**2. Critical elements for the study of biomarkers**

Prostate cancer imposes an ever increasing healthcare burden to males worldwide due to do their higher life expectancies, the prevalence of high fat diets and sedentary lifestyles, exposure to environmental pollutants, sexual habits, etc (Albini & Sporn, 2007; De Marzo et al., 2004; DeMarzo, Nelson, Isaacs, & Epstein, 2003; Hammarsten & Hogstedt, 2002; Jemal, Siegel, Xu, & Ward, 2010; Pfeffer et al., 2002). Early detection is of vital importance in reducing mortality. However, the early detection of cancer is hampered by the lack of effective analytical methods. This lack in analytical efficiency has often resulted in the erroneous assessment and derivation of biological indicators, or biomarkers, of prostate cancer disease (Balk, Ko, & Bubley, 2003; Thompson et al., 2005). The reasons for the ineffective utility of these biomarkers are multi-fold and include the following, (i) they lack specificity and selectivity to the cancer type of interest, (ii) their reproducible detection is poor, (iii) the sensitivity of available methods, especially as they refer to biological fluids

ionization (LC-MS) for the analysis of proteins derived from prostate cancer clinical specimens (S. D. Garbis et al., 2011; S. D. Garbis et al., 2008). One of the several challenges of the serum and plasma proteomic methods involve the removal of high abundant proteins (i.e. albumin, IgGs, etc.) for the in-depth analysis of the lower abundant proteins where potential biomarkers can be revealed (S. D. Garbis et al., 2011; Hanash et al., 2008). However, their removal typically results in the co-removal of a significant percentage of the lower abundant tissue specific proteins. At the same token, the co-analysis of both high and low abundance proteins and their endogenously occurring cleavage products (**serum degradome**) may confer greater insight on serum biochemistry and cancer biology. The principle themes to be covered in the present book chapter includes the development and application of quantitative bottom-up LC-MS proteomic methods in the analytical characterization of (i) fresh frozen cancerous breast and prostate tissue biopsy specimens to define proteins expressed by the tumour microenvironment, (ii) the discovery of tissue specific serum biomarkers that are secreted in the systemic circulation of clinical utility to the medical practitioner, (iii) the future perspective on the use of targeted and highthroughput LC-MS based analysis approaches for the validation of biomarker discovery findings spanning large scale specimens sets including healthy specimen cohorts], and (iv) the use of lab-on-chip formats to further enhance LC-MS analysis sensitivity, selectivity and specificity at multiple orders of magnitude lower clinical specimen amounts currently used. The analytical attributes intrinsic to these methods allow the generation of a panel of protein biomarkers with multiple molecular features as reflected on measurable analytical variables that include the chromatographic retention times indexes, the concentration level, the amino acid sequence of the proteolytic peptide, uniquely traceable or surrogate, to one particular protein, and its *in vivo* modification status. The uniqueness in molecular features encoded in a given biomaker panel is accomplished by an ensemble of analytical variables that are explicitly dependent on the collective physico-chemical properties of the proteins and their surrogate peptides that constitute this panel. The end-product from the use of such methods is the determination of tumor "signatures" at the serum or plasma level based on rationally derived protein-panels with a high degree of specificity and sensitivity that uniquely identify a particular cancer type, its stage and its applicability to personalized intervention

protocols.

**2. Critical elements for the study of biomarkers** 

Prostate cancer imposes an ever increasing healthcare burden to males worldwide due to do their higher life expectancies, the prevalence of high fat diets and sedentary lifestyles, exposure to environmental pollutants, sexual habits, etc (Albini & Sporn, 2007; De Marzo et al., 2004; DeMarzo, Nelson, Isaacs, & Epstein, 2003; Hammarsten & Hogstedt, 2002; Jemal, Siegel, Xu, & Ward, 2010; Pfeffer et al., 2002). Early detection is of vital importance in reducing mortality. However, the early detection of cancer is hampered by the lack of effective analytical methods. This lack in analytical efficiency has often resulted in the erroneous assessment and derivation of biological indicators, or biomarkers, of prostate cancer disease (Balk, Ko, & Bubley, 2003; Thompson et al., 2005). The reasons for the ineffective utility of these biomarkers are multi-fold and include the following, (i) they lack specificity and selectivity to the cancer type of interest, (ii) their reproducible detection is poor, (iii) the sensitivity of available methods, especially as they refer to biological fluids such as serum and plasma, is poor relative to the natural abundance levels of the tissuespecific secreted or shedded molecular entities of disease, and (iv) the majority of the available analytical protocols measure biomarkers at the DNA and mRNA level, which may not reflect the phenotypic aspects of disease (Adewale et al., 2008; Buchen, 2011; Lin et al., 2005; Rahbar et al., 2011; Sawyers, 2008; Turteltaub et al., 2011). In addition, the availability of more selective prognosis strategies may also help identify patient cohorts, or even single individuals, eligible for adjuvant therapy (i.e., **personalized medicine**). Hence, new biomarkers for asymptomatic prediction, diagnosis, prognosis and response to treatment at the protein level are warranted to improve clinical intervention. It is assumed that one of the critical parameters for the staging of disease and/or treatment intervention is the difference in concentration levels found for the respective biomarkers. This especially becomes true when the complexity of the derived proteomes is decoded in the form of biological pathways and their networks that allow the interrogation of novel candidate protein markers as physiologic targets. Consequent to with this notion, the family of protein markers that will encompass the molecular biology of carcinogenesis will include not only tissue specific proteins but also proteins that reflect systemic changes that predispose a seemingly healthy individual to a longer-term initiation to event of carcinogenesis (Adewale et al., 2008; Buchen, 2011; DeMarzo et al., 2004; Hanahan & Weinberg, 2011; Joyce, 2005; Rahbar et al., 2011; Sawyers, 2008; Turteltaub et al., 2011). In addition to protein markers, and in particular enzyme species, other biological indicators of disease and its predisposition, may include co-factors (i.e., vitamin species) and protein end-products such as 1° and 2° metabolites in the form nucleic acids, amino acids, fatty acids and xenobiotic species in their parent and biotransformed moieties. This integrated monitoring of these biomolecular entities at multiple levels may impart more accuracy and reliability in functionally capturing biochemical pathways of disease. A general example may include an *in vivo* phosphorylation at the catalytic domain of a protein substrate leading to the inhibition of the metabolism of its affiliated ligand. The absence of biotransformed ligand constitutes a proof-positive indicator in the functional annotation of the protein under consideration. A case in point is the polymorphism of the enzyme species 5-methyltetrahydrofolate reductase (5-MTHFR) leading to altered concentration levels of 5-methyl tetrahydrofolate (5MTHF), a metabolically active form of folic acid. The polymorphism of 5- MTHFR has been implicated as a cause to the sub-clinical deficiency of folic acid observed in the older human adult populations, despite their adequate intake of this essential nutrient. The ability, therefore, to quantitatively monitor both the polymorphic 5-MTHFR enzyme and its biotransformation product 5-MTHF can better capture this event (Antoniades et al., 2009; S. D. Garbis, Melse-Boonstra, West, & van Breemen, 2001; Melse-Boonstra et al., 2006; Yetley et al., 2011). The unique analytical versatility and adaptability of MS based methods in detecting diverse biomolecular species imparts a unique opportunity in both customizing and validating key mechanisms of disease and its etiology. From this perspective, modulating these **mechanism based biomarkers** may cause the induction or inhibition of a given carcinogenesis pathway (Kocher & Superti-Furga, 2007). Consequently, such types of biomarkers make for better candidates as treatment targets that can be modulated with medicinal agents and other clinical intervention schemes. Our working hypothesis is based on the assumption that the key difference between the early, asymptomatic disease (low disease burden) versus that of late stage, metastatic disease (high disease burden) is the concentration level found for these mechanistic biomarkers either in their native

The Discovery of Cancer Tissue Specific Proteins in Serum: Case Studies on Prostate Cancer 337

and nano- flow derivatives (Wilm, 2011). As a soft-ionization source, ESI made it possible to introduce the thermally labile biomolecular species to become introduced to the gas phase from its initial liquid phase in its charged state with an intact chemical integrity. Consequently, the ESI source allowed the interfacing of liquid phase sample introduction systems (i.e., liquid chromatography and capillary electrophoresis) with the vacuum-system encased MS platforms (i.e., quadrupolar, ion trapping, time-of-flight, or hybrids thereof, etc.). (Cox & Mann, 2011; Cravatt et al., 2007; Diamandis, 2004; Kocher & Superti-Furga, 2007; Nilsson et al., 2010; Walther & Mann, 2010). The development of novel analytical methods that are based on the combined use of liquid chromatography and tandem mass spectrometry (LC-MS) techniques for the bottom-up or top-down proteome analysis of a wide spectrum of both low and high abundant proteins in clinical tissue and sera dates back to the late nineties with the introduction of the Multi-Dimensional Protein Identification Technology (MudPIT) by John Yates (Fournier, Gilmore, Martin-Brown, & Washburn, 2007). The MuDPIT approaches constituted an alternative to the Two-Dimensional Gel Electrophoresis (2DGe) approaches in their ability to capture and identify a wider spectrum of proteins and at lower abundance levels. These in-depth LC-MS proteomic methods employ the orthogonal use of various high-performance liquid chromatographic (HPLC) chemistries, based on the principles of strong ion exchange (XIC), size-exclusion (SEC), hydrophilic interaction (HILIC), affinity capture (biological and chemical), reverse phase (RPC) and others. These separation techniques allow the isolation, separation and enrichment of proteins and surrogate peptides found in extracts derived form clinical specimens such as tissues, blood plasma and sera. Overall, the LC-MS proteomic methods incorporate the combined use of both nano-electrospray ionization (nESI) and off-line matrix-assisted laser desorption ionization (MALDI) interfaces, to ensure the broadest possible surrogate peptide coverage for a given protein. The bottom-up analysis approach, which is based on the analysis of surrogate tryptic peptides, is well suited for a robust and sensitive protein analysis strategy (taking into consideration individual protein hydrophobicity, charge, or post-translational modification). These complementary methodological approaches provide a more comprehensive and reproducible proteomics assessment of clinical tissue and sera specimens. This has become yet more evident with the use of the latest tandem MS-MS analyzer platforms that include the quadrupole time-offlight QqTOF and Orbitrap based geometries. These MS platforms exhibit high-sensitivity (limit of detection < 10 fmol on-column allowing the use of very low signal accumulation times) and ultra-high resolution (≥ 30,000, translating to 1-3 ppm mass accuracies) at very high signal sampling speeds (≥ 30 Hz). Such performance characteristics allow the detection > 3,000 proteins at > 99% confidence derived from cell culture lysates and spanning over 4 orders of magnitude natural concentration abundance in a single LC-MS analysis run (Cox & Mann, 2011; Liu, Belov, Jaitly, Qian, & Smith, 2007; Mann & Kelleher, 2008; Ong & Mann, 2005). One of several key advantages of the non-gel LC-MS based methods is that they allow the analysis of a much wider spectrum of proteins than that typically covered with the classical gel-based approaches. This spectrum includes proteins that are membrane bound or membrane associated; proteins that exhibit alkaline (pI > 8) and acidic (pI < 5) character; proteins with low (<10 kDa) or high (>200 kDa) molecular weights; and proteins that have undergone *in vivo* modifications (i.e. phosphorylation, acetylation, methylation, glycosylation, etc.) occurring in minor molar ratios (oftentimes < 1:1000) relative to their

Fig. 1. A key parameter to the utility of a given biomarker is its concentration level in the assessment of disease and its treatment. The current detection methods detect biomarker levels that reflect late stage disease wherein treatment options are limited. The more sensitive and selective the analysis method the greater the effectiveness in capturing the disease progression at the initiation stage wherein its reversal is possible with cancer chemoprevention, nutritional/functional food intervention, and other low-toxicity treatment protocols.

or *in vivo* modified form (i.e., post translational modified proteins, biotransformed 1° and 2° metabolites). As such, the ability to capture very low levels of these protein markers and their surrogate end-products provides greater assurance in capturing their disease potential at the progression or even initiation stage whose effects can be reversed with less toxic intervention protocols (see Figure 1). The present discourse will focus on protein-based markers of prostate carcinogenesis.
