y.; Yin,

X. (2010). Immunoglobulin gene locus events in epithelial cells of lactating mouse

& Brockhausen, I. (1994). Alterations of O-glycan biosynthesis in human colon

C-type lectin-like molecule-1 for antibody-mediated immunotherapy in acute

C.C. & Qiu, X. (2009). Immunoglobulin gene transcripts have distinct VHDJH

Waldmann, T.A. (2003). Immunotherapy: past, present and future. *Nat Med.* 9(3): 269-77. Wang, R.F. & Rosenberg, S.A. (1999). Human tumor antigens for cancer vaccine

promote growth and survival of tumor cells. *Cancer Res.* 63: 6488-6495. Rosenberg, S.A.(1995). The development of new cancer therapies based on the molecular identification of cancer regression antigens.*Cancer J Sci Am*. 1(2):90-100. Sanz L.; Blanco, B. & Alvarez-Vallina, L. (2004).Antibodies and gene therapy: teaching old

Hormone therapy and ovarian cancer. *JAMA* 302(3): 298-305.

'magic bullets' new tricks. *Trends Immunol*. 25(2):85-91.

cancer immunotherapy. *Curr Opin Immunol*.6(5):741-5.

cancer vaccine. *Int J Nanomedicine*. 5:889-900.

development. *Immunol Rev*. 170:85-100.

cancer tissues. *Glycobiology.* 4(6):873-84.

myeloid leukemia. *Haematologica.* 95: 71-78.

mammary glands. *Cell Mol Life Sci*. 67(6): 985-94.

the human ovary. *Mol Cell Endocrinol.* 202(1-2): 145-53.

*Investigations*, in press.

*Immunol.* 10: 725-734.

*Biol Reprod.* 46: 937-948.

*N Engl J Med.* 334(1): 1-6.

progress*. J Clin Oncol*. 10;26(11):1774-7.

Carbohydrate-associated immunodominant epitope of CA215. *Immunological* 

studies of a sperm acrosome antigen recognized by HS-63 monoclonal antibody.

Pearson, D.L. & Davidson, M. (1996). Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer.

Zheng, J.; Deng, Y.; Hou, C.; Tang, P.; Zhang, S. & Zhang, Y. (2003). Human epithelial cancers secrete immunoglobulin G with unidentified specificity to


Huang, J.; Zhang, L.; Ma, T.; Zhang, P. & Qiu, X. (2008). Expression of immunoglobulin gene

Jemal, A.; Murray, T.; Samuels, A.; Ghafoor, A.; Ward, E. & Thun, M.J. (2003). Cancer

Kang, S.K.; Choi, K.C.; Yang, H.S. & Leung, P.C. (2003) Potential role of gonadotrophin-

Kantoff, P.W.; Higano, C.S.; Shore, N.D.; Berger, E.R.; Small, E.J.; Penson, D.F.; Redfern,

Lee, G.; Chen, K.W.; Sheu, F.S.; Tsang, A.; Chao, K.C. & Ng, H.T. (1992). Studies of a tumor-

Lee, G.; Wu, Q.; Li, C.H.; Ting, H.H. & Chien, C.H. (2006). Recent studies of a new carbohydrate-associated pan cancer marker, CA215. *J Clin Ligand Assay.* 29: 47-51. Lee, G.; Laflamme, E.; Chien, C.H. & Ting, H.H. (2008). Molecular identity of a pan cancer

Lee G. (2009). Cancer cell-expressed immunoglobulins: CA215 as a pan cancer marker and

Lee, G. & Ge, B. (2009). Cancer cell expressions of immunoglobulin heavy chains with unique carbohydrateassociated biomarker. *Cancer Biomark..* 5: 177-188. Lee, G.; Chu, R.A. & Ting, H.H. (2009). Preclinical assessment of anti-cancer drugs by using

Lee, G. & Ge, B. (2010a). Growth inhibition of tumor cells in vitro by using monoclonal

Lee, G. & Ge, B. (2010b). Inhibition of in vitro tumor cell growth by RP215 monoclonal

Lee, G.; Cheung, A.P.; Ge, B.; Zhu, M.; Li, P.P.; Hsu, E. & Huang, T-K. (2010a).

Lee, G.; Ge, B.; Huang, T-K.; Zheng, G.; Duan, J. & Wang, I.H. (2010b). Positive identification

Lee G. & Azadi. (2011). Peptide mapping and glycoanalysis of cancer cell-expressed

Lee, G.; Zhu, M.; Ge, B. & Potzold, S. (2011a). Widespread expression of immunoglobulin superfamily proteins in cancer cells. *Cancer Immunol Immunother.* in press.

antibodies against gonadotropin- releasing hormone receptor. *Cancer Immunol* 

antibody and antibodies raised against its anti-idiotype antibodies. *Cancer Immunol* 

MonoclonalAntiidiotype Antibodies against Carbohydrate-associate Epitope for Anti-Cancer Vaccine Development. *J Vaccin Vaccinat*. 1:106. doi:10.4172/2157-

of CA215 pan cancer biomarker from serum specimens of cancer patients. *Cancer* 

glycoproteins, CA215 recognized by RP215 monoclonal antibody. *The Journal of* 

*Cytochem.* 57(4): 339-49.

*Relat Cancer.* 10: 169-77.

*Engl. J. Med.* 363(5): 411-22.

marker, CA215. *Cancer Biol Ther.*7: 2007-2014.

its diagnostic applications. *Cancer Biomark*. 5(3):137-42.

RP215 monoclonal antibody. *Cancer Biol Ther.* 8: 161-162.

*Immunother.* 35: 19-26.

*Immunother.* 59: 1011-9.

7560.1000106.

*Biomark.* 6(2): 111-7.

*Carbohydrate Chemistry*, in press.

*Immunother.* 59(9): 1347-56.

statistics. *Cancer J Clin.* 53, 1, 5-26.

with classical V-(D)-J rearrangement in mouse testis and epididymis. *J Histochem* 

releasing hormone (GnRH)-I and GnRH-II in the ovary and ovarian cancer. *Endocr* 

C.H.; Ferrari, A.C.; Dreicer, R.; Sims, R.B.; Xu, Y.; Frohlich, M.W. & Schellhammer, P.F. (2010). Sipuleucel-T immunotherapy for castration-resistant prostate cancer. *N.* 

associated antigen, COX-1, recognized by a monoclonal antibody. *Cancer Immunol* 


recombination characteristics in human epithelial cancer cells. *J Biol Chem.* 284(20): 13610-9.

Zou, W. (2006).Regulatory T cells, tumour immunity and immunotherapy. *Nat Rev Immunol*. 6(4):295-307.

Zou, W. (2006).Regulatory T cells, tumour immunity and immunotherapy. *Nat Rev Immunol*.

13610-9.

6(4):295-307.

recombination characteristics in human epithelial cancer cells. *J Biol Chem.* 284(20):

### *Edited by Samir A. Farghaly*

Worldwide, Ovarian carcinoma continues to be responsible for more deaths than all other gynecologic malignancies combined. International leaders in the field address the critical biologic and basic science issues relevant to the disease. The book details the molecular biological aspects of ovarian cancer. It provides molecular biology techniques of understanding this cancer. The techniques are designed to determine tumor genetics, expression, and protein function, and to elucidate the genetic mechanisms by which gene and immunotherapies may be perfected. It provides an analysis of current research into aspects of malignant transformation, growth control, and metastasis. A comprehensive spectrum of topics is covered providing up to date information on scientific discoveries and management considerations.

Ovarian Cancer

Basic Science Perspective

*Edited by Samir A. Farghaly*

ISBN 978-953-307-812-0

ISBN 978-953-51-6640-5

Ovarian Cancer - Basic Science Perspective

Photo by viach80 / iStock