**Meet the editor**

Samir A. Farghaly is a Physician / Scientist and national and international expert in Obstetrics and Gynecology at Joan and Sanford I. Weill College of Medicine and the New York Presbyterian Hospital/ Weill Cornell Medical center- Cornell University, New York, NY – USA. He received his M.D. from London University and his PhD degree in molecular biology from London University.

He was affiliated with major London University teaching hospitals, Columbia University College of Physicians and Surgeons/ Columbia University medical center, New York, NY-USA. He received several national and international clinical and research awards. He has been an invited speaker at several national and international conferences on Women's health, Molecular genetic of female cancers, Gynecological cancer and oncologic radical surgical techniques. He is a member of several national and international societies, organizations, foundations of Women health and Cancer. He is an editor, member of editorial boards, editorial advisory boards of Cancer Science & Therapy, Women's Health, Clinical & Experimental Obstetrics and Gynecology, Biomedical Sciences, Open Journal of Obstetrics & Gynecology, Carcinogenesis& Mutagenesis, and current Angiogenesis. He is a reviewer for several medical journals on Obstetrics & Gynecology, Cancer, and Surgery. He has published 78 articles in reputed peer review journals. He has written several books chapters, and is an editor of (2) books on ovarian cancer.

Contents

**Preface IX** 

Chapter 1 **Epidemiology and Etiology of Ovarian Cancer 3**  Ana Fernández Montes, Jesús García Gómez, Miguel Nuñez Viejo, Miguel Alonso Bermejo, Susana Alonso Urrutia and Jesús García Mata

**Serum Biomarker Proteins in Ovarian Cancer 51** 

Christine Sers, Reinhold Schafer and Irina Nazarenko

Chapter 6 **Oncogenic Pathway Signatures and Survival Outcome 105**  Xuan Bich Trinh, Peter A. Van Dam, Luc Y. Dirix, Steven J. van Laere and Wiebren A. A. Tjalma

Kyle Bauckman, Christie Campla and Meera Nanjundan

**and Its Prognostic and Clinical Relevance** 1**39**  Malgorzata Banys, Natalia Krawczyk and Tanja Fehm

Chapter 9 **Sensitive Detection of Epithelial Ovarian Cancer Biomarkers** 

Yuri Markushin and Noureddine Melikechi

**Using Tag-Laser Induced Breakdown Spectroscopy 153** 

Chapter 7 **Dysregulated TGF Signaling in Ovarian Cancer 121** 

Claudine Rancourt, Isabelle Matte, Denis Lane and Alain Piché

Chapter 2 **Inflammation and Ovarian Cancer 17**  Antonio Macciò and Clelia Madeddu

Chapter 3 **Photonic Sensor System for Screening** 

Chapter 4 **The Role of MUC16 Mucin (CA125) in** 

Debra Wawro, Shelby Zimmerman, Robert Magnusson and Peter Koulen

Chapter 5 **Apoptosis Pathways in Ovarian Cancer 85** 

Chapter 8 **New Tumor Biomarkers in Ovarian Cancer** 

**the Pathogenesis of Ovarian Cancer 67** 

### Contents

#### **Preface XI**


X Contents


### Preface

Worldwide, 204,449 new cases of ovarian cancer are diagnosed each year, with an estimated 124,860 disease-related deaths. In the United States, ovarian cancer is the leading cause of gynecologic cancer–related morbidity and mortality due to the difficulty in detecting early-stage disease. Ovarian cancer is the fifth leading cause of death by cancer in the USA, only behind lung, breast, colorectal and pancreatic.

The contributors come from several academic medical institutions in the USA, Europe and Asia. The purpose of this book is to provide a broad background of several aspects of basic sciences related to ovarian cancer. The book provides state-of-the-art information on the molecular genetics and biology of ovarian cancer and new approaches to its diagnosis and management. Better understandings of the molecular events that underlie ovarian cancer development are very much needed.

The epidemiology of ovarian cancer has been extensively studied; it is known that the incidence of ovarian cancer increases with age. Epithelial ovarian cancer is predominantly a disease of perimenopausal and postmenopausal women, with 80% of ovarian cancers occurring after the age of 40. Approximately 10% of all ovarian cancers can be associated with a familial genetic predisposition. The majority of hereditary ovarian cancers can be linked to two currently known syndromes, hereditary breast and ovarian cancer (HBOC) and hereditary nonpolyposis colorectal cancer (HNPCC). Epidemiology and etiology of hereditary and non-hereditary ovarian cancer is reviewed in Chapter 1. The role of inflammation in promoting ovarian tumorgenesis and cancer progression is presented in Chapter 2. Development of a portable sensor system for screening of serum biomarkers proteins in ovarian cancer is discussed in Chapter 3. The implications of MUC16 (CA125) mucin in the pathogenesis of ovarian cancer is reviewed in chapter 4. The cellular oncogenic pathways that have an effect on survival outcome by a bioinformatical approach in ovarian cancer are covered in Chapter 5. The mechanisms of H-REV 107-1/ HRLS3/ PLA2G16 and its related gene TIG/ RIG1/ PARRES suppression in ovarian cancer is reviewed in Chapter 6. Dysregulated Transforming Growth Factor B (TGFB) signaling in ovarian cancer development is discussed in Chapter 7. New biomarkers and hematogenous tumor cell dissemination in ovarian cancer is detailed in Chapter 8. The development of a transgenic mouse model and optimal techniques that yield sensitive detection of proteins is known to play a role in epithelial ovarian cancer is presented in

#### X Preface

Chapter 9. A Homeobox gene as molecular linkage between embryonic development and ovarian cancer is discussed in Chapter 10. Transcriptomic analysis of human ovarian cancer cells and changes mediated by luteinizing hormone receptor activation is discussed in Chapter 11. Known risk factors/conditions that make women susceptible to ovarian cancer and potential biomarkers for early diagnosis is presented in Chapter 12. Ectoenzymes in epithelial ovarian carcinoma as potential biomarkers and therapeutic targets are discussed in Chapter 13. Tumor suppressor gene p53 and its regulators MD M2 and MD M4 in ovarian cancer and their relationship with clinical and pathological presentations are reviewed in Chapter 14. Novel development updates in DNA copy number variations as pertains to ovarian cancer and identifying the most successful markers to be utilized in clinics are discussed in Chapter 15. Endogenous low-level nitric oxide and its action via cyclic GMP/protein kinase G type –I alpha signaling pathway and enhancement of Src tyrosine kinase activity and promotions of cell proliferation/DNA synthesis in ovarian cancer is presented in Chapter 16. Vascular Endothelial Growth Factor (VEGF) as a potent mediator of angiogenesis in epithelial ovarian cancer is reviewed in Chapter 17. Autotaxin as a target for the treatment of drug resistant ovarian cancer is discussed in Chapter 18. Finally, CA125, PK215 and GHR 106 antibodies as potential anti-cancer drugs for the treatment of ovarian cancer is presented in Chapter 19.

This book volume is intended for all clinicians and basic medical scientists caring for women with ovarian cancer, including attending surgeons and physicians, fellows, and residents in the disciplines of gynecologic oncology, medical oncology, and primary care. Also PhD students and post-doctoral fellows in basic medical sciences.

I hope that you find this book very useful, and benefit from the extensive experience of the knowledgeable team of contributors who have authored its contents.

**Samir A. Farghaly, MD, PhD** 

The Joan and Sanford Weill Medical College of Cornell University The New York Presbyterian Hospital Cornell University Medical Center, New York USA

X Preface

Chapter 9. A Homeobox gene as molecular linkage between embryonic development and ovarian cancer is discussed in Chapter 10. Transcriptomic analysis of human ovarian cancer cells and changes mediated by luteinizing hormone receptor activation is discussed in Chapter 11. Known risk factors/conditions that make women susceptible to ovarian cancer and potential biomarkers for early diagnosis is presented in Chapter 12. Ectoenzymes in epithelial ovarian carcinoma as potential biomarkers and therapeutic targets are discussed in Chapter 13. Tumor suppressor gene p53 and its regulators MD M2 and MD M4 in ovarian cancer and their relationship with clinical and pathological presentations are reviewed in Chapter 14. Novel development updates in DNA copy number variations as pertains to ovarian cancer and identifying the most successful markers to be utilized in clinics are discussed in Chapter 15. Endogenous low-level nitric oxide and its action via cyclic GMP/protein kinase G type –I alpha signaling pathway and enhancement of Src tyrosine kinase activity and promotions of cell proliferation/DNA synthesis in ovarian cancer is presented in Chapter 16. Vascular Endothelial Growth Factor (VEGF) as a potent mediator of angiogenesis in epithelial ovarian cancer is reviewed in Chapter 17. Autotaxin as a target for the treatment of drug resistant ovarian cancer is discussed in Chapter 18. Finally, CA125, PK215 and GHR 106 antibodies as potential anti-cancer drugs for the

This book volume is intended for all clinicians and basic medical scientists caring for women with ovarian cancer, including attending surgeons and physicians, fellows, and residents in the disciplines of gynecologic oncology, medical oncology, and primary care. Also PhD students and post-doctoral fellows in basic medical sciences.

I hope that you find this book very useful, and benefit from the extensive experience of

The Joan and Sanford Weill Medical College of Cornell University

**Samir A. Farghaly, MD, PhD** 

USA

The New York Presbyterian Hospital

Cornell University Medical Center, New York

the knowledgeable team of contributors who have authored its contents.

treatment of ovarian cancer is presented in Chapter 19.

**1** 

*Spain* 

**Epidemiology and Etiology of Ovarian Cancer** 

Ovarian cancer is the second most common gynecological malignancy following uterine

There are important differences in their incidence across the world. In Europe in 2008,

In United States, ovarian cancer was diagnosed in 21,880 with 13,850 cancer deaths last year.

Higher incidence rates are observed in North America and European countries exceeding 10 per 100.000 inhabitants. Lower rates are observed in South America (7,7 per 100.000) and

Such geographical variations are due to differences in oral contraceptive use practices, pregnancy history, breast-feeding and other hormonal factors. (Permuth –Wey & Sellers,

The relative risk for developing ovarian cancer is 1.39% (lifetime risk). It affects 12.9 per 100,000 women per year. Incidence rate of ovarian cancer increases with aging, being more

At diagnosis, mean age is 63 years, and 62% of patients have advanced disease. Inherited

Five year overall survival is 93.5% for localized disease, 73.4% for locoregional disease

Genetic studies on ovarian cancer indicate that most of the cases are sporadic while 5 to 10

Three histological subgroups have been described: epithelial tumours, stromal tumours and germ-cell tumours. Ninety percent of cases are epithelial tumours arising from the ovarian surface epithelium or Mullerian derivatives. These tumours are typical in postmenopausal women. The World Health Organization classification defines six more histotypes: serous,

According to their architectural features like glandular or papillary components, carcinomas have been classified into three histological grades, well differentiated, moderately

estimated incidence was 66,734 cases with an estimated mortality of 41,929 women.

corpus cancer and it is the fifth leading cause of cancer death in women.

ovarian cancer presents at younger age. (www.Seer.gov,Ferlay et al 2010)

(regional lymph node involvement) and 27.6% for distant disease.

mucinous, endometrioid, clear cell and squamous cell carcinomas.

Malignant germ cell tumour affects younger women. (De Vita et al,2009) Despite the high incidence, ovarian cancer etiology is still poorly understood.

percent are inherited, generally due to germline mutations.

Both incidence and mortality are declining in USA and Europe.

Southern Asia (7,5 per 100.000). (Parkin et al, 2005)

prevalent in the eighth decade of life.

differentiated, poorly or undifferentiated.

**1. Introduction** 

2009)

Ana Fernández Montes, Jesús García Gómez, Miguel Nuñez Viejo, Miguel Alonso Bermejo, Susana Alonso Urrutia and Jesús García Mata

*Complexo Hospitalario Universitario de Ourense* 

## **Epidemiology and Etiology of Ovarian Cancer**

Ana Fernández Montes, Jesús García Gómez, Miguel Nuñez Viejo, Miguel Alonso Bermejo, Susana Alonso Urrutia and Jesús García Mata *Complexo Hospitalario Universitario de Ourense Spain* 

#### **1. Introduction**

Ovarian cancer is the second most common gynecological malignancy following uterine corpus cancer and it is the fifth leading cause of cancer death in women.

There are important differences in their incidence across the world. In Europe in 2008, estimated incidence was 66,734 cases with an estimated mortality of 41,929 women.

In United States, ovarian cancer was diagnosed in 21,880 with 13,850 cancer deaths last year. Both incidence and mortality are declining in USA and Europe.

Higher incidence rates are observed in North America and European countries exceeding 10 per 100.000 inhabitants. Lower rates are observed in South America (7,7 per 100.000) and Southern Asia (7,5 per 100.000). (Parkin et al, 2005)

Such geographical variations are due to differences in oral contraceptive use practices, pregnancy history, breast-feeding and other hormonal factors. (Permuth –Wey & Sellers, 2009)

The relative risk for developing ovarian cancer is 1.39% (lifetime risk). It affects 12.9 per 100,000 women per year. Incidence rate of ovarian cancer increases with aging, being more prevalent in the eighth decade of life.

At diagnosis, mean age is 63 years, and 62% of patients have advanced disease. Inherited ovarian cancer presents at younger age. (www.Seer.gov,Ferlay et al 2010)

Five year overall survival is 93.5% for localized disease, 73.4% for locoregional disease (regional lymph node involvement) and 27.6% for distant disease.

Genetic studies on ovarian cancer indicate that most of the cases are sporadic while 5 to 10 percent are inherited, generally due to germline mutations.

Three histological subgroups have been described: epithelial tumours, stromal tumours and germ-cell tumours. Ninety percent of cases are epithelial tumours arising from the ovarian surface epithelium or Mullerian derivatives. These tumours are typical in postmenopausal women. The World Health Organization classification defines six more histotypes: serous, mucinous, endometrioid, clear cell and squamous cell carcinomas.

According to their architectural features like glandular or papillary components, carcinomas have been classified into three histological grades, well differentiated, moderately differentiated, poorly or undifferentiated.

Malignant germ cell tumour affects younger women. (De Vita et al,2009)

Despite the high incidence, ovarian cancer etiology is still poorly understood.

Epidemiology and Etiology of Ovarian Cancer 3

Several case-control studies have demonstrated that parous women are estimated to have a 30-60% lower risk for ovarian cancer. Increasing parity seems to reduce risk further. In a recent case-control study between parous and non parous women, higher parity, increased age at first or last birth, and time since last birth were associated with reduced risk of ovarian cancer. This was due to endometrioid and clear cell histology. This link was correlated with reduced risk of epithelial ovarian cancer in another studies. (Titus-Ernstoff et al,2001;Hinkula et al,2006;Whiteman et al,2000) In another prospective study which examined several hormonal factor in 121.700 healthy nurses between 35 to 55 years a statistically significant inverse association was observed between parity and ovarian cancer risk (relative risk [RR] = 0.84; 95% confidence interval [CI] = 0.77-0.91 per pregnancy) ; age at first birth was not associated independently with risk (Hankinson et al,1995). A history of incomplete pregnancy does not influence a woman's risk of epithelial ovarian cancer (Dick et al,2009). Age at last birth also has been strongly associated with a reduced risk of ovarian cancer. Women with a last birth after age 30 to 35 years have a 58% decreased risk for ovarian cancer compared with nulliparous women. One theory to explain this also called the exfoliate theory is based on the suspicion that older women are more likely than younger women to have accumulated transformed surface epithelial ovarian cells, and progestins as suggested before may induced apoptosis of this cells, reducing the account of cells

Breastfeeding suppresses the secretion of pituitary gonadotropins leading to anovulation. Several studies have demonstrated an inverse association between ovarian cancer and lactation especially for non mucinous subtypes. An increasing period of breastfeeding has also been reported to decrease ovarian cancer risk. (Negri et al,2005;Chiafafrino et al,2005;Chiaffarino et al,2007;Jordan et al,2010) Danforth et al demonstrated that breastfeeding 18 or more months was associated with a significant decrease in ovarian cancer risk compared to never breastfeeding (RR=0.66, 95% CI 0.46-0.96). For each month of breastfeeding the relative risk decreased by 2 percent (RR=0.98 per month, 95% CI 0.97-1.00).

Endometriosis and its hormonally regulated lesions may trigger a local inflammatory reaction with activation of macrophages releasing cytokines and growth factors. Some clinical series have identified the coexistence of endometriosis and ovarian cancer

A Canadian cohort study also confirmed this association. They found an anticipation of 5, 5 years between people with endometriosis and ovarian cancer and also an increased risk of

Pelvic inflammatory disease has been linked to an increased risk of ovarian cancer, and more if it occurred at an early age, if the women were nulliparous, infertile or had

Common clinical presentations of polycystic ovarian syndrome (PCOS) include obesity, hirsutism, infertility and menstrual abnormalities. Women with PCOS has an elevated

susceptible of malignant transformation (Whiteman et al,2003).

particularly clear cell histology. (Ness et al,2000;Orezzoli et al,2008)

**2.1.5 Pelvic inflammatory disease and polycystic ovarian syndrome** 

**2.1.3 Breastfeeding** 

(Danforth et al,2007)

**2.1.4 Endometriosis**

ovarian cancer. (Ariset al,2010)

experienced recurrent episodes.(Risch,1995)

The learning objective of this chapter is to review some hormonal, environmental, inherited risk and protective factors associated with ovarian cancer.

#### **2. Risk and protective factors**

#### **2.1 Reproductive and hormonal factors**

Hormones such as estrogen and progesterone are believed to be involved in promoting ovarian carcinogenesis. Several hypotheses have been postulated.

The "incessant ovulation theory" holds that the risk of ovarian cancer is increased through the repetitive ovulatory microtrauma to the ovarian epithelium. The number of ovulatory cycles increases the rate of cellular division associated with the repair of the surface epithelium after each ovulation, thereby increasing the likelihood of spontaneous mutations that might promote carcinogenesis.

Breast-feeding, pregnancy or oral anticonceptive that suppress ovulation would have a protective effect. (Casagrande et al,1979).

The "pituitary gonadotropin hypothesis" indicates that high levels of estrogens and gonadotropins such as luteinizing hormone and follicle-stimulating hormone would over estimulate ovarian epithelium causing increased proliferation and subsequent malignant transformation (Cramer et al,1983).

Another hypothesis has described that androgens may stimulate ovarian cancer formation whereas progestin are protective. (Risch et al,1998)

The "inflammation hypothesis" proposes that factors such as endometriosis, pelvic inflammatory disease and other inflammatory conditions may stimulate cancer formation. (Ness et al,2000)

The last hypothesis, also called "the ovarian stromal hypothesis" states that there may be a failure of the apoptosis of the granulose and theca cells after ovulation which continued producing steroid hormones, thereby stimulating the formation of cancer. (Vo et al,2007; Purdie et al,2003;Permuth-Wey & Sellers,2009)

#### **2.1.1 Early menarche and late menopause**

Due to support the incessant ovulation hypothesis early age at menarche (less than 12 years) and late age at menopause (more than 50 years) should increase the number of ovulatory cycles. Several epidemiological studies have examined this relationship showing a slight increase among women with early age at menarche, Odds Ratio (OR) ranging from 1,1-1,5 and women with late age at menopause with OR ranking from 1,4-4,6 (Permuth-Wey& Sellers,2009).

In contrast to these data another prospective study in healthy nurses found no association between age at menarche and menopause and ovarian cancer risk. (Hankinson et al,1995)

#### **2.1.2 Pregnancy**

Nulliparous women tend to have more ovulatory cycles than multiparous women. It has been shown that with each full ovulation year there is a 6 percent increase in risk of ovarian cancer. This finding is specially relevant in the 20 to 29 year age group in which the risk is highest with a 20 percent increase.(Purdie et al,2003) Pregnancy also causes anovulation and suppresses secretion of pituitary gonadotropins. Maternal age of last birth is also implicated in decreasing the risk of ovarian cancer if the last birth was at age of 35 or greater.

The learning objective of this chapter is to review some hormonal, environmental, inherited

Hormones such as estrogen and progesterone are believed to be involved in promoting

The "incessant ovulation theory" holds that the risk of ovarian cancer is increased through the repetitive ovulatory microtrauma to the ovarian epithelium. The number of ovulatory cycles increases the rate of cellular division associated with the repair of the surface epithelium after each ovulation, thereby increasing the likelihood of spontaneous mutations

Breast-feeding, pregnancy or oral anticonceptive that suppress ovulation would have a

The "pituitary gonadotropin hypothesis" indicates that high levels of estrogens and gonadotropins such as luteinizing hormone and follicle-stimulating hormone would over estimulate ovarian epithelium causing increased proliferation and subsequent malignant

Another hypothesis has described that androgens may stimulate ovarian cancer formation

The "inflammation hypothesis" proposes that factors such as endometriosis, pelvic inflammatory disease and other inflammatory conditions may stimulate cancer formation.

The last hypothesis, also called "the ovarian stromal hypothesis" states that there may be a failure of the apoptosis of the granulose and theca cells after ovulation which continued producing steroid hormones, thereby stimulating the formation of cancer. (Vo et al,2007;

Due to support the incessant ovulation hypothesis early age at menarche (less than 12 years) and late age at menopause (more than 50 years) should increase the number of ovulatory cycles. Several epidemiological studies have examined this relationship showing a slight increase among women with early age at menarche, Odds Ratio (OR) ranging from 1,1-1,5 and women with late age at menopause with OR ranking from 1,4-4,6 (Permuth-Wey&

In contrast to these data another prospective study in healthy nurses found no association between age at menarche and menopause and ovarian cancer risk. (Hankinson et al,1995)

Nulliparous women tend to have more ovulatory cycles than multiparous women. It has been shown that with each full ovulation year there is a 6 percent increase in risk of ovarian cancer. This finding is specially relevant in the 20 to 29 year age group in which the risk is highest with a 20 percent increase.(Purdie et al,2003) Pregnancy also causes anovulation and suppresses secretion of pituitary gonadotropins. Maternal age of last birth is also implicated in

decreasing the risk of ovarian cancer if the last birth was at age of 35 or greater.

risk and protective factors associated with ovarian cancer.

ovarian carcinogenesis. Several hypotheses have been postulated.

**2. Risk and protective factors** 

that might promote carcinogenesis.

transformation (Cramer et al,1983).

(Ness et al,2000)

Sellers,2009).

**2.1.2 Pregnancy** 

protective effect. (Casagrande et al,1979).

whereas progestin are protective. (Risch et al,1998)

Purdie et al,2003;Permuth-Wey & Sellers,2009)

**2.1.1 Early menarche and late menopause** 

**2.1 Reproductive and hormonal factors** 

Several case-control studies have demonstrated that parous women are estimated to have a 30-60% lower risk for ovarian cancer. Increasing parity seems to reduce risk further. In a recent case-control study between parous and non parous women, higher parity, increased age at first or last birth, and time since last birth were associated with reduced risk of ovarian cancer. This was due to endometrioid and clear cell histology. This link was correlated with reduced risk of epithelial ovarian cancer in another studies. (Titus-Ernstoff et al,2001;Hinkula et al,2006;Whiteman et al,2000) In another prospective study which examined several hormonal factor in 121.700 healthy nurses between 35 to 55 years a statistically significant inverse association was observed between parity and ovarian cancer risk (relative risk [RR] = 0.84; 95% confidence interval [CI] = 0.77-0.91 per pregnancy) ; age at first birth was not associated independently with risk (Hankinson et al,1995). A history of incomplete pregnancy does not influence a woman's risk of epithelial ovarian cancer (Dick et al,2009). Age at last birth also has been strongly associated with a reduced risk of ovarian cancer. Women with a last birth after age 30 to 35 years have a 58% decreased risk for ovarian cancer compared with nulliparous women. One theory to explain this also called the exfoliate theory is based on the suspicion that older women are more likely than younger women to have accumulated transformed surface epithelial ovarian cells, and progestins as suggested before may induced apoptosis of this cells, reducing the account of cells susceptible of malignant transformation (Whiteman et al,2003).

#### **2.1.3 Breastfeeding**

Breastfeeding suppresses the secretion of pituitary gonadotropins leading to anovulation. Several studies have demonstrated an inverse association between ovarian cancer and lactation especially for non mucinous subtypes. An increasing period of breastfeeding has also been reported to decrease ovarian cancer risk. (Negri et al,2005;Chiafafrino et al,2005;Chiaffarino et al,2007;Jordan et al,2010) Danforth et al demonstrated that breastfeeding 18 or more months was associated with a significant decrease in ovarian cancer risk compared to never breastfeeding (RR=0.66, 95% CI 0.46-0.96). For each month of breastfeeding the relative risk decreased by 2 percent (RR=0.98 per month, 95% CI 0.97-1.00). (Danforth et al,2007)

#### **2.1.4 Endometriosis**

Endometriosis and its hormonally regulated lesions may trigger a local inflammatory reaction with activation of macrophages releasing cytokines and growth factors. Some clinical series have identified the coexistence of endometriosis and ovarian cancer particularly clear cell histology. (Ness et al,2000;Orezzoli et al,2008)

A Canadian cohort study also confirmed this association. They found an anticipation of 5, 5 years between people with endometriosis and ovarian cancer and also an increased risk of ovarian cancer. (Ariset al,2010)

#### **2.1.5 Pelvic inflammatory disease and polycystic ovarian syndrome**

Pelvic inflammatory disease has been linked to an increased risk of ovarian cancer, and more if it occurred at an early age, if the women were nulliparous, infertile or had experienced recurrent episodes.(Risch,1995)

Common clinical presentations of polycystic ovarian syndrome (PCOS) include obesity, hirsutism, infertility and menstrual abnormalities. Women with PCOS has an elevated

Epidemiology and Etiology of Ovarian Cancer 5

already prevented some 200,000 ovarian cancers and 100,000 ovarian cancer related

This was also reported in both carriers and non-carriers of BRCA1 mutation. Reduced risk of ovarian cancer was associated with the use of oral contraceptives, odds ratio of 0.54 (95% confidence interval (CI): 0.26, 1.13) for carriers and 0.55 (95% CI: 0.41, 0.73) for non-carriers. Tubal ligation and increasing parity were also associated with reduced risk. (McGuire et

Use for more than five years confers a protective factor for up to 10 years after

Tubal ligation has been documented to decrease the risk of development epithelial ovarian cancer, especially endometrioid tumours. This has been postulated as a result of the reducing utero-ovarian flow and altering local hormonal and growth factor levels. This was

Obesity and increasing body mass index (BMI) have been associated with ovarian cancer risk. In a combined study of cohorts BMI was not associated with ovarian cancer risk in postmenopausal women but was positively associated with risk in premenopausal women (Schoute et al,2008). A metanalysis also concluded that being obese (defined as a body mass index over 30) or overweight in the premenopausal years is associated with an increased risk of ovarian cancer, suggesting a possible influence of menopausal status on the

The risk of ovarian cancer may result from changes in synthesis and bioavailability of

Exposure to talc was associated with ovarian cancer risk due to perineal migration in the past. Noneless a metanalysis did not find any association.(Harlow et al,1992;Huncharek et al,2007) Cigarette smoking increases risk of mucinous and borderline ovarian tumours but not other

Hankinson et al studied the relationship between ovarian cancer and several environmental factors. They found in a prospective study which examined 110,454 women that compared with never-smokers, neither current nor past smoking was associated with ovarian cancer risk overall; however, both situations were associated with mucinous tumors (n = 69; rate ratio [RR], past = 2.02 [95% confidence interval (CI), 1.15-3.55]; RR, current = 2.22 [95% CI, 1.16-4.24]). A modest inverse association between caffeine intake and ovarian cancer risk was observed (RR, top vs bottom quintile = 0.80; 95% CI, 0.60-1.07 [P = .03]), which was strongest for women who had never used either oral contraceptives (RR = 0.65; 95% CI, 0.46- 0.92 [P for heterogeneity = .02]) or postmenopausal hormones (RR = 0.57; 95% CI, 0.36-0.91 [P for heterogeneity = .13]). Alcohol was not associated with ovarian cancer risk (Hankinson

Another data from alcohol and caffeine intake and ovarian cancer risk are inconclusive.

La Vecchia et al found in a case- control study between italian women that meat consumption over 7 portions versus less than 4 portions of meat per week (RR:1,6;95%CI:1,21-2,12)

also demonstrated for hysterectomy. (Parazzini et al,1993;Tung et al,2003)

deaths.(Beral et al,2008)

al,2004)

et al,2008).

discontinuation.

**2.1.8 Tubal ligation and hysterectomy** 

endogenous hormonal environment.(Olsen et al,2007)

endogenous sex esteroids seen in obese women. (Vo et al,2007)

histological subtypes. (Zhang et al,2004;Rossing et al,2008).

The impact of diet and physical activity is unknown.

**3. Environmental factors** 

luteinizing hormone to follicle stimulating hormone ratio, hyperandrogenism and abnormal estrogens secretion. Ovarian cancer risk seems higher among women who does not use oral contraceptives. However these data are controversial. Balen et al,2001)

#### **2.1.6 Hormone replacement**

The use of hormonal agents such as infertility treatment and their association with ovarian cancer has been subject of discussion for years. The Women's Health Initiative (WHI) study found an increased risk for ovarian cancer with a hazard ratio of 1,58.(Anderson et al,2003)

A metanalysis of eight cohort and 19 case-control studies found a summary relative risk (RR) of 1.24 (95% confidence interval [CI] 1.15-1.34) from cohort studies and a summary odds ratio [OR] of 1.19 (95%CI 1.02-1.40) from case-control studies for ever Hormone replacement therapy (HRT) use. Association was stronger among ERT (estrogen replacement treatment) user than EPRT (estrogen-progestin replacement treatment) user. Based on data abstracted from six case-control studies, duration of HRT use was not significant. The summary risk estimates for less than 5 years, 6-10, and more than 10 years use were 1.02, 1.13, and 1.21, respectively and 95%CI for each estimate crossed 1.0.(Zhou et al,2008) Another observational study from UK in postmenopausal women with no risk factor for ovarian cancer reported that current users of HRT were significantly more likely to develop and die from ovarian cancer than never users (relative risk 1.20 [95% CI 1.09-1.32; p=0.0002] for incident disease and 1.23 [1.09-1.38; p=0.0006] for death). Ovarian cancer increased with increasing duration of use, but did not differ significantly by type of preparation used, its constituents, or mode of administration. Serous carcinoma was more common associated than mucinous, endometrioid, or clear cell tumours. Past users of HRT were not at an increased risk of ovarian cancer. (Beral t al,2007)

The time association between the duration of use of HRT and the risk of development ovarian cancer seems to be between 5 and 10 years and may last up to 29 years after HRT use has stopped. (Danforth et al,2007) In contrast to these findings a recent Danish study found no overall increased risk of ovarian cancer was showed after any use of gonadotrophins, clomifene , human chorionic gonadotrophin , or gonadotrophin releasing hormone. Furthermore, no associations were found between all four groups of fertility drugs and number of cycles of use, length of follow-up, or parity.(Jense et al,2009)

#### **2.1.7 Oral contraception**

Several studies have demonstrated that oral contraception decreases the risk of ovarian cancer due to reduction in ovulatory cycles.

Women using oral contraceptives had a risk reduction of ovarian cancer of at least 30 to 40 percent with Lower risk with longer time of use. Use oral contraceptive for more than five years was found to have a stronger reduction than use for less than five years.

In a large review of twelve case-controlled studies in the United States , use of oral contraceptives and reduction ovarian cancer risk had an overall odds ratio of 0,67(95%CI 0,37-1,2) in white women.(Whittemore et al, 1992).

This protective effect continued 15 to 20 years after ceased and was independent of any specific type of oral contraceptive formulation. (Bosetti et al,2002;La Vecchia et al,2006).

In another reanalisis of data of 45 epidemiological studies use of oral contraceptives confers long-term protection against ovarian cancer suggesting that oral contraceptives have

luteinizing hormone to follicle stimulating hormone ratio, hyperandrogenism and abnormal estrogens secretion. Ovarian cancer risk seems higher among women who does not use oral

The use of hormonal agents such as infertility treatment and their association with ovarian cancer has been subject of discussion for years. The Women's Health Initiative (WHI) study found an increased risk for ovarian cancer with a hazard ratio of 1,58.(Anderson et

A metanalysis of eight cohort and 19 case-control studies found a summary relative risk (RR) of 1.24 (95% confidence interval [CI] 1.15-1.34) from cohort studies and a summary odds ratio [OR] of 1.19 (95%CI 1.02-1.40) from case-control studies for ever Hormone replacement therapy (HRT) use. Association was stronger among ERT (estrogen replacement treatment) user than EPRT (estrogen-progestin replacement treatment) user. Based on data abstracted from six case-control studies, duration of HRT use was not significant. The summary risk estimates for less than 5 years, 6-10, and more than 10 years use were 1.02, 1.13, and 1.21, respectively and 95%CI for each estimate crossed 1.0.(Zhou et al,2008) Another observational study from UK in postmenopausal women with no risk factor for ovarian cancer reported that current users of HRT were significantly more likely to develop and die from ovarian cancer than never users (relative risk 1.20 [95% CI 1.09-1.32; p=0.0002] for incident disease and 1.23 [1.09-1.38; p=0.0006] for death). Ovarian cancer increased with increasing duration of use, but did not differ significantly by type of preparation used, its constituents, or mode of administration. Serous carcinoma was more common associated than mucinous, endometrioid, or clear cell tumours. Past users of HRT

The time association between the duration of use of HRT and the risk of development ovarian cancer seems to be between 5 and 10 years and may last up to 29 years after HRT use has stopped. (Danforth et al,2007) In contrast to these findings a recent Danish study found no overall increased risk of ovarian cancer was showed after any use of gonadotrophins, clomifene , human chorionic gonadotrophin , or gonadotrophin releasing hormone. Furthermore, no associations were found between all four groups of fertility

Several studies have demonstrated that oral contraception decreases the risk of ovarian

Women using oral contraceptives had a risk reduction of ovarian cancer of at least 30 to 40 percent with Lower risk with longer time of use. Use oral contraceptive for more than five

In a large review of twelve case-controlled studies in the United States , use of oral contraceptives and reduction ovarian cancer risk had an overall odds ratio of 0,67(95%CI

This protective effect continued 15 to 20 years after ceased and was independent of any specific type of oral contraceptive formulation. (Bosetti et al,2002;La Vecchia et al,2006). In another reanalisis of data of 45 epidemiological studies use of oral contraceptives confers long-term protection against ovarian cancer suggesting that oral contraceptives have

drugs and number of cycles of use, length of follow-up, or parity.(Jense et al,2009)

years was found to have a stronger reduction than use for less than five years.

contraceptives. However these data are controversial. Balen et al,2001)

were not at an increased risk of ovarian cancer. (Beral t al,2007)

**2.1.6 Hormone replacement** 

**2.1.7 Oral contraception** 

cancer due to reduction in ovulatory cycles.

0,37-1,2) in white women.(Whittemore et al, 1992).

al,2003)

already prevented some 200,000 ovarian cancers and 100,000 ovarian cancer related deaths.(Beral et al,2008)

This was also reported in both carriers and non-carriers of BRCA1 mutation. Reduced risk of ovarian cancer was associated with the use of oral contraceptives, odds ratio of 0.54 (95% confidence interval (CI): 0.26, 1.13) for carriers and 0.55 (95% CI: 0.41, 0.73) for non-carriers. Tubal ligation and increasing parity were also associated with reduced risk. (McGuire et al,2004)

Use for more than five years confers a protective factor for up to 10 years after discontinuation.

#### **2.1.8 Tubal ligation and hysterectomy**

Tubal ligation has been documented to decrease the risk of development epithelial ovarian cancer, especially endometrioid tumours. This has been postulated as a result of the reducing utero-ovarian flow and altering local hormonal and growth factor levels. This was also demonstrated for hysterectomy. (Parazzini et al,1993;Tung et al,2003)

#### **3. Environmental factors**

Obesity and increasing body mass index (BMI) have been associated with ovarian cancer risk. In a combined study of cohorts BMI was not associated with ovarian cancer risk in postmenopausal women but was positively associated with risk in premenopausal women (Schoute et al,2008). A metanalysis also concluded that being obese (defined as a body mass index over 30) or overweight in the premenopausal years is associated with an increased risk of ovarian cancer, suggesting a possible influence of menopausal status on the endogenous hormonal environment.(Olsen et al,2007)

The risk of ovarian cancer may result from changes in synthesis and bioavailability of endogenous sex esteroids seen in obese women. (Vo et al,2007)

Exposure to talc was associated with ovarian cancer risk due to perineal migration in the past. Noneless a metanalysis did not find any association.(Harlow et al,1992;Huncharek et al,2007)

Cigarette smoking increases risk of mucinous and borderline ovarian tumours but not other histological subtypes. (Zhang et al,2004;Rossing et al,2008).

Hankinson et al studied the relationship between ovarian cancer and several environmental factors. They found in a prospective study which examined 110,454 women that compared with never-smokers, neither current nor past smoking was associated with ovarian cancer risk overall; however, both situations were associated with mucinous tumors (n = 69; rate ratio [RR], past = 2.02 [95% confidence interval (CI), 1.15-3.55]; RR, current = 2.22 [95% CI, 1.16-4.24]). A modest inverse association between caffeine intake and ovarian cancer risk was observed (RR, top vs bottom quintile = 0.80; 95% CI, 0.60-1.07 [P = .03]), which was strongest for women who had never used either oral contraceptives (RR = 0.65; 95% CI, 0.46- 0.92 [P for heterogeneity = .02]) or postmenopausal hormones (RR = 0.57; 95% CI, 0.36-0.91 [P for heterogeneity = .13]). Alcohol was not associated with ovarian cancer risk (Hankinson et al,2008).

Another data from alcohol and caffeine intake and ovarian cancer risk are inconclusive.

The impact of diet and physical activity is unknown.

La Vecchia et al found in a case- control study between italian women that meat consumption over 7 portions versus less than 4 portions of meat per week (RR:1,6;95%CI:1,21-2,12)

Epidemiology and Etiology of Ovarian Cancer 7

disease in absolute numbers (Carlson et al,1994). In relative numbers familiar ovarian cancer confers a 4,6 percent relative risk (95% CI =2,1-8,7) of this disease in the proband's mother

At least 10 percent of ovarian tumours are hereditary and associated with highly penetrant,

The two most common hereditary cancer syndromes associated with ovarian cancer include Hereditary Breast Ovarian Cancer that accounts for approximately 90 percent of the cases and Ovarian Cancer and Hereditary Nonpoliposis Colorectal Cancer (Lynch Syndrome) that

Hereditary ovarian cancer syndromes appears to be genotypically and phenotypically an

Women who carry disease specific alleles for BRCA1 and BRCA2 are at significantly higher risk of epithelial ovarian cancer than general population. The BRCA1 is an oncosuppresor gene located on chromosome 17q21. It was first identified in 1994 and contains small delections or insertions that result in premature stop codons that shorten (truncate) its protein product. This gene participates in chromatin remodelling processes and when mutation occurs cellular controls are unchecked resulting in cellular overgrowing. Alterations in this gene are found in 75 percent of families with hereditary breast and ovarian cancer. On the other hand BRCA2 is a suppressor gene located on chromosome 13q. Its alterations are found in 10 to 20 percent of families with hereditary breast and ovarian

More tan 2600 mutations have been found in those chromosomes. They have been described in 1/800 people in the general (White) and 1/40-50 in ashkenazi Jewish. Mutations in these genes lead to inability to regulate cell death and uncontrolled cell growth leading to cancer.

The average cumulative risks in BRCA1-mutation carriers by age 70 years were 39 percent (18%-54%) for ovarian cancer. The corresponding estimates for BRCA2 were 11 percent

**Type of Cancer BRCA Mutation Carriers (%) General Population (%)** 

Table 1. Estimated risk of developing cancer by age 70 in BRCA mutation carriers with the

Breast (women) 50-85 11

Breast (men) ≤6 Rare

Ovarian (BRCA1) 40-60 1,5

Ovarian (BRCA2) 10-20 1,5

and 1,66 relative risk (95% CI=0,2-5,9) in the proband's sister.(Ziogas et al,2000)

**4.2 Hereditary factors** 

cancer.

(Carroll et al,2008)

general population.

(2.4%-19%). (Antoniou et al,2003)

autosomal dominant genetic predisposition.

accounts for the 10 percent of the cases.(Russo et al,2009)

heterogeneous disease characterized by variable clinical courses.

**4.2.1 Hereditary Breast - Ovarian Cancer (HBOC) syndrome** 

increased ovarian cancer risk and also the consumption of butter versus fat consumption (RR:1,9;95% CI:1,20-3,11). However some confounding factors were present in the study like body weight, parity, socioeconomic status and contraceptive use. The Women's Health Initiative Dietary Modification Randomized Controlled Trial demonstrated decreased ovarian cancer risk in postmenopausal women after four years of a low-fat diet, although this was not statistically significative. Increased daily fiber intake; the use of carotene, vitamin C, vitamin E, and unsaturated fatty acids; and increased physical activity were moderately associated with a decreased risk of ovarian cancer. However, several confounding factors may coexist, and there is limited evidence to support recommending specific lifestyle modifications to reduce ovarian cancer risk. (Prentice et al,2007)

Another prospective study did not find some relation between consumption of antioxidant vitamins from foods or supplements, or intake of fruits and vegetables, and the incidence of ovarian carcinoma (Fairfield et al,2001).

Milk, calcium and lactose intake were associated with reduced risk in another case-control study. The odds ratio for ovarian cancer was 0.46 (95% confidence interval: 0.27, 0.76) among women in the highest quartile of dietary calcium intake versus the lowest (p for trend = 0.0006). The significant dietary association was limited to dairy sources of calcium (p for trend = 0.003), although a nonsignificant inverse gradient in risk was also found in relation to calcium supplement intake (Goodman et al,2002).

Non steroidal anti-inflammatory drugs have been described as a protective factor of ovarian cancer.

Several hypotheses have been postulated like interruption prostaglandin synthesis, apoptosis induction and reduction local inflammatory processes.

Two case–control studies have found a relationship between acetaminophen use and reduction in ovarian cancer risk. (Rosenberg et al, 2000; Cramer et al,1998)

Despite this, the influence of environmental factors in the etiology of ovarian cancer is controversial.

#### **4. Genetic factors**

One of the most significant risk factors of ovarian cancer is a familial history of the disease. Mutations in genes involved in DNA repair (BRCA, MSH-2, MLH-1, PMS 1 and 2) increases risk of cancer in some individuals.

It is estimated that approximately 7 percent of women with ovarian cancer have a positive family history of the disease. (Nguyen et al, 1994)

Genetic factors account for 10 to 15 percent of ovarian cancer cases.

Population-based studies have identified a personal history of breast cancer (particularly at young age) or a family history of either breast or ovarian cancer as one of the strongest risk factors, increasing woman's risk two to six fold. Hereditary ovarian cancer generally occurs in women about 10 years earlier than sporadic disease (Negri et al,2003;Nguyen et al,1994; Parazzini et al 1992; Stratton et al 1998;Sutcliffe et al 2000; Ziogas et al 2000).

We should differentiate genetic factors into two different subtypes as are familiar ovarian cancer and hereditary ovarian cancer.

#### **4.1 Familiar aggregation**

Women with a single family member affected by epithelial ovarian cancer have a 4 to 5 percent risk, while those with two affected relatives have a 7 percent risk for developing the disease in absolute numbers (Carlson et al,1994). In relative numbers familiar ovarian cancer confers a 4,6 percent relative risk (95% CI =2,1-8,7) of this disease in the proband's mother and 1,66 relative risk (95% CI=0,2-5,9) in the proband's sister.(Ziogas et al,2000)

#### **4.2 Hereditary factors**

6 Ovarian Cancer – Basic Science Perspective

increased ovarian cancer risk and also the consumption of butter versus fat consumption (RR:1,9;95% CI:1,20-3,11). However some confounding factors were present in the study like body weight, parity, socioeconomic status and contraceptive use. The Women's Health Initiative Dietary Modification Randomized Controlled Trial demonstrated decreased ovarian cancer risk in postmenopausal women after four years of a low-fat diet, although this was not statistically significative. Increased daily fiber intake; the use of carotene, vitamin C, vitamin E, and unsaturated fatty acids; and increased physical activity were moderately associated with a decreased risk of ovarian cancer. However, several confounding factors may coexist, and there is limited evidence to support recommending specific lifestyle modifications to reduce ovarian

Another prospective study did not find some relation between consumption of antioxidant vitamins from foods or supplements, or intake of fruits and vegetables, and the incidence of

Milk, calcium and lactose intake were associated with reduced risk in another case-control study. The odds ratio for ovarian cancer was 0.46 (95% confidence interval: 0.27, 0.76) among women in the highest quartile of dietary calcium intake versus the lowest (p for trend = 0.0006). The significant dietary association was limited to dairy sources of calcium (p for trend = 0.003), although a nonsignificant inverse gradient in risk was also found in

Non steroidal anti-inflammatory drugs have been described as a protective factor of ovarian

Several hypotheses have been postulated like interruption prostaglandin synthesis,

Two case–control studies have found a relationship between acetaminophen use and

Despite this, the influence of environmental factors in the etiology of ovarian cancer is

One of the most significant risk factors of ovarian cancer is a familial history of the disease. Mutations in genes involved in DNA repair (BRCA, MSH-2, MLH-1, PMS 1 and 2) increases

It is estimated that approximately 7 percent of women with ovarian cancer have a positive

Population-based studies have identified a personal history of breast cancer (particularly at young age) or a family history of either breast or ovarian cancer as one of the strongest risk factors, increasing woman's risk two to six fold. Hereditary ovarian cancer generally occurs in women about 10 years earlier than sporadic disease (Negri et al,2003;Nguyen et al,1994;

We should differentiate genetic factors into two different subtypes as are familiar ovarian

Women with a single family member affected by epithelial ovarian cancer have a 4 to 5 percent risk, while those with two affected relatives have a 7 percent risk for developing the

cancer risk. (Prentice et al,2007)

cancer.

controversial.

**4. Genetic factors** 

risk of cancer in some individuals.

cancer and hereditary ovarian cancer.

**4.1 Familiar aggregation** 

family history of the disease. (Nguyen et al, 1994)

ovarian carcinoma (Fairfield et al,2001).

relation to calcium supplement intake (Goodman et al,2002).

apoptosis induction and reduction local inflammatory processes.

Genetic factors account for 10 to 15 percent of ovarian cancer cases.

Parazzini et al 1992; Stratton et al 1998;Sutcliffe et al 2000; Ziogas et al 2000).

reduction in ovarian cancer risk. (Rosenberg et al, 2000; Cramer et al,1998)

At least 10 percent of ovarian tumours are hereditary and associated with highly penetrant, autosomal dominant genetic predisposition.

The two most common hereditary cancer syndromes associated with ovarian cancer include Hereditary Breast Ovarian Cancer that accounts for approximately 90 percent of the cases and Ovarian Cancer and Hereditary Nonpoliposis Colorectal Cancer (Lynch Syndrome) that accounts for the 10 percent of the cases.(Russo et al,2009)

Hereditary ovarian cancer syndromes appears to be genotypically and phenotypically an heterogeneous disease characterized by variable clinical courses.

#### **4.2.1 Hereditary Breast - Ovarian Cancer (HBOC) syndrome**

Women who carry disease specific alleles for BRCA1 and BRCA2 are at significantly higher risk of epithelial ovarian cancer than general population. The BRCA1 is an oncosuppresor gene located on chromosome 17q21. It was first identified in 1994 and contains small delections or insertions that result in premature stop codons that shorten (truncate) its protein product. This gene participates in chromatin remodelling processes and when mutation occurs cellular controls are unchecked resulting in cellular overgrowing. Alterations in this gene are found in 75 percent of families with hereditary breast and ovarian cancer. On the other hand BRCA2 is a suppressor gene located on chromosome 13q. Its alterations are found in 10 to 20 percent of families with hereditary breast and ovarian cancer.

More tan 2600 mutations have been found in those chromosomes. They have been described in 1/800 people in the general (White) and 1/40-50 in ashkenazi Jewish. Mutations in these genes lead to inability to regulate cell death and uncontrolled cell growth leading to cancer. (Carroll et al,2008)

The average cumulative risks in BRCA1-mutation carriers by age 70 years were 39 percent (18%-54%) for ovarian cancer. The corresponding estimates for BRCA2 were 11 percent (2.4%-19%). (Antoniou et al,2003)


Table 1. Estimated risk of developing cancer by age 70 in BRCA mutation carriers with the general population.

Epidemiology and Etiology of Ovarian Cancer 9

Salpingo-oophorectomy has demonstrated a risk reduction of ovarian cancer over 90 percent and a 50 percent for breast cancer with a mean follow up time of 5 years. (Agnantis et

Rebbeck et al report that bilateral salpingo-oophorectomy was associated with a statistically significant risk reduction of BRCA1/2-associated ovarian or fallopian tube cancer (HR = 0.21; 95% CI = 0.12 to 0.39), which confers an absolute risk reduction near 80 percent of

Another neoplasm has been associated with these mutations. In BRCA 1 carriers primary peritoneal cancer, fallopian tube cancer and prostate cancer have been described. In BRAC 2 carriers there are also an increased risk for melanoma, pancreatic cancer, gastric cancer and

Lynch and co-workers described in 1966 a syndrome that conferred a susceptibility to colorectal cancer with predilection to the right of the splenic flexure but with no excess of adenomatous polyps in younger than expected in adult patients (<45 years) (Lynch et

This is an autosomal dominant syndrome which increases risk of colorectal endometrial, ovarian, gastric, pancreatic , renal and biliary tract cancer and it is a result of mutations in mismatch repair (MMR) genes including at least four chromosomes (2p,3p,7p,2q).These genes form heterodimers which recognize and repair deoxyribonucleic acid mistakes during

Watson et al determined a 6,7 percent lifetime risk for ovarian cancer in proven or probable

Amsterdam criteria were first described in 1990 called Amsterdam I. They were revised in

1. At least 3 relatives with histologically confirmed colorectal cancer, 1 of whom is a

1. 3 or more relatives with an associated cancer (colorectal cancer, or cancer of the

5. Familial adenomatous polyposis (FAP) should be excluded in cases of colorectal

al,2004;Dowdy et al,2004)

al,1967)

transcription.

1999 (Vasen et al, 1999).

**Amsterdam I** 

**Amsterdam II**

carcinoma;

Table 3. Amsterdam I and II.

biliary tract cancer. (Llort et al,2010)

ovarian and fallopian tube neoplasm.(Rebbeck et al,2009)

MSH2 and MSH1 mutation carriers (Watson et al,2008).

first degree relative of the other 2 2. At least 2 successive generations involved. 3. At least 1 of the cancers diagnosed before age 50. 4. Familial adenomatous polyposis should be excluded.

2. 2 or more successive generations affected;

**4.2.2 Hereditary nonpolyposis colorectal cancer (lynch syndrome)** 

Some clinical criteria have been described to identificate Lynch syndrome.

endometrium, small intestine, ureter or renal pelvis);

3. 1 or more relatives diagnosed before the age of 50 years; 4. 1 should be a first-degree relative of the other two;

6. Tumours should be verified by pathologic examination

In contrast to Lynch syndrome there are no defined criteria for this hereditary syndrome. Some criteria have been described and these include several cases of breast cancer diagnosed before the age of 50, one or more relatives with both breast and ovarian cancer, the presence of BRCA1 or BRCA 2 germline mutation. These criteria vary between the different Cooperative Groups.

#### **Independent of Family History**


**Families with two affected breast or ovarian cancer an one of the next characteristics:** 


**Families with 3 or more affected members with breast or ovarian cancer** 

Table 2. Criteria for Mutation in BRCA1-BRCA 2 genes study.

Special mention deserves triple negative breast cancer associated with familiar history of breast or ovarian cancer and younger age at diagnosis. It confers a special risk for BRCA1 mutation although criteria have not yet been defined (Young et al, 2009;Haffty et al, 2006)

Some statistical models have been investigated to estimate the risk of having a germline mutation in BRCA1 and BRCA 2 genes like Boadicea, BRCAPRO, Manchester, IBIS, Myriad II, U Penn.

Ovarian cancers associated with BRCA1-2 mutation are typically high grade serous bilateral carcinomas.

There exist some controversies about the prognosis of these neoplasms. The information derives from retrospective studies, with intrinsic bias due to inadequate sample size and also the lack of adequate controls.

Some case-control and population studies found no difference in survival between general population and mutations carriers (Brunet et al,1997;Johannsson et al,1998) Another studies show a more favourable outcome in mutation carriers.(Rubin et al,1996)

Tan et al. described in a small case-control study that BRCA-positive patients had higher overall (95.5% v 59.1%; P = .002) and complete response rates (81.8% v 43.2%; P = .004) to first line chemotherapy treatment, higher responses to second and third line platinumbased chemotherapy (second line, 91.7% v 40.9% [P = .004]; third line, 100% v 14.3% [P = .005]) and longer progression free interval. A significant improvement in median OS in BRCA-positive patients compared with controls was observed from both time of diagnosis (8.4 v 2.9 years; P < .002) and time of first relapse (5 v 1.6 years; P < .001). BRCA status, stage, and length of first response were independent prognostic factors from time of first relapse. (Tan et al,2008)

Some preventive strategies like bilateral salpingo-oophorectomy or mastectomy have been developed to prevent these neoplasms.

In contrast to Lynch syndrome there are no defined criteria for this hereditary syndrome. Some criteria have been described and these include several cases of breast cancer diagnosed before the age of 50, one or more relatives with both breast and ovarian cancer, the presence of BRCA1 or BRCA 2 germline mutation. These criteria vary between the

**Families with two affected breast or ovarian cancer an one of the next characteristics:** 

Special mention deserves triple negative breast cancer associated with familiar history of breast or ovarian cancer and younger age at diagnosis. It confers a special risk for BRCA1 mutation although criteria have not yet been defined (Young et al, 2009;Haffty et al, 2006) Some statistical models have been investigated to estimate the risk of having a germline mutation in BRCA1 and BRCA 2 genes like Boadicea, BRCAPRO, Manchester, IBIS, Myriad

Ovarian cancers associated with BRCA1-2 mutation are typically high grade serous bilateral

There exist some controversies about the prognosis of these neoplasms. The information derives from retrospective studies, with intrinsic bias due to inadequate sample size and

Some case-control and population studies found no difference in survival between general population and mutations carriers (Brunet et al,1997;Johannsson et al,1998) Another studies

Tan et al. described in a small case-control study that BRCA-positive patients had higher overall (95.5% v 59.1%; P = .002) and complete response rates (81.8% v 43.2%; P = .004) to first line chemotherapy treatment, higher responses to second and third line platinumbased chemotherapy (second line, 91.7% v 40.9% [P = .004]; third line, 100% v 14.3% [P = .005]) and longer progression free interval. A significant improvement in median OS in BRCA-positive patients compared with controls was observed from both time of diagnosis (8.4 v 2.9 years; P < .002) and time of first relapse (5 v 1.6 years; P < .001). BRCA status, stage, and length of first response were independent prognostic factors from time

Some preventive strategies like bilateral salpingo-oophorectomy or mastectomy have been

show a more favourable outcome in mutation carriers.(Rubin et al,1996)

Patient with synchronous or metacronous breast and ovarian cancer

**Families with 3 or more affected members with breast or ovarian cancer** 

different Cooperative Groups.

**Independent of Family History** 

Breast cáncer before 30 years

Both two cases before 50 years

also the lack of adequate controls.

of first relapse. (Tan et al,2008)

developed to prevent these neoplasms.

Male breast cancer

II, U Penn.

carcinomas.

Bilateral breast cancer before 40 years

Ovarian, primary peritoneal or Fallopian tube cancer

Table 2. Criteria for Mutation in BRCA1-BRCA 2 genes study.

One bilateral case and the other before 50 years

Salpingo-oophorectomy has demonstrated a risk reduction of ovarian cancer over 90 percent and a 50 percent for breast cancer with a mean follow up time of 5 years. (Agnantis et al,2004;Dowdy et al,2004)

Rebbeck et al report that bilateral salpingo-oophorectomy was associated with a statistically significant risk reduction of BRCA1/2-associated ovarian or fallopian tube cancer (HR = 0.21; 95% CI = 0.12 to 0.39), which confers an absolute risk reduction near 80 percent of ovarian and fallopian tube neoplasm.(Rebbeck et al,2009)

Another neoplasm has been associated with these mutations. In BRCA 1 carriers primary peritoneal cancer, fallopian tube cancer and prostate cancer have been described. In BRAC 2 carriers there are also an increased risk for melanoma, pancreatic cancer, gastric cancer and biliary tract cancer. (Llort et al,2010)

#### **4.2.2 Hereditary nonpolyposis colorectal cancer (lynch syndrome)**

Lynch and co-workers described in 1966 a syndrome that conferred a susceptibility to colorectal cancer with predilection to the right of the splenic flexure but with no excess of adenomatous polyps in younger than expected in adult patients (<45 years) (Lynch et al,1967)

This is an autosomal dominant syndrome which increases risk of colorectal endometrial, ovarian, gastric, pancreatic , renal and biliary tract cancer and it is a result of mutations in mismatch repair (MMR) genes including at least four chromosomes (2p,3p,7p,2q).These genes form heterodimers which recognize and repair deoxyribonucleic acid mistakes during transcription.

Watson et al determined a 6,7 percent lifetime risk for ovarian cancer in proven or probable MSH2 and MSH1 mutation carriers (Watson et al,2008).

Some clinical criteria have been described to identificate Lynch syndrome.

Amsterdam criteria were first described in 1990 called Amsterdam I. They were revised in 1999 (Vasen et al, 1999).

#### **Amsterdam I**


#### **Amsterdam II**


Table 3. Amsterdam I and II.

Epidemiology and Etiology of Ovarian Cancer 11

Ovarian cancer is the second most common gynecological malignancy and the fifth leading cause of cancer death. Some histological subgroups have been described. Etiology is still poorly understood. Hypotheses relating to incessant ovulation, excessive gonadotropin secretion have been involved as etiological explanations. Based upon epidemiological research there is evidence that certain reproductive factors are associated with ovarian cancer risk. There are some hormonal factors that have special importance. Each childbirth incurs a 15 to 20 percent reduction risk. Breastfeeding also represents a protective factor. Oral contraceptive use for 5 years or longer reduced about half the risk compared to never users. In contrast to these protective factors hormone replacement therapy compared with never users increases the risk and this is associated with longer use. Some inflammatory disorders like pelvic inflammatory disease and endometriosis are associated with an increased risk. The significance of environmental factors like obesity, cigarette smoking, vegetable consumption etc is not yet established .Finally some genetic disorders like BRCA 1 and 2 mutations and Lynch syndrome have been involved as risk factors for this disease. A deeper understanding of these risk factors is important in order to establish preventive

Agnantis NJ, Paraskevaidis E, Roukos D. Preventing breast, ovarian cancer in BRCA

Anderson GL, Judd HL, Kaunitz AM, Barad DH, Beresford SA, Pettinger M et al. Effects of

Antoniou A, Pharoah PD, Narod S, Risch HA, Eyfjord JE, Hopper JL et al. Average risks of

Aris A. Endometriosis-associated ovarian cancer: A ten-year cohort study of women living

Balen A. Polycystic ovary syndrome and cancer.Hum Reprod Update. 2001 Nov-

Beral V, Doll R, Hermon C, Peto R, Reeves G.Lancet .Ovarian cancer and oral

Group on Epidemiological Studies of Ovarian Cancer. 2008 ;371:303-14. Beral V, Bull D, Green J, Reeves G, et al, Million Women Study Collaborators. Ovarian

Bosetti C, Negri E, Trichopoulos D, Franceschi S, Beral V, Tzonou A et al. Long-term effects of oral contraceptives on ovarian cancer risk.Int J Cancer. 2002 ;102(3):262-5.

in the Estrie Region of Quebec, Canada.J Ovarian Res. 2010 19;3:2.

carriers: rational of prophylactic surgery and promises of surveillance.Ann Surg

estrogen plus progestin on gynecologic cancers and associated diagnostic procedures: the Women's Health Initiative randomized trial. *JAMA.*

breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies.Am J

contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23,257 women with ovarian cancer and 87,303 controls. Collaborative

cancer and hormone replacement therapy in the Million Women Study.Lancet. 2007

**5. Conclusion** 

strategies for this fatal disease

Oncol. 2004 Dec;11(12):1030-4.

Hum Genet. 2003;72(5):1117-30.

2003;290(13):1739-48.

Dec;7(6):522-5.

;369(9574):1703-10.

**6. References** 

Then in 1996 Bethesda criteria were redacted to encompass a greater number of patients who may be carriers of a mutation.

They have found to be more sensitive than Amsterdam criteria.

Bethesda Criteria:


Revised Bethesda criteria:


(Rodriguez-Bigas et al,1997;Umar et al,2004)

Ovarian cancer from this syndrome at diagnosis is ten years earlier than in general population and survival is similar as sporadic ovarian cancer. It represents all histopathologic subtypes.(Crijnen et al,2005)

There are no proven strategies that have demonstrated an impact on survival in this setting.


Table 4. Risk Factors Associated with Ovarian Cancer.

#### **5. Conclusion**

10 Ovarian Cancer – Basic Science Perspective

Then in 1996 Bethesda criteria were redacted to encompass a greater number of patients

2. Individuals with two HNPCC-related cancers, including synchronous and metachronous colorectal cancers or associated extracolonic cancers an individual and a

One of the cancers diagnosed at age <45 years, and the adenoma diagnosed at age <40

3. Individuals with colorectal cancer or endometrial cancer diagnosed at age <45 years. 4. Individuals with right-sided colorectal cancer with an undifferentiated pattern

5. Individuals with signet-ring-cell-type colorectal cancer diagnosed at age <45 years.

2. Presence of synchronous, metachronous colorectal, or other HNPCC-associated

3. CRC with the MSI-H histology (presence of tumor-inltrating lymphocytes, Crohn'slike lymphocytic reaction, mucinous/signet-ring differentiation, or medullary growth

4. CRC in 1 or more rst-degree relatives with an HNPCC-related tumor, with 1 of the

5. CRC diagnosed in 2 or more rst- or second-degree relatives with HNPCC- related

Ovarian cancer from this syndrome at diagnosis is ten years earlier than in general population and survival is similar as sporadic ovarian cancer. It represents all

There are no proven strategies that have demonstrated an impact on survival in this setting.

Early menopause

Hysterctomy Late menarche Low fat diet Tubal Ligation

additional pregnacy)

**Decreased Risk**  Breastfeeding for 18 months or more

Multiparity (risk decreases with each

who may be carriers of a mutation.

first-degree relative with: either colorectal cancer

and/or a colorectal adenoma

Revised Bethesda criteria:

tumours, regardless of age.

tumors, regardless of age.

Delayed childbearing Early menarche Endometriosis

than five years

predisposition Genetic syndromes Hight fat diet Late menopause Low parity

pattern), in patient 60 years of age.

(Rodriguez-Bigas et al,1997;Umar et al,2004)

histopathologic subtypes.(Crijnen et al,2005)

**Increased Risk** 

Estrogen replacement therapy for more

Table 4. Risk Factors Associated with Ovarian Cancer.

Family History suggesting genetic

and/or HNPCC-related extracolonic cancer

Bethesda Criteria:

years

They have found to be more sensitive than Amsterdam criteria.

1. Individuals with cancer in families meeting the Amsterdam criteria

(solid/cribiform) on histopathology diagnosed at age <45 years

6. Individuals with adenomas diagnosed at age <40 years

1. CRC diagnosed in individual under age 50 years.

cancers being diagnosed under age 50 years.

Ovarian cancer is the second most common gynecological malignancy and the fifth leading cause of cancer death. Some histological subgroups have been described. Etiology is still poorly understood. Hypotheses relating to incessant ovulation, excessive gonadotropin secretion have been involved as etiological explanations. Based upon epidemiological research there is evidence that certain reproductive factors are associated with ovarian cancer risk. There are some hormonal factors that have special importance. Each childbirth incurs a 15 to 20 percent reduction risk. Breastfeeding also represents a protective factor. Oral contraceptive use for 5 years or longer reduced about half the risk compared to never users. In contrast to these protective factors hormone replacement therapy compared with never users increases the risk and this is associated with longer use. Some inflammatory disorders like pelvic inflammatory disease and endometriosis are associated with an increased risk. The significance of environmental factors like obesity, cigarette smoking, vegetable consumption etc is not yet established .Finally some genetic disorders like BRCA 1 and 2 mutations and Lynch syndrome have been involved as risk factors for this disease. A deeper understanding of these risk factors is important in order to establish preventive strategies for this fatal disease

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**2** 

*Italy* 

**Inflammation and Ovarian Cancer** 

*1Department of Obstetrics and Gynecology, Sirai Hospital, Carbonia, 2Department of Medical Oncology, University of Cagliari, Cagliari* 

Epithelial ovarian cancer (EOC) is a highly lethal gynaecological cancer for which overall prognosis has remained poor over the past few decades. A number of theories have been postulated in an effort to explain the aetiology of EOC. Noteworthy, these theories likely are not mutually exclusive, as they all converge more or less on the role of inflammation in promoting ovarian tumorigenesis and cancer progression. The tumour milieu in which ovarian carcinoma develops has been described as one enriched with a broad spectrum of pro-inflammatory cytokines and chemokines. In particular, several of these cytokines (such as tumour necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6) produced by tumour itself or/and activated immune cells, besides stimulating cancer cell growth, have been shown to influence clinical disease status and prognosis, by reducing responsiveness to chemotherapy and inducing symptoms such as anorexia, altered energy metabolism, anaemia, weight loss, depression and fatigue. Recent data show that cytokine antagonists may have a role to play in the treatment of ovarian cancer. Their action by inhibiting both production and activity of inflammatory cytokines seems to obtain the control of angiogenetic and apoptotic events, the reversal of chemoresistance, the improvement of systemic symptoms and prognosis. In the light of our scientific research and the most recent experimental and clinical advances our chapter will review the most relevant and recent findings on the role of proinflammatory cytokines in the pathogenesis and prognosis of

ovarian cancer and the possible therapeutic implications.

2010a, Clendenen et al., 2011).

**2. Role of inflammation in the etiopathogenesis of ovarian cancer** 

A number of studies suggest that factors related to inflammation of the ovarian surface epithelium (OSE), such as ovulation, endometriosis and pelvic inflammatory diseases, are associated with an increased risk for EOC. In particular, inflammatory mediators and several cytokines produced by activated innate immune cells, such as TNF-α, IL-1β and IL-6 and IL-6, have been shown to promote EOC genesis, growth and progression (Nowak et al.,

The most important hypothesis to arise about EOC carcinogenesis was the ovulation theory, which relates ovarian cancer risk to incessant ovulation. To support this hypothesis, there is growing interest in the etiologic role of inflammation that accompanies each ovulation

**1. Introduction** 

Antonio Macciò1 and Clelia Madeddu2

Ziogas A, Gildea M, Cohen P, Bringman D, Taylor TH, Seminara D et al. Cancer risk estimates for family members of a population-based family registry for breast and ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2000 ;9(1):103-11.

### **Inflammation and Ovarian Cancer**

Antonio Macciò1 and Clelia Madeddu2

*1Department of Obstetrics and Gynecology, Sirai Hospital, Carbonia, 2Department of Medical Oncology, University of Cagliari, Cagliari Italy* 

#### **1. Introduction**

16 Ovarian Cancer – Basic Science Perspective

Ziogas A, Gildea M, Cohen P, Bringman D, Taylor TH, Seminara D et al. Cancer risk

ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2000 ;9(1):103-11.

estimates for family members of a population-based family registry for breast and

Epithelial ovarian cancer (EOC) is a highly lethal gynaecological cancer for which overall prognosis has remained poor over the past few decades. A number of theories have been postulated in an effort to explain the aetiology of EOC. Noteworthy, these theories likely are not mutually exclusive, as they all converge more or less on the role of inflammation in promoting ovarian tumorigenesis and cancer progression. The tumour milieu in which ovarian carcinoma develops has been described as one enriched with a broad spectrum of pro-inflammatory cytokines and chemokines. In particular, several of these cytokines (such as tumour necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6) produced by tumour itself or/and activated immune cells, besides stimulating cancer cell growth, have been shown to influence clinical disease status and prognosis, by reducing responsiveness to chemotherapy and inducing symptoms such as anorexia, altered energy metabolism, anaemia, weight loss, depression and fatigue. Recent data show that cytokine antagonists may have a role to play in the treatment of ovarian cancer. Their action by inhibiting both production and activity of inflammatory cytokines seems to obtain the control of angiogenetic and apoptotic events, the reversal of chemoresistance, the improvement of systemic symptoms and prognosis. In the light of our scientific research and the most recent experimental and clinical advances our chapter will review the most relevant and recent findings on the role of proinflammatory cytokines in the pathogenesis and prognosis of ovarian cancer and the possible therapeutic implications.

#### **2. Role of inflammation in the etiopathogenesis of ovarian cancer**

A number of studies suggest that factors related to inflammation of the ovarian surface epithelium (OSE), such as ovulation, endometriosis and pelvic inflammatory diseases, are associated with an increased risk for EOC. In particular, inflammatory mediators and several cytokines produced by activated innate immune cells, such as TNF-α, IL-1β and IL-6 and IL-6, have been shown to promote EOC genesis, growth and progression (Nowak et al., 2010a, Clendenen et al., 2011).

The most important hypothesis to arise about EOC carcinogenesis was the ovulation theory, which relates ovarian cancer risk to incessant ovulation. To support this hypothesis, there is growing interest in the etiologic role of inflammation that accompanies each ovulation

Inflammation and Ovarian Cancer 19

hypothesis, patients with chronic aspirin, nonsteroidal anti-inflammatory drug, or

Multiple genetic alterations are implicated in ovarian carcinogenesis, but clinical and genetic evidence support two wide categories of EOC carcinogenesis: those of low-grade and highgrade pathways. Gene and protein analyses of tumours of these two different subtypes also suggest different pathogenesis: K-Ras, BRAF, and PTEN mutations are more frequently observed in low-grade tumours, whereas P53 mutation is predominantly present in highgrade tumours, but rarely in other subtypes or low malignant potential (LMP) tumors. Moreover, HER2 and AKT are overexpressed in high-grade carcinomas but rarely in lowgrade and LMP tumours. Overexpression of human leukocyte antigen-G (HLA-G), which may provide a mechanism of immune escape for the tumour, has been noted in a high percentage of high-grade carcinomas but is absent in low-grade or LMP neoplasma (Landen, 2008). Moreover, the new proposed histological classification of EOC in type I slow growing tumours and type II rapidly growing and highly aggressive tumours is accompanied by a specific expression of the inflammatory markers: glucose transporter protein-1 (Glut-1), inducible nitric oxide synthase (iNOS), COX-1, COX-2) and nuclear factor kappa B. In detail, overexpression of COX-1, COX-2, iNOS, and Glut-1 was significantly higher in type II tumours and was associated with a poorer median survival as compared with those with type I tumours. Therefore, the distinct expression of these markers may explain the different biologic behaviour of these 2 tumour types and provide

Although EOC can be subdivided by grade, their histological subtypes also differ. Serous, endometrioid, and mucinous adenocarcinomas have difference in clinical outcomes even if not as dramatic as those between high- and low-grade cancers. However, genomic studies have demonstrated that mucinous adenocarcinomas often harbour mutations and have peculiar gene expression similar to LMP tumours and to benign cystadenomas. Specifically, mutations in K-RAS have been described in borderline, low-grade tumours and mucinous adenocarcinomas, but are very rare in high-grade serous carcinomas. Moreover, endometrioid adenocarcinomas harbour PTEN mutations (similar to endometrioid tumours of the uterine endometrium) more frequently than do serous or mucinous subtype. The discovery of these genetic mutations allowed hypothesizing a model of multistep carcinogenesis of ovarian cancer (Landen, 2008). To become a clinically evident tumour ovarian cancer cells must overcome many protective mechanisms: these include unchecked proliferation, evading apoptosis, angiogenesis, stromal invasion, separation and survival away from the primary tumour, and implantation and growth within new tissues. Within the dual pathway model, it is clear that the tumour cell and its environment must acquire the above characteristics. Although the order in which these occur is likely variable, early alterations in dominant genes may dictate the specic path that is followed, such as K-RAS leading to an LMP tumour and early occurrence of a p53 alteration leading to genetic instability and rapid progression to a high-grade phenotype. Many researchers show a role for inflammation in tumour initiation, promotion, progression and metastatisation. In particular most studies focused their attention to IL-6 signalling which seems to play the main role (Lane et al., 2011). IL-6 is one of the major immunoregulatory cytokines present in the EOC microenvironment. Both ovarian cancer cells and tumor-associated macrophages

acetaminophen use have a reduced risk of EOC (Altinoz & Korkmaz, 2004).

**3.1 Cytokines as cancer growth factors** 

targets for therapy (Ali-Fehmi et al., 2011).

(Landen et al., 2008). Ovarian surface epithelium adjacent to the site of ovulation may be exposed to inflammatory and oxidative status with consequent risk of malignant transformation. Intriguingly, the same ovulatory process together with the repair steps immediately after liberation of the ovum, are characterized by the generation of an enormous amount of cytokines/chemokines and matrix-remodeling enzymes, including prostaglandins, bioactive eicosanoids, plasminogen activators, collagenases, interleukins (ILs), TNF-α and various growth factors (Macciò et al., 1994) as well as by the recruitment of activated immune cells to the wounded epithelial surface, entailing the global activation of the pro-inflammatory network. Recently, it has been hypothesized that high grade serous ovarian cancer, endometrioid and clear cell cancers arise from fallopian tube epithelium and share a common pathogenic mechanism, i.e. iron-induced oxidative stress derived from retrograde menstruation. Fimbriae floating in bloody peritoneal fluid are exposed to the action of catalytic iron and to the genotoxic effect of reactive oxygen species, generated from haemolysis of erythrocytes by pelvic activated macrophages and by the cytokines secreted from themselves. In summary, both incessant ovulation and oxido-reductive fallopian tube epithelial damage hypotheses have provided evidence that inflammatory responses induced under physiological conditions may foster the development of EOC.

A growing body of evidence suggests that, although genetic events in the tumour cells themselves are definitely crucial, host and stromal factors in the tumour microenvironment are equally important. A clinically overt tumour includes not only cancer cells but also matrix components, stromal cells and inammatory cells. In particular, in EOC peritoneal and stromal alterations alongside with their lymphomocytes components and associated cytokines may be permissive for cancer growth and spread. Likewise, cytokine production also by tumour cells themselves can both promote their growth and inhibits apoptosis in an autocrine manner. Therefore, inflammation seems to contribute to every step of carcinogenesis, including tumour initiation, promotion, and progression. On the other hand, tumour cells can produce immunogenic proteins that are recognized as foreign, potentially thus inducing an antineoplastic immune response. Actually, the tumour uses these immunological interactions to evade recognition and destruction by immune cells, i.e. Fas ligand production to induce lymphocyte apoptosis (Mantovani et al., 1999a) and HLA-G secretion to inhibit natural-killer cell activity. Then, although the importance of the host antitumor immune response, as demonstrated by the finding that increased T-cell inltration into the tumour is associated with improved survival (Zhang et al., 2003), the real role of immune system in containing tumour growth remains to be fully dened (Landen, 2008).

#### **3. Proinflammatory cytokines in the progression of EOC**

Components of the inflammatory pathway, including free radicals, cytokines, NF-κB, signal transducer and activator of transcription-3 (STAT-3), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), prostaglandins, and vascular endothelial growth factor (VEGF) have been shown to contribute to the development of various malignancies, including EOC. In particular, COX-2 was found to be highly expressed in non-mucinous ovarian cancers, and its expression was correlated with poor prognostic factors, such as stage, residual disease status and presence of ascites (Ferrandina et al. 2002a). Consistently with this

(Landen et al., 2008). Ovarian surface epithelium adjacent to the site of ovulation may be exposed to inflammatory and oxidative status with consequent risk of malignant transformation. Intriguingly, the same ovulatory process together with the repair steps immediately after liberation of the ovum, are characterized by the generation of an enormous amount of cytokines/chemokines and matrix-remodeling enzymes, including prostaglandins, bioactive eicosanoids, plasminogen activators, collagenases, interleukins (ILs), TNF-α and various growth factors (Macciò et al., 1994) as well as by the recruitment of activated immune cells to the wounded epithelial surface, entailing the global activation of the pro-inflammatory network. Recently, it has been hypothesized that high grade serous ovarian cancer, endometrioid and clear cell cancers arise from fallopian tube epithelium and share a common pathogenic mechanism, i.e. iron-induced oxidative stress derived from retrograde menstruation. Fimbriae floating in bloody peritoneal fluid are exposed to the action of catalytic iron and to the genotoxic effect of reactive oxygen species, generated from haemolysis of erythrocytes by pelvic activated macrophages and by the cytokines secreted from themselves. In summary, both incessant ovulation and oxido-reductive fallopian tube epithelial damage hypotheses have provided evidence that inflammatory responses induced

A growing body of evidence suggests that, although genetic events in the tumour cells themselves are definitely crucial, host and stromal factors in the tumour microenvironment are equally important. A clinically overt tumour includes not only cancer cells but also matrix components, stromal cells and inammatory cells. In particular, in EOC peritoneal and stromal alterations alongside with their lymphomocytes components and associated cytokines may be permissive for cancer growth and spread. Likewise, cytokine production also by tumour cells themselves can both promote their growth and inhibits apoptosis in an autocrine manner. Therefore, inflammation seems to contribute to every step of carcinogenesis, including tumour initiation, promotion, and progression. On the other hand, tumour cells can produce immunogenic proteins that are recognized as foreign, potentially thus inducing an antineoplastic immune response. Actually, the tumour uses these immunological interactions to evade recognition and destruction by immune cells, i.e. Fas ligand production to induce lymphocyte apoptosis (Mantovani et al., 1999a) and HLA-G secretion to inhibit natural-killer cell activity. Then, although the importance of the host antitumor immune response, as demonstrated by the finding that increased T-cell inltration into the tumour is associated with improved survival (Zhang et al., 2003), the real role of immune system in containing tumour growth remains to be fully dened

Components of the inflammatory pathway, including free radicals, cytokines, NF-κB, signal transducer and activator of transcription-3 (STAT-3), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), prostaglandins, and vascular endothelial growth factor (VEGF) have been shown to contribute to the development of various malignancies, including EOC. In particular, COX-2 was found to be highly expressed in non-mucinous ovarian cancers, and its expression was correlated with poor prognostic factors, such as stage, residual disease status and presence of ascites (Ferrandina et al. 2002a). Consistently with this

under physiological conditions may foster the development of EOC.

**3. Proinflammatory cytokines in the progression of EOC** 

(Landen, 2008).

hypothesis, patients with chronic aspirin, nonsteroidal anti-inflammatory drug, or acetaminophen use have a reduced risk of EOC (Altinoz & Korkmaz, 2004).

#### **3.1 Cytokines as cancer growth factors**

Multiple genetic alterations are implicated in ovarian carcinogenesis, but clinical and genetic evidence support two wide categories of EOC carcinogenesis: those of low-grade and highgrade pathways. Gene and protein analyses of tumours of these two different subtypes also suggest different pathogenesis: K-Ras, BRAF, and PTEN mutations are more frequently observed in low-grade tumours, whereas P53 mutation is predominantly present in highgrade tumours, but rarely in other subtypes or low malignant potential (LMP) tumors. Moreover, HER2 and AKT are overexpressed in high-grade carcinomas but rarely in lowgrade and LMP tumours. Overexpression of human leukocyte antigen-G (HLA-G), which may provide a mechanism of immune escape for the tumour, has been noted in a high percentage of high-grade carcinomas but is absent in low-grade or LMP neoplasma (Landen, 2008). Moreover, the new proposed histological classification of EOC in type I slow growing tumours and type II rapidly growing and highly aggressive tumours is accompanied by a specific expression of the inflammatory markers: glucose transporter protein-1 (Glut-1), inducible nitric oxide synthase (iNOS), COX-1, COX-2) and nuclear factor kappa B. In detail, overexpression of COX-1, COX-2, iNOS, and Glut-1 was significantly higher in type II tumours and was associated with a poorer median survival as compared with those with type I tumours. Therefore, the distinct expression of these markers may explain the different biologic behaviour of these 2 tumour types and provide targets for therapy (Ali-Fehmi et al., 2011).

Although EOC can be subdivided by grade, their histological subtypes also differ. Serous, endometrioid, and mucinous adenocarcinomas have difference in clinical outcomes even if not as dramatic as those between high- and low-grade cancers. However, genomic studies have demonstrated that mucinous adenocarcinomas often harbour mutations and have peculiar gene expression similar to LMP tumours and to benign cystadenomas. Specifically, mutations in K-RAS have been described in borderline, low-grade tumours and mucinous adenocarcinomas, but are very rare in high-grade serous carcinomas. Moreover, endometrioid adenocarcinomas harbour PTEN mutations (similar to endometrioid tumours of the uterine endometrium) more frequently than do serous or mucinous subtype. The discovery of these genetic mutations allowed hypothesizing a model of multistep carcinogenesis of ovarian cancer (Landen, 2008). To become a clinically evident tumour ovarian cancer cells must overcome many protective mechanisms: these include unchecked proliferation, evading apoptosis, angiogenesis, stromal invasion, separation and survival away from the primary tumour, and implantation and growth within new tissues. Within the dual pathway model, it is clear that the tumour cell and its environment must acquire the above characteristics. Although the order in which these occur is likely variable, early alterations in dominant genes may dictate the specic path that is followed, such as K-RAS leading to an LMP tumour and early occurrence of a p53 alteration leading to genetic instability and rapid progression to a high-grade phenotype. Many researchers show a role for inflammation in tumour initiation, promotion, progression and metastatisation. In particular most studies focused their attention to IL-6 signalling which seems to play the main role (Lane et al., 2011). IL-6 is one of the major immunoregulatory cytokines present in the EOC microenvironment. Both ovarian cancer cells and tumor-associated macrophages

Inflammation and Ovarian Cancer 21

promoter is inhibited by p53 and the retinoblastoma (Rb) gene product. The overexpression of IL-6 in many malignancies may occur as a result of the loss of one of these negative

The physiological activity of IL-6 is complex, including both pro-inflammatory and antiinflammatory effects in the immune system. In fact, relative to its specific immunomodulating capacity, IL-6 is an activator or an inhibitor of T-cell responses, depending to the length of time of its activities. This combination of pro-inflammatory and anti-inflammatory effects suggests that IL-6 may play a role in regulating the control of immune system activation during the different phases of EOC evolution. IL-6 modulates the transcription of several liver-specific genes during acute inflammatory states, particularly Creactive protein (CRP) and hepcidin. IL-6 can also up-regulates the multidrug resistance 1 (MDR-1) gene through activation of NF-IL6, which, in turn, transactivates the MDR-1 gene through a Y-box motif. IL-6 blood levels are high in numerous infectious, inflammatory, and autoimmune diseases and in cancer in association with increased synthesis of other cytokines and specific immunological challenge. Human diseases that involve prolonged inflammation and in particularly advanced EOC frequently exhibit cachexia with loss of muscle mass and IL-6 seems to be the key mediator of these processes as well. It is noteworthy that high circulating levels of IL-6 have also been linked to insulin resistance, high body mass index and obesity. IL-6 also exerts its effects on the central nervous system, where it regulates glial cell activation and modulate mood as well as induce severe

IL-6 signals primarily by its binding to a specific receptor (IL-6R) which is a member of the Class I cytokine receptor family. Functional Class I receptors contain high-affinity ligandbinding components and signal-transducing components, and are thus multichain receptor complexes that often share the signal-transducing element. Then, IL-6 signals through a protein complex including the membrane-bound non-signalling α-receptor subunit (IL-6R achain gp80 or CD126) and two signal-transducing gp130 subunits (IL6-Rβ-chain gp130 or CD130), this second chain of the receptor resulting in the formation of high-avidity IL-6 binding receptors (Lo, 2011). More precisely: the ligand-binding portion of the IL-6R is an 80-kDa molecule associates directly with IL-6 that exists both in a membrane-bound and a soluble form; the signal transducing component of the IL-6R complex is glycoprotein 130 (gp130), sometimes called IL-6Rb-chain. The gp130 functions as an affinity converter because the resulting affinity of IL-6 for the ternary complex is approximately 10-11 M instead of 10- 9 M for IL-6R. While gp130 is expressed ubiquitously, gp80 is physiologically mainly expressed on hepatocytes and specialized subsets of leukocytes, including neutrophils, monocytes/macrophages, and T and B lymphocytes. However, IL-6 can also signal via a soluble receptor (sIL-6R or gp55 chain) that lacks the transmembrane and cytoplasmatic components. Soluble IL-6R (sIL-6R) can be generated by two mechanisms: 1) Metalloproteinase mediated cleavage ("shedding") of the membrane bound form of the IL-6R and 2) expression of an alternatively spliced IL-6R variant that lacks the transmembrane domain. Neutrophils and macrophages in addition to some cell lines have been shown to produce sIL-6R. Activated sIL-6R binds to membrane-bound gp130 subunits in a process known as trans-signalling. Therefore, unlike other soluble cytokine receptors, which are generally antagonists, sIL-6R is an agonist molecule, promoting IL-6 activity. This ability may explain a possible activation of gp130 despite the lack of gp80, if sIL-6R molecules

regulators of transcription.

depressive symptoms.

produce IL-6, and it is to date known that high serum levels of IL-6 are related with specific immune and metabolic alterations which finally lead to cancer cachexia, the main cause of death of EOC patients. IL-6 has also been demonstrated to be involved in autocrine growth of ovarian cancer cells [19-21] as well as in tumorigenesis and progression of ovarian cancer cells particularly by increasing their capacity to secrete matrix metalloproteinase (MMP)-9 (Rabinovich et al., 2007). Then, IL-6 could stimulate the proliferation of tumour cells either directly and/or by promoting angiogenesis. In fact, IL-6 has an important role, precisely through tumour angiogenesis, in promoting the development of ascites as well as the spread of ovarian cancer thus leading to fast progression and short survival. (Lane, 2011; Lo, 2011). The high levels of IL-6 enhance the immune suppressive status of the tumour microenvironment by inhibiting IL-2 synthesis, T cell activation and proliferation, and promoting lymphocytes apoptosis (Macciò, 1998; Mantovani, 1999a). Furthermore, IL-6 may divert the immune response from Th1 towards a suppressive Th2 response although controversial data have been reported. Another inflammatory cytokine TNF-α that is constitutively expressed in the malignant ovarian surface epithelium generates and sustains a network of other mediators that promote tumour growth and peritoneal spread. Constitutive production of TNF-a is associated with greater release of IL-6 itself as well as other chemokines as: CCL2 and CXCL12, macrophage migration-inhibitory factor (MIF) and VEGF. In turn, these factors may act in an autocrine/paracrine manner to promote colonization of the peritoneum and neovascularization of developing tumour deposits. Moreover, also estrogens by the modulation of proinflammatory cytokines, and in particular IL-6, are involved in regulating the growth and progression of EOC. Estrogens not only enhance cytokines production but also modulate the expression of their receptors. In turn, IL-6 and IL-8 also promote ovarian cancer cells growth through an oestrogen receptor pathway. Therefore, these findings provide a novel mechanism that oestrogens, IL-6 and IL-8 may form a common amplifying signalling cascade to modulate ovarian cancer cells growth and progression (Yang et al., 2009).

From what has been written it can be deduced that IL-6 is the cytokine mainly involved in EOC carcinogenesis and progression. IL-6 is a 26-kDa glycopeptide whose gene is found on chromosome 7, produced by antigen presenting cells (APCs) such as dendritic cells, macrophages and B cells among other cells of the haematopoietic system. It is also produced by a variety of non-haematopoietic cells including keratinocytes, fibroblasts, epithelial cells, and neoplastic cells. IL-6 gene transcription is induced in many different normal tissues in response to stimuli, such as RNA and DNA virus infection, bacterial endotoxin, lipopolysaccharide and other inflammatory cytokines as TNF-, IL-1, and platelet-derived growth factor (PDGF) and the interferons (IFNs). It has been previously named hepatocytestimulating factor, cytotoxic T-cell differentiation factor, B-cell differentiation factor, B-cell stimulatory factor 2, hybridoma/plasmacytoma growth factor, monocyte granulocyte inducer type 2 and thrombopoietin. The many names reflect the pleiotropism of IL-6. IL-6 affects virtually every organ, most notably the immune system and in particular, it is an essential factor for the normal development and function of both T and B lymphocytes and has broad actions on cells of the haematopoietic system. Efficient induction of the IL-6 promoter requires the interaction of several transcription factors, including the CAAT enhancer-binding protein (C/EBP) family members and nuclear factor kB (NF-kB). Nuclear factor for IL-6 (NF-IL6, C/EBP-b) and NF-kB interact with each other to synergistically upregulate the IL-6 promoter, just like NF-IL6 (C/EBP-b) and NF-IL6b (C/EBP-d). The IL-6

produce IL-6, and it is to date known that high serum levels of IL-6 are related with specific immune and metabolic alterations which finally lead to cancer cachexia, the main cause of death of EOC patients. IL-6 has also been demonstrated to be involved in autocrine growth of ovarian cancer cells [19-21] as well as in tumorigenesis and progression of ovarian cancer cells particularly by increasing their capacity to secrete matrix metalloproteinase (MMP)-9 (Rabinovich et al., 2007). Then, IL-6 could stimulate the proliferation of tumour cells either directly and/or by promoting angiogenesis. In fact, IL-6 has an important role, precisely through tumour angiogenesis, in promoting the development of ascites as well as the spread of ovarian cancer thus leading to fast progression and short survival. (Lane, 2011; Lo, 2011). The high levels of IL-6 enhance the immune suppressive status of the tumour microenvironment by inhibiting IL-2 synthesis, T cell activation and proliferation, and promoting lymphocytes apoptosis (Macciò, 1998; Mantovani, 1999a). Furthermore, IL-6 may divert the immune response from Th1 towards a suppressive Th2 response although controversial data have been reported. Another inflammatory cytokine TNF-α that is constitutively expressed in the malignant ovarian surface epithelium generates and sustains a network of other mediators that promote tumour growth and peritoneal spread. Constitutive production of TNF-a is associated with greater release of IL-6 itself as well as other chemokines as: CCL2 and CXCL12, macrophage migration-inhibitory factor (MIF) and VEGF. In turn, these factors may act in an autocrine/paracrine manner to promote colonization of the peritoneum and neovascularization of developing tumour deposits. Moreover, also estrogens by the modulation of proinflammatory cytokines, and in particular IL-6, are involved in regulating the growth and progression of EOC. Estrogens not only enhance cytokines production but also modulate the expression of their receptors. In turn, IL-6 and IL-8 also promote ovarian cancer cells growth through an oestrogen receptor pathway. Therefore, these findings provide a novel mechanism that oestrogens, IL-6 and IL-8 may form a common amplifying signalling cascade to modulate

ovarian cancer cells growth and progression (Yang et al., 2009).

From what has been written it can be deduced that IL-6 is the cytokine mainly involved in EOC carcinogenesis and progression. IL-6 is a 26-kDa glycopeptide whose gene is found on chromosome 7, produced by antigen presenting cells (APCs) such as dendritic cells, macrophages and B cells among other cells of the haematopoietic system. It is also produced by a variety of non-haematopoietic cells including keratinocytes, fibroblasts, epithelial cells, and neoplastic cells. IL-6 gene transcription is induced in many different normal tissues in response to stimuli, such as RNA and DNA virus infection, bacterial endotoxin, lipopolysaccharide and other inflammatory cytokines as TNF-, IL-1, and platelet-derived growth factor (PDGF) and the interferons (IFNs). It has been previously named hepatocytestimulating factor, cytotoxic T-cell differentiation factor, B-cell differentiation factor, B-cell stimulatory factor 2, hybridoma/plasmacytoma growth factor, monocyte granulocyte inducer type 2 and thrombopoietin. The many names reflect the pleiotropism of IL-6. IL-6 affects virtually every organ, most notably the immune system and in particular, it is an essential factor for the normal development and function of both T and B lymphocytes and has broad actions on cells of the haematopoietic system. Efficient induction of the IL-6 promoter requires the interaction of several transcription factors, including the CAAT enhancer-binding protein (C/EBP) family members and nuclear factor kB (NF-kB). Nuclear factor for IL-6 (NF-IL6, C/EBP-b) and NF-kB interact with each other to synergistically upregulate the IL-6 promoter, just like NF-IL6 (C/EBP-b) and NF-IL6b (C/EBP-d). The IL-6 promoter is inhibited by p53 and the retinoblastoma (Rb) gene product. The overexpression of IL-6 in many malignancies may occur as a result of the loss of one of these negative regulators of transcription.

The physiological activity of IL-6 is complex, including both pro-inflammatory and antiinflammatory effects in the immune system. In fact, relative to its specific immunomodulating capacity, IL-6 is an activator or an inhibitor of T-cell responses, depending to the length of time of its activities. This combination of pro-inflammatory and anti-inflammatory effects suggests that IL-6 may play a role in regulating the control of immune system activation during the different phases of EOC evolution. IL-6 modulates the transcription of several liver-specific genes during acute inflammatory states, particularly Creactive protein (CRP) and hepcidin. IL-6 can also up-regulates the multidrug resistance 1 (MDR-1) gene through activation of NF-IL6, which, in turn, transactivates the MDR-1 gene through a Y-box motif. IL-6 blood levels are high in numerous infectious, inflammatory, and autoimmune diseases and in cancer in association with increased synthesis of other cytokines and specific immunological challenge. Human diseases that involve prolonged inflammation and in particularly advanced EOC frequently exhibit cachexia with loss of muscle mass and IL-6 seems to be the key mediator of these processes as well. It is noteworthy that high circulating levels of IL-6 have also been linked to insulin resistance, high body mass index and obesity. IL-6 also exerts its effects on the central nervous system, where it regulates glial cell activation and modulate mood as well as induce severe depressive symptoms.

IL-6 signals primarily by its binding to a specific receptor (IL-6R) which is a member of the Class I cytokine receptor family. Functional Class I receptors contain high-affinity ligandbinding components and signal-transducing components, and are thus multichain receptor complexes that often share the signal-transducing element. Then, IL-6 signals through a protein complex including the membrane-bound non-signalling α-receptor subunit (IL-6R achain gp80 or CD126) and two signal-transducing gp130 subunits (IL6-Rβ-chain gp130 or CD130), this second chain of the receptor resulting in the formation of high-avidity IL-6 binding receptors (Lo, 2011). More precisely: the ligand-binding portion of the IL-6R is an 80-kDa molecule associates directly with IL-6 that exists both in a membrane-bound and a soluble form; the signal transducing component of the IL-6R complex is glycoprotein 130 (gp130), sometimes called IL-6Rb-chain. The gp130 functions as an affinity converter because the resulting affinity of IL-6 for the ternary complex is approximately 10-11 M instead of 10- 9 M for IL-6R. While gp130 is expressed ubiquitously, gp80 is physiologically mainly expressed on hepatocytes and specialized subsets of leukocytes, including neutrophils, monocytes/macrophages, and T and B lymphocytes. However, IL-6 can also signal via a soluble receptor (sIL-6R or gp55 chain) that lacks the transmembrane and cytoplasmatic components. Soluble IL-6R (sIL-6R) can be generated by two mechanisms: 1) Metalloproteinase mediated cleavage ("shedding") of the membrane bound form of the IL-6R and 2) expression of an alternatively spliced IL-6R variant that lacks the transmembrane domain. Neutrophils and macrophages in addition to some cell lines have been shown to produce sIL-6R. Activated sIL-6R binds to membrane-bound gp130 subunits in a process known as trans-signalling. Therefore, unlike other soluble cytokine receptors, which are generally antagonists, sIL-6R is an agonist molecule, promoting IL-6 activity. This ability may explain a possible activation of gp130 despite the lack of gp80, if sIL-6R molecules

Inflammation and Ovarian Cancer 23

probably the target proteins of STAT3 (Zhang, 2010). Increased levels of pSTAT3 are correlated with increased expression of HER-2/neu, EGFR and proliferation but not apoptosis markers. Unlike other molecules involved in oncogenesis, no genetic mutations or amplifications have been identified for STAT3, suggesting that persistent STAT3 activity is caused mostly by the dysregulation of upstream molecules, such as receptors with intrinsic tyrosine kinase activity (e.g., EGFR or HER-2/neu) and, in particular, endogenous or exogenous IL-6. Moreover, the regulation and functions of Stat proteins are highly dependent on the cell type, the activating stimulus and the cellular context, especially the activity of other signalling pathways and transcription factors that interact with the Stat proteins. Consequently, depending on the cellular context, STAT3 may mediate conflicting responses in terms of cell proliferation, differentiation or apoptosis. For example, the concurrent coexpression of dominant-negative STAT3 and the oncoprotein Ras does not arrest Ras-induced transformation, suggesting that STAT3 signalling is only one of several pathways required for cell transformation induced by this oncogenic tyrosine kinase. In addition, STAT3 demonstrates a histotype-specific pattern of expression. High levels of expression were observed more commonly in those histotypes with aggressive biologic behaviour (undifferentiated, clear cell, and serous carcinomas) than in those histotypes with

Results from a recent study (Saydmohammed et al., 2010) confirm that IL-6 secretion increases during malignant progression of ovarian epithelial cells and found that IL-6 expression levels are not always correlated with the expression or subcellular location of pSTAT3 in ovarian carcinoma, supporting the finding that IL-6 is involved in other signalling pathways, independent of STAT3. Moreover, given the observations that cancer cells can constitutively express STAT3 in the absence of stimulation by any known ligand and that expression of STAT3 is higher in ovarian carcinoma than in normal ovarian tissue, it is possible to speculate that the constitutive activation of STAT3 in ovarian cancer cells could be because of aberrant EGFR signalling. In agreement with this possibility, it has been observed a significant correlation between high levels of pSTAT3 expression and the overexpression of EGFR and HER-2/neu in EOC (Bast et al., 1993). Alternatively, the constitutive activation of STAT3 in EOC may be caused by the elevation of Src and focal adhesion kinase levels (Rosen et al., 2006) More recently, a significant activation of both STAT-3 and its upstream activator JAK-2, has been demonstrate in high-grade ovarian carcinomas compared with normal ovaries and benign tumours. The association between STAT3 activation and migratory phenotype of ovarian cancer cells was investigated by EGFinduced epithelial-mesenchymal transition (EMT) in ovarian cancer cell lines. Ligand activation of EGFR induced a fibroblast-like morphology and migratory phenotype, consistent with the upregulation of mesenchyme-associated N-cadherin, vimentin and nuclear translocation of beta-catenin. This occurred concomitantly with activation of the downstream JAK2/STAT3 pathway. The cell lines expressed the IL-6R and treatment with EGF resulted in enhanced IL-6 expression and release in the serum-free medium. Exogenous addition of IL-6 stimulated STAT3 activation and enhanced migration. Blocking antibodies against IL-6R inhibited both IL-6 production and EGF- and IL-6-induced migration. Specific inhibition of STAT3 activation by a JAK2-specific inhibitor blocked STAT3 phosphorylation, cell motility, induction of N-cadherin and vimentin expression and IL6 production. These data suggest that the activated status of STAT3 in high-grade EOC may occur directly through activation of

less aggressive behaviour (mucinous and endometrioid carcinomas).

circulate in great quantity, as demonstrated in certain pathological states. Accordingly, it was observed that cells lacking IL-6R expression are responsive to IL-6 stimulation especially during inflammatory conditions. As demonstrated in EOC, this alternate pathway serves as the major signalling in inducing endothelial hyperpermeability and increasing transendothelial migration of cancer cells, thus contributing to cancer progression. Moreover, elevated levels of sIL-6R in malignant ascites from ovarian cancer patients are associated with poor prognosis (Lo, Cancer Res 2011; 7: 424-34). The increase of IL6R expression as well as of the soluble spliced variant of IL6R in malignant ovarian tumours are regulated by cancerassociated inflammation (Rath et al., 2010). Therefore, in advanced EOC IL6R is overexpressed mainly because of increases in a sIL6R variant, which can influence its evolution and prognosis. In addition to sIL-6R, soluble gp130 (sgp130) also exists in human serum and acts as an antagonist of the IL-6/sIL-6R complex.

Once IL-6 binds its receptor and gp130 homodimerization occurs, a signalling cascade is triggered. X-Ray crystallography has shown that two heterotrimers of IL-6, IL-6R and gp130 associate to form a hexameric complex. Through formation of this complex, members of the cytoplasmic Janus kinase (Jak) family of tyrosine kinases bind to gp130 inducing phosphorylation of downstream targets. The Janus kinases activation is followed by the recruitment of signal transducers and activators of transcriptions (STATs). One phosphorylated, STATs translocate to the nucleus where they promote gene transcription. IL-6R stimulation also recruits other signal transduction molecules, including SH2 domaincontaining tyrosine phosphatase (SHP2) and suppressor of cytokine signalling (SOCS). Both SHP2 and SOCS may subsequently down-regulate IL-6 signalling. Jak1 is thought to be the most relevant for IL-6 signalling although Jak2 and Tyk2 also transduce some of the IL-6 signals. In some instances, IL-6 acts with other factors, such as heparin-binding epithelial growth factor and hepatocyte growth factor in controlling proliferation and function of various cell types. Blocking IL-6 by specific anti-receptor drugs may thus be of benefit in many pathological situations.

The best-described substrate for Jaks in IL-6 signaling is the STAT 3, a transcription factor that in its inactive form remains in the cytoplasm but after phosphorylation forms homodimers that are actively being transported to the nucleus to induce gene transcription. Increasing evidence indicates that tumour cells express constitutively activated Stat proteins, particularly STAT3, independent of dysregulation of upstream molecules, disabled inhibitory mechanisms or identifiable ligand stimulation. Stat3 overexpression also may promote cell proliferation and transformation into a tumour phenotype. Overexpression and overactivation of Stat3 is found in EOC tissue and the constitutive activation of Stat3 signalling pathway may play an important role in the invasion and prognosis. The expressions of Stat3 and phosphorylated (p)-Stat3 in EOC are significantly higher than in normal ovarian epithelial tissues or benign ovarian tumour and the expression of Stat3 protein is highly correlated with the expression of p-Stat3 protein. The nuclear localization of p-Stat3 predicts a poor prognosis: in fact, pSTAT3 expression is significantly correlated with disease stage, degree of differentiation and lymph node metastasis (Min & Wei-hong, 2009). Recent studies suggest that STAT3 is a key factor for EOC chemoresistance, showing that STAT3 decoy oligodeoxynucleotides (ODN), its specific antagonist, inhibited cancer cell invasive power and enhanced sensitivity to paclitaxel. The mechanism involves the inhibition of EMMPRIN, P-gp, and pAkt by STAT3 decoy ODN. These three proteins are

circulate in great quantity, as demonstrated in certain pathological states. Accordingly, it was observed that cells lacking IL-6R expression are responsive to IL-6 stimulation especially during inflammatory conditions. As demonstrated in EOC, this alternate pathway serves as the major signalling in inducing endothelial hyperpermeability and increasing transendothelial migration of cancer cells, thus contributing to cancer progression. Moreover, elevated levels of sIL-6R in malignant ascites from ovarian cancer patients are associated with poor prognosis (Lo, Cancer Res 2011; 7: 424-34). The increase of IL6R expression as well as of the soluble spliced variant of IL6R in malignant ovarian tumours are regulated by cancerassociated inflammation (Rath et al., 2010). Therefore, in advanced EOC IL6R is overexpressed mainly because of increases in a sIL6R variant, which can influence its evolution and prognosis. In addition to sIL-6R, soluble gp130 (sgp130) also exists in human serum and acts as

Once IL-6 binds its receptor and gp130 homodimerization occurs, a signalling cascade is triggered. X-Ray crystallography has shown that two heterotrimers of IL-6, IL-6R and gp130 associate to form a hexameric complex. Through formation of this complex, members of the cytoplasmic Janus kinase (Jak) family of tyrosine kinases bind to gp130 inducing phosphorylation of downstream targets. The Janus kinases activation is followed by the recruitment of signal transducers and activators of transcriptions (STATs). One phosphorylated, STATs translocate to the nucleus where they promote gene transcription. IL-6R stimulation also recruits other signal transduction molecules, including SH2 domaincontaining tyrosine phosphatase (SHP2) and suppressor of cytokine signalling (SOCS). Both SHP2 and SOCS may subsequently down-regulate IL-6 signalling. Jak1 is thought to be the most relevant for IL-6 signalling although Jak2 and Tyk2 also transduce some of the IL-6 signals. In some instances, IL-6 acts with other factors, such as heparin-binding epithelial growth factor and hepatocyte growth factor in controlling proliferation and function of various cell types. Blocking IL-6 by specific anti-receptor drugs may thus be of benefit in

The best-described substrate for Jaks in IL-6 signaling is the STAT 3, a transcription factor that in its inactive form remains in the cytoplasm but after phosphorylation forms homodimers that are actively being transported to the nucleus to induce gene transcription. Increasing evidence indicates that tumour cells express constitutively activated Stat proteins, particularly STAT3, independent of dysregulation of upstream molecules, disabled inhibitory mechanisms or identifiable ligand stimulation. Stat3 overexpression also may promote cell proliferation and transformation into a tumour phenotype. Overexpression and overactivation of Stat3 is found in EOC tissue and the constitutive activation of Stat3 signalling pathway may play an important role in the invasion and prognosis. The expressions of Stat3 and phosphorylated (p)-Stat3 in EOC are significantly higher than in normal ovarian epithelial tissues or benign ovarian tumour and the expression of Stat3 protein is highly correlated with the expression of p-Stat3 protein. The nuclear localization of p-Stat3 predicts a poor prognosis: in fact, pSTAT3 expression is significantly correlated with disease stage, degree of differentiation and lymph node metastasis (Min & Wei-hong, 2009). Recent studies suggest that STAT3 is a key factor for EOC chemoresistance, showing that STAT3 decoy oligodeoxynucleotides (ODN), its specific antagonist, inhibited cancer cell invasive power and enhanced sensitivity to paclitaxel. The mechanism involves the inhibition of EMMPRIN, P-gp, and pAkt by STAT3 decoy ODN. These three proteins are

an antagonist of the IL-6/sIL-6R complex.

many pathological situations.

probably the target proteins of STAT3 (Zhang, 2010). Increased levels of pSTAT3 are correlated with increased expression of HER-2/neu, EGFR and proliferation but not apoptosis markers. Unlike other molecules involved in oncogenesis, no genetic mutations or amplifications have been identified for STAT3, suggesting that persistent STAT3 activity is caused mostly by the dysregulation of upstream molecules, such as receptors with intrinsic tyrosine kinase activity (e.g., EGFR or HER-2/neu) and, in particular, endogenous or exogenous IL-6. Moreover, the regulation and functions of Stat proteins are highly dependent on the cell type, the activating stimulus and the cellular context, especially the activity of other signalling pathways and transcription factors that interact with the Stat proteins. Consequently, depending on the cellular context, STAT3 may mediate conflicting responses in terms of cell proliferation, differentiation or apoptosis. For example, the concurrent coexpression of dominant-negative STAT3 and the oncoprotein Ras does not arrest Ras-induced transformation, suggesting that STAT3 signalling is only one of several pathways required for cell transformation induced by this oncogenic tyrosine kinase. In addition, STAT3 demonstrates a histotype-specific pattern of expression. High levels of expression were observed more commonly in those histotypes with aggressive biologic behaviour (undifferentiated, clear cell, and serous carcinomas) than in those histotypes with less aggressive behaviour (mucinous and endometrioid carcinomas).

Results from a recent study (Saydmohammed et al., 2010) confirm that IL-6 secretion increases during malignant progression of ovarian epithelial cells and found that IL-6 expression levels are not always correlated with the expression or subcellular location of pSTAT3 in ovarian carcinoma, supporting the finding that IL-6 is involved in other signalling pathways, independent of STAT3. Moreover, given the observations that cancer cells can constitutively express STAT3 in the absence of stimulation by any known ligand and that expression of STAT3 is higher in ovarian carcinoma than in normal ovarian tissue, it is possible to speculate that the constitutive activation of STAT3 in ovarian cancer cells could be because of aberrant EGFR signalling. In agreement with this possibility, it has been observed a significant correlation between high levels of pSTAT3 expression and the overexpression of EGFR and HER-2/neu in EOC (Bast et al., 1993). Alternatively, the constitutive activation of STAT3 in EOC may be caused by the elevation of Src and focal adhesion kinase levels (Rosen et al., 2006) More recently, a significant activation of both STAT-3 and its upstream activator JAK-2, has been demonstrate in high-grade ovarian carcinomas compared with normal ovaries and benign tumours. The association between STAT3 activation and migratory phenotype of ovarian cancer cells was investigated by EGFinduced epithelial-mesenchymal transition (EMT) in ovarian cancer cell lines. Ligand activation of EGFR induced a fibroblast-like morphology and migratory phenotype, consistent with the upregulation of mesenchyme-associated N-cadherin, vimentin and nuclear translocation of beta-catenin. This occurred concomitantly with activation of the downstream JAK2/STAT3 pathway. The cell lines expressed the IL-6R and treatment with EGF resulted in enhanced IL-6 expression and release in the serum-free medium. Exogenous addition of IL-6 stimulated STAT3 activation and enhanced migration. Blocking antibodies against IL-6R inhibited both IL-6 production and EGF- and IL-6-induced migration. Specific inhibition of STAT3 activation by a JAK2-specific inhibitor blocked STAT3 phosphorylation, cell motility, induction of N-cadherin and vimentin expression and IL6 production. These data suggest that the activated status of STAT3 in high-grade EOC may occur directly through activation of

Inflammation and Ovarian Cancer 25

induce in vitro a number of phenomena similar to those that follow antigenic activity in vivo. The secretion of macrophagic cytokines, the production of IL-2 by CD4+ lymphocytes and the RIL-2 expression on lymphocyte membrane are the defining moments of these events. For these reasons, the entity of the lymphocyte blastic response depends on the quantity of cytokines produced, the number of RIL-2 expressed and the physiologic interaction of IL-2 with its receptor. Lymphocytes inability both to produce adequate quantities of IL-2 and to express physiological amount of RIL-2 seems to be the crucial feature of this specific lymphocyte functional deficit in EOC patients. In our studies, patients peripheral blood mononuclear cells (PBMC) proliferative response to PHA, anti-CD3 mAb and human recombinant IL-2 (HurIL-2) alone was significantly lower in comparison to controls and it was not modified by the addition of human recombinant IL-2 (HurIL-2) to the culture media. Furthermore, also the expression of CD25 and CD122 subunits of membrane-bound IL-2R on patients' PBMC after stimulation with PHA or CD3mAb was lower than that seen in controls (Macciò et al., 1998). A very important finding of our researches highlights that this impairment of T cells response was associated with increased circulating levels of proinflammatory cytokines (IL-1α, IL-1β, IL-6, TNF-α) and other

mediators of inflammation such as fibrinogen, CRP and sIL-2R (Figure 1).

Fig. 1. Aspecific activation of immune system during the evolution of the ovarian cancer leads to immunodepression associated to high serum levels of inflammatory cytokines and acute phase proteins. Abbreviations: ROS, Reactive Oxygen Species, RIL-2, IL-2 receptor,

In particular, it is extremely interesting that IL-6 and CRP have been shown to be able to suppress T cell responses and several studies suggested that they might interfere with the immunological mechanisms underlying the antitumor activity of IL-2. Moreover, it is known that CRP, typically induced by IL-6, is involved in the binding of complement to cytotoxic CD3+ cells and plays a key role in the inhibition of cytotoxic activity of NK cells. Then, IL-6 can be an activator or an inhibitor of T-cell responses, depending its effects by the time and duration of its activity. This interaction of pro-inflammatory and antiinflammatory activities suggests that IL-6 may play a role in regulating the control of immune system activation during the different phases of in EOC progression. A widely accepted model of tumour and immune cell interaction, termed immunoediting, describes an initial restriction of tumour cell growth, but maintains that the immune system ultimately selects for tumour cells with reduced immunogenicity that subsequently prevail

CRP, C-reactive protein.

EGFR or IL-6R or indirectly through induction of IL-6R signalling. Such activation of STAT3 suggests a rationale for a combination of anti-STAT3 and EGFR/IL-6R therapy to suppress the peritoneal spread of ovarian cancer (Colomiere et al., 2009).

In addition to STAT3 also the Ras protein can be activated in response to IL-6. After Ras activation, hyperphosphorylation of mitogen-activated protein kinase (MAPK) occurs as well as an increase in its serine/threonine kinase activity. MAPK then phosphorylates the NF-IL6 transcription factor on serine 231 and threonine 235, a process that is essential for DNA binding. NF-IL6 has a basic leucine zipper motif and is a member of the C/EBP family of transcription factors. NF-IL6 activates the promoter regions of various acute-phase protein genes in the liver. Thus, when IL-6 binds to a cell through IL-6Ra/gp130 complexes, a series of events takes place that leads to the activation of STATs and NF-IL6, switching on target genes. OSE cells immortalized with mutant H-Ras or K-Ras lead to cells that grow slowly but progressively with serous papillary histology in the peritoneal cavity. Gene expression profile analysis of these transformed cells showed an increased expression of several cytokines, mainly IL-6, which are up regulated by the NF-kB pathway. Each of these cytokines might provide targets for therapeutic intervention in EOC with RAS mutation.

#### **3.2 Cytokines and modulation of immune system**

The host immune response comprises a multitude of highly developed interconnected biological processes involving both cellular and humoral responses that cooperate to eliminate foreign bodies and repair the site of injury. The innate arm of the immune activity provides rapid reactions prior to the development of highly specific adaptive responses. In the context of a malignant tumour, many of the suppressive and stimulatory properties of innate immunity may influence tumour progression in both positive and negative ways. The activation of the cell-mediated immunity by macrophages, T lymphocytes, and natural killer cells has been suggested as a specific mechanism performed by the body to counteract oncogenesis and tumour growth. During their activation processes these cells release several soluble factors (cytokines) that send stimulatory or inhibitory signals to the different immune cell types. Interleukin-1, IL-2 and TNF-α are the main mediators of cell-mediated immune response. Interleukin-1 and TNF-α are potent inductors of IL-6 that, in turn, regulates their production, acts as a second signal for the production of IL-2 and induces on cytotoxic T lymphocytes the expression of IL-2 receptor (RIL-2). IL-2 is the key cytokine in the regulation of the antineoplastic immunity. The activity of IL-2 is strictly dependent on its binding to specific membrane receptor (IL-2R). Lymphocyte activation is followed by an increased expression of IL-2R and release of its α subunit from the membrane receptor in a soluble form (sIL-2R). Hence, sIL-2R serum levels provide direct evidence of immune system activation. Then, the synergistic effect of IL-2 and other cytokines deriving from the activated immune system may play an active role in the cytotoxic attack against tumour by counteracting neoplastic cells growth. However, some cytokines, such as IL-1, IL-6 and TNF-α may favour tumour progression. Indeed, several studies of our research group have shown in vitro that the immune system of EOC patients is inefficient to various mitogen stimuli in terms of lymphocyte proliferative response and that the severity of the immune deficit is proportionate to the stage of disease and to the performance status (PS) of patients (Mantovani et al., 2000, 2003). The reduced lymphocytes proliferative response to mitogens, such as phytohaemagglutinin (PHA) and anti-CD3 monoclonal antibody (mAb), must be considered as an index of more complex functional alterations. In fact, these mitogens

EGFR or IL-6R or indirectly through induction of IL-6R signalling. Such activation of STAT3 suggests a rationale for a combination of anti-STAT3 and EGFR/IL-6R therapy to suppress the

In addition to STAT3 also the Ras protein can be activated in response to IL-6. After Ras activation, hyperphosphorylation of mitogen-activated protein kinase (MAPK) occurs as well as an increase in its serine/threonine kinase activity. MAPK then phosphorylates the NF-IL6 transcription factor on serine 231 and threonine 235, a process that is essential for DNA binding. NF-IL6 has a basic leucine zipper motif and is a member of the C/EBP family of transcription factors. NF-IL6 activates the promoter regions of various acute-phase protein genes in the liver. Thus, when IL-6 binds to a cell through IL-6Ra/gp130 complexes, a series of events takes place that leads to the activation of STATs and NF-IL6, switching on target genes. OSE cells immortalized with mutant H-Ras or K-Ras lead to cells that grow slowly but progressively with serous papillary histology in the peritoneal cavity. Gene expression profile analysis of these transformed cells showed an increased expression of several cytokines, mainly IL-6, which are up regulated by the NF-kB pathway. Each of these cytokines might

The host immune response comprises a multitude of highly developed interconnected biological processes involving both cellular and humoral responses that cooperate to eliminate foreign bodies and repair the site of injury. The innate arm of the immune activity provides rapid reactions prior to the development of highly specific adaptive responses. In the context of a malignant tumour, many of the suppressive and stimulatory properties of innate immunity may influence tumour progression in both positive and negative ways. The activation of the cell-mediated immunity by macrophages, T lymphocytes, and natural killer cells has been suggested as a specific mechanism performed by the body to counteract oncogenesis and tumour growth. During their activation processes these cells release several soluble factors (cytokines) that send stimulatory or inhibitory signals to the different immune cell types. Interleukin-1, IL-2 and TNF-α are the main mediators of cell-mediated immune response. Interleukin-1 and TNF-α are potent inductors of IL-6 that, in turn, regulates their production, acts as a second signal for the production of IL-2 and induces on cytotoxic T lymphocytes the expression of IL-2 receptor (RIL-2). IL-2 is the key cytokine in the regulation of the antineoplastic immunity. The activity of IL-2 is strictly dependent on its binding to specific membrane receptor (IL-2R). Lymphocyte activation is followed by an increased expression of IL-2R and release of its α subunit from the membrane receptor in a soluble form (sIL-2R). Hence, sIL-2R serum levels provide direct evidence of immune system activation. Then, the synergistic effect of IL-2 and other cytokines deriving from the activated immune system may play an active role in the cytotoxic attack against tumour by counteracting neoplastic cells growth. However, some cytokines, such as IL-1, IL-6 and TNF-α may favour tumour progression. Indeed, several studies of our research group have shown in vitro that the immune system of EOC patients is inefficient to various mitogen stimuli in terms of lymphocyte proliferative response and that the severity of the immune deficit is proportionate to the stage of disease and to the performance status (PS) of patients (Mantovani et al., 2000, 2003). The reduced lymphocytes proliferative response to mitogens, such as phytohaemagglutinin (PHA) and anti-CD3 monoclonal antibody (mAb), must be considered as an index of more complex functional alterations. In fact, these mitogens

peritoneal spread of ovarian cancer (Colomiere et al., 2009).

provide targets for therapeutic intervention in EOC with RAS mutation.

**3.2 Cytokines and modulation of immune system** 

induce in vitro a number of phenomena similar to those that follow antigenic activity in vivo. The secretion of macrophagic cytokines, the production of IL-2 by CD4+ lymphocytes and the RIL-2 expression on lymphocyte membrane are the defining moments of these events. For these reasons, the entity of the lymphocyte blastic response depends on the quantity of cytokines produced, the number of RIL-2 expressed and the physiologic interaction of IL-2 with its receptor. Lymphocytes inability both to produce adequate quantities of IL-2 and to express physiological amount of RIL-2 seems to be the crucial feature of this specific lymphocyte functional deficit in EOC patients. In our studies, patients peripheral blood mononuclear cells (PBMC) proliferative response to PHA, anti-CD3 mAb and human recombinant IL-2 (HurIL-2) alone was significantly lower in comparison to controls and it was not modified by the addition of human recombinant IL-2 (HurIL-2) to the culture media. Furthermore, also the expression of CD25 and CD122 subunits of membrane-bound IL-2R on patients' PBMC after stimulation with PHA or CD3mAb was lower than that seen in controls (Macciò et al., 1998). A very important finding of our researches highlights that this impairment of T cells response was associated with increased circulating levels of proinflammatory cytokines (IL-1α, IL-1β, IL-6, TNF-α) and other mediators of inflammation such as fibrinogen, CRP and sIL-2R (Figure 1).

Fig. 1. Aspecific activation of immune system during the evolution of the ovarian cancer leads to immunodepression associated to high serum levels of inflammatory cytokines and acute phase proteins. Abbreviations: ROS, Reactive Oxygen Species, RIL-2, IL-2 receptor, CRP, C-reactive protein.

In particular, it is extremely interesting that IL-6 and CRP have been shown to be able to suppress T cell responses and several studies suggested that they might interfere with the immunological mechanisms underlying the antitumor activity of IL-2. Moreover, it is known that CRP, typically induced by IL-6, is involved in the binding of complement to cytotoxic CD3+ cells and plays a key role in the inhibition of cytotoxic activity of NK cells. Then, IL-6 can be an activator or an inhibitor of T-cell responses, depending its effects by the time and duration of its activity. This interaction of pro-inflammatory and antiinflammatory activities suggests that IL-6 may play a role in regulating the control of immune system activation during the different phases of in EOC progression. A widely accepted model of tumour and immune cell interaction, termed immunoediting, describes an initial restriction of tumour cell growth, but maintains that the immune system ultimately selects for tumour cells with reduced immunogenicity that subsequently prevail

Inflammation and Ovarian Cancer 27

on the inltration of ovarian cancer by both CD4+ and CD8+ TILs and show a positive correlation between T-cell inltration and prognosis (Yigit et al. 2010). Napoletano et al. demonstrated that primary debulking in ovarian cancer is associated with a reduction of circulating Tregs and an increase in CD8+ T-cell function (Napoletano et al., 2010). Leffers at al. reported that a high TIL/Treg ratio independently predicts increased survival and suggest that it is not so much the presence of Treg as the presence of TIL in general to be

A central mechanism whereby both TIL and/or TAL contribute to invasive proliferation of tumour cells is through the production of the cytokines and chemokines that increase both the migration and survival of tumour cells. These cytokines present in the blood and in large quantities in neoplastic effusions can also be produced by cancer cells and have been

In conclusion the development of EOC is associated with changes in the peritoneal cavity microenvironment. Immune cells in the ovarian stromal microenvironment play an important role in ovarian tumorigenesis and progression (Wertel et al., 2011). In turn, tumour cells develop several mechanisms to evade anti-tumour immunity by developing an immunosuppressive microenvironment by the production of different factors (cytokines), which impairs differentiation, maturation, and function of antigen-presenting cells. Once transformed ovarian epithelial cells develop an immunoediting process occurs in which immune cells and their mediators dictate the growth and progression of EOC (Thompson & Mok, 2009). Then, as described above chronic inflammation is associated with initiation and/or progression of the most common EOC types and the balance between pro- and anti-inflammatory cytokines is critical for host immune response to

Several studies, including some from our group (Macciò et al., 1998, 2009), demonstrated the correlation existing between the severity of chronic inflammation, advanced stage and poor outcome in patients with epithelial ovarian cancer. Epithelial ovarian cancer is an immunogenic tumour and exploits many suppressive ways to escape immune eradication. High circulating levels of proinflammatory cytokines, such as IL-1, Il-6, and TNF- have been found in EOC patients with advanced stage of disease and an unfavourable prognosis. The prognostic role of various cytokines has been studied, but no absolutely firm conclusions can be drawn so far. It is likely that cytokines involved in Th1 response predict for better prognosis, while the opposite is expected in those associated with Th2 response. Moreover, proinflammatory cytokines play an important role in the mechanisms inducing the complex clinical condition known as cancer-related anorexia/cachexia (CACS). One of the metabolic changes present in this syndrome is the hepatic synthesis of C-reactive protein (CRP). High serum levels of CRP are associated with a poor prognosis in EOC patients and can negatively influence the therapeutic response to HurIL-2. This is extremely important since IL-2 initiates the activation of T and NK cells and it is also essential for the maintenance of self-tolerance through generation and maintenance of Tregs or by activation-induced cell death to eliminate self reactive T cells. Interestingly, IL-6 is a potent inducer of CRP exerting its regulatory effect

responsible for the observed survival effect (Leffers et al. 2009).

associated with prognosis in EOC (Gavalas et al., 2010)**.** 

**4. Proinflammatory cytokines and prognosis** 

tumours.

over the host immune system. Therefore, whereas the immune system may initially be protective against tumour development, its efficacy may diminish over time and it may ultimately facilitate tumour progression. Indeed, the mechanisms by which the tumour can evade immune system control are manifold. Despite immune-cells have for long been known for their roles primarily in immune cancer surveillance, many tumour cell types secrete immunosuppressive cytokines such as transforming growth factor-beta, IL-6, IL-10 and IL-13, and chemokines that can also recruit cells that negatively regulate immunity such as T-regulatory cells, myeloid suppressor cells, NK cells and macrophage subsets (Robinson-Smith et al., 2007). Jeannin P et al. in a very recent work (Jeannin et al., 2011) reported that ovarian cancer ascites switched monocyte differentiation into tumour-associated macrophages (TAM)-like cells, that exhibit most phenotypic and functional characteristics of TAMs, suggesting that soluble mediators are involved in the differentiation of monocytes into TAM-like cells. TAMs, the most abundant immunosuppressive myeloid cells in the tumour microenvironment, exhibit an IL-10 (high) and IL-12 (low) profile called M2, opposite to the immunostimulatory M1. The same authors observed that the leukaemiainhibitory factor and IL-6, present at high concentrations in ovarian cancer ascites, skew monocyte differentiation into TAM-like cells by increasing macrophage colony-stimulating factor consumption. These data reveal a new tumour-escape mechanism associated with TAMs generation through an IL-6 mediated effect. An interesting published study by Nowak et al. confirmed that in the presence of autologous ovarian cancer cells, peripheral blood mononuclear cells from patients with advanced EOC produced higher amount of immunosuppressive (Il-10, TGF-beta) and proinflammatory (IL-6) cytokines with downregulation of T cells response (Nowak et al., 2010b). In the context of EOC, two specific leukocyte subsets have been demonstrated to significantly promote tumour growth: regulatory T cells (Tregs) and pro-angiogenic/immunosuppressive myeloid cells, the latter exhibiting the phenotypic attributed of macrophages (Cubillos-Ruiz et al., 2010). Globally, all ovarian cancer-associated myeloid cell subsets impair the function of anti-tumour T cells, (Scarlett et al., 2009) the only element in the ovarian cancer microenvironment known to exert clinically relevant spontaneous immune pressure against tumour progression. The accumulation of tumour Tregs predicts poor survival in EOC patients. Curiel and colleagues (Curiel et al., 2004) first demonstrated a crucial role for Tregs in ovarian cancer-mediated immunosuppression. They showed that solid tumour masses and malignant ascites of human ovarian cancer accumulate variable levels of Tregs (CD3+CD4+CD25+ GITR+CTLA-4+CCR7+FoxP3hi), while non-malignant ascites or normal ovaries did not contain a significant proportion of these cells. Interestingly, Tregs were found to be specifically recruited to tumour locations via CCL22, a cytokine expressed by tumour cells and microenvironmental myeloid cells. Ovarian tumours and tumour microenvironment macrophages are major sources of CCL22. Tregs isolated from ovarian cancer ascites were functionally active, as they inhibited the proliferation of autologous T cells stimulated in vitro with DCs pulsed with tumour antigens, and also prevented the anti-tumour activity of adoptively transferred T cells. Giuntoli et al. reported that a high CD4+/CD8+ ratio in ascites, which may indicate the presence of Tregs, is associated with poor outcome (Giuntoli et al., 2009). Other studies investigating the signicance of the role of intratumoral infiltrates (TIL) or tumour associated lymphocytes (TAL) in these events have been reported. By contrast, there is accumulating evidence that the presence both of TIL or TAL, such as those found in neoplastic effusions, is quantitatively related with improved clinical outcome in ovarian cancer (Kim et al., 2009). In fact, recent studies report

over the host immune system. Therefore, whereas the immune system may initially be protective against tumour development, its efficacy may diminish over time and it may ultimately facilitate tumour progression. Indeed, the mechanisms by which the tumour can evade immune system control are manifold. Despite immune-cells have for long been known for their roles primarily in immune cancer surveillance, many tumour cell types secrete immunosuppressive cytokines such as transforming growth factor-beta, IL-6, IL-10 and IL-13, and chemokines that can also recruit cells that negatively regulate immunity such as T-regulatory cells, myeloid suppressor cells, NK cells and macrophage subsets (Robinson-Smith et al., 2007). Jeannin P et al. in a very recent work (Jeannin et al., 2011) reported that ovarian cancer ascites switched monocyte differentiation into tumour-associated macrophages (TAM)-like cells, that exhibit most phenotypic and functional characteristics of TAMs, suggesting that soluble mediators are involved in the differentiation of monocytes into TAM-like cells. TAMs, the most abundant immunosuppressive myeloid cells in the tumour microenvironment, exhibit an IL-10 (high) and IL-12 (low) profile called M2, opposite to the immunostimulatory M1. The same authors observed that the leukaemiainhibitory factor and IL-6, present at high concentrations in ovarian cancer ascites, skew monocyte differentiation into TAM-like cells by increasing macrophage colony-stimulating factor consumption. These data reveal a new tumour-escape mechanism associated with TAMs generation through an IL-6 mediated effect. An interesting published study by Nowak et al. confirmed that in the presence of autologous ovarian cancer cells, peripheral blood mononuclear cells from patients with advanced EOC produced higher amount of immunosuppressive (Il-10, TGF-beta) and proinflammatory (IL-6) cytokines with downregulation of T cells response (Nowak et al., 2010b). In the context of EOC, two specific leukocyte subsets have been demonstrated to significantly promote tumour growth: regulatory T cells (Tregs) and pro-angiogenic/immunosuppressive myeloid cells, the latter exhibiting the phenotypic attributed of macrophages (Cubillos-Ruiz et al., 2010). Globally, all ovarian cancer-associated myeloid cell subsets impair the function of anti-tumour T cells, (Scarlett et al., 2009) the only element in the ovarian cancer microenvironment known to exert clinically relevant spontaneous immune pressure against tumour progression. The accumulation of tumour Tregs predicts poor survival in EOC patients. Curiel and colleagues (Curiel et al., 2004) first demonstrated a crucial role for Tregs in ovarian cancer-mediated immunosuppression. They showed that solid tumour masses and malignant ascites of human ovarian cancer accumulate variable levels of Tregs (CD3+CD4+CD25+ GITR+CTLA-4+CCR7+FoxP3hi), while non-malignant ascites or normal ovaries did not contain a significant proportion of these cells. Interestingly, Tregs were found to be specifically recruited to tumour locations via CCL22, a cytokine expressed by tumour cells and microenvironmental myeloid cells. Ovarian tumours and tumour microenvironment macrophages are major sources of CCL22. Tregs isolated from ovarian cancer ascites were functionally active, as they inhibited the proliferation of autologous T cells stimulated in vitro with DCs pulsed with tumour antigens, and also prevented the anti-tumour activity of adoptively transferred T cells. Giuntoli et al. reported that a high CD4+/CD8+ ratio in ascites, which may indicate the presence of Tregs, is associated with poor outcome (Giuntoli et al., 2009). Other studies investigating the signicance of the role of intratumoral infiltrates (TIL) or tumour associated lymphocytes (TAL) in these events have been reported. By contrast, there is accumulating evidence that the presence both of TIL or TAL, such as those found in neoplastic effusions, is quantitatively related with improved clinical outcome in

ovarian cancer (Kim et al., 2009). In fact, recent studies report

on the inltration of ovarian cancer by both CD4+ and CD8+ TILs and show a positive correlation between T-cell inltration and prognosis (Yigit et al. 2010). Napoletano et al. demonstrated that primary debulking in ovarian cancer is associated with a reduction of circulating Tregs and an increase in CD8+ T-cell function (Napoletano et al., 2010). Leffers at al. reported that a high TIL/Treg ratio independently predicts increased survival and suggest that it is not so much the presence of Treg as the presence of TIL in general to be responsible for the observed survival effect (Leffers et al. 2009).

A central mechanism whereby both TIL and/or TAL contribute to invasive proliferation of tumour cells is through the production of the cytokines and chemokines that increase both the migration and survival of tumour cells. These cytokines present in the blood and in large quantities in neoplastic effusions can also be produced by cancer cells and have been associated with prognosis in EOC (Gavalas et al., 2010)**.** 

In conclusion the development of EOC is associated with changes in the peritoneal cavity microenvironment. Immune cells in the ovarian stromal microenvironment play an important role in ovarian tumorigenesis and progression (Wertel et al., 2011). In turn, tumour cells develop several mechanisms to evade anti-tumour immunity by developing an immunosuppressive microenvironment by the production of different factors (cytokines), which impairs differentiation, maturation, and function of antigen-presenting cells. Once transformed ovarian epithelial cells develop an immunoediting process occurs in which immune cells and their mediators dictate the growth and progression of EOC (Thompson & Mok, 2009). Then, as described above chronic inflammation is associated with initiation and/or progression of the most common EOC types and the balance between pro- and anti-inflammatory cytokines is critical for host immune response to tumours.

#### **4. Proinflammatory cytokines and prognosis**

Several studies, including some from our group (Macciò et al., 1998, 2009), demonstrated the correlation existing between the severity of chronic inflammation, advanced stage and poor outcome in patients with epithelial ovarian cancer. Epithelial ovarian cancer is an immunogenic tumour and exploits many suppressive ways to escape immune eradication. High circulating levels of proinflammatory cytokines, such as IL-1, Il-6, and TNF- have been found in EOC patients with advanced stage of disease and an unfavourable prognosis. The prognostic role of various cytokines has been studied, but no absolutely firm conclusions can be drawn so far. It is likely that cytokines involved in Th1 response predict for better prognosis, while the opposite is expected in those associated with Th2 response. Moreover, proinflammatory cytokines play an important role in the mechanisms inducing the complex clinical condition known as cancer-related anorexia/cachexia (CACS). One of the metabolic changes present in this syndrome is the hepatic synthesis of C-reactive protein (CRP). High serum levels of CRP are associated with a poor prognosis in EOC patients and can negatively influence the therapeutic response to HurIL-2. This is extremely important since IL-2 initiates the activation of T and NK cells and it is also essential for the maintenance of self-tolerance through generation and maintenance of Tregs or by activation-induced cell death to eliminate self reactive T cells. Interestingly, IL-6 is a potent inducer of CRP exerting its regulatory effect

Inflammation and Ovarian Cancer 29

cancer and induced delivery of survival signals to PDC. In turn, the tumour microenvironmental PDC induced IL-10 expressing Tregs, which are correlated to poor prognosis and shorter progression-free survival. In the case of Tregs it has been exhibited that CCL22 plays a central role in inducing influx of these cells into tumour sites by binding to CCR4 that is expressed on Treg surface. Interferon gamma (IFN-γ) plays a stimulatory role for macrophages turning them from immunosuppressive to immunostimulatory cells. It also skewed monocyte differentiation from associatedassociated macrophages (TAM) like cells to M1-polarized immunostimulatory macrophages. Taken together these data show that IFN-γ overcomes TAM-induced immunosuppression by preventing TAM generation and functions. Furthermore, cytokines such as IL-18 and stroma derived factor 1 (SDF-1) have been shown to be correlated with poor prognosis in ovarian cancer patients, but further studies are required

In the course of its evolution cancer induces in the host changes of the immune system and energy metabolism that affect its clinical conditions so deeply that in some cases they are responsible for patient's death. Several symptoms are associated to these events and involve


It is difficult to establish the exact moment when such changes actually start, but it could be hypothesized that they are the consequence of the interactions between the tumour and the host. The hypothesis that the presence of the tumour and its continuous growth are responsible for the increased energy expenditure and for the progressive weight loss has been considered the most reliable so far. Indeed, the presence in the host of continuously growing neoplastic tissue justifies by itself the increased energy needs; moreover, it is accompanied by enhanced energy expenditure associated with the chronic activation of the immune system, trying to counteract the tumour, which is energetically very costly (25-30% of the basal metabolic rate, i.e. 1750-2080 kJ/day) (Straub et al., 2010). The resulting metabolic scenario is that of two systems that require a continuous supply of energy substrates, particularly glucose. Glucose oxidation to CO2 and H2O is the main energy source produced as ATP, NADH and FADH. A further glucose amount is also involved for the synthesis, through the phosphate pentose pathway, of compounds with high reducing power as NADPH and reduced glutathione (GSH), essential for the neutralisation of reactive oxygen species (ROS) produced during the various steps of the energy metabolism. ROS are intermediate compounds derived from the univalent reduction of molecular oxygen by electrons and protons, characterized by the presence of an unpaired electron in the farthest external orbital, which makes them particularly unstable (hydrogen peroxide:

to fully evaluate them in the tumour microenvironment and the periphery.


**4.1 Inflammation and metabolic changes** 

various organs and systems:



metabolism) - Immunodepression

on CRP synthesis at the pretranslational level. IL-6 levels have been shown to be increased in advanced ovarian cancer patients' serum and to correlate with poor prognosis and reduced overall survival (Scambia et al., 1995). Elevated levels are also present in malignant ascites from EOC patients (Plante et al., 1994) and a positive correlation has been found between IL-6 concentration in ascites and residual disease after debulking. Additionally, IL-6 levels are remarkably higher at recurrence compared to primary advanced disease, thus opening an opportunity for inhibition of IL-6 expression in the prevention of recurrence. EOC is known to spread primarily by tumour cell implantations in peritoneal cavity. Therefore, ascites may be an ideal fluid compartment to unravel the immune status of the peritoneal cavity (Mantovani et al, 1999, 1997). Recently, Yigit R et al. (Yigit et al., 2011) observed high expression of pro-inflammatory cytokines IL-6, IL-8 and immune suppressive cytokines IL-10, CCL22 and TGF-β in most samples of ovarian cancer ascites whereas Th1 (IL-12p70, IFN-γ) and Th2 (IL-4, IL-5) cytokines were only detectable in few samples. TGF-β was only detected in latent form, questioning its immune suppressive role. At advanced stage, they also observed a negative correlation with CCL22 levels and Th1/2 cytokine expression. A cytokine that seems to be heavily involved in tumour immunosuppression is the transforming growth factor beta (TGF-β), a protein that affects proliferation, activation, and differentiation of immune cells and inhibits antitumor immune response. In cancer cells, the production of TGF-β is increased and, in turn, raises their proteolytic activity and binding to cell adhesion molecules in the extracellular matrix. TGF-β can also convert effector T cells into Tregs. It has been reported that it can also promote angiogenesis and that this process can be blocked by anti-TGF-β antibodies. TGF-β blockade almost completely eradicate ascites formation and significantly inhibit the expression of VEGF, which is the major contributor to ascites formation. At the same time, TGF-β blockade prevent 'abnormalization' of diaphragm lymphatic vessels and improve ascites drainage (Liao et al., 2011). Also TNF-α is produced by tumour cells and can induce autocrine proliferation and disease progression in ovarian cancer. The autocrine action of TNFα may have direct effects on tumour cell spread via acting on the chemokine receptor CXCR4 and stimulating new blood vessel formation in the peritoneum by inducing expression of VEGF and CXCL12. In contrast, TNF-α levels have also been inversely correlated with the presence of CD4+ CD25+ cells, and have been shown to directly downregulate Tregs. This might indicate a favourable effect of this cytokine on prognosis and underlines the complexity of the functions that each of these factors may possess. Then, reports on whether TNF- is a signature of poor or better prognosis vary. Another cytokine that was shown to be associated with the growth of cancer cells and tumour proliferation is IL-1. A family of proteins called chemokines (CC) may also be influencing cellular composition in biological fluids. Recent studies have demonstrated the presence of mRNA for CCL2, CCL3, CCL4, and CCL5 in EOC by in situ hybridization. Moreover, CCL5 has been shown to be secreted by CD4+ T cells, recruits CCR5+ dendritic cells to the tumour location, and activates them through CD40-CD40L interactions. The newly matured dendritic cells prime tumour-specific CD8+ cells thus providing with long-term protection. Also in the protein-rich ascitic fluid, different chemokine molecules are expressed, with CCL2 being the predominant one. In addition, chemokine stromal-derived factor-1 (CXCL-1) induced the migration of plasmacytoid dendritic cells (PDC) into the tumour microenvironment in cases of ovarian

on CRP synthesis at the pretranslational level. IL-6 levels have been shown to be increased in advanced ovarian cancer patients' serum and to correlate with poor prognosis and reduced overall survival (Scambia et al., 1995). Elevated levels are also present in malignant ascites from EOC patients (Plante et al., 1994) and a positive correlation has been found between IL-6 concentration in ascites and residual disease after debulking. Additionally, IL-6 levels are remarkably higher at recurrence compared to primary advanced disease, thus opening an opportunity for inhibition of IL-6 expression in the prevention of recurrence. EOC is known to spread primarily by tumour cell implantations in peritoneal cavity. Therefore, ascites may be an ideal fluid compartment to unravel the immune status of the peritoneal cavity (Mantovani et al, 1999, 1997). Recently, Yigit R et al. (Yigit et al., 2011) observed high expression of pro-inflammatory cytokines IL-6, IL-8 and immune suppressive cytokines IL-10, CCL22 and TGF-β in most samples of ovarian cancer ascites whereas Th1 (IL-12p70, IFN-γ) and Th2 (IL-4, IL-5) cytokines were only detectable in few samples. TGF-β was only detected in latent form, questioning its immune suppressive role. At advanced stage, they also observed a negative correlation with CCL22 levels and Th1/2 cytokine expression. A cytokine that seems to be heavily involved in tumour immunosuppression is the transforming growth factor beta (TGF-β), a protein that affects proliferation, activation, and differentiation of immune cells and inhibits antitumor immune response. In cancer cells, the production of TGF-β is increased and, in turn, raises their proteolytic activity and binding to cell adhesion molecules in the extracellular matrix. TGF-β can also convert effector T cells into Tregs. It has been reported that it can also promote angiogenesis and that this process can be blocked by anti-TGF-β antibodies. TGF-β blockade almost completely eradicate ascites formation and significantly inhibit the expression of VEGF, which is the major contributor to ascites formation. At the same time, TGF-β blockade prevent 'abnormalization' of diaphragm lymphatic vessels and improve ascites drainage (Liao et al., 2011). Also TNF-α is produced by tumour cells and can induce autocrine proliferation and disease progression in ovarian cancer. The autocrine action of TNFα may have direct effects on tumour cell spread via acting on the chemokine receptor CXCR4 and stimulating new blood vessel formation in the peritoneum by inducing expression of VEGF and CXCL12. In contrast, TNF-α levels have also been inversely correlated with the presence of CD4+ CD25+ cells, and have been shown to directly downregulate Tregs. This might indicate a favourable effect of this cytokine on prognosis and underlines the complexity of the functions that each of these factors may possess. Then, reports on whether TNF- is a signature of poor or better prognosis vary. Another cytokine that was shown to be associated with the growth of cancer cells and tumour proliferation is IL-1. A family of proteins called chemokines (CC) may also be influencing cellular composition in biological fluids. Recent studies have demonstrated the presence of mRNA for CCL2, CCL3, CCL4, and CCL5 in EOC by in situ hybridization. Moreover, CCL5 has been shown to be secreted by CD4+ T cells, recruits CCR5+ dendritic cells to the tumour location, and activates them through CD40-CD40L interactions. The newly matured dendritic cells prime tumour-specific CD8+ cells thus providing with long-term protection. Also in the protein-rich ascitic fluid, different chemokine molecules are expressed, with CCL2 being the predominant one. In addition, chemokine stromal-derived factor-1 (CXCL-1) induced the migration of plasmacytoid dendritic cells (PDC) into the tumour microenvironment in cases of ovarian cancer and induced delivery of survival signals to PDC. In turn, the tumour microenvironmental PDC induced IL-10 expressing Tregs, which are correlated to poor prognosis and shorter progression-free survival. In the case of Tregs it has been exhibited that CCL22 plays a central role in inducing influx of these cells into tumour sites by binding to CCR4 that is expressed on Treg surface. Interferon gamma (IFN-γ) plays a stimulatory role for macrophages turning them from immunosuppressive to immunostimulatory cells. It also skewed monocyte differentiation from associatedassociated macrophages (TAM) like cells to M1-polarized immunostimulatory macrophages. Taken together these data show that IFN-γ overcomes TAM-induced immunosuppression by preventing TAM generation and functions. Furthermore, cytokines such as IL-18 and stroma derived factor 1 (SDF-1) have been shown to be correlated with poor prognosis in ovarian cancer patients, but further studies are required to fully evaluate them in the tumour microenvironment and the periphery.

#### **4.1 Inflammation and metabolic changes**

In the course of its evolution cancer induces in the host changes of the immune system and energy metabolism that affect its clinical conditions so deeply that in some cases they are responsible for patient's death. Several symptoms are associated to these events and involve various organs and systems:


It is difficult to establish the exact moment when such changes actually start, but it could be hypothesized that they are the consequence of the interactions between the tumour and the host. The hypothesis that the presence of the tumour and its continuous growth are responsible for the increased energy expenditure and for the progressive weight loss has been considered the most reliable so far. Indeed, the presence in the host of continuously growing neoplastic tissue justifies by itself the increased energy needs; moreover, it is accompanied by enhanced energy expenditure associated with the chronic activation of the immune system, trying to counteract the tumour, which is energetically very costly (25-30% of the basal metabolic rate, i.e. 1750-2080 kJ/day) (Straub et al., 2010). The resulting metabolic scenario is that of two systems that require a continuous supply of energy substrates, particularly glucose. Glucose oxidation to CO2 and H2O is the main energy source produced as ATP, NADH and FADH. A further glucose amount is also involved for the synthesis, through the phosphate pentose pathway, of compounds with high reducing power as NADPH and reduced glutathione (GSH), essential for the neutralisation of reactive oxygen species (ROS) produced during the various steps of the energy metabolism. ROS are intermediate compounds derived from the univalent reduction of molecular oxygen by electrons and protons, characterized by the presence of an unpaired electron in the farthest external orbital, which makes them particularly unstable (hydrogen peroxide:

Inflammation and Ovarian Cancer 31

In detail, IL-1 exerts a specific effect on reducing food intake and influences meal size and duration: IL-1 has an anorectic action by directly decreasing neuropeptide Y (NPY) neurotransmission and secondarily by increasing corticotrophin-releasing factor (CRF), which in turn acts on the satiety circuitry inhibiting food intake. IL-1 has also been demonstrated to inhibit serum levels of growth hormone (GH) by increasing CRF and somatostatin levels. The decreased synthesis of GH leads to reduced synthesis of the insulinlike growth factors (IGFs), which in turn influences the muscle protein turnover and the autocrine and paracrine regulation of muscle mass proliferation. TNF-α has been shown to promote lipolysis and inhibit lipogenesis and plays a key role in the depletion of adipose tissue mass seen in cachexia. It has been proposed that an elevation in plasma levels of TNFα is responsible for the metabolic alterations in adipose tissue seen in advanced cancer patients. Lipid metabolism is a complex sequence of events that determine whether the triglyceride pool within the adipocyte increases, due to the processes of free fatty acid (FFA) uptake and lipogenesis, or decreases, due to the process of lipolysis. Circulating lipoproteins and triglycerides are first converted into FFA by the action of lipoprotein lipase (LPL), which is secreted by the adipocyte. FFA can then enter the adipocyte via a fatty acid transporter and, once inside the adipocyte, they are converted into the triglyceride by a multi-step-regulated enzymatic reaction, which involves acyl-CoA synthetase. In addition, triglyceride can be formed from the uptake of glucose, via glucose transporters (GLUT) 1 and 4, into the adipocyte. The glucose can then be converted into triglyceride by the actions of a series of enzymes, which include acetyl-CoA carboxylase and fatty acid synthase. A large body of evidence now supports a role for TNF-α in modulating these processes. TNF-α inhibits LPL activity by down-regulating its protein expression. In addition, TNF-α has been shown to reduce the expression of FFA transporters in adipose tissue. TNF-α could thus hinder the synthesis and entry of FFA into the adipocyte, curtailing an increase in the intracellular triglyceride pool size. Studies have also suggested that TNF-α may decrease the expression of enzymes involved in lipogenesis. Specifically, it has been suggested that acetyl-CoA carboxylase and fatty acid synthase are down regulated. Acyl-CoA synthase expression and activity have also been suggested to be down regulated by TNF-α. TNF-α has been found to promote lipolysis. TNF-α has been implicated as a factor associated with the development of insulin resistance. A positive association between plasma insulin levels and TNF-α mRNA from subcutaneous adipose tissue has been found in women, finding which is supported by a further study showing increased adipose TNF-α secretion in obese patients with insulin resistance. Extensive research has highlighted several potential mechanisms by which TNF-α induces insulin resistance. These include: accelerated lipolysis and a concomitant increase in circulating FFA concentrations, down regulation of GLUT4 synthesis, down-regulation of insulin receptor, insulin receptor substrate-1 (IRS-1) synthesis and increased Ser/Thr phosphorylation of IRS-1. Interleukin-6 is another proinflammatory cytokine with cachectic effects. The presence of tumour in mouse models was associated with early CACS and production of IL-6, whom serum levels correlated with the severity of CACS. Vice versa, the administration of anti-IL-6 antibody inhibits the comparison of CACS symptoms thus demonstrating the central pathogenetic role of this cytokine in cachectic syndrome. In vitro studies have demonstrated that IL-6 induces, similarly to IL-1, the hypothalamic release of CRF. Moreover, IL-6 acts on β

pancreatic cells similarly to IL-1 (Mantovani et al., 2001).

H2O2, superoxide anione: O2- ; hydroxyl radical: OH°). As they are partly useful, but potentially toxic, compounds the body has a number of control mechanisms that limit their activity once they have been used for the scheduled objective. In particular superoxide dismutase (SOD) metabolises O2 to H2O2, whereas catalase and various glutathione peroxidase (GPx) metabolise it to H2O and alcohol. ROS which have not been eliminated for the lack of these antioxidants, have a negative oxidative action on polyunsaturated fatty acids circulating proteins, membrane rich of disulphur bridge, enzymes and DNA, determining irreversible damage both to cell architecture and function. Under such conditions, detoxification systems, sustained by reducing compounds, should be adequately present. These reducing compounds, which are called natural detoxificants, are thus essential for a normal cell activity.

The energy metabolism in cancer patients is affected by the presence, during the disease evolution, of symptoms such as anorexia, nausea and vomiting, which prevent a normal nutrition and thus a regular supply of glucose, lipids, proteins and vitamins. Antiblastic treatments and the same molecules (cytokines), which regulate both the tumour development and the immune system functions, are responsible for these symptoms (Bennani-Baiti & Davis, 2008). In this context, the finding that neoplastic patients in advanced stages show a severe impairment of immunologic functions characterized by impaired cell-mediated immunity and elevated serum levels of macrophage cytokines (IL-1, IL-6, TNF-a) and inflammation acute phase proteins (fibrinogen and CRP) is of great importance (Macciò et al., 1998). Evidence that high serum concentrations of cytokines and inflammatory proteins are associated with high levels of ROS and low levels of SOD and GPx is also of particular interest (Mantovani et al., 2002).

Thus, in neoplastic patients tumour growth and immune system activation determine an overall metabolic picture characterized by:


Therefore, the metabolic changes described in the neoplastic patients are to be attributed to the chronic action of some cytokines (in particular IL-1, IL-6 and TNF-) produced both by activated immune system and tumour cells (Argiles & Lopez-Soriano, 1999; Delano & Moldawer, 2006). It may be hypothesised that, during the initial phases of neoplastic disease, the synthesis of proinflammatory cytokines leads to an efficient antineoplastic effect. However, the inability of the immune system to definitively counteract tumour growth (Hagemann et al., 2006) determines the chronicisation of cytokine activity with deleterious effects on cell metabolism, body composition, nutritional status and immune system efficiency. Indeed, the chronic action of cytokines is the main cause of the metabolic abnormalities characterising advanced ovarian cancer patient (Figure 2).

potentially toxic, compounds the body has a number of control mechanisms that limit their activity once they have been used for the scheduled objective. In particular superoxide

peroxidase (GPx) metabolise it to H2O and alcohol. ROS which have not been eliminated for the lack of these antioxidants, have a negative oxidative action on polyunsaturated fatty acids circulating proteins, membrane rich of disulphur bridge, enzymes and DNA, determining irreversible damage both to cell architecture and function. Under such conditions, detoxification systems, sustained by reducing compounds, should be adequately present. These reducing compounds, which are called natural detoxificants,

The energy metabolism in cancer patients is affected by the presence, during the disease evolution, of symptoms such as anorexia, nausea and vomiting, which prevent a normal nutrition and thus a regular supply of glucose, lipids, proteins and vitamins. Antiblastic treatments and the same molecules (cytokines), which regulate both the tumour development and the immune system functions, are responsible for these symptoms (Bennani-Baiti & Davis, 2008). In this context, the finding that neoplastic patients in advanced stages show a severe impairment of immunologic functions characterized by impaired cell-mediated immunity and elevated serum levels of macrophage cytokines (IL-1, IL-6, TNF-a) and inflammation acute phase proteins (fibrinogen and CRP) is of great importance (Macciò et al., 1998). Evidence that high serum concentrations of cytokines and inflammatory proteins are associated with high levels of ROS and low levels of SOD and

Thus, in neoplastic patients tumour growth and immune system activation determine an

Difficulty to introduce these substances with food because of anorexia, nausea and

Resorting to glucogenesis with depletion of protein and lipid stores and thus loss of

Difficult to use the newly formed glucose because of hypoinsulinemia and/or

 Oxidative damage induced by ROS on DNA, membrane lipoprotein, and enzymes and coenzymes that play a major role in the regulation of the main cell anabolic and

Therefore, the metabolic changes described in the neoplastic patients are to be attributed to the chronic action of some cytokines (in particular IL-1, IL-6 and TNF-) produced both by activated immune system and tumour cells (Argiles & Lopez-Soriano, 1999; Delano & Moldawer, 2006). It may be hypothesised that, during the initial phases of neoplastic disease, the synthesis of proinflammatory cytokines leads to an efficient antineoplastic effect. However, the inability of the immune system to definitively counteract tumour growth (Hagemann et al., 2006) determines the chronicisation of cytokine activity with deleterious effects on cell metabolism, body composition, nutritional status and immune system efficiency. Indeed, the chronic action of cytokines is the main cause of the metabolic

abnormalities characterising advanced ovarian cancer patient (Figure 2).

; hydroxyl radical: OH°). As they are partly useful, but

to H2O2, whereas catalase and various glutathione

H2O2, superoxide anione: O2-

dismutase (SOD) metabolises O2-

are thus essential for a normal cell activity.

GPx is also of particular interest (Mantovani et al., 2002).

Increased glucose, lipid and protein requirements;

overall metabolic picture characterized by:

peripheral resistance to insulin;

catabolic pathways.

vomiting;

weight;

In detail, IL-1 exerts a specific effect on reducing food intake and influences meal size and duration: IL-1 has an anorectic action by directly decreasing neuropeptide Y (NPY) neurotransmission and secondarily by increasing corticotrophin-releasing factor (CRF), which in turn acts on the satiety circuitry inhibiting food intake. IL-1 has also been demonstrated to inhibit serum levels of growth hormone (GH) by increasing CRF and somatostatin levels. The decreased synthesis of GH leads to reduced synthesis of the insulinlike growth factors (IGFs), which in turn influences the muscle protein turnover and the autocrine and paracrine regulation of muscle mass proliferation. TNF-α has been shown to promote lipolysis and inhibit lipogenesis and plays a key role in the depletion of adipose tissue mass seen in cachexia. It has been proposed that an elevation in plasma levels of TNFα is responsible for the metabolic alterations in adipose tissue seen in advanced cancer patients. Lipid metabolism is a complex sequence of events that determine whether the triglyceride pool within the adipocyte increases, due to the processes of free fatty acid (FFA) uptake and lipogenesis, or decreases, due to the process of lipolysis. Circulating lipoproteins and triglycerides are first converted into FFA by the action of lipoprotein lipase (LPL), which is secreted by the adipocyte. FFA can then enter the adipocyte via a fatty acid transporter and, once inside the adipocyte, they are converted into the triglyceride by a multi-step-regulated enzymatic reaction, which involves acyl-CoA synthetase. In addition, triglyceride can be formed from the uptake of glucose, via glucose transporters (GLUT) 1 and 4, into the adipocyte. The glucose can then be converted into triglyceride by the actions of a series of enzymes, which include acetyl-CoA carboxylase and fatty acid synthase. A large body of evidence now supports a role for TNF-α in modulating these processes. TNF-α inhibits LPL activity by down-regulating its protein expression. In addition, TNF-α has been shown to reduce the expression of FFA transporters in adipose tissue. TNF-α could thus hinder the synthesis and entry of FFA into the adipocyte, curtailing an increase in the intracellular triglyceride pool size. Studies have also suggested that TNF-α may decrease the expression of enzymes involved in lipogenesis. Specifically, it has been suggested that acetyl-CoA carboxylase and fatty acid synthase are down regulated. Acyl-CoA synthase expression and activity have also been suggested to be down regulated by TNF-α. TNF-α has been found to promote lipolysis. TNF-α has been implicated as a factor associated with the development of insulin resistance. A positive association between plasma insulin levels and TNF-α mRNA from subcutaneous adipose tissue has been found in women, finding which is supported by a further study showing increased adipose TNF-α secretion in obese patients with insulin resistance. Extensive research has highlighted several potential mechanisms by which TNF-α induces insulin resistance. These include: accelerated lipolysis and a concomitant increase in circulating FFA concentrations, down regulation of GLUT4 synthesis, down-regulation of insulin receptor, insulin receptor substrate-1 (IRS-1) synthesis and increased Ser/Thr phosphorylation of IRS-1. Interleukin-6 is another proinflammatory cytokine with cachectic effects. The presence of tumour in mouse models was associated with early CACS and production of IL-6, whom serum levels correlated with the severity of CACS. Vice versa, the administration of anti-IL-6 antibody inhibits the comparison of CACS symptoms thus demonstrating the central pathogenetic role of this cytokine in cachectic syndrome. In vitro studies have demonstrated that IL-6 induces, similarly to IL-1, the hypothalamic release of CRF. Moreover, IL-6 acts on β pancreatic cells similarly to IL-1 (Mantovani et al., 2001).

Inflammation and Ovarian Cancer 33

obtained by some authors in tuberculosis patients (van Crevel et al., 2002), it can be suggested that the prolonged severe inflammatory response associated to the most advanced stages of EOC is responsible for the energy metabolism impairment thus downregulating and exhausting leptin production. Indeed, the stimulation of leptin synthesis by aerobic glucose metabolism is mediated through the production of ATP and through the effect of glucose oxidation on cellular redox status and pyruvate cycling. Therefore, oxidative stress, in advanced cancer patients, consequent to the low energy reserves and the inability to utilize efficiently the energy substrates, particularly glucose, may be considered the direct evidence of the metabolic impairment of which leptin is the most important parameter. Accordingly, our results demonstrated that in advanced EOC patients the lowest leptin levels and the highest IL-6 levels correlated with the highest levels of ROS and the lowest levels of GPx, the most sensitive among antioxidants to nutritional status being a selenium-dependent enzyme. In keeping with these hypotheses, our prospective study, which analyzed the changes of the above reported parameters during the course of disease in advanced EOC patients, showed that in patients who achieved objective complete response after the primary antineoplastic treatment, IL-6 levels fell to normal values and leptin increased significantly. Then, patients who achieved progression of disease (PD) showed a significant increase of IL-6 accompanied by a significant decrease of leptin. The patients with further PD had a progressive increase of IL-6, which reached the highest concentrations in the terminal phases of disease, associated with a significant increase of CRP and fibrinogen and a further decrease of leptin. Importantly, when PD occurred leptin did not decreased proportionally to body weight that fell significantly only in the terminal phases of disease. Leptin changes strictly reflected changes of IL-6 in accordance to tumour response or disease progression (Maccio et al., 2009). It may be suggested that leptin variation reflected the changes of energy metabolism, induced by cytokines released from the tumour itself or by the aspecific activation of the immune system, even before they caused a significant body weight loss due to anorexia and muscle and fat wasting. In light of these results we can hypothesize that in EOC patients the reduced leptin production functions as a signal of increased energy expenditure and low energy reserves during the progression of the neoplastic disease. Leptin decrease in advanced EOC patients should induce an adaptative reduction of energy expenditure and an increase of appetite and food intake in response to the metabolic impairment induced by tumour growth and cancerrelated inflammation. The signal activated by the drop of leptin levels might therefore constitute the evidence of the metabolic hyperactivity of the tumour and the host immune system and the subsequent defence attempt of the host to reduce energy expenditure when energy is scarce. Leptin levels fell together with a significant weight loss, probably induced by the prolonged action of inflammatory mediators, only in the last phases of the neoplastic disease. Indeed, chronic inflammation results in severe alterations of cell metabolism, with deleterious effects on body composition, nutritional status and immune system efficiency. Therefore, IL-6 and leptin play a central role as early markers of the main metabolic alterations associated to the progression of advanced EOC, and therefore their assessment should be included in monitoring the disease outcome, especially when cancer is no longer curable with

standard antineoplastic treatments and quality of life becomes the primary endpoint.

As widely written on, several studies have shown that inflammatory cytokines, and in particular IL-6, play a central role in the evolution of EOC and the mechanisms by which IL-

**4.2 Inflammation-related symptoms** 

Fig. 2. Role of proinflammatory cytokines in inducing metabolic changes of advanced epithelial ovarian cancer patients. Abbreviations: IL, Interelukin; TNF, Tumour Necrosis Factor, CRH, corticotrophin releasing hormone; GH, growth hormone; IGF, Insulin growth factor.

Findings from our group demonstrated a relationship between serum levels of IL-6 and leptin, one of the most important parameters of the body energy metabolism (Macciò et al., 2008) in advanced EOC patients leptin levels were significantly lower in comparison to controls and were inversely correlated with weight, BMI, stage, PS, circulating cytokines, CRP and fibrinogen. Furthermore, multivariate regression analysis demonstrated that IL-6, besides stage of disease, was an independent predictive factor of leptin levels. These results are in accordance with those of other important studies performed on a wide population of newly diagnosed EOC patients (Mor et al., 2005; Visintin et al., 2008). Leptin, released from adipocytes into the systemic circulation proportionally to fat mass, acts as a master hormone controlling energy metabolism and weight balance. Additionally, this adipokine controls several other critical systems, including endocrine axis, bone metabolism, as well as the immune/inflammatory response. Noteworthy, our study showed that serum leptin levels evaluated in 104 ovarian cancer patients at different stage of disease (stage I-IV) were dependent both from stage of disease and serum IL-6 levels, independently of patient BMI. This finding was in contrast to the great majority of studies in cancer patients that have concluded that BMI and weight are the most important determinants of circulating leptin levels; however, it is to be noted that in the majority of these papers the impact of weight loss and the pattern of serum leptin concentration before diagnosis or study enrolment are unknown. Indeed, experimental and clinical studies have clarified that leptin production is not only strictly related to body weight and fat but it is also influenced by glucose utilization ability (Havel, 2004). Acute caloric deprivation and increased energy expenditure result in a large decrease of leptin synthesis, before major changes in body weight or fat mass have actually occurred (Chan et al., 2003). Consistently with this evidence and the findings

Fig. 2. Role of proinflammatory cytokines in inducing metabolic changes of advanced epithelial ovarian cancer patients. Abbreviations: IL, Interelukin; TNF, Tumour Necrosis Factor, CRH, corticotrophin releasing hormone; GH, growth hormone; IGF, Insulin growth

Findings from our group demonstrated a relationship between serum levels of IL-6 and leptin, one of the most important parameters of the body energy metabolism (Macciò et al., 2008) in advanced EOC patients leptin levels were significantly lower in comparison to controls and were inversely correlated with weight, BMI, stage, PS, circulating cytokines, CRP and fibrinogen. Furthermore, multivariate regression analysis demonstrated that IL-6, besides stage of disease, was an independent predictive factor of leptin levels. These results are in accordance with those of other important studies performed on a wide population of newly diagnosed EOC patients (Mor et al., 2005; Visintin et al., 2008). Leptin, released from adipocytes into the systemic circulation proportionally to fat mass, acts as a master hormone controlling energy metabolism and weight balance. Additionally, this adipokine controls several other critical systems, including endocrine axis, bone metabolism, as well as the immune/inflammatory response. Noteworthy, our study showed that serum leptin levels evaluated in 104 ovarian cancer patients at different stage of disease (stage I-IV) were dependent both from stage of disease and serum IL-6 levels, independently of patient BMI. This finding was in contrast to the great majority of studies in cancer patients that have concluded that BMI and weight are the most important determinants of circulating leptin levels; however, it is to be noted that in the majority of these papers the impact of weight loss and the pattern of serum leptin concentration before diagnosis or study enrolment are unknown. Indeed, experimental and clinical studies have clarified that leptin production is not only strictly related to body weight and fat but it is also influenced by glucose utilization ability (Havel, 2004). Acute caloric deprivation and increased energy expenditure result in a large decrease of leptin synthesis, before major changes in body weight or fat mass have actually occurred (Chan et al., 2003). Consistently with this evidence and the findings

factor.

obtained by some authors in tuberculosis patients (van Crevel et al., 2002), it can be suggested that the prolonged severe inflammatory response associated to the most advanced stages of EOC is responsible for the energy metabolism impairment thus downregulating and exhausting leptin production. Indeed, the stimulation of leptin synthesis by aerobic glucose metabolism is mediated through the production of ATP and through the effect of glucose oxidation on cellular redox status and pyruvate cycling. Therefore, oxidative stress, in advanced cancer patients, consequent to the low energy reserves and the inability to utilize efficiently the energy substrates, particularly glucose, may be considered the direct evidence of the metabolic impairment of which leptin is the most important parameter. Accordingly, our results demonstrated that in advanced EOC patients the lowest leptin levels and the highest IL-6 levels correlated with the highest levels of ROS and the lowest levels of GPx, the most sensitive among antioxidants to nutritional status being a selenium-dependent enzyme. In keeping with these hypotheses, our prospective study, which analyzed the changes of the above reported parameters during the course of disease in advanced EOC patients, showed that in patients who achieved objective complete response after the primary antineoplastic treatment, IL-6 levels fell to normal values and leptin increased significantly. Then, patients who achieved progression of disease (PD) showed a significant increase of IL-6 accompanied by a significant decrease of leptin. The patients with further PD had a progressive increase of IL-6, which reached the highest concentrations in the terminal phases of disease, associated with a significant increase of CRP and fibrinogen and a further decrease of leptin. Importantly, when PD occurred leptin did not decreased proportionally to body weight that fell significantly only in the terminal phases of disease. Leptin changes strictly reflected changes of IL-6 in accordance to tumour response or disease progression (Maccio et al., 2009). It may be suggested that leptin variation reflected the changes of energy metabolism, induced by cytokines released from the tumour itself or by the aspecific activation of the immune system, even before they caused a significant body weight loss due to anorexia and muscle and fat wasting. In light of these results we can hypothesize that in EOC patients the reduced leptin production functions as a signal of increased energy expenditure and low energy reserves during the progression of the neoplastic disease. Leptin decrease in advanced EOC patients should induce an adaptative reduction of energy expenditure and an increase of appetite and food intake in response to the metabolic impairment induced by tumour growth and cancerrelated inflammation. The signal activated by the drop of leptin levels might therefore constitute the evidence of the metabolic hyperactivity of the tumour and the host immune system and the subsequent defence attempt of the host to reduce energy expenditure when energy is scarce. Leptin levels fell together with a significant weight loss, probably induced by the prolonged action of inflammatory mediators, only in the last phases of the neoplastic disease. Indeed, chronic inflammation results in severe alterations of cell metabolism, with deleterious effects on body composition, nutritional status and immune system efficiency. Therefore, IL-6 and leptin play a central role as early markers of the main metabolic alterations associated to the progression of advanced EOC, and therefore their assessment should be included in monitoring the disease outcome, especially when cancer is no longer curable with standard antineoplastic treatments and quality of life becomes the primary endpoint.

#### **4.2 Inflammation-related symptoms**

As widely written on, several studies have shown that inflammatory cytokines, and in particular IL-6, play a central role in the evolution of EOC and the mechanisms by which IL-

Inflammation and Ovarian Cancer 35

CRA is typically normochromic, normocytic with a low reticulocyte count. Bone marrow iron stores are adequate or increased, but iron reutilization is impaired, as shown by normal or increased ferritin levels and low serum iron levels and iron-binding capacity. In CRA, erythroid progenitor cells respond normally to erythropoietin (EPO), but EPO production is often not optimal for the level of anaemia. EOC patients and in particulary those in advanced stages of disease, suffer of anaemia similar to anaemia of inammation. In these patients the lowest hemoglobin (Hb) levels are linked with the highest concentrations of markers of inammation, such as proinammatory cytokines (IL-6, IL-1, TNF-a), CRP, and Fibrinogen, and with the lowest leptin levels. Statistical analysis conrmed that Hb inversely correlates with stage and ECOG PS, proinammatory cytokines, CRP, Fibrinogen, and ROS but positively correlated with leptin and GPx. By multivariate regression analysis, only stage of disease and IL-6 levels are independent factors in determining Hb levels. In accordance with these data, Van der Zee et al (van der Zee et al., 1995) demonstrated that higher levels of IL-6 in cystic uids from patients with malignant versus benign ovarian tumors correlate with decreased Hb levels and increased platelet counts as marker of inflammatory status. Several researchers have also demonstrated that IL-6 is both necessary and sufcient for the induction of hepcidin, an iron regulatory hormone responsible for inammation-induced iron disutilization resulting in the anaemia associated with acute and chronic infections, chronic kidney disease, and neoplastic disease. Of note, in an our study (Macciò et al., 2005) we demonstrated a signicant positive correlation between IL-6 and other markers of inammation and oxidative stress. Thus, high serum level of IL-6 may be considered an indicator of the inammatory and pro-oxidative status of patients with EOC and they could be linked also with a specic production of IL-6 by ovarian cancer cells. Although, it is not completely clear the mechanism through which the high levels of inammatory mediators could induce CRA, several studies showed that proinammatory cytokines blunt HUrEPO response to anaemia and impair erythroid colony formation in response to HUrEPO. Additionally, proinammatory cytokines and the acute-phase proteins impair iron metabolism, inhibiting the reticuloendothelial iron stores with low iron circulating levels. Furthermore, the presence of proinammatory cytokines in patients with EOC is associated with increased production of ROS either as a reection of inammation or as a consequence of their metabolic effects. Several studies demonstrated that ROSs are capable of inhibiting the production of EPO from kidney tissue. Takeda et al. (Takeda et al., 2002) hypothesized that also nutritional status, probably through leptin action, may affect erythropoiesis and demonstrated that BMI and leptin were inversely correlated with rHuEPO dose required in patients receiving hemodialysis. Indeed, *in vitro* studies have suggested that leptin plays a role in enhancing erythropoiesis but, certainly, this hypothesis needs more denitive analysis. Therefore, the results we have reported suggest that anaemia in patients with EOC is, at least in part, the consequence of cancer-related chronic inammation. Cancer-related anaemia must be recognized as a constitutional feature of patients with advanced neoplasms and not necessarily as just a consequence of antineoplastic treatments. Indeed, it has been widely demonstrated that CRA is associated with poor response to treatment and decreased survival, and with a decline in energy and activity levels, quality of life, and cognitive functions. An increased understanding of the

pathogenesis of CRA may help identify the most appropriate treatment strategies.

EOC patients, who have the poorest survival rate among gynaecologic cancer patients show high rates of depression. Depression among cancer patients has frequently been attributed

**4.2.2 Inflammation and depression** 

6 may influence disease progression and outcome are extremely complex and multifactorial. In fact IL-6, as well as IL-1 and TNF-α, is responsible for symptoms such as anorexia, nausea and vomiting, weight loss and altered energy metabolism. Furthermore, high IL-6 levels are associated with an impaired efficiency of immune cells both in terms of PBMC reduced blastic response and membrane-bound IL-2 receptor expression (Macciò et al., 1998). In the same way, recent data shown that IL-6 exerts a central role in the pathogenesis of cancerrelated anaemia. Additionally, elevated serum IL-6 levels account for its endocrine activity leading to severe impairment of physical, functional and psychosocial well-being (depression, anxiety, reduced social interaction) and fatigue.

#### **4.2.1 Cancer-related anaemia**

Anaemia is present in more than 30% of patients with EOC at the time of initial presentation. The severity of this particular form of anaemia called cancer-related anaemia (CRA) has been associated with more aggressive tumour hystotypes and is able to influence the response to treatment and the patients' performance status (PS). The biologic and hematologic characteristics of CRA are similar to those observed in anaemia occurring in chronic inammatory diseases. Several *in vitro* and *in vivo* studies demonstrated that high levels of proinflammatory cytokines and increased oxidative stress contribute both to the development of anaemia and to the resistance to human recombinant erythropoietin (HurEPO) (Figure 3).

Fig. 3. Pathogenetic mechanisms of cancer-related anaemia. Abbreviations: ROS, Reactive Oxygen Species; IL, Interleukin; TNF, Tumor Necrosis Factor; IFN, Interferon; CRP, Creactive protein; EPO, erythropoietin.

6 may influence disease progression and outcome are extremely complex and multifactorial. In fact IL-6, as well as IL-1 and TNF-α, is responsible for symptoms such as anorexia, nausea and vomiting, weight loss and altered energy metabolism. Furthermore, high IL-6 levels are associated with an impaired efficiency of immune cells both in terms of PBMC reduced blastic response and membrane-bound IL-2 receptor expression (Macciò et al., 1998). In the same way, recent data shown that IL-6 exerts a central role in the pathogenesis of cancerrelated anaemia. Additionally, elevated serum IL-6 levels account for its endocrine activity leading to severe impairment of physical, functional and psychosocial well-being

Anaemia is present in more than 30% of patients with EOC at the time of initial presentation. The severity of this particular form of anaemia called cancer-related anaemia (CRA) has been associated with more aggressive tumour hystotypes and is able to influence the response to treatment and the patients' performance status (PS). The biologic and hematologic characteristics of CRA are similar to those observed in anaemia occurring in chronic inammatory diseases. Several *in vitro* and *in vivo* studies demonstrated that high levels of proinflammatory cytokines and increased oxidative stress contribute both to the development of anaemia and to the resistance to human recombinant erythropoietin (HurEPO) (Figure 3).

Fig. 3. Pathogenetic mechanisms of cancer-related anaemia. Abbreviations: ROS, Reactive Oxygen Species; IL, Interleukin; TNF, Tumor Necrosis Factor; IFN, Interferon; CRP, C-

(depression, anxiety, reduced social interaction) and fatigue.

**4.2.1 Cancer-related anaemia** 

reactive protein; EPO, erythropoietin.

CRA is typically normochromic, normocytic with a low reticulocyte count. Bone marrow iron stores are adequate or increased, but iron reutilization is impaired, as shown by normal or increased ferritin levels and low serum iron levels and iron-binding capacity. In CRA, erythroid progenitor cells respond normally to erythropoietin (EPO), but EPO production is often not optimal for the level of anaemia. EOC patients and in particulary those in advanced stages of disease, suffer of anaemia similar to anaemia of inammation. In these patients the lowest hemoglobin (Hb) levels are linked with the highest concentrations of markers of inammation, such as proinammatory cytokines (IL-6, IL-1, TNF-a), CRP, and Fibrinogen, and with the lowest leptin levels. Statistical analysis conrmed that Hb inversely correlates with stage and ECOG PS, proinammatory cytokines, CRP, Fibrinogen, and ROS but positively correlated with leptin and GPx. By multivariate regression analysis, only stage of disease and IL-6 levels are independent factors in determining Hb levels. In accordance with these data, Van der Zee et al (van der Zee et al., 1995) demonstrated that higher levels of IL-6 in cystic uids from patients with malignant versus benign ovarian tumors correlate with decreased Hb levels and increased platelet counts as marker of inflammatory status. Several researchers have also demonstrated that IL-6 is both necessary and sufcient for the induction of hepcidin, an iron regulatory hormone responsible for inammation-induced iron disutilization resulting in the anaemia associated with acute and chronic infections, chronic kidney disease, and neoplastic disease. Of note, in an our study (Macciò et al., 2005) we demonstrated a signicant positive correlation between IL-6 and other markers of inammation and oxidative stress. Thus, high serum level of IL-6 may be considered an indicator of the inammatory and pro-oxidative status of patients with EOC and they could be linked also with a specic production of IL-6 by ovarian cancer cells. Although, it is not completely clear the mechanism through which the high levels of inammatory mediators could induce CRA, several studies showed that proinammatory cytokines blunt HUrEPO response to anaemia and impair erythroid colony formation in response to HUrEPO. Additionally, proinammatory cytokines and the acute-phase proteins impair iron metabolism, inhibiting the reticuloendothelial iron stores with low iron circulating levels. Furthermore, the presence of proinammatory cytokines in patients with EOC is associated with increased production of ROS either as a reection of inammation or as a consequence of their metabolic effects. Several studies demonstrated that ROSs are capable of inhibiting the production of EPO from kidney tissue. Takeda et al. (Takeda et al., 2002) hypothesized that also nutritional status, probably through leptin action, may affect erythropoiesis and demonstrated that BMI and leptin were inversely correlated with rHuEPO dose required in patients receiving hemodialysis. Indeed, *in vitro* studies have suggested that leptin plays a role in enhancing erythropoiesis but, certainly, this hypothesis needs more denitive analysis. Therefore, the results we have reported suggest that anaemia in patients with EOC is, at least in part, the consequence of cancer-related chronic inammation. Cancer-related anaemia must be recognized as a constitutional feature of patients with advanced neoplasms and not necessarily as just a consequence of antineoplastic treatments. Indeed, it has been widely demonstrated that CRA is associated with poor response to treatment and decreased survival, and with a decline in energy and activity levels, quality of life, and cognitive functions. An increased understanding of the pathogenesis of CRA may help identify the most appropriate treatment strategies.

#### **4.2.2 Inflammation and depression**

EOC patients, who have the poorest survival rate among gynaecologic cancer patients show high rates of depression. Depression among cancer patients has frequently been attributed

Inflammation and Ovarian Cancer 37

ascites, secreted cortisol may be inadequate to suppress IL-6. In turn, depression may contribute to enhanced IL-6 secretion. In fact, depression has been associated with systemic elevations in norepinephrine which is known to enhance IL-6 secretion by ovarian tumor cells in vitro, potentially setting up a positive feedback loop for IL-6 in the tumor microenvironment. It is also possible that all of these pathways may operate simultaneously

Fatigue is one of the most common and distressing side effects of cancer and its treatment and may persist long after successful treatment completion. Subjective and objective evidence suggest that a third to half of patients developing EOC report symptoms at 3 or more months prior to diagnosis (Lurie et al., 2009; Arriba et al., 2010). Fatigue may be part of these symptom complex (Smith, 2006). Cancer-related fatigue (CRF) has been defined by National Comprehensive Cancer Network as "a distressing persistent subjective sense of tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning". It can adversely affect emotional, physical and mental well-being. CRF can also affect patients' abilities to function in terms of their usual social activities, and their ability to carry on with their normal working lives. The two most plausible mechanism include an abnormal or prolonged inflammatory response and/or disruption to the HPA axis. Emerging evidence suggests that inflammatory processes may be involved in cancer-related fatigue both during and after treatment. Indeed, a wide range of different changes of the immune system has been shown in patients suffering from fatigue. The most common are deficit of cell-mediated immunity associated with high serum levels of the proinflammatory cytokines. Each of these cytokines can determine by themselves the symptomatology typical of patient suffering from fatigue. Indeed, it is well known that these cytokines play important actions both on the central nervous system and the endocrine system and at various sites involved in the regulation of energy metabolism. The same proinflammatory cytokines involved in cachexia and associated with chronic inflammation are potent stimulators of the HPA axis. Moreover, changes in the HPA axis may be caused by a number of different factors relevant to neoplastic disease: cancer itself and/or cancer treatment can alter the function of the HPA axis resulting in endocrine changes that cause or contribute to fatigue. All these findings highlight multiple and complex mechanisms through which the immune system function disorders may lead to fatigue. Moreover, the close link between fatigue and depression in cancer patients suggests that a common mechanism could underlie the development of both. Since serotonin is a principal (but not a sole) contributor to depression, the model would predict that serotonin-influencing interventions effective against clinical depression might also prove beneficial for fatigue. Furthermore, it has been proposed that patients with cancer, particularly those with anorexia–cachexia, have altered muscle protein metabolism,

Since the causes of fatigue are not fully understood, it is very difficult to treat it appropriately. The National Comprehensive Cancer Network's clinical guidelines also provide further options for cancer-related fatigue management. These suggest initially treating any underlying reversible causes of fatigue (e.g. anaemia, poor nutrition or depression) and attending to general supportive measures and psychosocial support. A recent review (Minton et al., 2010) has examined drug treatment for fatigue as it represents

(Weinrib et al., 2010).

**4.2.3 Inflammation and fatigue** 

which may also contribute to cancer-related fatigue.

to the stress of a potentially life-threatening diagnosis and the difficulties of cancer treatment. However, several recent studies among cancer patients have found associations between depression, elevated levels of the proinflammatory cytokine and/or dysregulation of the neuroendocrine hormone cortisol (Costanzo et al., 2005). Inflammation has been implicated in the pathogenesis of depression and it has been proposed that inflammatory cytokines such as IL-6 may contribute to depression in cancer patients. Also in healthy adults, elevated IL-6 has been associated with depressive symptoms and clinical depression. In particularly IL-6 has profound effects on the CNS, inducing a syndrome of "sickness behaviors" characterized by anhaedonia and vegetative symptoms including fatigue, malaise, anorexia, difficulty concentrating, reduced activity, sleep impairments, and disinterest in activities. Proinflammatory cytokines exert differential effects on affective and vegetative depression, with more prominent effects on vegetative symptoms. Affective and vegetative depressive symptoms are thought to occur via distinct mechanisms, with vegetative symptoms occurring significantly earlier than mood disturbance. Depressive symptoms are also associated with hypercortisolemia, downregulated glucocorticoid receptors, and general dysregulation of the hypothalamic pituitary adrenocortical (HPA) axis. With chronic stress and depression, the negative feedback system regulating cortisol may become impaired and diurnal cortisol rhythms altered, particularly with respect to evening cortisol. There is a well-characterized feedback loop whereby IL-6 stimulates HPA secretion of cortisol which, in turn, exerts negative feedback on IL-6 for inflammatory control. Persistent inflammation is associated with HPA abnormalities and may contribute to the hypercortisolemia seen in depression. In particular, in advanced-stage EOC patients, assessed prior to surgery, elevations of IL-6 associated with both affective and vegetative depressive symptoms have been documented (Lutgendorf et al., 2008). Early-stage patients had levels of IL-6 and depressive symptoms that were greater than those observed in LMP patients but lower than those in patients with advanced disease. Elevated IL-6 was also related to greater disturbances in the diurnal cortisol rhythm among advanced patients, with the elevated plasma and ascites levels of IL-6 related to higher evening cortisol as well as higher afternoon cortisol and cortisol AUC. These results are consistent with the "proinflammatory cytokine theory of depression" in suggesting that pathophysiologic elevations in circulating inflammatory mediators may lead to the appearance of depressive symptomatology via cytokine regulation of CNS function.

Proinflammatory cytokines influence the CNS via several direct pathways, including passage through permeability area of the blood-brain barrier and stimulation of afferent fibers in the vagus nerve. These fibers transmit information to specific brain nuclei with subsequent downstream effects on multiple central processes including induction of cytokines, neurotransmitters, stimulation of the HPA axis and development of sickness behaviors. Relationships between IL-6 and vegetative depression without any associations between affective depression and IL-6 are consistent with the possibility that inflammatory mechanisms may contribute specifically to vegetative symptoms, whereas other mechanisms may underlie affective symptoms of depression. Chronic inflammation can induce glucocorticoid resistance and lead to a hyperactive HPA axis. The resultant HPA dysregulation and high levels of cortisol may contribute to depression, providing an indirect pathway linking IL-6 and depression. Then, the excessive production of IL-6 by ovarian carcinomas may set up a chronic proinflammatory state, eliciting sickness behaviors in the CNS and hypersecretion and dysregulation of the HPA axis, both contributing to depressive symptomatology. Because of extremely high levels of tumor-secreted IL-6, particularly in

to the stress of a potentially life-threatening diagnosis and the difficulties of cancer treatment. However, several recent studies among cancer patients have found associations between depression, elevated levels of the proinflammatory cytokine and/or dysregulation of the neuroendocrine hormone cortisol (Costanzo et al., 2005). Inflammation has been implicated in the pathogenesis of depression and it has been proposed that inflammatory cytokines such as IL-6 may contribute to depression in cancer patients. Also in healthy adults, elevated IL-6 has been associated with depressive symptoms and clinical depression. In particularly IL-6 has profound effects on the CNS, inducing a syndrome of "sickness behaviors" characterized by anhaedonia and vegetative symptoms including fatigue, malaise, anorexia, difficulty concentrating, reduced activity, sleep impairments, and disinterest in activities. Proinflammatory cytokines exert differential effects on affective and vegetative depression, with more prominent effects on vegetative symptoms. Affective and vegetative depressive symptoms are thought to occur via distinct mechanisms, with vegetative symptoms occurring significantly earlier than mood disturbance. Depressive symptoms are also associated with hypercortisolemia, downregulated glucocorticoid receptors, and general dysregulation of the hypothalamic pituitary adrenocortical (HPA) axis. With chronic stress and depression, the negative feedback system regulating cortisol may become impaired and diurnal cortisol rhythms altered, particularly with respect to evening cortisol. There is a well-characterized feedback loop whereby IL-6 stimulates HPA secretion of cortisol which, in turn, exerts negative feedback on IL-6 for inflammatory control. Persistent inflammation is associated with HPA abnormalities and may contribute to the hypercortisolemia seen in depression. In particular, in advanced-stage EOC patients, assessed prior to surgery, elevations of IL-6 associated with both affective and vegetative depressive symptoms have been documented (Lutgendorf et al., 2008). Early-stage patients had levels of IL-6 and depressive symptoms that were greater than those observed in LMP patients but lower than those in patients with advanced disease. Elevated IL-6 was also related to greater disturbances in the diurnal cortisol rhythm among advanced patients, with the elevated plasma and ascites levels of IL-6 related to higher evening cortisol as well as higher afternoon cortisol and cortisol AUC. These results are consistent with the "proinflammatory cytokine theory of depression" in suggesting that pathophysiologic elevations in circulating inflammatory mediators may lead to the appearance of depressive

symptomatology via cytokine regulation of CNS function.

Proinflammatory cytokines influence the CNS via several direct pathways, including passage through permeability area of the blood-brain barrier and stimulation of afferent fibers in the vagus nerve. These fibers transmit information to specific brain nuclei with subsequent downstream effects on multiple central processes including induction of cytokines, neurotransmitters, stimulation of the HPA axis and development of sickness behaviors. Relationships between IL-6 and vegetative depression without any associations between affective depression and IL-6 are consistent with the possibility that inflammatory mechanisms may contribute specifically to vegetative symptoms, whereas other mechanisms may underlie affective symptoms of depression. Chronic inflammation can induce glucocorticoid resistance and lead to a hyperactive HPA axis. The resultant HPA dysregulation and high levels of cortisol may contribute to depression, providing an indirect pathway linking IL-6 and depression. Then, the excessive production of IL-6 by ovarian carcinomas may set up a chronic proinflammatory state, eliciting sickness behaviors in the CNS and hypersecretion and dysregulation of the HPA axis, both contributing to depressive symptomatology. Because of extremely high levels of tumor-secreted IL-6, particularly in ascites, secreted cortisol may be inadequate to suppress IL-6. In turn, depression may contribute to enhanced IL-6 secretion. In fact, depression has been associated with systemic elevations in norepinephrine which is known to enhance IL-6 secretion by ovarian tumor cells in vitro, potentially setting up a positive feedback loop for IL-6 in the tumor microenvironment. It is also possible that all of these pathways may operate simultaneously (Weinrib et al., 2010).

#### **4.2.3 Inflammation and fatigue**

Fatigue is one of the most common and distressing side effects of cancer and its treatment and may persist long after successful treatment completion. Subjective and objective evidence suggest that a third to half of patients developing EOC report symptoms at 3 or more months prior to diagnosis (Lurie et al., 2009; Arriba et al., 2010). Fatigue may be part of these symptom complex (Smith, 2006). Cancer-related fatigue (CRF) has been defined by National Comprehensive Cancer Network as "a distressing persistent subjective sense of tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning". It can adversely affect emotional, physical and mental well-being. CRF can also affect patients' abilities to function in terms of their usual social activities, and their ability to carry on with their normal working lives. The two most plausible mechanism include an abnormal or prolonged inflammatory response and/or disruption to the HPA axis. Emerging evidence suggests that inflammatory processes may be involved in cancer-related fatigue both during and after treatment. Indeed, a wide range of different changes of the immune system has been shown in patients suffering from fatigue. The most common are deficit of cell-mediated immunity associated with high serum levels of the proinflammatory cytokines. Each of these cytokines can determine by themselves the symptomatology typical of patient suffering from fatigue. Indeed, it is well known that these cytokines play important actions both on the central nervous system and the endocrine system and at various sites involved in the regulation of energy metabolism. The same proinflammatory cytokines involved in cachexia and associated with chronic inflammation are potent stimulators of the HPA axis. Moreover, changes in the HPA axis may be caused by a number of different factors relevant to neoplastic disease: cancer itself and/or cancer treatment can alter the function of the HPA axis resulting in endocrine changes that cause or contribute to fatigue. All these findings highlight multiple and complex mechanisms through which the immune system function disorders may lead to fatigue. Moreover, the close link between fatigue and depression in cancer patients suggests that a common mechanism could underlie the development of both. Since serotonin is a principal (but not a sole) contributor to depression, the model would predict that serotonin-influencing interventions effective against clinical depression might also prove beneficial for fatigue. Furthermore, it has been proposed that patients with cancer, particularly those with anorexia–cachexia, have altered muscle protein metabolism, which may also contribute to cancer-related fatigue.

Since the causes of fatigue are not fully understood, it is very difficult to treat it appropriately. The National Comprehensive Cancer Network's clinical guidelines also provide further options for cancer-related fatigue management. These suggest initially treating any underlying reversible causes of fatigue (e.g. anaemia, poor nutrition or depression) and attending to general supportive measures and psychosocial support. A recent review (Minton et al., 2010) has examined drug treatment for fatigue as it represents

Inflammation and Ovarian Cancer 39

inversely associated with their response to cisplatin and paclitaxel. Moreover, both exogenous and endogenous IL-6 induce cisplatin and paclitaxel resistance in non-IL-6 producing cells, whereas deleting of endogenous IL-6 expression in IL-6-overexpressing cells promotes the sensitivity of these cells to these anticancer drugs. Meanwhile, IL-6 mediated resistance of EOC cells exhibits decreased proteolytic activation of caspase-3 and a number of studies have shown that the anti-apoptotic ability of IL-6 was associated with expression of the Bcl-2 family proteins that are typically associated with resistance to chemotherapy. Then, the main mechanism of drug resistance induced by IL-6 is exerted in a dose dependent manner by the increased expression of Bcl-2 family proteins. Other lines of evidence suggest that also the activation of Ras/MEK/ERK and PI3K/Akt, the most important cell survival signalling, protects EOC cells from chemotherapy. It has been shown that cisplatin treatment modulates ERK and that activation of ERK protects ovarian cancer cells from cisplatin-induced death. The inhibition *in vitro* of ERK signalling by a MEK1/2 inhibitor blocked ERK activation and increased cisplatin sensitivity in specific EOC cell lines. Also, the inactivation of Akt and its downstream targets sensitizes human ovarian cancer cells to cisplatin and paclitaxel. Worthy of note, it is specifically IL-6 to be able to induce activation of ERK and Akt in ovarian cancer cells and that the use of specific inhibitors of these two signal transducers, inhibits IL-6-induced cisplatin and paclitaxel resistance. Taken together, these data suggest that IL-6 promotes chemoresistance of ovarian cancer cells via activation of multiple signal transduction pathways including ERK cascade and PI3K/Akt pathway. These results provide support for these signal transduction

Another major downstream component of the IL-6 signalling pathway is STAT3. Duan et al. (Duan et al., 2006) has reported that inhibition of STAT3 expression increases the sensitivity of ovarian cancer cell lines to paclitaxel treatment *in vitro*, suggesting that the STAT3 pathway may also be involved in chemoresistance of ovarian cancer cells. They found that IL-6 induced phosphorylation of STAT3 in several, but not all, of the examined ovarian cancer cell lines. However, it is possible that STAT3 could be activated also through IL-6 independent mechanisms such as Src, epidermal growth factor receptor, or other cytokines

In conclusion, IL-6 secreted by ovarian cancer and/or immune cells may contribute to the refractoriness of these cells to conventional chemotherapy through down-regulation of various signalling step. IL-6-induced chemoresistance may be associated with increase of both multidrug resistance-related genes (MDR1 and GSTpi) and apoptosis inhibitory proteins (Bcl-2, BclxL and XIAP), as well as activation of Ras/MEK/ERK and PI3K/Akt. Then, modulation of IL-6 expression or its related signalling pathways may be a promising

Also COX-2 could represent a possible new marker of sensitivity to platinum-based chemotherapy in ovarian cancer. In a study by Ferrandina et al in a population of advanced ovarian cancer patients, COX-2 positivity was found in a statistically significant higher percentage of unresponsive cases than in patients responding to chemotherapy (Ferrandina et al., 2002b). The association between COX-2 positivity and poor chance of response to treatment was retained in multivariate analysis. The ability of COX-2 to predict tumour sensitivity to chemotherapy is not dependent on EGFR or Her-2/neu status and could be independently associated with prognosis. Therefore, in this context, the availability of

agents able to specifically interfere with COX-2 is of potential interest.

pathways as a strategy for reversing drug resistance.

like oncostatin in different cancer cells.

strategy of treatment for drug-resistant EOC.

one of the ways this problem can be tackled. The review authors looked at trials in all types of cancer and at all stages of treatment. Fifty studies met the inclusion criteria but only 31 (7104 participants) were deemed suitable for detailed analysis as they explored fatigue in sufficient detail. They found mixed results with some drugs showing an effect on fatigue. In particular the authors concluded that Methylphenidate, a stimulant drug that improves concentration, is effective for the management of cancer-related fatigue but the small samples used in the available studies mean more research is needed to confirm its role. Erythropoietin and darbopoetin, drugs that improve anaemia, are effective in the management of cancer-related fatigue. Research on inflammation and cancer-related fatigue helps to elucidate the biological basis for this common and troublesome symptom and may also promote the development of targeted therapies. In particular, use of cytokine antagonists may be a promising direction for intervention efforts. There is preliminary evidence that TNF-α blockade with etanercept is safe and effective in reducing fatigue among patients with advanced cancer (Monk et al., 2006), but effects among patients with early stage cancer and cancer survivors have not been determined. Behavioural and mind– body interventions also show considerable promise for treating fatigue and other cancerrelated symptoms, and there is preliminary evidence for their effects on immune function (Carlson et al., 2003; Fairey et al., 2005; Stevinson et al., 2009). These treatments may be more palatable to EOC patients than pharmacologic therapies and are another important avenue for research efforts.

#### **4.3 Chemoresistance**

It has been shown that increased IL-6 concentration in serum and ascites of EOC patients correlates with chemoresistance. In particular, the IL-6 signalling cascade in ovarian cancer cells has been associated with the development of cisplatin and paclitaxel resistance (Wang et al, 2010). The underlining mechanisms of IL-6-mediated chemoresistance in ovarian cancer cells are not so clear. However, some studies showed that IL-6 is associated with increased expression of multidrug resistance-related genes, apoptosis inhibitory proteins (Bcl-2, Bcl-xL and XIAP) as well as activation of Ras/MEK/Erk and PI3K/Akt signalling. Moreover, IL-6 signalling prevents chemotherapy-induced endothelial cells apoptosis (Lo et al., 2011). Thus, interference with IL-6 pathway may offer opportunities for new strategies in ovarian cancer therapy. Using a monoclonal antibody that specifically blocks IL-6 signalling (siltuximab), Guo et al. demonstrated *in vitro* that the combination of siltuximab with paclitaxel increased the sensitivity of ovarian tumour cells to paclitaxel (Guo et al., 2010). In vitro studies with ovarian cancer cell lines confirm that generation of paclitaxel-resistant sublines is often associated with increased IL-6 mRNA expression and protein secretion. As well known, IL-6 acts through a hexametric receptor, which contains the ligand-binding IL-6a chain and the common cytokine receptor signal-transducing subunit gp130. The binding

of IL-6 to gp130 activates multiple signal transduction pathways such as signal transducers and activators of transcription (JAK/STATs) pathway, Ras/MEK (mitogen-activated protein or extracellular signal-regulated kinase kinase)/ERK (extracellular signal-regulated kinase) pathway, and PI3K (phosphotidylinositol 3 kinase)/Akt pathway. Recently, evidence suggests that activation of Ras/MEK/ERK and PI3K/Akt signalling pathways play an important role in chemoresistance of EOC. A research by Wang et al (Wang et al., 2010) firstly demonstrated that autocrine production of IL-6 by ovarian cancer cell lines is

one of the ways this problem can be tackled. The review authors looked at trials in all types of cancer and at all stages of treatment. Fifty studies met the inclusion criteria but only 31 (7104 participants) were deemed suitable for detailed analysis as they explored fatigue in sufficient detail. They found mixed results with some drugs showing an effect on fatigue. In particular the authors concluded that Methylphenidate, a stimulant drug that improves concentration, is effective for the management of cancer-related fatigue but the small samples used in the available studies mean more research is needed to confirm its role. Erythropoietin and darbopoetin, drugs that improve anaemia, are effective in the management of cancer-related fatigue. Research on inflammation and cancer-related fatigue helps to elucidate the biological basis for this common and troublesome symptom and may also promote the development of targeted therapies. In particular, use of cytokine antagonists may be a promising direction for intervention efforts. There is preliminary evidence that TNF-α blockade with etanercept is safe and effective in reducing fatigue among patients with advanced cancer (Monk et al., 2006), but effects among patients with early stage cancer and cancer survivors have not been determined. Behavioural and mind– body interventions also show considerable promise for treating fatigue and other cancerrelated symptoms, and there is preliminary evidence for their effects on immune function (Carlson et al., 2003; Fairey et al., 2005; Stevinson et al., 2009). These treatments may be more palatable to EOC patients than pharmacologic therapies and are another important avenue

It has been shown that increased IL-6 concentration in serum and ascites of EOC patients correlates with chemoresistance. In particular, the IL-6 signalling cascade in ovarian cancer cells has been associated with the development of cisplatin and paclitaxel resistance (Wang et al, 2010). The underlining mechanisms of IL-6-mediated chemoresistance in ovarian cancer cells are not so clear. However, some studies showed that IL-6 is associated with increased expression of multidrug resistance-related genes, apoptosis inhibitory proteins (Bcl-2, Bcl-xL and XIAP) as well as activation of Ras/MEK/Erk and PI3K/Akt signalling. Moreover, IL-6 signalling prevents chemotherapy-induced endothelial cells apoptosis (Lo et al., 2011). Thus, interference with IL-6 pathway may offer opportunities for new strategies in ovarian cancer therapy. Using a monoclonal antibody that specifically blocks IL-6 signalling (siltuximab), Guo et al. demonstrated *in vitro* that the combination of siltuximab with paclitaxel increased the sensitivity of ovarian tumour cells to paclitaxel (Guo et al., 2010). In vitro studies with ovarian cancer cell lines confirm that generation of paclitaxel-resistant sublines is often associated with increased IL-6 mRNA expression and protein secretion. As well known, IL-6 acts through a hexametric receptor, which contains the ligand-binding IL-6a chain and the common cytokine receptor signal-transducing subunit gp130. The binding of IL-6 to gp130 activates multiple signal transduction pathways such as signal transducers and activators of transcription (JAK/STATs) pathway, Ras/MEK (mitogen-activated protein or extracellular signal-regulated kinase kinase)/ERK (extracellular signal-regulated kinase) pathway, and PI3K (phosphotidylinositol 3 kinase)/Akt pathway. Recently, evidence suggests that activation of Ras/MEK/ERK and PI3K/Akt signalling pathways play an important role in chemoresistance of EOC. A research by Wang et al (Wang et al., 2010) firstly demonstrated that autocrine production of IL-6 by ovarian cancer cell lines is

for research efforts.

**4.3 Chemoresistance** 

inversely associated with their response to cisplatin and paclitaxel. Moreover, both exogenous and endogenous IL-6 induce cisplatin and paclitaxel resistance in non-IL-6 producing cells, whereas deleting of endogenous IL-6 expression in IL-6-overexpressing cells promotes the sensitivity of these cells to these anticancer drugs. Meanwhile, IL-6 mediated resistance of EOC cells exhibits decreased proteolytic activation of caspase-3 and a number of studies have shown that the anti-apoptotic ability of IL-6 was associated with expression of the Bcl-2 family proteins that are typically associated with resistance to chemotherapy. Then, the main mechanism of drug resistance induced by IL-6 is exerted in a dose dependent manner by the increased expression of Bcl-2 family proteins. Other lines of evidence suggest that also the activation of Ras/MEK/ERK and PI3K/Akt, the most important cell survival signalling, protects EOC cells from chemotherapy. It has been shown that cisplatin treatment modulates ERK and that activation of ERK protects ovarian cancer cells from cisplatin-induced death. The inhibition *in vitro* of ERK signalling by a MEK1/2 inhibitor blocked ERK activation and increased cisplatin sensitivity in specific EOC cell lines. Also, the inactivation of Akt and its downstream targets sensitizes human ovarian cancer cells to cisplatin and paclitaxel. Worthy of note, it is specifically IL-6 to be able to induce activation of ERK and Akt in ovarian cancer cells and that the use of specific inhibitors of these two signal transducers, inhibits IL-6-induced cisplatin and paclitaxel resistance. Taken together, these data suggest that IL-6 promotes chemoresistance of ovarian cancer cells via activation of multiple signal transduction pathways including ERK cascade and PI3K/Akt pathway. These results provide support for these signal transduction pathways as a strategy for reversing drug resistance.

Another major downstream component of the IL-6 signalling pathway is STAT3. Duan et al. (Duan et al., 2006) has reported that inhibition of STAT3 expression increases the sensitivity of ovarian cancer cell lines to paclitaxel treatment *in vitro*, suggesting that the STAT3 pathway may also be involved in chemoresistance of ovarian cancer cells. They found that IL-6 induced phosphorylation of STAT3 in several, but not all, of the examined ovarian cancer cell lines. However, it is possible that STAT3 could be activated also through IL-6 independent mechanisms such as Src, epidermal growth factor receptor, or other cytokines like oncostatin in different cancer cells.

In conclusion, IL-6 secreted by ovarian cancer and/or immune cells may contribute to the refractoriness of these cells to conventional chemotherapy through down-regulation of various signalling step. IL-6-induced chemoresistance may be associated with increase of both multidrug resistance-related genes (MDR1 and GSTpi) and apoptosis inhibitory proteins (Bcl-2, BclxL and XIAP), as well as activation of Ras/MEK/ERK and PI3K/Akt. Then, modulation of IL-6 expression or its related signalling pathways may be a promising strategy of treatment for drug-resistant EOC.

Also COX-2 could represent a possible new marker of sensitivity to platinum-based chemotherapy in ovarian cancer. In a study by Ferrandina et al in a population of advanced ovarian cancer patients, COX-2 positivity was found in a statistically significant higher percentage of unresponsive cases than in patients responding to chemotherapy (Ferrandina et al., 2002b). The association between COX-2 positivity and poor chance of response to treatment was retained in multivariate analysis. The ability of COX-2 to predict tumour sensitivity to chemotherapy is not dependent on EGFR or Her-2/neu status and could be independently associated with prognosis. Therefore, in this context, the availability of agents able to specifically interfere with COX-2 is of potential interest.

Inflammation and Ovarian Cancer 41

could play a significant role in the future of cancer and adjuvant cancer therapies

STAT3 activation is also induced by hypoxia that is commonly observed in many solid tumours and represents a major obstacle to chemo- or radiation therapy. In an experimental animal model it has been shown that exposure of mice containing human ovarian cancer xenograft tumour to hyperbaric oxygen (HBO) obtained a significant reduction in tumour volume, associated with a significant decrease of STAT3 (Tyr 705) activation and cyclin-D1 protein/mRNA levels. Interestingly, HBO exposure, in combination with weekly administration of cisplatin, also significantly reduced the tumour volume. Therefore, therapeutic strategies able to increase tumour oxygenation may be able to inhibit key steps, such as STAT3 activation, involved in the ovarian tumour progression. (Selvendiran et al., 2010). Moreover, the reduced effectiveness of conventional chemotherapeutic drugs cisplatin and taxol in eliminating the hypoxic ovarian cancer cells suggests a role for pSTAT3 in cellular resistance to chemotherapy. It has been shown that inhibition of STAT3 followed by treatment with cisplatin or taxol resulted in a significant increase in apoptosis supporting the hypothesis that hypoxia-induced STAT3 activation is responsible for chemoresistance (Selvendiran et al., 2009). According to this evidence the correction of anaemia and the maintenance of adequate Hb levels during cancer chemotherapy should be addressed as a fundamental outcome in the therapeutic

Disruption of STAT3 could also be therefore an effective approach to control EOC tumorigenesis. Among the several compounds tested for chemoprevention of EOC curcumin is one of the most interesting and studied. Curcumin is a dihydroxyphenolic compound, whose anti-tumour mechanisms involve regulation of STAT-3 and the negative regulators of STAT-3, including suppressors of cytokine signalling proteins (SOCS-1 and SOCS-3), protein inhibitors of activated STAT (PIAS-1 and PIAS-3), and SH2 domaincontaining phosphatases (SHP-1 and SHP-2). Treatment of ovarian cancer cells with curcumin induced a dose- and time-dependent decrease of constitutive IL-6 expression and IL-6-induced STAT-3 phosphorylation, which is associated with decreased cell viability and increased cleavage of caspase-3. Moreover, curcumin suppresses JAK-STAT signalling also via activation of PIAS-3, thus attenuating STAT-3 phosphorylation and tumour cell growth (Saydmohammed et al., 2010). The activity of curcumin on STAT3 is also mediated by its ability to inhibit lysophosphatidic acid (LPA) which is a biolipid that stimulates tumour cell invasion and metastasis by inducing phosphorylation of STAT3 as well as IL-6 and IL-8 secretion, which in turn results in STAT3 phosphorylation. Treatment of the cells with curcumin inhibited LPA-induced IL-6 and IL-8 secretion and STAT3 phosphorylation,

Since the same inflammatory mediators that promote tumour growth also are responsible for cancer-related symptoms, i.e., cachexia/anorexia, anaemia, fatigue, pain, debilitation and shortened survival, a concerted effort should be made to attack inflammation alongside with other anticancer measures at initial diagnosis with the consequent probability of improving both patient quality of life and survival (MacDonald, 2007). Therefore, counteracting cancerrelated inflammation is certainly a key target in the therapeutic approach of symptoms associated to advanced cancer, especially in EOC patients who are diagnosed at advanced stage and suffer of severe distressing symptoms. A suggestive example of how the

leading to blocked ovarian cancer cell motility (Seo et al., 2010).

(Lavecchia et al., 2011).

strategies of EOC.

#### **5. Inflammation and possible therapeutic implications**

Our knowledge on ovarian cancer-related inflammation offers innovative therapeutic strategies. For many years, all efforts to treat cancer have concentrated on the destruction/inhibition of tumour cells. Strategies to modulate the host microenvironment offer a complementary perspective. Primary proinflammatory cytokines represent the main targets and ongoing results in this direction justify continuing efforts (Colotta et al., 2009). In particular, IL-6, as described above, plays a central role in EOC in promoting tumour growth and progression and influencing its prognosis and related symptoms. Collectively, all data available in the literature and reported in the previous sections of this chapter lead to hypothesize that IL-6 antagonists may have therapeutic activity in patients with ovarian cancer via inhibition of a tumour-promoting cytokine network. Accordingly to this evidence, Coward et al. (Coward et al., 2011) carried out an experimental study to assess the activity of the anti-human-IL-6 antibody siltuximab (CNTO328) in tissue culture of EOC and human ovarian cancer xenografts. The authors demonstrated that IL-6 is expressed both in malignant cells and infiltrating leukocytes, endothelial cells and stromal fibroblast. In addition, they found that high IL-6 expression in EOC cells was associated with poor prognosis. Vice versa, IL-6 inhibition prevents the constitutive production of IL-6 and other inflammatory and angiogenic mediators by EOC cells. Additionally, siltuximab had also a significant inhibitory effect on tumour cell proliferation, macrophage infiltration and angiogenesis. In the same paper Coward et al presented the results of a single arm phase II clinical trial of the anti-human IL-6 monoclonal antibody siltuximab in women with recurrent ovarian cancer. Interestingly, they showed that siltuximab, given as a single agent, has some clinical activity in recurrent, platinum-resistant ovarian cancer. A total of eight patients achieved radiological disease stabilisation, which lasted six months or more in four cases. One of these eight also had normalisation of CA125 that lasted for 12 weeks, giving an overall partial response by combined RECIST/CA125 criteria. Noteworthy, partial response was accompanied by a reduction in 18F FDG uptake as detected by PET/TC imaging. Moreover, siltuximab treatment induced a decline in plasma levels of CRP, CCL2, CXCL12, VEGF and IL-8. Also a significant increase in Hb levels occurred in the majority of patients. The study by Coward et al is the first clinical study of anti-IL-6 therapy carried out in a population of EOC patients. Several experimental studies support the rationale for using this anti-IL-6 mAb in EOC. In fact, it has been demonstrated that siltuximab specifically suppress IL-6-induced STAT3 phosphorylation and STAT3 nuclear translocation, as well as the levels of Stat3 downstream proteins such as MCL-1, Bcl-X (L), and surviving, thus targeting the main intracellular mediator of the effects of cytokines on EOC cells growth (Guo et al., 2010).

Indeed, as well described above, STAT3 is constitutively active in EOC and leads to increased expression of genes regulating survival and proliferation, and drives the malignant behaviour of these cells. Therefore, the identification of novel compounds that selectively inhibit STAT3 activity may lead to additional useful tools to reduce cancerassociated cell proliferation, inflammation, and chemotherapeutic resistance. A potent and selective STAT3 inhibitor has been identified through the use of high throughput screening, synthetic medicinal chemistry, and molecular assays. Due to the central role of aberrant STAT3 signalling in ovarian cancer pathogenesis, this compound may provide a useful starting point for the development of chemical scaffolds to block STAT3 signalling for cancer therapy (Madoux et al., 2010). In particular, STAT3 dimerization inhibitors

Our knowledge on ovarian cancer-related inflammation offers innovative therapeutic strategies. For many years, all efforts to treat cancer have concentrated on the destruction/inhibition of tumour cells. Strategies to modulate the host microenvironment offer a complementary perspective. Primary proinflammatory cytokines represent the main targets and ongoing results in this direction justify continuing efforts (Colotta et al., 2009). In particular, IL-6, as described above, plays a central role in EOC in promoting tumour growth and progression and influencing its prognosis and related symptoms. Collectively, all data available in the literature and reported in the previous sections of this chapter lead to hypothesize that IL-6 antagonists may have therapeutic activity in patients with ovarian cancer via inhibition of a tumour-promoting cytokine network. Accordingly to this evidence, Coward et al. (Coward et al., 2011) carried out an experimental study to assess the activity of the anti-human-IL-6 antibody siltuximab (CNTO328) in tissue culture of EOC and human ovarian cancer xenografts. The authors demonstrated that IL-6 is expressed both in malignant cells and infiltrating leukocytes, endothelial cells and stromal fibroblast. In addition, they found that high IL-6 expression in EOC cells was associated with poor prognosis. Vice versa, IL-6 inhibition prevents the constitutive production of IL-6 and other inflammatory and angiogenic mediators by EOC cells. Additionally, siltuximab had also a significant inhibitory effect on tumour cell proliferation, macrophage infiltration and angiogenesis. In the same paper Coward et al presented the results of a single arm phase II clinical trial of the anti-human IL-6 monoclonal antibody siltuximab in women with recurrent ovarian cancer. Interestingly, they showed that siltuximab, given as a single agent, has some clinical activity in recurrent, platinum-resistant ovarian cancer. A total of eight patients achieved radiological disease stabilisation, which lasted six months or more in four cases. One of these eight also had normalisation of CA125 that lasted for 12 weeks, giving an overall partial response by combined RECIST/CA125 criteria. Noteworthy, partial response was accompanied by a reduction in 18F FDG uptake as detected by PET/TC imaging. Moreover, siltuximab treatment induced a decline in plasma levels of CRP, CCL2, CXCL12, VEGF and IL-8. Also a significant increase in Hb levels occurred in the majority of patients. The study by Coward et al is the first clinical study of anti-IL-6 therapy carried out in a population of EOC patients. Several experimental studies support the rationale for using this anti-IL-6 mAb in EOC. In fact, it has been demonstrated that siltuximab specifically suppress IL-6-induced STAT3 phosphorylation and STAT3 nuclear translocation, as well as the levels of Stat3 downstream proteins such as MCL-1, Bcl-X (L), and surviving, thus targeting the main intracellular mediator of the effects of cytokines on EOC cells growth

Indeed, as well described above, STAT3 is constitutively active in EOC and leads to increased expression of genes regulating survival and proliferation, and drives the malignant behaviour of these cells. Therefore, the identification of novel compounds that selectively inhibit STAT3 activity may lead to additional useful tools to reduce cancerassociated cell proliferation, inflammation, and chemotherapeutic resistance. A potent and selective STAT3 inhibitor has been identified through the use of high throughput screening, synthetic medicinal chemistry, and molecular assays. Due to the central role of aberrant STAT3 signalling in ovarian cancer pathogenesis, this compound may provide a useful starting point for the development of chemical scaffolds to block STAT3 signalling for cancer therapy (Madoux et al., 2010). In particular, STAT3 dimerization inhibitors

**5. Inflammation and possible therapeutic implications** 

(Guo et al., 2010).

could play a significant role in the future of cancer and adjuvant cancer therapies (Lavecchia et al., 2011).

STAT3 activation is also induced by hypoxia that is commonly observed in many solid tumours and represents a major obstacle to chemo- or radiation therapy. In an experimental animal model it has been shown that exposure of mice containing human ovarian cancer xenograft tumour to hyperbaric oxygen (HBO) obtained a significant reduction in tumour volume, associated with a significant decrease of STAT3 (Tyr 705) activation and cyclin-D1 protein/mRNA levels. Interestingly, HBO exposure, in combination with weekly administration of cisplatin, also significantly reduced the tumour volume. Therefore, therapeutic strategies able to increase tumour oxygenation may be able to inhibit key steps, such as STAT3 activation, involved in the ovarian tumour progression. (Selvendiran et al., 2010). Moreover, the reduced effectiveness of conventional chemotherapeutic drugs cisplatin and taxol in eliminating the hypoxic ovarian cancer cells suggests a role for pSTAT3 in cellular resistance to chemotherapy. It has been shown that inhibition of STAT3 followed by treatment with cisplatin or taxol resulted in a significant increase in apoptosis supporting the hypothesis that hypoxia-induced STAT3 activation is responsible for chemoresistance (Selvendiran et al., 2009). According to this evidence the correction of anaemia and the maintenance of adequate Hb levels during cancer chemotherapy should be addressed as a fundamental outcome in the therapeutic strategies of EOC.

Disruption of STAT3 could also be therefore an effective approach to control EOC tumorigenesis. Among the several compounds tested for chemoprevention of EOC curcumin is one of the most interesting and studied. Curcumin is a dihydroxyphenolic compound, whose anti-tumour mechanisms involve regulation of STAT-3 and the negative regulators of STAT-3, including suppressors of cytokine signalling proteins (SOCS-1 and SOCS-3), protein inhibitors of activated STAT (PIAS-1 and PIAS-3), and SH2 domaincontaining phosphatases (SHP-1 and SHP-2). Treatment of ovarian cancer cells with curcumin induced a dose- and time-dependent decrease of constitutive IL-6 expression and IL-6-induced STAT-3 phosphorylation, which is associated with decreased cell viability and increased cleavage of caspase-3. Moreover, curcumin suppresses JAK-STAT signalling also via activation of PIAS-3, thus attenuating STAT-3 phosphorylation and tumour cell growth (Saydmohammed et al., 2010). The activity of curcumin on STAT3 is also mediated by its ability to inhibit lysophosphatidic acid (LPA) which is a biolipid that stimulates tumour cell invasion and metastasis by inducing phosphorylation of STAT3 as well as IL-6 and IL-8 secretion, which in turn results in STAT3 phosphorylation. Treatment of the cells with curcumin inhibited LPA-induced IL-6 and IL-8 secretion and STAT3 phosphorylation, leading to blocked ovarian cancer cell motility (Seo et al., 2010).

Since the same inflammatory mediators that promote tumour growth also are responsible for cancer-related symptoms, i.e., cachexia/anorexia, anaemia, fatigue, pain, debilitation and shortened survival, a concerted effort should be made to attack inflammation alongside with other anticancer measures at initial diagnosis with the consequent probability of improving both patient quality of life and survival (MacDonald, 2007). Therefore, counteracting cancerrelated inflammation is certainly a key target in the therapeutic approach of symptoms associated to advanced cancer, especially in EOC patients who are diagnosed at advanced stage and suffer of severe distressing symptoms. A suggestive example of how the

Inflammation and Ovarian Cancer 43

proportion of advanced EOC patients, were enrolled. All patients were given as basic treatment polyphenols plus antioxidant agents alpha-lipoic acid, carbocysteine, and vitamins A, C, and E, all orally administered. Then patients were randomly assigned to one of five treatment arms: arm 1, MPA (500 mg/day) or MA (320 mg/day); arm 2, oral supplementation with EPA; arm 3, L-carnitine (4 g/day); arm 4, thalidomide (200 mg/day);

Treatment duration was 4 months. Analysis of variance showed a significant difference between treatment arms. A post hoc analysis showed the superiority of arm 5 over the others for all primary endpoints. An analysis of changes from baseline showed that LBM (by dual-energy X-ray absorptiometry and by L3 computed tomography) significantly increased in arm 5. REE decreased significantly and fatigue improved significantly in arm 5. Appetite increased significantly in arm 5; IL-6 decreased significantly in arm 5 and arm 4; Glasgow Prognostic Score (GPS) and Eastern Cooperative Oncology Group (ECOG) performance status (PS) score decreased significantly in arm 5, arm 4, and arm 3. Toxicity was quite negligible, and was comparable between arms. In conclusion, the most effective treatment in terms of all three primary efficacy endpoints and the secondary endpoints appetite, IL-6, GPS, and ECOG PS score was the combination regimen that included all selected agents.

Fig. 4. Phase III randomised clinical trial of five different arms of treatment for cancer

Proinflammatory cytokines, and in particular IL-6, as demonstrated in the present chapter, are involved in the development and progression of EOC. They are also associated with fatigue, depression, anaemia, pain and cachexia that impact significantly quality of life. Strategies to inhibit the effect of inflammation and such cytokines might therefore have a profound effect on quality of life and survival. In particular, IL-6 antagonism seems to have the most promising therapeutic activity in EOC patients but further clinical trials testing it

and arm 5, a combination of the above (Figure 4).

cachexia: treatment plan.

**6. Conclusion** 

modulation of inflammation may be useful in the care of EOC patients is represented by the efficacy of lactoferrin in association to rHuEPO in the treatment of chemotherapy-induced anaemia. In fact, lactoferrin is a specific protein involved in iron transport mechanisms, which has also an important role in host defence against infection and excessive inflammation. Results from a recent open label randomised phase III study of our group (Macciò et al., 2010), including EOC patients (20% in each arm), demonstrated that lactoferrin plus rHuEPO was able to increase Hb levels with a efficacy similar to iron i.v. in term of haematopoietic response but with a better capacity to modulate iron homeostasis and inflammation (as demonstrated by decrease of ferritin and CRP levels in patients treated with lactoferrin).

Specific inhibition of proinflammatory cytokines, and particularly IL-6, has also been tested in the therapeutic approach of cancer-related cachexia. Preliminary results form a phase I study showed that i.v. infusion of a specific anti-IL-6 MoAb was able to reverse fatigue, increase haemoglobin and albumin, and improve muscle strength (Clarke et al., 2009). However, according to the most recent findings, the best management of cancer-related symptoms, such as weight loss, muscle wasting, anorexia, anaemia, fatigue, which globally define the clinical picture of cachexia, requires a multimodal approach by a multidisciplinary team and is best commenced earlier rather than later (Bosaeus, 2008). Intervention should include dietary counselling, nutritional and vitamin supplementation, exercise concordant with the patient's physical condition, anti-inflammatory agents, anabolic drugs and the most adequate symptom managements.

In the context of such combined approaches, one of the most intriguing ones was an open phase II trial published by our group (Mantovani et al., 2006) which aimed to test the safety and efficacy of an integrated treatment based on diet, pharmaconutritional support administered orally, and drugs in a population of cachectic patients with advanced cancer at different sites, including also a significant percentage of EOC patients. The treatment consisted of diet with high polyphenols content (400 mg), antioxidant treatment (300 mg/day alpha lipoic acid+2.7 g/day carbocysteine lysine salt+400 mg/day vitamin E+30,000 IU/day vitamin A+500 mg/day vitamin C), and pharmaconutritional support enriched with two cans per day (n−3)-PUFA (eicosapentaenoic acid and docosahexaenoic acid), 500 mg/day MPA and 200 mg/day selective cyclooxygenase-2 inhibitor celecoxib. The treatment duration was 4 months. Body weight increased significantly from baseline, as did LBM and appetite. There was an important decrease of proinflammatory cytokines IL-6 and TNF-α, and a negative relationship worthy of note was found between LBM and IL-6 changes. As for quality of life, there was a significant improvement in the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30, Euro QL-5D and fatigue assessed by Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) scores. The results overall showed the treatment to be both safe (without significant adverse events) and effective as for increase of body weight, increase of LMB, decrease of proinflammatory cytokines, improvement of quality of life parameters, amelioration of fatigue symptom. On the basis of these results, we started a phase III randomized clinical trial (Mantovani et al., 2010) to establish which was the most effective and safest treatment of CACS and oxidative stress in improving selected key variables as primary endpoints: increase of LBM, decrease of REE, increase of total daily physical activity, decrease of IL-6 and TNF-α, and improvement of fatigue. Three hundred thirty-two assessable patients with cancer-related anorexia/cachexia syndrome, including a significant

modulation of inflammation may be useful in the care of EOC patients is represented by the efficacy of lactoferrin in association to rHuEPO in the treatment of chemotherapy-induced anaemia. In fact, lactoferrin is a specific protein involved in iron transport mechanisms, which has also an important role in host defence against infection and excessive inflammation. Results from a recent open label randomised phase III study of our group (Macciò et al., 2010), including EOC patients (20% in each arm), demonstrated that lactoferrin plus rHuEPO was able to increase Hb levels with a efficacy similar to iron i.v. in term of haematopoietic response but with a better capacity to modulate iron homeostasis and inflammation (as demonstrated by decrease of ferritin and CRP levels in patients

Specific inhibition of proinflammatory cytokines, and particularly IL-6, has also been tested in the therapeutic approach of cancer-related cachexia. Preliminary results form a phase I study showed that i.v. infusion of a specific anti-IL-6 MoAb was able to reverse fatigue, increase haemoglobin and albumin, and improve muscle strength (Clarke et al., 2009). However, according to the most recent findings, the best management of cancer-related symptoms, such as weight loss, muscle wasting, anorexia, anaemia, fatigue, which globally define the clinical picture of cachexia, requires a multimodal approach by a multidisciplinary team and is best commenced earlier rather than later (Bosaeus, 2008). Intervention should include dietary counselling, nutritional and vitamin supplementation, exercise concordant with the patient's physical condition, anti-inflammatory agents,

In the context of such combined approaches, one of the most intriguing ones was an open phase II trial published by our group (Mantovani et al., 2006) which aimed to test the safety and efficacy of an integrated treatment based on diet, pharmaconutritional support administered orally, and drugs in a population of cachectic patients with advanced cancer at different sites, including also a significant percentage of EOC patients. The treatment consisted of diet with high polyphenols content (400 mg), antioxidant treatment (300 mg/day alpha lipoic acid+2.7 g/day carbocysteine lysine salt+400 mg/day vitamin E+30,000 IU/day vitamin A+500 mg/day vitamin C), and pharmaconutritional support enriched with two cans per day (n−3)-PUFA (eicosapentaenoic acid and docosahexaenoic acid), 500 mg/day MPA and 200 mg/day selective cyclooxygenase-2 inhibitor celecoxib. The treatment duration was 4 months. Body weight increased significantly from baseline, as did LBM and appetite. There was an important decrease of proinflammatory cytokines IL-6 and TNF-α, and a negative relationship worthy of note was found between LBM and IL-6 changes. As for quality of life, there was a significant improvement in the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30, Euro QL-5D and fatigue assessed by Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) scores. The results overall showed the treatment to be both safe (without significant adverse events) and effective as for increase of body weight, increase of LMB, decrease of proinflammatory cytokines, improvement of quality of life parameters, amelioration of fatigue symptom. On the basis of these results, we started a phase III randomized clinical trial (Mantovani et al., 2010) to establish which was the most effective and safest treatment of CACS and oxidative stress in improving selected key variables as primary endpoints: increase of LBM, decrease of REE, increase of total daily physical activity, decrease of IL-6 and TNF-α, and improvement of fatigue. Three hundred thirty-two assessable patients with cancer-related anorexia/cachexia syndrome, including a significant

anabolic drugs and the most adequate symptom managements.

treated with lactoferrin).

proportion of advanced EOC patients, were enrolled. All patients were given as basic treatment polyphenols plus antioxidant agents alpha-lipoic acid, carbocysteine, and vitamins A, C, and E, all orally administered. Then patients were randomly assigned to one of five treatment arms: arm 1, MPA (500 mg/day) or MA (320 mg/day); arm 2, oral supplementation with EPA; arm 3, L-carnitine (4 g/day); arm 4, thalidomide (200 mg/day); and arm 5, a combination of the above (Figure 4).

Treatment duration was 4 months. Analysis of variance showed a significant difference between treatment arms. A post hoc analysis showed the superiority of arm 5 over the others for all primary endpoints. An analysis of changes from baseline showed that LBM (by dual-energy X-ray absorptiometry and by L3 computed tomography) significantly increased in arm 5. REE decreased significantly and fatigue improved significantly in arm 5. Appetite increased significantly in arm 5; IL-6 decreased significantly in arm 5 and arm 4; Glasgow Prognostic Score (GPS) and Eastern Cooperative Oncology Group (ECOG) performance status (PS) score decreased significantly in arm 5, arm 4, and arm 3. Toxicity was quite negligible, and was comparable between arms. In conclusion, the most effective treatment in terms of all three primary efficacy endpoints and the secondary endpoints appetite, IL-6, GPS, and ECOG PS score was the combination regimen that included all selected agents.

Fig. 4. Phase III randomised clinical trial of five different arms of treatment for cancer cachexia: treatment plan.

#### **6. Conclusion**

Proinflammatory cytokines, and in particular IL-6, as demonstrated in the present chapter, are involved in the development and progression of EOC. They are also associated with fatigue, depression, anaemia, pain and cachexia that impact significantly quality of life. Strategies to inhibit the effect of inflammation and such cytokines might therefore have a profound effect on quality of life and survival. In particular, IL-6 antagonism seems to have the most promising therapeutic activity in EOC patients but further clinical trials testing it

Inflammation and Ovarian Cancer 45

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**3** 

**Photonic Sensor System for Screening** 

*3University of Missouri – Kansas City, School of Medicine, Departments of* 

Debra Wawro1, Shelby Zimmerman1, Robert Magnusson1,2 and Peter Koulen3 *1Resonant Sensors Incorporated (RSI), Arlington, TX* 

**Serum Biomarker Proteins in Ovarian Cancer**

*2University of Texas at Arlington, Department of Electrical Engineering, Arlington, TX* 

*Ophthalmology and Basic Medical Science, Vision Research Center, Kansas City, MO \*USA* 

Ovarian cancer is among the most deadly types of cancers among women, with about 21,990 new cases diagnosed every year in the United States (American Cancer Society, 2011). About 15,460 of these women will die from ovarian cancer. If diagnosed while the cancer is still localized, survival rates of at least 5 years are likely. Unfortunately, less than 20% of cases are found at an early stage due to the absence of reproducible and definitive diagnostic tools. Because ovarian cancers occur deep in the pelvis, there are often few symptoms until the cancer is at an advanced stage. Furthermore, many of the symptoms of ovarian cancer (such as back pain, fatigue, and abdominal bloat) are common and difficult to distinguish from those not caused by cancer. Because of this lack of symptom specificity, most ovarian cancers are substantially advanced at the time of diagnosis. Staging of the cancer is critically important in order to determine the most effective treatment modality. Currently there are no routine clinical diagnostic assays using urinalysis or seranalysis for early screening or staging of ovarian cancer. However, there are several research studies (Bignotti et al., 2007; and Liotta et

al., 2005) that identify potential biomarker indicators that can be used for this purpose.

When a woman is suspected of having ovarian cancer, medical diagnostics typically include an ultrasound of the abdomen and pelvis as well as a blood test that includes measurement of the CA-125 protein levels (American Cancer Society, 2011). CA 125 is a protein biomarker found in greater concentration in tumor cells than in other cells of the body. However, since CA-125 levels can be elevated due to other benign causes, it is primarily used to monitor women with a known cancer of the ovary to determine treatment efficacy. Measurement of CA-125 levels is not accepted as a sufficient test for an early screening indicator in ovarian cancer. Thus, improved methods are needed to provide a specific and early screen for this

Based on "Optical nanotechnology enables rapid label-free diagnostics for cancer biomarker screening," by D. Wawro, S. Zimmerman, R. Magnusson and P. Koulen which appeared in

**1. Introduction** 

deadly disease.

Proceedings of SPIE 8090, 80900S (2011). \*


### **Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer**

Debra Wawro1, Shelby Zimmerman1,

Robert Magnusson1,2 and Peter Koulen3 *1Resonant Sensors Incorporated (RSI), Arlington, TX 2University of Texas at Arlington, Department of Electrical Engineering, Arlington, TX 3University of Missouri – Kansas City, School of Medicine, Departments of Ophthalmology and Basic Medical Science, Vision Research Center, Kansas City, MO \*USA* 

#### **1. Introduction**

50 Ovarian Cancer – Basic Science Perspective

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oligodeoxynucleotides on cell invasion and chemosensitivity in human epithelial ovarian cancer cells. *Cancer Genetic and Cytogenetics* Vol. 197, No. 1 (February 2010), Ovarian cancer is among the most deadly types of cancers among women, with about 21,990 new cases diagnosed every year in the United States (American Cancer Society, 2011). About 15,460 of these women will die from ovarian cancer. If diagnosed while the cancer is still localized, survival rates of at least 5 years are likely. Unfortunately, less than 20% of cases are found at an early stage due to the absence of reproducible and definitive diagnostic tools. Because ovarian cancers occur deep in the pelvis, there are often few symptoms until the cancer is at an advanced stage. Furthermore, many of the symptoms of ovarian cancer (such as back pain, fatigue, and abdominal bloat) are common and difficult to distinguish from those not caused by cancer. Because of this lack of symptom specificity, most ovarian cancers are substantially advanced at the time of diagnosis. Staging of the cancer is critically important in order to determine the most effective treatment modality. Currently there are no routine clinical diagnostic assays using urinalysis or seranalysis for early screening or staging of ovarian cancer. However, there are several research studies (Bignotti et al., 2007; and Liotta et al., 2005) that identify potential biomarker indicators that can be used for this purpose.

When a woman is suspected of having ovarian cancer, medical diagnostics typically include an ultrasound of the abdomen and pelvis as well as a blood test that includes measurement of the CA-125 protein levels (American Cancer Society, 2011). CA 125 is a protein biomarker found in greater concentration in tumor cells than in other cells of the body. However, since CA-125 levels can be elevated due to other benign causes, it is primarily used to monitor women with a known cancer of the ovary to determine treatment efficacy. Measurement of CA-125 levels is not accepted as a sufficient test for an early screening indicator in ovarian cancer. Thus, improved methods are needed to provide a specific and early screen for this deadly disease.

Based on "Optical nanotechnology enables rapid label-free diagnostics for cancer biomarker screening," by D. Wawro, S. Zimmerman, R. Magnusson and P. Koulen which appeared in Proceedings of SPIE 8090, 80900S (2011). \*

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 53

spectrum analyzer. Test time is limited solely by the chemical binding dynamics between the receptor and its target. Specificity is imparted on the sensor surface by covalently attaching a selective layer (such as antibodies). It is multifunctional as only the sensitizing surface layer needs to be chemically altered to detect different targets. Repeatable fabrication processes are in place to produce the resonant grating sensor element in low-cost

Since the resonance layer is polarization-sensitive, separate resonance peaks occur for incident TE (electric vector normal to the plane of incidence) and TM (magnetic vector normal to the plane of incidence) polarization states. This dual-peak feature provides crossreferenced data useful for increasing detection accuracy. These distinct resonant modes interact differently with the surrounding media, enabling the polarization-based differentiation. This sensor technology is broadly applicable to medical diagnostics, drug discovery and development, industrial process control, and environmental monitoring.

The coupling of a freely propagating electromagnetic wave to a state of confinement at a periodic surface is presently the subject of considerable research activity. Periodic structures with subwavelength features provide effective means of achieving such coupling. The resulting strong localization of energy at a dielectric (or metallic) layer is of interest for numerous photonic applications including biosensors, light sources, nonlinear frequency converters, and particle traps. Magnusson et al. disclosed GMR filters that were tunable on variation in resonance structure parameters (Magnusson & Wang, 1992). Wawro et al. presented GMR biosensor embodiments as well as system architectures (Wawro et al., 2000). Thus, spectral or angular variations induced via layer thickness change or on change in refractive index in surrounding media or in device layers can be used to sense these changes (Magnusson et al., 2011; Wawro et al., 2006; 2010). Additional aspects of GMR sensors in various applications have been discussed in the literature (Cunningham et al., 2002; Kikuta

Thin-film structures containing waveguide layers and periodic elements, under the correct conditions, exhibit the GMR effect. Most commonly, GMR biosensors are designed to operate in reflection. In this configuration, an incident wave is phase-matched, by the periodic element, to a leaky waveguide mode. It is reradiated in the specular-reflection direction as it propagates along the waveguide and constructively interferes with the directly reflected wave. Conversely and equivalently, the phase of the reradiated leaky mode in the forward, directly transmitted wave direction is radians out of phase with the direct unguided transmitted wave, thereby extinguishing the transmitted light (Rosenblatt, 1997). This picture of the resonance effect pertains to a reflection, or bandstop, filter. Other operation configurations are possible, such as in transmission

Figure 2 shows the measured and calculated spectral reflectance of a dielectric GMR device (Priambodo et al., 2003). It acts as a bandstop filter with the spectrum of interest reflected in a narrow band with relatively low sidebands. Although the theoretical calculation predicts 100% peak efficiency for a plane wave incidence, it is diminished in practice by various factors such as material and scattering losses, incident beam divergence, and the lateral

polymer and other dielectric materials.

et al., 2001).

mode, or as a bandpass filter.

device size; here, the experimental peak is 90%.

**2.1 Guided-mode resonance technology overview** 

In this work, we describe a high-accuracy, label-free biosensor system that can provide effective detection of an array of biomarker proteins in serum to accurately diagnose and stage ovarian cancer. While there are currently no established clinical diagnostic assays using urinalysis or seranalysis, experimentally and clinically identified targets (Bignotti et al., 2007; and Liotta et al., 2005) can be categorized into two groups: group 1 consists of biomarker proteins that are up-regulated twofold or higher in metastatic over primary ovarian serous papillary carcinoma (such as Fibronectin), and group 2 consists of biomarker proteins that are up-regulated twofold or higher in primary over metastatic ovarian serous papillary carcinoma (such as Apolipoprotein A-1). This differentiation yields accurate diagnosis of the disease and staging information that can be used to monitor presymptomatic aspects of the disease, disease progression, and the efficacy of therapies.

Conventional blood diagnostic testing methods such as immunoassay approaches require time-intensive processing and washing steps, and they are not easily integrated in a clinical setting. To address these needs, we utilize a real-time photonic biosensor technology that provides rapid results with minimal processing steps and the capability to test for an array of biomarkers in a single sample.

#### **2. Label-free diagnostic approach**

The diagnostic screening system that is central to this work applies an optical approach based on the guided-mode resonance (GMR) effect that occurs in subwavelength dielectric waveguide gratings. As shown in Fig. 1, when these diffractive elements are illuminated with a broadband light source, a specific wavelength of light is reflected (or transmitted) at a specific angle. The binding interaction between an immobilized receptor and its analyte can be monitored in real time without the use of reporter labels (such as fluorescent or radioactive tags) by following the corresponding resonance wavelength shift with an optical

Fig. 1. (a) Schematic of a label-free GMR sensor system (single channel illustrated) operating in reflection mode. The collimated beam from a broadband source is incident on the sensor at normal incidence. The reflected spectral response is monitored in real time with an optical spectrum analyzer. (b) As binding events occur at the sensor surface, resonance peak changes (only one polarization depicted in plot) can be tracked as a function of the wavelength.

spectrum analyzer. Test time is limited solely by the chemical binding dynamics between the receptor and its target. Specificity is imparted on the sensor surface by covalently attaching a selective layer (such as antibodies). It is multifunctional as only the sensitizing surface layer needs to be chemically altered to detect different targets. Repeatable fabrication processes are in place to produce the resonant grating sensor element in low-cost polymer and other dielectric materials.

Since the resonance layer is polarization-sensitive, separate resonance peaks occur for incident TE (electric vector normal to the plane of incidence) and TM (magnetic vector normal to the plane of incidence) polarization states. This dual-peak feature provides crossreferenced data useful for increasing detection accuracy. These distinct resonant modes interact differently with the surrounding media, enabling the polarization-based differentiation. This sensor technology is broadly applicable to medical diagnostics, drug discovery and development, industrial process control, and environmental monitoring.

#### **2.1 Guided-mode resonance technology overview**

52 Ovarian Cancer – Basic Science Perspective

In this work, we describe a high-accuracy, label-free biosensor system that can provide effective detection of an array of biomarker proteins in serum to accurately diagnose and stage ovarian cancer. While there are currently no established clinical diagnostic assays using urinalysis or seranalysis, experimentally and clinically identified targets (Bignotti et al., 2007; and Liotta et al., 2005) can be categorized into two groups: group 1 consists of biomarker proteins that are up-regulated twofold or higher in metastatic over primary ovarian serous papillary carcinoma (such as Fibronectin), and group 2 consists of biomarker proteins that are up-regulated twofold or higher in primary over metastatic ovarian serous papillary carcinoma (such as Apolipoprotein A-1). This differentiation yields accurate diagnosis of the disease and staging information that can be used to monitor presymptomatic aspects of the disease, disease progression, and the efficacy of therapies. Conventional blood diagnostic testing methods such as immunoassay approaches require time-intensive processing and washing steps, and they are not easily integrated in a clinical setting. To address these needs, we utilize a real-time photonic biosensor technology that provides rapid results with minimal processing steps and the capability to test for an array

The diagnostic screening system that is central to this work applies an optical approach based on the guided-mode resonance (GMR) effect that occurs in subwavelength dielectric waveguide gratings. As shown in Fig. 1, when these diffractive elements are illuminated with a broadband light source, a specific wavelength of light is reflected (or transmitted) at a specific angle. The binding interaction between an immobilized receptor and its analyte can be monitored in real time without the use of reporter labels (such as fluorescent or radioactive tags) by following the corresponding resonance wavelength shift with an optical

Fig. 1. (a) Schematic of a label-free GMR sensor system (single channel illustrated) operating in reflection mode. The collimated beam from a broadband source is incident on the sensor at normal incidence. The reflected spectral response is monitored in real time with an optical

spectrum analyzer. (b) As binding events occur at the sensor surface, resonance peak changes (only one polarization depicted in plot) can be tracked as a function of the

(a) (b)

Analyte Antibody

TM resonant mode

Reflected narrowband

light

TE resonant mode

of biomarkers in a single sample.

wavelength.

Sensor

Incident broadband light

Sample fluid

**2. Label-free diagnostic approach** 

The coupling of a freely propagating electromagnetic wave to a state of confinement at a periodic surface is presently the subject of considerable research activity. Periodic structures with subwavelength features provide effective means of achieving such coupling. The resulting strong localization of energy at a dielectric (or metallic) layer is of interest for numerous photonic applications including biosensors, light sources, nonlinear frequency converters, and particle traps. Magnusson et al. disclosed GMR filters that were tunable on variation in resonance structure parameters (Magnusson & Wang, 1992). Wawro et al. presented GMR biosensor embodiments as well as system architectures (Wawro et al., 2000). Thus, spectral or angular variations induced via layer thickness change or on change in refractive index in surrounding media or in device layers can be used to sense these changes (Magnusson et al., 2011; Wawro et al., 2006; 2010). Additional aspects of GMR sensors in various applications have been discussed in the literature (Cunningham et al., 2002; Kikuta et al., 2001).

Thin-film structures containing waveguide layers and periodic elements, under the correct conditions, exhibit the GMR effect. Most commonly, GMR biosensors are designed to operate in reflection. In this configuration, an incident wave is phase-matched, by the periodic element, to a leaky waveguide mode. It is reradiated in the specular-reflection direction as it propagates along the waveguide and constructively interferes with the directly reflected wave. Conversely and equivalently, the phase of the reradiated leaky mode in the forward, directly transmitted wave direction is radians out of phase with the direct unguided transmitted wave, thereby extinguishing the transmitted light (Rosenblatt, 1997). This picture of the resonance effect pertains to a reflection, or bandstop, filter. Other operation configurations are possible, such as in transmission mode, or as a bandpass filter.

Figure 2 shows the measured and calculated spectral reflectance of a dielectric GMR device (Priambodo et al., 2003). It acts as a bandstop filter with the spectrum of interest reflected in a narrow band with relatively low sidebands. Although the theoretical calculation predicts 100% peak efficiency for a plane wave incidence, it is diminished in practice by various factors such as material and scattering losses, incident beam divergence, and the lateral device size; here, the experimental peak is 90%.

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 55

Fig. 3. Profile of the leaky mode at resonance for a typical GMR sensor device. The

with an evanescent tail penetrating into the cover region (shown in Figs. 3 and 4).

The structure of the local fields associated with the resonant leaky modes is key to sensor applications. The leaky mode is a surface state that propagates along the surface, providing maximal interaction with any attached molecular or chemical layer. In the technology discussed herein, the sensing field (a resonant leaky mode) is maximized in the grating layer

Fig. 4**.** Snapshot of the standing-wave pattern associated with the leaky mode in Fig. 3. The size of the region is 22. Results are obtained with rigorous coupled wave analysis.

The GMR biosensor devices used in this work are based upon a single-layer waveguide grating design. We fabricated these with low-cost submicron molding methods in our labs, and they can be purchased from numerous commercial sources. We utilize polymers that are imprinted with submicron grating patterns and coated with a high-index dielectric material (such as TiO2 or HfO2) to realize resonant sensors. Figure 5 shows an example of a

amplitude is normalized to the incident-wave amplitude.

**2.3 Sensor element fabrication** 

GMR sensor.

These resonant structures, tunable on change of refractive index and/or thickness, have clear applications for biosensors. The buildup of the attaching biolayer can be monitored in real time, without use of chemical tags, by following the corresponding resonance shift.

Fig. 2. Comparison between experiment and theory for a dielectric resonance element. The parameters used for the theoretical curve fit are close to the nominal values; they are nC=1.0, nH=1.454 (SiO2), n2=1.975 (HfO2), ns=1.454, d1=135 nm, f=0.58, d2=208 nm, =446 nm, normal incidence. Rigorous coupled-wave analysis (RCWA) is used for the computations (Gaylord & Moharam, 1985).

#### **2.2 Biosensor operation**

In addition to the reflection/transmission properties of propagating electromagnetic waves, the near-field properties of resonant periodic lattices, including localization and fieldstrength enhancement, are of interest in sensor applications. The near-field patterns associated with a typical filter, similar to that in Fig. 2 in structure, are shown in Figs. 3 and 4 with a normally incident TE-polarized wave. Numerical results are obtained with rigorous coupled-wave analysis (RCWA) (Gaylord & Moharam, 1985) to provide quantitative information on relative field strengths and spatial extents associated with the near fields. As shown in Fig. 3, the S0 wave (S0 denotes the electric field of the zero order) propagates with reflected-wave amplitude close to unity, producing the standing-wave pattern shown by interference with the unit-amplitude input wave used in our model. Thus, at resonance, most of the energy is reflected back. The evanescent, first-order diffracted waves S1 and S-1 constitute the counter-propagating leaky modes. We see that the maximum field value is located in the HfO2 layer with the evanescent tails gradually penetrating into the substrate and cover. Figure 4 shows the standing wave pattern formed by the counter-propagating S-1 and S+1 waves at a certain instant of time; the field scale is color coded as shown. Since the S1 space harmonics correspond to localized waves, they can be very strong at resonance; here, the field enhancement is ~x10 as seen in Fig. 3. Depending on the level of grating modulation (= nH2 – nL2), the field amplitude can range from ~x10-x1000 in the layer relative to the input wave amplitude that represents a large increase in local intensity I~S2. The maximum amplitude of S1 is approximately inversely proportional to the modulation strength. In general, small modulation implies narrow linewidth and a large resonator Q factor Q=

These resonant structures, tunable on change of refractive index and/or thickness, have clear applications for biosensors. The buildup of the attaching biolayer can be monitored in real time, without use of chemical tags, by following the corresponding resonance shift.

> Experiment Theory

> > SiO2

Fig. 2. Comparison between experiment and theory for a dielectric resonance element. The parameters used for the theoretical curve fit are close to the nominal values; they are nC=1.0, nH=1.454 (SiO2), n2=1.975 (HfO2), ns=1.454, d1=135 nm, f=0.58, d2=208 nm, =446 nm, normal incidence. Rigorous coupled-wave analysis (RCWA) is used for the computations

In addition to the reflection/transmission properties of propagating electromagnetic waves, the near-field properties of resonant periodic lattices, including localization and fieldstrength enhancement, are of interest in sensor applications. The near-field patterns associated with a typical filter, similar to that in Fig. 2 in structure, are shown in Figs. 3 and 4 with a normally incident TE-polarized wave. Numerical results are obtained with rigorous coupled-wave analysis (RCWA) (Gaylord & Moharam, 1985) to provide quantitative information on relative field strengths and spatial extents associated with the near fields. As shown in Fig. 3, the S0 wave (S0 denotes the electric field of the zero order) propagates with reflected-wave amplitude close to unity, producing the standing-wave pattern shown by interference with the unit-amplitude input wave used in our model. Thus, at resonance, most of the energy is reflected back. The evanescent, first-order diffracted waves S1 and S-1 constitute the counter-propagating leaky modes. We see that the maximum field value is located in the HfO2 layer with the evanescent tails gradually penetrating into the substrate and cover. Figure 4 shows the standing wave pattern formed by the counter-propagating S-1 and S+1 waves at a certain instant of time; the field scale is color coded as shown. Since the S1 space harmonics correspond to localized waves, they can be very strong at resonance; here, the field enhancement is ~x10 as seen in Fig. 3. Depending on the level of grating modulation (= nH2 – nL2), the field amplitude can range from ~x10-x1000 in the layer relative to the input wave amplitude that represents a large increase in local intensity I~S2. The maximum amplitude of S1 is approximately inversely proportional to the modulation strength. In general, small modulation implies narrow linewidth and a large resonator Q

(Gaylord & Moharam, 1985).

745 750 755 760 765 770 775 780

Wavelength (nm)

**2.2 Biosensor operation** 

0.0

0.2

0.4

Reflectance

0.6

0.8

1.0

factor Q=

HfO2

nH

nC = nL

f

Fused silica ns

nL

nd d

Fig. 3. Profile of the leaky mode at resonance for a typical GMR sensor device. The amplitude is normalized to the incident-wave amplitude.

The structure of the local fields associated with the resonant leaky modes is key to sensor applications. The leaky mode is a surface state that propagates along the surface, providing maximal interaction with any attached molecular or chemical layer. In the technology discussed herein, the sensing field (a resonant leaky mode) is maximized in the grating layer with an evanescent tail penetrating into the cover region (shown in Figs. 3 and 4).

Fig. 4**.** Snapshot of the standing-wave pattern associated with the leaky mode in Fig. 3. The size of the region is 22. Results are obtained with rigorous coupled wave analysis.

#### **2.3 Sensor element fabrication**

The GMR biosensor devices used in this work are based upon a single-layer waveguide grating design. We fabricated these with low-cost submicron molding methods in our labs, and they can be purchased from numerous commercial sources. We utilize polymers that are imprinted with submicron grating patterns and coated with a high-index dielectric material (such as TiO2 or HfO2) to realize resonant sensors. Figure 5 shows an example of a GMR sensor.

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 57

test. By using GMR sensor technology, real-time results can be obtained with no required washing steps. Results are limited only by the binding dynamics of the ligand-receptor interactions (typically less than 30 minutes). This greatly simplifies medical diagnostic testing approaches, and it will enable doctor offices and hospitals to perform routine

Numerous characterization experiments have been performed for a variety of biological and chemical materials utilizing GMR sensors and the Vides bioassay spectroscopic reader system developed by Resonant Sensors Incorporated (shown in Fig. 6). In this work, we evaluate this label-free screening tool for the detection of biomarker proteins fibronectin and apolipoprotein A-1 (ApoA-1), which are relevant in ovarian cancer. The sensor plate (shown in Fig. 6(b)) is incorporated in the bottom of a bottomless microarray plate. Each well is sensitized to detect a target analyte by immobilizing a selective layer (such as highly specific antibodies). The spectroscopic sensor system approach (as shown in Fig. 1) tracks the GMR resonance peak wavelength changes as a function of time during a biochemical interaction. The relative peak shift is correlated to a concentration for a particular analyte in a serum or cell culture sample. We use an in vitro cell model for ovarian cancer to provide the relevant expressed biomarker proteins under test. Additionally, we investigate the impact of nonspecific binding and cross reactivity in complex samples such as human serum and cell

(a) (b)

Fig. 6. (a) A benchtop spectroscopic detection system utilizing GMR biosensor technology developed by Resonant Sensors Incorporated (RSI). In this arrangement, the spectral reflectance is monitored with an optical spectrum analyzer, and the peak wavelength is tracked as a function of time during a biochemical event. (b) This bioassay reader utilizes 96-

Human cell lines are used for the detection of relevant biomarker proteins and feasibility of sensor operation in complex samples. In order to combine the highest possible clinical relevance for the most financially viable research plan, the *in vitro* models for ovarian cancer

well (shown here) or 384-well (not shown) sensor array plates.

screening on a much larger scale with dramatically less labor.

**3. Experiments** 

media.

**3.1** *In vitro* **cell model** 

Fig. 5. Submicron resonant grating. (a) Atomic force microscope (AFM) picture of a ~520-nm period grating contact printed in an optical polymer. (b) A picture of a submicron molded grating. The grating is coated with a thin high-index layer (TiO2 or HfO2) to realize a GMR sensor element.

#### **2.4 Competing approaches**

Numerous optical sensors for bio- and chemical detection have been developed commercially and in research literature. Key label-free technologies include the surfaceplasmon resonance sensor (Homola, 2003; Raether, 1988), MEMS-based sensors, nanosensors (rods and particles), resonant mirror, Bragg grating sensors, waveguide sensors, waveguide interferometric sensors, ellipsometry, and grating coupled sensors (Cunningham, 1998; Cooper, 2006). Other methods include immunomagnetic separation, polymerase chain reaction, and standard immunoassay approaches that incorporate fluorescent, absorptive, radioactive, and luminescence labels. The GMR sensor approach has advantages and distinctions relative to these technologies, including features such as polarization diversity and low-power, portable system formats.

In our opinion, although dramatically different in concept and function, the surfaceplasmon resonance (SPR) sensor (Homola, 2003; Raether, 1988) comes closest in features and operation to the GMR sensor discussed here. The term surface plasmon (SP) refers to an electromagnetic field charge-density oscillation that can occur at the interface between a conductor and a dielectric (for example, gold/glass interface). An SP mode can be resonantly excited by parallel-polarized (TM, electric vector in the plane of the page) incident light but not with TE polarized light. Phase matching occurs by employing a metallized diffraction grating, or by using total internal reflection from a high-index material, such as in prism coupling or an evanescent field from a guided wave. When an SPR surface wave is excited, an absorption minimum occurs in a specific wavelength band. Since only a single polarization (TM) can physically be used for detection, refractive index and thickness attachments cannot simultaneously be resolved in one measurement. This is particularly important in chemical sensor applications where binding kinetics includes conformational and density changes at the sensor surface.

Standard label-based immunoassay tests involve extensive and complicated incubation and washing steps. In this approach, results are not obtained until 4-24 hours after starting the test. By using GMR sensor technology, real-time results can be obtained with no required washing steps. Results are limited only by the binding dynamics of the ligand-receptor interactions (typically less than 30 minutes). This greatly simplifies medical diagnostic testing approaches, and it will enable doctor offices and hospitals to perform routine screening on a much larger scale with dramatically less labor.

### **3. Experiments**

56 Ovarian Cancer – Basic Science Perspective

Fig. 5. Submicron resonant grating. (a) Atomic force microscope (AFM) picture of a ~520-nm period grating contact printed in an optical polymer. (b) A picture of a submicron molded grating. The grating is coated with a thin high-index layer (TiO2 or HfO2) to realize a GMR

Numerous optical sensors for bio- and chemical detection have been developed commercially and in research literature. Key label-free technologies include the surfaceplasmon resonance sensor (Homola, 2003; Raether, 1988), MEMS-based sensors, nanosensors (rods and particles), resonant mirror, Bragg grating sensors, waveguide sensors, waveguide interferometric sensors, ellipsometry, and grating coupled sensors (Cunningham, 1998; Cooper, 2006). Other methods include immunomagnetic separation, polymerase chain reaction, and standard immunoassay approaches that incorporate fluorescent, absorptive, radioactive, and luminescence labels. The GMR sensor approach has advantages and distinctions relative to these technologies, including features such as

In our opinion, although dramatically different in concept and function, the surfaceplasmon resonance (SPR) sensor (Homola, 2003; Raether, 1988) comes closest in features and operation to the GMR sensor discussed here. The term surface plasmon (SP) refers to an electromagnetic field charge-density oscillation that can occur at the interface between a conductor and a dielectric (for example, gold/glass interface). An SP mode can be resonantly excited by parallel-polarized (TM, electric vector in the plane of the page) incident light but not with TE polarized light. Phase matching occurs by employing a metallized diffraction grating, or by using total internal reflection from a high-index material, such as in prism coupling or an evanescent field from a guided wave. When an SPR surface wave is excited, an absorption minimum occurs in a specific wavelength band. Since only a single polarization (TM) can physically be used for detection, refractive index and thickness attachments cannot simultaneously be resolved in one measurement. This is particularly important in chemical sensor applications where binding kinetics includes

Standard label-based immunoassay tests involve extensive and complicated incubation and washing steps. In this approach, results are not obtained until 4-24 hours after starting the

(a) (b)

polarization diversity and low-power, portable system formats.

conformational and density changes at the sensor surface.

sensor element.

**2.4 Competing approaches** 

Numerous characterization experiments have been performed for a variety of biological and chemical materials utilizing GMR sensors and the Vides bioassay spectroscopic reader system developed by Resonant Sensors Incorporated (shown in Fig. 6). In this work, we evaluate this label-free screening tool for the detection of biomarker proteins fibronectin and apolipoprotein A-1 (ApoA-1), which are relevant in ovarian cancer. The sensor plate (shown in Fig. 6(b)) is incorporated in the bottom of a bottomless microarray plate. Each well is sensitized to detect a target analyte by immobilizing a selective layer (such as highly specific antibodies). The spectroscopic sensor system approach (as shown in Fig. 1) tracks the GMR resonance peak wavelength changes as a function of time during a biochemical interaction. The relative peak shift is correlated to a concentration for a particular analyte in a serum or cell culture sample. We use an in vitro cell model for ovarian cancer to provide the relevant expressed biomarker proteins under test. Additionally, we investigate the impact of nonspecific binding and cross reactivity in complex samples such as human serum and cell media.

Fig. 6. (a) A benchtop spectroscopic detection system utilizing GMR biosensor technology developed by Resonant Sensors Incorporated (RSI). In this arrangement, the spectral reflectance is monitored with an optical spectrum analyzer, and the peak wavelength is tracked as a function of time during a biochemical event. (b) This bioassay reader utilizes 96 well (shown here) or 384-well (not shown) sensor array plates.

#### **3.1** *In vitro* **cell model**

Human cell lines are used for the detection of relevant biomarker proteins and feasibility of sensor operation in complex samples. In order to combine the highest possible clinical relevance for the most financially viable research plan, the *in vitro* models for ovarian cancer

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 59

glycoprotein that is known to be produced by some ovarian cancer cell lines. To provide selectivity to fibronectin, anti-fibronectin monoclonal antibodies are immobilized on the sensor surface using commercial silane surface chemistries and cross-linking agents. Known standard concentrations of the target analyte fibronectin are diluted in a reagent diluent solution in phosphate buffered saline (PBS, pH 7.4). This reagent provides a BSA blocking agent to minimize nonspecific binding during the reaction. Both TE and TM polarization resonances are tracked for each concentration. Neat reagent diluent is used as a reference blank and subtracted from the data in Fig. 7. Binding is monitored for 1 hour at 37°C. At the end of the binding, any loose or unbound fibronectin is rinsed away in PBS, and a postbinding measurement is taken. Final data is shown using the relative peak shifts recorded pre- and post-binding in PBS. Both TE and TM resonances trend similarly, with the TM peak having slightly better detection sensitivity. The limit of detection for this assay is ~20

Fig. 7. Resonance peak shift as a function of concentration for fibronectin binding to its matched antibody on the sensor surface. Both TE and TM polarization resonances are

Figure 8 illustrates fibronectin detected in Caov-3 cell culture media and supernatant. The TM resonance peak shift for the test sample (unknown) is compared to the standard concentration (known) to obtain a measured concentration of 439.1 ng/ml for Caov-3 media and 996.7 ng/ml for Caov-3 supernatant. This indicates that the cell line is expressing fibronectin under culture conditions. Additional concentration measurements were performed for detection of fibronectin in TOV-21G media and cell culture supernatant. Summarized results comparing measured concentrations of both Caov-3 and TOV-21G are shown in Fig. 9. For TOV-21G (a stage IIIC ovarian cancer cell line), fibronectin levels are

tracked. Results are repeated in quadruplicate and averaged.

reduced during cell culture.

ng/ml.

are chosen based on human cell lines that had been derived directly from patients with ovarian cancer and are not from other types of cancer with ovarian side effects/metastases. Additionally, the *in vitro* models are established (used by ovarian cancer researchers in peerreviewed publications) and reproducible (available through ATCC).

Two different cell lines are used to provide samples for the detection of ovarian cancer biomarker proteins as shown in Table 1. The cell culture supernatant, which contains the expressed biomarker proteins, is measured to determine the concentrations of fibronectin and apolipoprotein A-1 (detailed in next section). We culture both cell lines as follows:

#### **3.1.1 Cell culture growth**

Cells are thawed and transferred to a 15 ml conical tube. Cells are spun at 200 x g for 1 minute. The supernatant is removed and replaced with 1 ml of Complete Medium (MCDB 105 and Medium 199, with fetal bovine serum). A cell count is done on the Nexcelom T4 Cellometer (Nexcelom Bioscience LLC, Lawrence, MA). Cells are then seeded in two 75 cm2 flasks per vial of cells.


Table 1. Ovarian cancer cell lines used in this work.

#### **3.1.2 Sub-culturing or passage**

The media is removed and collected for supernatant. The media is replaced with 0.25% trypsin/EDTA, and the flask is placed in an incubator for approximately 3-5 minutes. Once cells are detached, the suspension is removed and placed in a 15 ml Falcon tube. The cell suspension is spun at 200 x g for 1 minute. The trypsin is removed, and the cell pellet is resuspended in Complete Medium (amount varies depending on confluence). The suspension is seeded into a fresh flask.

#### **3.1.3 Supernatant collection**

To collect supernatant, the media is removed from the culture flask, placed in 50 ml Falcon tube, and spun at 300 x g for 1 minute. The supernatant is removed and placed in a fresh 50 ml falcon tube. The tubes are then frozen at -80°C.

#### **3.2 Protein biomarker screening**

Detection of the proteins fibronectin and ApoA-1 are performed in a variety of sample backgrounds, including a reagent diluent (containing bovine serum albumin, BSA), human serum, cell media, and cell culture supernatant. Figure 7 illustrates the spectral resonance peak shifts due to the binding of the ovarian cancer biomarker fibronectin in various concentrations. Fibronectin is a high-molecular weight (~440 kDa) extracellular matrix

are chosen based on human cell lines that had been derived directly from patients with ovarian cancer and are not from other types of cancer with ovarian side effects/metastases. Additionally, the *in vitro* models are established (used by ovarian cancer researchers in peer-

Two different cell lines are used to provide samples for the detection of ovarian cancer biomarker proteins as shown in Table 1. The cell culture supernatant, which contains the expressed biomarker proteins, is measured to determine the concentrations of fibronectin and apolipoprotein A-1 (detailed in next section). We culture both cell lines as follows:

Cells are thawed and transferred to a 15 ml conical tube. Cells are spun at 200 x g for 1 minute. The supernatant is removed and replaced with 1 ml of Complete Medium (MCDB 105 and Medium 199, with fetal bovine serum). A cell count is done on the Nexcelom T4 Cellometer (Nexcelom Bioscience LLC, Lawrence, MA). Cells are then seeded in two 75 cm2

The media is removed and collected for supernatant. The media is replaced with 0.25% trypsin/EDTA, and the flask is placed in an incubator for approximately 3-5 minutes. Once cells are detached, the suspension is removed and placed in a 15 ml Falcon tube. The cell suspension is spun at 200 x g for 1 minute. The trypsin is removed, and the cell pellet is resuspended in Complete Medium (amount varies depending on confluence). The suspension

To collect supernatant, the media is removed from the culture flask, placed in 50 ml Falcon tube, and spun at 300 x g for 1 minute. The supernatant is removed and placed in a fresh 50

Detection of the proteins fibronectin and ApoA-1 are performed in a variety of sample backgrounds, including a reagent diluent (containing bovine serum albumin, BSA), human serum, cell media, and cell culture supernatant. Figure 7 illustrates the spectral resonance peak shifts due to the binding of the ovarian cancer biomarker fibronectin in various concentrations. Fibronectin is a high-molecular weight (~440 kDa) extracellular matrix

adenocarcinoma human HTB-75 (Karlan & Lagasse, 1994)

human CRL-11730 (Provencher et al., 1993)

reviewed publications) and reproducible (available through ATCC).

**Name description source ATCC # Ref.** 

**3.1.1 Cell culture growth** 

flasks per vial of cells.

TOV-21G

Caov-3 Epithelial ovarian papillary

Stage IIIC

**3.1.2 Sub-culturing or passage** 

is seeded into a fresh flask.

**3.1.3 Supernatant collection** 

**3.2 Protein biomarker screening** 

Epithelial ovarian poorly differentiated primary malignant adenocarcinoma;

Table 1. Ovarian cancer cell lines used in this work.

ml falcon tube. The tubes are then frozen at -80°C.

glycoprotein that is known to be produced by some ovarian cancer cell lines. To provide selectivity to fibronectin, anti-fibronectin monoclonal antibodies are immobilized on the sensor surface using commercial silane surface chemistries and cross-linking agents. Known standard concentrations of the target analyte fibronectin are diluted in a reagent diluent solution in phosphate buffered saline (PBS, pH 7.4). This reagent provides a BSA blocking agent to minimize nonspecific binding during the reaction. Both TE and TM polarization resonances are tracked for each concentration. Neat reagent diluent is used as a reference blank and subtracted from the data in Fig. 7. Binding is monitored for 1 hour at 37°C. At the end of the binding, any loose or unbound fibronectin is rinsed away in PBS, and a postbinding measurement is taken. Final data is shown using the relative peak shifts recorded pre- and post-binding in PBS. Both TE and TM resonances trend similarly, with the TM peak having slightly better detection sensitivity. The limit of detection for this assay is ~20 ng/ml.

Fig. 7. Resonance peak shift as a function of concentration for fibronectin binding to its matched antibody on the sensor surface. Both TE and TM polarization resonances are tracked. Results are repeated in quadruplicate and averaged.

Figure 8 illustrates fibronectin detected in Caov-3 cell culture media and supernatant. The TM resonance peak shift for the test sample (unknown) is compared to the standard concentration (known) to obtain a measured concentration of 439.1 ng/ml for Caov-3 media and 996.7 ng/ml for Caov-3 supernatant. This indicates that the cell line is expressing fibronectin under culture conditions. Additional concentration measurements were performed for detection of fibronectin in TOV-21G media and cell culture supernatant. Summarized results comparing measured concentrations of both Caov-3 and TOV-21G are shown in Fig. 9. For TOV-21G (a stage IIIC ovarian cancer cell line), fibronectin levels are reduced during cell culture.

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 61

for the range from 32 ng/ml to 125 ng/ml (with an R2 value of 0.989). Based on this linear fit, the fresh cell culture media is found to contain ~59 ng/ml ApoA-1 while the cell culture

Fig. 10. Resonance peak shifts measured for detection of apolipoprotein A-1. Known

quadruplicate and averaged.

standards are measured (shown in blue) to obtain a calibration curve that is used to quantify the unknown samples (shown in red and green). Samples are run in quadruplicate and averaged, with major outliers removed. Standard deviation is negligible (shown on plot). Figure 11 illustrates detection of the biomarker ApoA-1 in culture media and supernatant for the Caov-3 and TOV-21G cell lines. The TM resonance peak shift for the test sample (unknown) is compared to the standard concentration (shown in Fig. 10) to obtain a measured concentration for each sample. Summarized results comparing measured concentrations of both cell media and culture supernatant are shown in Fig. 11. For the TOV-21G cell line, ApoA-1 is increased (or expressed) in the measured supernatant. In the Caov-3, the measured amount in the supernatant is reduced during culture. Tests are run in

Fig. 11. Comparison of the measured ApoA-1 in cell culture media and expressed supernatant for two different ovarian cancer cell lines. Standard deviation is negligible (shown on plot).

supernatant contains ~89 ng/ml. Binding is monitored for 1 hour at 37°C.

Fig. 8. Resonance peak shift as a function of concentration for detection of fibronectin. Standards are generated in a reagent diluent background. Caov-3 supernatant (green) and media (red) sample resonance shifts are compared to the known concentration resonance shifts (standard curve in blue) to obtain Fibronectin concentrations. All measurements are repeated in quadruplicate and averaged. Some standard deviations are too small to display on chart.

Fig. 9. Comparison of the measured fibronectin in cell culture media and expressed supernatant for two different ovarian cancer cell lines.

Detection of the expressed biomarker protein ApoA-1 in the ovarian cancer cell culture supernatant was also quantified for cell lines Caov-3 and TOV-21G. ApoA-1 is a protein component of high-density lipoprotein in plasma, and it has an approximate molecular weight of 28 kDa. In this experiment, anti-ApoA-1 antibodies are immobilized on the sensor surface to provide targeted selectivity for detection. Figure 10 illustrates measured TM-resonance shifts for standard known concentrations of ApoA-1 in reagent diluent (shown in blue). We also measure unknown amounts of ApoA-1 in fresh (unused) cell culture media and in ovarian cancer cell supernatant. The known standards are used to generate a linear calibration curve

Fig. 8. Resonance peak shift as a function of concentration for detection of fibronectin. Standards are generated in a reagent diluent background. Caov-3 supernatant (green) and media (red) sample resonance shifts are compared to the known concentration resonance shifts (standard curve in blue) to obtain Fibronectin concentrations. All measurements are repeated in quadruplicate and averaged. Some standard deviations are too small to display on chart.

Fig. 9. Comparison of the measured fibronectin in cell culture media and expressed

Detection of the expressed biomarker protein ApoA-1 in the ovarian cancer cell culture supernatant was also quantified for cell lines Caov-3 and TOV-21G. ApoA-1 is a protein component of high-density lipoprotein in plasma, and it has an approximate molecular weight of 28 kDa. In this experiment, anti-ApoA-1 antibodies are immobilized on the sensor surface to provide targeted selectivity for detection. Figure 10 illustrates measured TM-resonance shifts for standard known concentrations of ApoA-1 in reagent diluent (shown in blue). We also measure unknown amounts of ApoA-1 in fresh (unused) cell culture media and in ovarian cancer cell supernatant. The known standards are used to generate a linear calibration curve

supernatant for two different ovarian cancer cell lines.

for the range from 32 ng/ml to 125 ng/ml (with an R2 value of 0.989). Based on this linear fit, the fresh cell culture media is found to contain ~59 ng/ml ApoA-1 while the cell culture supernatant contains ~89 ng/ml. Binding is monitored for 1 hour at 37°C.

Fig. 10. Resonance peak shifts measured for detection of apolipoprotein A-1. Known standards are measured (shown in blue) to obtain a calibration curve that is used to quantify the unknown samples (shown in red and green). Samples are run in quadruplicate and averaged, with major outliers removed. Standard deviation is negligible (shown on plot).

Figure 11 illustrates detection of the biomarker ApoA-1 in culture media and supernatant for the Caov-3 and TOV-21G cell lines. The TM resonance peak shift for the test sample (unknown) is compared to the standard concentration (shown in Fig. 10) to obtain a measured concentration for each sample. Summarized results comparing measured concentrations of both cell media and culture supernatant are shown in Fig. 11. For the TOV-21G cell line, ApoA-1 is increased (or expressed) in the measured supernatant. In the Caov-3, the measured amount in the supernatant is reduced during culture. Tests are run in quadruplicate and averaged.

Fig. 11. Comparison of the measured ApoA-1 in cell culture media and expressed supernatant for two different ovarian cancer cell lines. Standard deviation is negligible (shown on plot).

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 63

Fig. 13. Comparison of the TM resonant peak shift due to the binding of fibronectin (in human serum) to the fibronectin antibodies on the sensors surface versus the nonspecific binding on the sensor elements not coated with antibodies. Tests are run in quadruplicate

> **Standard Resonance Peak Shift (nm)**

Caov-3 500 0.129 0.135 104.7 TOV-21G 500 0.129 0.116 89.9

As shown in Fig. 1, there are separate resonance peaks for each polarization (TE and TM) that shift in response to a given measurement. By backfitting this dual-peak response into our rigorous electromagnetic coupled wave analysis codes (Gaylord & Moharam, 1985), we can determine two unknowns: surface changes due to analyte binding and bulk refractiveindex changes that occur due to sample background variations. First, we calculate and map the predicted TE and TM resonance peak shifts over a relevant range of added biolayer thicknesses (0 to 50 nm) and background index variations (n=1.33 to n=1.5). A simple matrix is applied to match the corresponding detection layer and background index when the two resonance peak shifts are known. This data is fitted assuming a known biolayer refractive index, with unknown values to be determined for the biolayer thicknesses and background index. To illustrate the utility of this approach, we use the ionic polymer poly (allylamine hydrochloride) to study binding interactions that involve biolayer adhesion and associated thickness change at the sensor surface (Magnusson et al., 2011). Two resonance peaks are tracked as the ionic polymer attaches a monolayer of material as shown in Fig. 14. After the polymer saturates, the measurement is paused and the sensor is washed to remove any unbound polymer. A post-binding measurement is made in DI water. The results in Fig. 15 show that the binding of the polymer layer to the sensor surface contributes most to the measured response. The fitted background drift is partially attributed to thermal changes in the sample during the measurement and imperfect model assumptions (such as polymer layer index). Improvements to the backfit model will further distinguish these contributions.

**Spiked Resonance Peak Shift (nm)** 

**Recovery %** 

and averaged.

**Medium Spike** 

**4. Dual-peak analysis** 

**(ng/ml)** 

Table 2. Fibronectin Spike and Recovery.

#### **3.3 Nonspecific binding**

To investigate the amount of nonspecific binding that might occur during the media/supernatant and serum experiments, we prepare a negative reference well using a blocked silanized well (no antibodies attached); it is compared to wells containing specific antibodies for ApoA-1 and fibronectin. The capture antibodies for ApoA-1 and fibronectin are monoclonal mouse antibodies that are chemically attached to the sensor surface using a silane-based crosslinking agent. After antibody attachment, the unbound sites are blocked with a blocking buffer (BSA). In Figure 12, a cell culture media sample (having ApoA-1 naturally present) is incubated (1 hour) on sensor wells containing antibodies specific for ApoA-1 and wells that have no antibodies present. Figure 12 illustrates the minimal shift results from the negative reference well (no antibodies) as compared to the well containing the specific antibodies (large shift). We also investigate the use of human serum as a sample background in the detection of fibronectin. Figure 13 illustrates the resonance peak shift results from a serum sample (naturally containing fibronectin) after incubation (1 hour) on a negative reference well (no antibodies) compared to the specific antibody coated region. Both of these results are based on the difference of initial and final PBS baseline readings.

Fig. 12. Comparison of the TM resonant peak shift due to binding of the biomarker ApoA-1 (in a cell media background) to the ApoA-1 antibodies on the sensors surface versus nonspecific binding on the sensor elements not coated with antibodies.

Since the cell media and supernatant samples are made up of complex matrices, we used a spike and recovery method (Thermo, 2007) for each biomarker protein assay to determine whether the protein detection is affected by a difference between the diluent used to prepare the standard curve and the cell media sample matrix. In spike and recovery experiments, a known amount of protein standard is added to the sample matrix (corresponding growth media for each cell line) and compared to a standard curve measured in diluent. The two sets of total resonance peak shift measurements are compared. Table 2 shows results for spike and recovery experiments performed for fibronectin in both Caov-3 and TOV-21G cell culture media. Measurements are based on the difference of initial and final baseline readings with pure reagent diluent or pure media used as negative controls and subtracted from the data. In both cases, the detected amount was within ~10% of the target.

Fig. 13. Comparison of the TM resonant peak shift due to the binding of fibronectin (in human serum) to the fibronectin antibodies on the sensors surface versus the nonspecific binding on the sensor elements not coated with antibodies. Tests are run in quadruplicate and averaged.


Table 2. Fibronectin Spike and Recovery.

#### **4. Dual-peak analysis**

62 Ovarian Cancer – Basic Science Perspective

To investigate the amount of nonspecific binding that might occur during the media/supernatant and serum experiments, we prepare a negative reference well using a blocked silanized well (no antibodies attached); it is compared to wells containing specific antibodies for ApoA-1 and fibronectin. The capture antibodies for ApoA-1 and fibronectin are monoclonal mouse antibodies that are chemically attached to the sensor surface using a silane-based crosslinking agent. After antibody attachment, the unbound sites are blocked with a blocking buffer (BSA). In Figure 12, a cell culture media sample (having ApoA-1 naturally present) is incubated (1 hour) on sensor wells containing antibodies specific for ApoA-1 and wells that have no antibodies present. Figure 12 illustrates the minimal shift results from the negative reference well (no antibodies) as compared to the well containing the specific antibodies (large shift). We also investigate the use of human serum as a sample background in the detection of fibronectin. Figure 13 illustrates the resonance peak shift results from a serum sample (naturally containing fibronectin) after incubation (1 hour) on a negative reference well (no antibodies) compared to the specific antibody coated region. Both of these results are based on the difference of initial and

Fig. 12. Comparison of the TM resonant peak shift due to binding of the biomarker ApoA-1 (in a cell media background) to the ApoA-1 antibodies on the sensors surface versus

Since the cell media and supernatant samples are made up of complex matrices, we used a spike and recovery method (Thermo, 2007) for each biomarker protein assay to determine whether the protein detection is affected by a difference between the diluent used to prepare the standard curve and the cell media sample matrix. In spike and recovery experiments, a known amount of protein standard is added to the sample matrix (corresponding growth media for each cell line) and compared to a standard curve measured in diluent. The two sets of total resonance peak shift measurements are compared. Table 2 shows results for spike and recovery experiments performed for fibronectin in both Caov-3 and TOV-21G cell culture media. Measurements are based on the difference of initial and final baseline readings with pure reagent diluent or pure media used as negative controls and subtracted from the data. In both cases, the detected

nonspecific binding on the sensor elements not coated with antibodies.

amount was within ~10% of the target.

**3.3 Nonspecific binding** 

final PBS baseline readings.

As shown in Fig. 1, there are separate resonance peaks for each polarization (TE and TM) that shift in response to a given measurement. By backfitting this dual-peak response into our rigorous electromagnetic coupled wave analysis codes (Gaylord & Moharam, 1985), we can determine two unknowns: surface changes due to analyte binding and bulk refractiveindex changes that occur due to sample background variations. First, we calculate and map the predicted TE and TM resonance peak shifts over a relevant range of added biolayer thicknesses (0 to 50 nm) and background index variations (n=1.33 to n=1.5). A simple matrix is applied to match the corresponding detection layer and background index when the two resonance peak shifts are known. This data is fitted assuming a known biolayer refractive index, with unknown values to be determined for the biolayer thicknesses and background index. To illustrate the utility of this approach, we use the ionic polymer poly (allylamine hydrochloride) to study binding interactions that involve biolayer adhesion and associated thickness change at the sensor surface (Magnusson et al., 2011). Two resonance peaks are tracked as the ionic polymer attaches a monolayer of material as shown in Fig. 14. After the polymer saturates, the measurement is paused and the sensor is washed to remove any unbound polymer. A post-binding measurement is made in DI water. The results in Fig. 15 show that the binding of the polymer layer to the sensor surface contributes most to the measured response. The fitted background drift is partially attributed to thermal changes in the sample during the measurement and imperfect model assumptions (such as polymer layer index). Improvements to the backfit model will further distinguish these contributions.

Photonic Sensor System for Screening Serum Biomarker Proteins in Ovarian Cancer 65

polarization diversity, these sensors employ multiple resonance peaks that are used to increase detection accuracy by providing multiple data points for each test. Work is ongoing to integrate this system into a portable detection unit that can be used in a point-of-care setting. Future work will include clinical sample validation and an expanded array of relevant biomarkers that can be tested in a single sample. This will provide a highly accurate

This work was supported in part by the National Science Foundation SBIR grant #0724407 (D.W), the National Cancer Institute SBIR grant #R43CA135960 (D.W. and P.K.) and the State of Texas Emerging Technology Fund (D.W.). Additional support was provided by the UT System Texas Nanoelectronics Research Superiority Award (R.M.), the Texas Instruments Distinguished University Chair in Nanoelectronics endowment (R.M.), the Vision Research Foundation of Kansas City (P.K.), and the Felix and Carmen Sabates Missouri Endowed Chair in Vision Research (P.K.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

American Cancer Society, (2011) *Ovarian Cancer Reference literature*, http://www.cancer.org. Bignotti, E.; et al. (2007). Gene expression profile of ovarian serous papillary carcinomas: identification of metastasis-associated genes. *Am. J. Obstet. Gynecol*. 196:245. Cooper, M. (2006). Current Biosensor Technologies in Drug Discovery. *Drug Discovery* 

Cunningham, A. (1998). Introduction to Bioanalytical Sensors. *John Wiley and Sons*, New

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Homola, J. (2003). Present and future of surface plasmon resonance biosensors. *Anal. Bioanal.* 

Karlan, B.Y. & Lagasse, L.D. (1994). Glucocorticoids stabilize HER-2/neu messenger RNA in human epithelial ovarian carcinoma cells. *Gynecologic Oncology* 53:70-77. Kikuta, H.; Maegawa, N.; Mizutani, A.; Iwata, K.; & Toyota, H. (2001). Refractive index

Liotta, L.; Lowenthal, M.; & Mehta, A. (2005). Importance of communication between

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direct biochemical assay technique. *Sens. Actuators* B. 81:316-328.

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rapid screening tool for early detection of ovarian cancer.

**6. Acknowledgements** 

**7. References** 

*World*. 68-82.

*Chem*. 377:528-539.

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Fig. 14. Resonance peak shifts as a function of time for binding ionic polymer to the sensor surface. Both TE and TM resonances are monitored. This medium has a molecular weight of 56,000 kDa.

Fig. 15. Results of backfitting to a simple model, thereby differentiating contributions from biolayer adhesion and background changes.

#### **5. Conclusions**

A novel diagnostic system to detect biomarkers relevant for diagnosis of ovarian cancer has been developed. This label-free sensor system can accurately and rapidly detect an array of protein markers with minimal sample processing requirements. Sensor performance was characterized for the biomarker proteins fibronectin and apoliprotein A-1 with limits of detection measured to be ~20 ng/ml in backgrounds of cell culture media and human serum. An *in vitro* cell culture model was used with established ovarian cancer cell lines to provide relevant samples for this work. Nonspecific binding effects were investigated for operation in serum backgrounds with minimal impact. Additionally, due to inherent polarization diversity, these sensors employ multiple resonance peaks that are used to increase detection accuracy by providing multiple data points for each test. Work is ongoing to integrate this system into a portable detection unit that can be used in a point-of-care setting. Future work will include clinical sample validation and an expanded array of relevant biomarkers that can be tested in a single sample. This will provide a highly accurate rapid screening tool for early detection of ovarian cancer.

#### **6. Acknowledgements**

64 Ovarian Cancer – Basic Science Perspective

TM TE

Resonance Wavelength Shift (nm)

biolayer adhesion and background changes.


0

0.005 0.01

Biolayer Thickness Change (

m)

0.015 0.02 0.025

56,000 kDa.

**5. Conclusions** 

0

0.1

0.2

0.3

0.4

Time (minutes)

0 10 20 30 40 50

DI Polymer DI

Fig. 14. Resonance peak shifts as a function of time for binding ionic polymer to the sensor surface. Both TE and TM resonances are monitored. This medium has a molecular weight of

Fig. 15. Results of backfitting to a simple model, thereby differentiating contributions from

DI Polymer DI

0 10 20 30 40 50

Time (minutes)



0.005

0.01 0.015 Background Index Change (RIU)

0.02

0.025 biolayer change

background change

0

A novel diagnostic system to detect biomarkers relevant for diagnosis of ovarian cancer has been developed. This label-free sensor system can accurately and rapidly detect an array of protein markers with minimal sample processing requirements. Sensor performance was characterized for the biomarker proteins fibronectin and apoliprotein A-1 with limits of detection measured to be ~20 ng/ml in backgrounds of cell culture media and human serum. An *in vitro* cell culture model was used with established ovarian cancer cell lines to provide relevant samples for this work. Nonspecific binding effects were investigated for operation in serum backgrounds with minimal impact. Additionally, due to inherent This work was supported in part by the National Science Foundation SBIR grant #0724407 (D.W), the National Cancer Institute SBIR grant #R43CA135960 (D.W. and P.K.) and the State of Texas Emerging Technology Fund (D.W.). Additional support was provided by the UT System Texas Nanoelectronics Research Superiority Award (R.M.), the Texas Instruments Distinguished University Chair in Nanoelectronics endowment (R.M.), the Vision Research Foundation of Kansas City (P.K.), and the Felix and Carmen Sabates Missouri Endowed Chair in Vision Research (P.K.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

#### **7. References**


**4** 

*Canada* 

**The Role of MUC16 Mucin (CA125) in the** 

Claudine Rancourt, Isabelle Matte, Denis Lane and Alain Piché\*

The majority of epithelial ovarian carcinomas (EOCs) are derived from the ovarian surface epithelium (OSE). EOCs are the most lethal of all gynecological malignancies. Most patients present with advanced diseases in which tumor cells are disseminated throughout the peritoneal cavity. MUC16 serum level is a well-established marker for ovarian cancer (OC) progression and disease response to treatment. MUC16 is a high molecular weight, membrane associated-mucin, which is aberrantly expressed in advanced serous EOC. MUC16 is also expressed at the surface of corneal and respiratory epithelial cells, and the surface of female reproductive tract epithelium. It is however not expressed by the normal OSE. Like other membrane-bounded mucins, this glycosylated protein is primarily involved in the lubrification of epithelial luminal surfaces. MUC16 glycoprotein possesses unique structural motifs as compared with other membrane-bounded mucins. Its ectodomain is composed of a large heavily O-glycosylated N-terminus and a tandem repeat region with over 60 tandem repeats. MUC16 C-terminal domain (CTD) is composed of an extracellular unique region which contains a potential proteolytic cleavage site, a transmembrane domain and a short cytoplasmic tail with possible phosphorylation sites. MUC16 domains most likely have various functions resulting in activation of signalling pathways which regulate different tumor cell phenotypes. Indeed, recent functional studies have begun to uncover the unique role of MUC16 in the pathogenesis of OC. The present review will discuss the

OC is the fifth cause of cancer-related death in women in North America, the second most common gynecological cancer, and the leading cause of death from gynecological malignancies (Ozols *et al*, 2004). One in 78 women will develop OC during her lifetime (Jemal *et al.,* 2010). In 2010, nearly 22,000 new cases were estimated to occur in the United States and approximately 14,000 women are expected to die from this disease (Jemal *et al.,* 2010). Similar incidence and mortality has been observed in Canada, relative to the total population. Although survival rates approach 90% in OC patients diagnosed at early stage, most patients

unique structure and functional roles of MUC16 in OC.

**2. Ovarian cancer overview** 

Corresponding Author

 \*

**1. Introduction** 

**Pathogenesis of Ovarian Cancer** 

*Département de Microbiologie et Infectiologie Faculté de Médecine, Université de Sherbrooke* 


## **The Role of MUC16 Mucin (CA125) in the Pathogenesis of Ovarian Cancer**

Claudine Rancourt, Isabelle Matte, Denis Lane and Alain Piché\* *Département de Microbiologie et Infectiologie Faculté de Médecine, Université de Sherbrooke Canada* 

#### **1. Introduction**

66 Ovarian Cancer – Basic Science Perspective

Priambodo, P.S.; Maldonado, T.A. & Magnusson, R. (2003). Fabrication and characterization

Provencher, D.M.; et al; (1993). Comparison of antigen expression on fresh and cultured

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Rosenblatt, D.; Sharon, A; & Friesem, A. (1997) Resonant Grating Waveguide Structures.

*Thermo Scientific Tech Tip* #58. (2007) Spike-and-recovery and linearity-of-dilution

Wawro, D.; Tibuleac, S.; Magnusson, R.; & Liu, H. (2000). Optical fiber endface biosensor

Wawro, D.; Tibuleac, S.; & Magnusson, R. (2006). Optical waveguide-mode resonant

Wawro, D.; Koulen, P.; Ding, Y.; Zimmerman, S. & Magnusson, R. (2010). Guided-mode

based on resonances in dielectric waveguide gratings. *Proceedings of the SPIE*. San

biosensors. *Optical Imaging Sensors and Systems for Homeland Security Applications*.

resonance sensor system for early detection of ovarian cancer. Optical Diagnostics and Sensing X: Toward Point-of-Care Diagnostics. *Proceedings of the SPIE*. San

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Francisco, California. 7572:75720D.

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Jose, California. 3911:86-94.

*Springer Verlag*, Berlin.

of high-quality waveguide-mode resonant optical filters. *Appl. Phys. Lett*. 83, 3248-

ascites cells and on solid tumors of patients with epithelial ovarian cancer.

The majority of epithelial ovarian carcinomas (EOCs) are derived from the ovarian surface epithelium (OSE). EOCs are the most lethal of all gynecological malignancies. Most patients present with advanced diseases in which tumor cells are disseminated throughout the peritoneal cavity. MUC16 serum level is a well-established marker for ovarian cancer (OC) progression and disease response to treatment. MUC16 is a high molecular weight, membrane associated-mucin, which is aberrantly expressed in advanced serous EOC. MUC16 is also expressed at the surface of corneal and respiratory epithelial cells, and the surface of female reproductive tract epithelium. It is however not expressed by the normal OSE. Like other membrane-bounded mucins, this glycosylated protein is primarily involved in the lubrification of epithelial luminal surfaces. MUC16 glycoprotein possesses unique structural motifs as compared with other membrane-bounded mucins. Its ectodomain is composed of a large heavily O-glycosylated N-terminus and a tandem repeat region with over 60 tandem repeats. MUC16 C-terminal domain (CTD) is composed of an extracellular unique region which contains a potential proteolytic cleavage site, a transmembrane domain and a short cytoplasmic tail with possible phosphorylation sites. MUC16 domains most likely have various functions resulting in activation of signalling pathways which regulate different tumor cell phenotypes. Indeed, recent functional studies have begun to uncover the unique role of MUC16 in the pathogenesis of OC. The present review will discuss the unique structure and functional roles of MUC16 in OC.

#### **2. Ovarian cancer overview**

OC is the fifth cause of cancer-related death in women in North America, the second most common gynecological cancer, and the leading cause of death from gynecological malignancies (Ozols *et al*, 2004). One in 78 women will develop OC during her lifetime (Jemal *et al.,* 2010). In 2010, nearly 22,000 new cases were estimated to occur in the United States and approximately 14,000 women are expected to die from this disease (Jemal *et al.,* 2010). Similar incidence and mortality has been observed in Canada, relative to the total population. Although survival rates approach 90% in OC patients diagnosed at early stage, most patients

<sup>\*</sup> Corresponding Author

Role of MUC16 Mucin in Ovarian Cancer 69

groups that rising and falling levels of serum MUC16 correlate with progression and regression of the disease and this formed the basis for monitoring MUC16 serum levels for patient follow-up (Bast *et al.,* 1983; Canney *et al.,* 1984; Vergote *et al.,* 1987). However, up to 20% of patients with advanced EOC have normal serum level of MUC16. Furthermore, MUC16 levels can be elevated in various benign diseases including menstruation, first trimester pregnancy, endometriosis, adenomyosis, salpingitis, uterine fibroids, chronic renal failure or in inflammation of the pleura, peritoneum or pericardium (Bagdwell *et al.,* 2007; Xiaofang *et al.,* 2007). MUC16 is therefore not specific for EOC. MUC16, as a single modality, is not currently use for screening of EOC because

Despite its recognized utility for the follow up of patients with EOC over the last three decades, the understanding of MUC16 structure became apparent only with the cloning of the gene in 2001. In addition, because of the lack of suitable cellular models, MUC16

Although MUC16 was recognized as a high molecular weight glycoprotein a few years after the description of OC125 monoclonal antibody (Davis *et al.,* 1986), and its structure confirmed by subsequent studies (Lloyd *et al.,* 1997; Lloyd *et al.,* 2001), it took 20 years before the MUC16 gene could be cloned (Yin and Llyod, 2001; O'Brien *et al.,* 2001; Yin *et al.,* 2002). The gene is located on chromosome 19p13.2 (Yin and Lloyd, 2001). The deduced amino acid sequence of MUC16 demonstrated that it resembles other membrane-bounded mucins with high serine, threonine and proline content. With a molecular weight of > 2 MDa, MUC16 is the largest membrane-bounded mucin known to date (O'Brien *et al.,* 2001; O'Brien *et al.,* 2002). This glycoprotein is composed of three major domains: an N-terminal domain, a large multiple repeat domain (up to 60 tandem repeats of 156 amino acids each) and a C-terminal domain (O'Brien *et al.,* 2001) (Fig. 1). The N-terminal domain and the repeat domain are heavily glycosylated with both O- and N-linked oligosaccharides (Kui *et al.,* 2003). The Cterminal domain is composed of an extracellular domain with sea urchin sperm protein, enterokinase and agrin (SEA) domains, a transmembrane domain to anchor the protein to the cellular membrane and a short cytoplasmic tail (31 amino acids) with potential serine, threonine and tyrosine phosphorylation sites. The phosphorylation of MUC16 cytoplasmic tail has been associated with its secretion (Fendrick *et al.,* 1997). The secretion of MUC16 is stimulated by epidermal growth factor (EGF) or tyrosine phosphatases (Konishi *et al.,* 1994).

Human MUC16 differs from other mucins by having 16 SEA domains located near the membrane-spanning sequence. Other membrane-bounded mucins usually have a single SEA domain (Duraisamy *et al.,* 2006). SEA domains consist of about 120 amino acids. Sequence analysis of MUC16 SEA modules showed that they display some sequence variability. The second MUC16 SEA domain however is relatively conserved and most closely resembles the SEA domain found in other mucins. It may therefore provide the preferential cleavage site, like as in MUC1 and MUC3, which allows release of MUC16 from the cell surface. This, however, remains to be confirmed. Unlike MUC1 and MUC4, MUC16 lacks an EGF-like domain. Through their EGF-like motif located at C-terminal domain (extracellular portion), MUC1 and MUC4 bind to growth factor receptor tyrosine kinases (RTKs) such as erbB family and fibroblast growth factor receptor 3 (FGFR3) (Li *et* 

of its lack of sensitivity and specificity.

**3. MUC16 structure** 

functions have remained mostly unknown until very recently.

Its shedding is decreased by glucocorticoids (Karlan *et al.,* 1988).

(~ 80%) are diagnosed with advanced diseases and metastases throughout the peritoneal cavity (Bast *et al.,* 2009). For these women, the 5-year survival rate is less than 30%.

Although OC may arise from all cell types composing the ovaries, EOC arising from the single-cell layer coelomic epithelium surrounding the ovaries, from postovulatory inclusion cysts or from the fimbriated end of the fallopian tube, are by far the most common (85-90% of all OC) (Ozols *et al*, 2004; Auersperg *et al.,* 2001; Landen *et al.,* 2008; Kindelberger *et al.,* 2007; Crum *et al.,* 2007 (2); Dubeau 2008; Kurman *et al.,* 2010). EOC presents substantial heterogeneity in terms of grade and histology. Most frequent EOCs divided into serous, mucinous, endometrioid and clear cell histotypes (Bast and al. 2009). Each histotype shows a distinctive gene expression and immunohistochemical profiling (Schwartz *et al.,* 2002; Ouellet *et al.,* 2005; Ouellet *et al.,* 2006; Ouellet *et al.,* 2008), and differs in the response to therapy (Bast *et al.,* 2009). Despite evidence of considerable heterogeneity in their histological phenotypes and molecular profiling (Bast *et al.,* 2009; Konstantinopoulos *et al.,* 2008; Soslow 2008), most cases of EOC are treated in a similar fashion.

Early detection of cancer patient remains an important objective in the field because over 70% of patients with EOC are diagnosed at late stage disease, with dissemination of tumor implants throughout the peritoneal cavity (Ozols *et al*, 2004; Aletti *et al.,* 2007; Goff *et al.,* 2000). Only 10-15% of these patients maintain a complete response after the initial therapy. The mean survival of patients that present with late stage disease, which is the case for most patients, is 39 months (Herzog 2004). Recurrence is associated with incurable diseases in most cases. The main obstacle to an effective treatment is the failure of the initial chemotherapy to eradicate a sufficient number of tumor cells to prevent disease recurrence. In this context, deficiency in the apoptotic cascade among tumor cells is a key hallmark of EOC.

The current standard treatment for advanced EOC consists of cytoreductive surgery and chemotherapy. Paclitaxel combined with platinum-based regimen is the standard first-line chemotherapy used for all patients with EOC (Colombo *et al.,* 2006). Serous EOC can be considered a chemosensitive neoplasm as most (80%) patients initially respond to the combination of paclitaxel and platinum-based drugs (McGuire *et al.,* 1996). However, 90% of the patients that initially responded will eventually develop chemotherapy-resistant diseases (Mano *et al.,* 2007). Although rarely curative, patients that do not respond to the first-line chemotherapy are given second-line and third-line regimens of chemotherapy in an attempt to prolong life and palliate symptoms.

Early on, MUC16 mucin has been recognized as a tumor-associated antigen because of its overexpression in EOC. Measurements of MUC16 serum level have been very useful over the years to monitor disease response or progression (Bast *et al.,* 2005). MUC16 is overexpressed in EOC, cleaved from the cell surface and detected into the peritoneal fluid and the blood. Since the characterization of the OC125 monoclonal antibody raised against the human ovarian cancer cell line OVCA433 in 1981 (Bast *et al.,* 1981; Bast *et al.,* 1983), a variety of MUC16-linked antibodies have been developed including VK8, M11 and 4H11 (Dharma Rao *et al.,* 2010; Nustad *et al.,* 2002). Except for 4H11 antibody, which recognizes an epitope in the noncleaved ectodomain of MUC16, other MUC16 antibodies bind to the glycosylated portion of the molecule. Measurement of serum MUC16 tumor antigen is an important part of the clinical management for EOC patients. Elevated levels of serum MUC16 are common in patients with advanced disease of serous histotype (~90%). It decreases to 50%-60% in patients with early stage OC. It was shown by several

(~ 80%) are diagnosed with advanced diseases and metastases throughout the peritoneal

Although OC may arise from all cell types composing the ovaries, EOC arising from the single-cell layer coelomic epithelium surrounding the ovaries, from postovulatory inclusion cysts or from the fimbriated end of the fallopian tube, are by far the most common (85-90% of all OC) (Ozols *et al*, 2004; Auersperg *et al.,* 2001; Landen *et al.,* 2008; Kindelberger *et al.,* 2007; Crum *et al.,* 2007 (2); Dubeau 2008; Kurman *et al.,* 2010). EOC presents substantial heterogeneity in terms of grade and histology. Most frequent EOCs divided into serous, mucinous, endometrioid and clear cell histotypes (Bast and al. 2009). Each histotype shows a distinctive gene expression and immunohistochemical profiling (Schwartz *et al.,* 2002; Ouellet *et al.,* 2005; Ouellet *et al.,* 2006; Ouellet *et al.,* 2008), and differs in the response to therapy (Bast *et al.,* 2009). Despite evidence of considerable heterogeneity in their histological phenotypes and molecular profiling (Bast *et al.,* 2009; Konstantinopoulos *et al.,*

Early detection of cancer patient remains an important objective in the field because over 70% of patients with EOC are diagnosed at late stage disease, with dissemination of tumor implants throughout the peritoneal cavity (Ozols *et al*, 2004; Aletti *et al.,* 2007; Goff *et al.,* 2000). Only 10-15% of these patients maintain a complete response after the initial therapy. The mean survival of patients that present with late stage disease, which is the case for most patients, is 39 months (Herzog 2004). Recurrence is associated with incurable diseases in most cases. The main obstacle to an effective treatment is the failure of the initial chemotherapy to eradicate a sufficient number of tumor cells to prevent disease recurrence. In this context, deficiency in the apoptotic cascade among tumor cells

The current standard treatment for advanced EOC consists of cytoreductive surgery and chemotherapy. Paclitaxel combined with platinum-based regimen is the standard first-line chemotherapy used for all patients with EOC (Colombo *et al.,* 2006). Serous EOC can be considered a chemosensitive neoplasm as most (80%) patients initially respond to the combination of paclitaxel and platinum-based drugs (McGuire *et al.,* 1996). However, 90% of the patients that initially responded will eventually develop chemotherapy-resistant diseases (Mano *et al.,* 2007). Although rarely curative, patients that do not respond to the first-line chemotherapy are given second-line and third-line regimens of chemotherapy in an

Early on, MUC16 mucin has been recognized as a tumor-associated antigen because of its overexpression in EOC. Measurements of MUC16 serum level have been very useful over the years to monitor disease response or progression (Bast *et al.,* 2005). MUC16 is overexpressed in EOC, cleaved from the cell surface and detected into the peritoneal fluid and the blood. Since the characterization of the OC125 monoclonal antibody raised against the human ovarian cancer cell line OVCA433 in 1981 (Bast *et al.,* 1981; Bast *et al.,* 1983), a variety of MUC16-linked antibodies have been developed including VK8, M11 and 4H11 (Dharma Rao *et al.,* 2010; Nustad *et al.,* 2002). Except for 4H11 antibody, which recognizes an epitope in the noncleaved ectodomain of MUC16, other MUC16 antibodies bind to the glycosylated portion of the molecule. Measurement of serum MUC16 tumor antigen is an important part of the clinical management for EOC patients. Elevated levels of serum MUC16 are common in patients with advanced disease of serous histotype (~90%). It decreases to 50%-60% in patients with early stage OC. It was shown by several

cavity (Bast *et al.,* 2009). For these women, the 5-year survival rate is less than 30%.

2008; Soslow 2008), most cases of EOC are treated in a similar fashion.

is a key hallmark of EOC.

attempt to prolong life and palliate symptoms.

groups that rising and falling levels of serum MUC16 correlate with progression and regression of the disease and this formed the basis for monitoring MUC16 serum levels for patient follow-up (Bast *et al.,* 1983; Canney *et al.,* 1984; Vergote *et al.,* 1987). However, up to 20% of patients with advanced EOC have normal serum level of MUC16. Furthermore, MUC16 levels can be elevated in various benign diseases including menstruation, first trimester pregnancy, endometriosis, adenomyosis, salpingitis, uterine fibroids, chronic renal failure or in inflammation of the pleura, peritoneum or pericardium (Bagdwell *et al.,* 2007; Xiaofang *et al.,* 2007). MUC16 is therefore not specific for EOC. MUC16, as a single modality, is not currently use for screening of EOC because of its lack of sensitivity and specificity.

Despite its recognized utility for the follow up of patients with EOC over the last three decades, the understanding of MUC16 structure became apparent only with the cloning of the gene in 2001. In addition, because of the lack of suitable cellular models, MUC16 functions have remained mostly unknown until very recently.

#### **3. MUC16 structure**

Although MUC16 was recognized as a high molecular weight glycoprotein a few years after the description of OC125 monoclonal antibody (Davis *et al.,* 1986), and its structure confirmed by subsequent studies (Lloyd *et al.,* 1997; Lloyd *et al.,* 2001), it took 20 years before the MUC16 gene could be cloned (Yin and Llyod, 2001; O'Brien *et al.,* 2001; Yin *et al.,* 2002). The gene is located on chromosome 19p13.2 (Yin and Lloyd, 2001). The deduced amino acid sequence of MUC16 demonstrated that it resembles other membrane-bounded mucins with high serine, threonine and proline content. With a molecular weight of > 2 MDa, MUC16 is the largest membrane-bounded mucin known to date (O'Brien *et al.,* 2001; O'Brien *et al.,* 2002). This glycoprotein is composed of three major domains: an N-terminal domain, a large multiple repeat domain (up to 60 tandem repeats of 156 amino acids each) and a C-terminal domain (O'Brien *et al.,* 2001) (Fig. 1). The N-terminal domain and the repeat domain are heavily glycosylated with both O- and N-linked oligosaccharides (Kui *et al.,* 2003). The Cterminal domain is composed of an extracellular domain with sea urchin sperm protein, enterokinase and agrin (SEA) domains, a transmembrane domain to anchor the protein to the cellular membrane and a short cytoplasmic tail (31 amino acids) with potential serine, threonine and tyrosine phosphorylation sites. The phosphorylation of MUC16 cytoplasmic tail has been associated with its secretion (Fendrick *et al.,* 1997). The secretion of MUC16 is stimulated by epidermal growth factor (EGF) or tyrosine phosphatases (Konishi *et al.,* 1994). Its shedding is decreased by glucocorticoids (Karlan *et al.,* 1988).

Human MUC16 differs from other mucins by having 16 SEA domains located near the membrane-spanning sequence. Other membrane-bounded mucins usually have a single SEA domain (Duraisamy *et al.,* 2006). SEA domains consist of about 120 amino acids. Sequence analysis of MUC16 SEA modules showed that they display some sequence variability. The second MUC16 SEA domain however is relatively conserved and most closely resembles the SEA domain found in other mucins. It may therefore provide the preferential cleavage site, like as in MUC1 and MUC3, which allows release of MUC16 from the cell surface. This, however, remains to be confirmed. Unlike MUC1 and MUC4, MUC16 lacks an EGF-like domain. Through their EGF-like motif located at C-terminal domain (extracellular portion), MUC1 and MUC4 bind to growth factor receptor tyrosine kinases (RTKs) such as erbB family and fibroblast growth factor receptor 3 (FGFR3) (Li *et* 

Role of MUC16 Mucin in Ovarian Cancer 71

Fig. 1. Schematic structure of MUC16 mucin. A. The major domains of MUC16 include the N-terminal domain, the tandem repeat domain and the C-terminal domain. The SEA modules contain a putative proteolytic cleavage site which divides MUC16 in two subunits. The extracellular larger subunit consists of the N-terminal (> 12,000 a.a.) and tandem repeat domains (156 a.a. each), and are heavily glycosylated. The smaller subunit contains SEA domains, a transmembrane domain (TM) and the cytoplasmic tail (31 a.a.). B. MUC16 is usually expressed at the apical surface of normal epithelial cells. In EOC cells, this pattern of expression is lost and MUC16 is expressed through the entire surface of the tumor cells. The

micrograph represents OVCAR3 cells probed with M11 antibody.

*al.,* 2001; Ren *et al.,* 2006; Schroeder *et al.,* 2001; Pochampalli *et al.*, 2007). The formation of heterodimer with RTKs causes cross-phosphorylation of their respective cytoplasmic domain leading to the activation of various signaling pathways (Bafna *et al.,* 2010). Because MUC16 lacks an RTK binding motif in its C-terminal domain, it is not clear whether MUC16-induced signaling is affected by RTKs although, as mentioned above, MUC16 release from the cell is stimulated by EGF. Consistent with the lack of an RTK binding motif, the intracellular interaction between MUC16 and β-catenin is not affected by EGF (Comamala *et al.,* 2011). MUC16 cytoplasmic tail contains a polybasic sequence of amino acids (RRRKK) which is predicted to bind to the ezrin/radixin/moesin (ERM) family of proteins (Fig. 2). This motif is not found in MUC1 and MUC4. The ERM proteins can interact with numerous membrane-associated proteins and the actin cytoskeleton. Consistently, MUC16 has recently been shown to interact with E-cadherin and β-catenin, and causes alteration in the actin cytoskeleton (Comamala *et al.,* 2011). However, it remains unclear whether MUC16/β-catenin and MUC16/E-cadherin interaction is mediated through the ERM motif of the MUC16 cytoplasmic tail. MUC1 cytoplasmic tail has been shown to bind to β-catenin and a serine-rich SXXXXXSSL motif in MUC1 is responsible for this interaction *in vitro* (Yamamoto *et al.,* 1997; Wen *et al.,* 2003; Huang *et al.,* 2005). This motif is notably absent in MUC16. Interestingly however, the binding of MUC1 to β-catenin in cells was independent of the serine-rich motif (Huang *et al.,* 2005). These observations suggest that MUC16 interaction with β-catenin is mediated by an indirect mechanism, probably through another protein. The positively charged R-K rich region of MUC16 cytoplasmic tail also constitutes a putative nuclear localization motif (Bafna *et al.,* 2010). Whether MUC16 cytoplasmic tail does indeed localize to the nucleus, as MUC1 cytoplasmic tail does (Wen *et al.,* 2003), remains to be determined. MUC1 nuclear localization suggests that it is cleaved and released from the membrane and traffic from the membrane to the cytoplasm and the nucleus.

Although MUC16 shares some structural similarities with other membrane-bounded mucins, it possesses many unique features suggesting that its signaling capabilities and functions may differ from other mucins.

#### **4. Expression of MUC16 in normal tissues and ovarian tumors**

Mucins are normally expressed by epithelial cells where they play a protective role. The extensive glycosylation of mucins provides a hydrophilic environment ideal for hydration and lubrication of epithelia. MUC16 is expressed at low levels in the normal airway epithelium but levels can increase in some chronic conditions such as cystic fibrosis (Hattrup *et al.,* 2008; Davies *et al.,* 2007; Gronowitz *et al.,* 2003). MUC16 is expressed at the apical surface of the ocular and conjonctival epithelium where it is part of the glycocalyx protecting corneal cells from bacterial infections and dryness (Argueso *et al.,* 2003; Blalock *et al.,* 2007). MUC16 is also found in lacrimal glands (Jäger *et al.,* 2007). Immunohistochemistry of human tissues using the OC125 antibody detected MUC16 expression in other epithelia such as the fetal coelomic epithelia and its derivatives such as Müllerian duct, fallopian tube, endometrium, and endocervix. MUC16 is also expressed by mesothelial cells of the peritoneum, pleura and pericardium (Kabawat *et al.,* 1983; Nap 1998). However, using OC125 or 4H11 antibodies, MUC16 expression is not found in normal adult colon, rectum, cervix, small intestine, liver, pancreatic ducts, spleen, kidney, skin and ovaries (Rao *et al.,* 2010).

*al.,* 2001; Ren *et al.,* 2006; Schroeder *et al.,* 2001; Pochampalli *et al.*, 2007). The formation of heterodimer with RTKs causes cross-phosphorylation of their respective cytoplasmic domain leading to the activation of various signaling pathways (Bafna *et al.,* 2010). Because MUC16 lacks an RTK binding motif in its C-terminal domain, it is not clear whether MUC16-induced signaling is affected by RTKs although, as mentioned above, MUC16 release from the cell is stimulated by EGF. Consistent with the lack of an RTK binding motif, the intracellular interaction between MUC16 and β-catenin is not affected by EGF (Comamala *et al.,* 2011). MUC16 cytoplasmic tail contains a polybasic sequence of amino acids (RRRKK) which is predicted to bind to the ezrin/radixin/moesin (ERM) family of proteins (Fig. 2). This motif is not found in MUC1 and MUC4. The ERM proteins can interact with numerous membrane-associated proteins and the actin cytoskeleton. Consistently, MUC16 has recently been shown to interact with E-cadherin and β-catenin, and causes alteration in the actin cytoskeleton (Comamala *et al.,* 2011). However, it remains unclear whether MUC16/β-catenin and MUC16/E-cadherin interaction is mediated through the ERM motif of the MUC16 cytoplasmic tail. MUC1 cytoplasmic tail has been shown to bind to β-catenin and a serine-rich SXXXXXSSL motif in MUC1 is responsible for this interaction *in vitro* (Yamamoto *et al.,* 1997; Wen *et al.,* 2003; Huang *et al.,* 2005). This motif is notably absent in MUC16. Interestingly however, the binding of MUC1 to β-catenin in cells was independent of the serine-rich motif (Huang *et al.,* 2005). These observations suggest that MUC16 interaction with β-catenin is mediated by an indirect mechanism, probably through another protein. The positively charged R-K rich region of MUC16 cytoplasmic tail also constitutes a putative nuclear localization motif (Bafna *et al.,* 2010). Whether MUC16 cytoplasmic tail does indeed localize to the nucleus, as MUC1 cytoplasmic tail does (Wen *et al.,* 2003), remains to be determined. MUC1 nuclear localization suggests that it is cleaved and released from the membrane and traffic

Although MUC16 shares some structural similarities with other membrane-bounded mucins, it possesses many unique features suggesting that its signaling capabilities and

Mucins are normally expressed by epithelial cells where they play a protective role. The extensive glycosylation of mucins provides a hydrophilic environment ideal for hydration and lubrication of epithelia. MUC16 is expressed at low levels in the normal airway epithelium but levels can increase in some chronic conditions such as cystic fibrosis (Hattrup *et al.,* 2008; Davies *et al.,* 2007; Gronowitz *et al.,* 2003). MUC16 is expressed at the apical surface of the ocular and conjonctival epithelium where it is part of the glycocalyx protecting corneal cells from bacterial infections and dryness (Argueso *et al.,* 2003; Blalock *et al.,* 2007). MUC16 is also found in lacrimal glands (Jäger *et al.,* 2007). Immunohistochemistry of human tissues using the OC125 antibody detected MUC16 expression in other epithelia such as the fetal coelomic epithelia and its derivatives such as Müllerian duct, fallopian tube, endometrium, and endocervix. MUC16 is also expressed by mesothelial cells of the peritoneum, pleura and pericardium (Kabawat *et al.,* 1983; Nap 1998). However, using OC125 or 4H11 antibodies, MUC16 expression is not found in normal adult colon, rectum, cervix, small intestine, liver, pancreatic ducts, spleen, kidney,

**4. Expression of MUC16 in normal tissues and ovarian tumors** 

from the membrane to the cytoplasm and the nucleus.

functions may differ from other mucins.

skin and ovaries (Rao *et al.,* 2010).

Fig. 1. Schematic structure of MUC16 mucin. A. The major domains of MUC16 include the N-terminal domain, the tandem repeat domain and the C-terminal domain. The SEA modules contain a putative proteolytic cleavage site which divides MUC16 in two subunits. The extracellular larger subunit consists of the N-terminal (> 12,000 a.a.) and tandem repeat domains (156 a.a. each), and are heavily glycosylated. The smaller subunit contains SEA domains, a transmembrane domain (TM) and the cytoplasmic tail (31 a.a.). B. MUC16 is usually expressed at the apical surface of normal epithelial cells. In EOC cells, this pattern of expression is lost and MUC16 is expressed through the entire surface of the tumor cells. The micrograph represents OVCAR3 cells probed with M11 antibody.

Role of MUC16 Mucin in Ovarian Cancer 73

significantly longer progression-free survival (PFS) and overall survival (Rustin *et al.,* 1996; Krivak *et al.*, 2009; van Altena *et al.,* 2010). Pretreatment MUC16 serum level is an independent predictor of PFS in patients with advanced EOC who received a standard chemotherapy regimen (Zorn *et al.,* 2009). In contrast, high MUC16 expression in EOC tissues has inconsistently been associated with overall survival. de la Cuesta *et al.,* found that, in a cohort of 50 EOC samples, patients with high tissue expression of MUC16 had a higher risk of death compared to patients with no expression (de la Cuesta *et al.,* 1999). However, in a much larger cohort of 778 EOC samples, Hogdall *et al.,* showed that latestage patients that lacked MUC16 tissue expression had a significantly poorer survival (Hogdall *et al.,* 2007). In addition, MUC16 expression had no prognostic value in early stage EOC (Hogdall *et al.,* 2007). Because the immunohistochemical detection of tissues MUC16 was based on antibodies that recognize glycosylated epitopes in the tandem repeats in these studies, the expression of a cleaved MUC16 lacking the N-terminal and the tandem repeats domains could not have been detected. Furthermore, the fact that we recently shown that a MUC16 construct consisting of the last C-terminal 283 amino acid was sufficient to promote tumorigenicity (Thériault *et al.,* 2011), is not inconsistent with the observation that late-stage EOC lacking MUC16 (as assessed by

immunohistochemistry) is associated with a worse prognosis.

pathways.

**5. MUC16 roles in the initiation and progression of ovarian cancer** 

Membrane-bounded mucins such as MUC1 and MUC4 are multifunctional molecules. Their large extracellular, heavily glycosylated domain promotes adequate hydration and lubrification of epithelia, and serve as a protective barrier with anti-adhesive properties. On the other hand, through their cytoplasmic tail, they activate various signaling

MUC16 is also seen as a multifunctional molecule with different domains involved in specific functions. Both secreted and membrane-bounded MUC16 have been shown to interact with galectin-1 (Seelenmeyer *et al.,*, 2002). The MUC16 C-terminal domain (last 1148 amino acids) appears to be sufficient for binding to galectin-1 but this interaction requires Olinked oligosaccharide chains which are found in the repeats of the MUC16. The biological significance of this interaction remains unclear but the cell surface recruitment of galectin-1 has been associated with processes such as regulation of cell adhesion (Perillo *et al.,* 1998). MUC16 facilitates cell-cell adhesion through its binding with mesothelin (Rump *et al.,* 2004; Gubbels *et al.,* 2006). The binding site for mesothelin on MUC16 is likely located in the 156 amino acid tandem repeats of the molecule (Gubbels *et al.,* 2006). MUC16 binds primarily to the N-terminus of the extracellular domain of mesothelin (residues 296-359) (Kaneko *et al.,* 2009). Mesothelin is a glycoprotein normally expressed on the mesothelial cells lining the peritoneal cavity (Chang *et al.,* 1996), and by ovarian tumor cells and mesotheliomas (Rump *et al.,* 2004). Mesothelin-MUC16 interaction could facilitate homotypic and heterotypic cellcell adhesion and peritoneal metastasis of ovarian tumors through the adhesion with mesothelial cells. This is consistent with our recent observation that MUC16 knockdown abolished homotypic cell-cell adhesion (Comamala *et al.,* 2011). MUC16 knockdown also promotes EOC cell motility and invasiveness (Comamala *et al.,* 2011). By regulating cell adhesion, cell motility and invasiveness, the extracellular portion of MUC16, through its interaction with galectin-1 and mesothelin, may thus play an important role in metastasis.

Fig. 2. Sequence of MUC1, MUC4 and MUC16 cytoplasmic tails. The intracellular sequence of the different mucins is shown along with protein interaction sites. MUC1 is the best characterized mucin. MUC1 cytoplasmic tail interacts with c-Src, GSK3β, PCKδ, β-catenin, p53, ERα, HSP70/90, Grb2, AP-2. Proteins with kinase activity are in blue whereas those without kinase activity are in yellow. HSP70 binds to MUC1 cytoplasmic tail in the same region as β-catenin. HSP90 binding to MUC1 depends on c-Src-induced Y-46 phosphorylation. MUC16 cytoplasmic tail has an ERM motif for potential interaction with the cytoskeleton. Both MUC1 and MUC16 contain a potential nuclear localization signal motif. MUC4 has no known interaction binding partners.

The expression of MUC16 in EOC tissues varies according to the histotype. Using tissues arrays, Hogdall *et al.,* reported that MUC16 was expressed by 85% of serous, 65% of endometrioid, 40% of clear cell and 36% of undifferentiated adenocarcinomas, but by only 12% of mucinous cancers (Hogdall *et al.*, 2007). Limited expression of MUC16 in mucinous EOC has also been reported by other groups (de la Cuesta *et al.,* 1999). These authors also showed that MUC16 tissues expression was significantly correlated with the FIGO stage but not with the histological grade (Hogdall *et al.*, 2007). In another study using tissues arrays, Rao *et al.,* found that 56%-66% of serous high grade EOC expressed MUC16 depending on the antibody used (OC125 vs 4H11) (Rao *et al.,* 2010). Other studies have also shown the lack MUC16 tissues expression in 15% to 25% of serous EOC (Lu *et al.,* 2004; Rosen *et al.,* 2005; Breitenecker *et al.*, 1989). MUC16 was also found to be expressed in a small percentage (3%-4%) of invasive breast carcinomas and 13% of lung carcinomas (Rao *et al.,* 2010).

Because MUC16 is expressed in a limited subset of early stage OC, in other types of cancers and in a number of benign conditions, its serum level is neither a sensitive nor a specific marker to detect early diseases. However, as mentioned previously, it is a useful marker to monitor response to treatment. In patients who reached complete response after standard primary treatment, MUC16 nadir serum values were associated with a

Fig. 2. Sequence of MUC1, MUC4 and MUC16 cytoplasmic tails. The intracellular sequence of the different mucins is shown along with protein interaction sites. MUC1 is the best characterized mucin. MUC1 cytoplasmic tail interacts with c-Src, GSK3β, PCKδ, β-catenin, p53, ERα, HSP70/90, Grb2, AP-2. Proteins with kinase activity are in blue whereas those without kinase activity are in yellow. HSP70 binds to MUC1 cytoplasmic tail in the same

phosphorylation. MUC16 cytoplasmic tail has an ERM motif for potential interaction with the cytoskeleton. Both MUC1 and MUC16 contain a potential nuclear localization signal

The expression of MUC16 in EOC tissues varies according to the histotype. Using tissues arrays, Hogdall *et al.,* reported that MUC16 was expressed by 85% of serous, 65% of endometrioid, 40% of clear cell and 36% of undifferentiated adenocarcinomas, but by only 12% of mucinous cancers (Hogdall *et al.*, 2007). Limited expression of MUC16 in mucinous EOC has also been reported by other groups (de la Cuesta *et al.,* 1999). These authors also showed that MUC16 tissues expression was significantly correlated with the FIGO stage but not with the histological grade (Hogdall *et al.*, 2007). In another study using tissues arrays, Rao *et al.,* found that 56%-66% of serous high grade EOC expressed MUC16 depending on the antibody used (OC125 vs 4H11) (Rao *et al.,* 2010). Other studies have also shown the lack MUC16 tissues expression in 15% to 25% of serous EOC (Lu *et al.,* 2004; Rosen *et al.,* 2005; Breitenecker *et al.*, 1989). MUC16 was also found to be expressed in a small percentage (3%-4%) of invasive breast carcinomas and 13% of lung carcinomas

Because MUC16 is expressed in a limited subset of early stage OC, in other types of cancers and in a number of benign conditions, its serum level is neither a sensitive nor a specific marker to detect early diseases. However, as mentioned previously, it is a useful marker to monitor response to treatment. In patients who reached complete response after standard primary treatment, MUC16 nadir serum values were associated with a

region as β-catenin. HSP90 binding to MUC1 depends on c-Src-induced Y-46

motif. MUC4 has no known interaction binding partners.

(Rao *et al.,* 2010).

significantly longer progression-free survival (PFS) and overall survival (Rustin *et al.,* 1996; Krivak *et al.*, 2009; van Altena *et al.,* 2010). Pretreatment MUC16 serum level is an independent predictor of PFS in patients with advanced EOC who received a standard chemotherapy regimen (Zorn *et al.,* 2009). In contrast, high MUC16 expression in EOC tissues has inconsistently been associated with overall survival. de la Cuesta *et al.,* found that, in a cohort of 50 EOC samples, patients with high tissue expression of MUC16 had a higher risk of death compared to patients with no expression (de la Cuesta *et al.,* 1999). However, in a much larger cohort of 778 EOC samples, Hogdall *et al.,* showed that latestage patients that lacked MUC16 tissue expression had a significantly poorer survival (Hogdall *et al.,* 2007). In addition, MUC16 expression had no prognostic value in early stage EOC (Hogdall *et al.,* 2007). Because the immunohistochemical detection of tissues MUC16 was based on antibodies that recognize glycosylated epitopes in the tandem repeats in these studies, the expression of a cleaved MUC16 lacking the N-terminal and the tandem repeats domains could not have been detected. Furthermore, the fact that we recently shown that a MUC16 construct consisting of the last C-terminal 283 amino acid was sufficient to promote tumorigenicity (Thériault *et al.,* 2011), is not inconsistent with the observation that late-stage EOC lacking MUC16 (as assessed by

#### **5. MUC16 roles in the initiation and progression of ovarian cancer**

immunohistochemistry) is associated with a worse prognosis.

Membrane-bounded mucins such as MUC1 and MUC4 are multifunctional molecules. Their large extracellular, heavily glycosylated domain promotes adequate hydration and lubrification of epithelia, and serve as a protective barrier with anti-adhesive properties. On the other hand, through their cytoplasmic tail, they activate various signaling pathways.

MUC16 is also seen as a multifunctional molecule with different domains involved in specific functions. Both secreted and membrane-bounded MUC16 have been shown to interact with galectin-1 (Seelenmeyer *et al.,*, 2002). The MUC16 C-terminal domain (last 1148 amino acids) appears to be sufficient for binding to galectin-1 but this interaction requires Olinked oligosaccharide chains which are found in the repeats of the MUC16. The biological significance of this interaction remains unclear but the cell surface recruitment of galectin-1 has been associated with processes such as regulation of cell adhesion (Perillo *et al.,* 1998). MUC16 facilitates cell-cell adhesion through its binding with mesothelin (Rump *et al.,* 2004; Gubbels *et al.,* 2006). The binding site for mesothelin on MUC16 is likely located in the 156 amino acid tandem repeats of the molecule (Gubbels *et al.,* 2006). MUC16 binds primarily to the N-terminus of the extracellular domain of mesothelin (residues 296-359) (Kaneko *et al.,* 2009). Mesothelin is a glycoprotein normally expressed on the mesothelial cells lining the peritoneal cavity (Chang *et al.,* 1996), and by ovarian tumor cells and mesotheliomas (Rump *et al.,* 2004). Mesothelin-MUC16 interaction could facilitate homotypic and heterotypic cellcell adhesion and peritoneal metastasis of ovarian tumors through the adhesion with mesothelial cells. This is consistent with our recent observation that MUC16 knockdown abolished homotypic cell-cell adhesion (Comamala *et al.,* 2011). MUC16 knockdown also promotes EOC cell motility and invasiveness (Comamala *et al.,* 2011). By regulating cell adhesion, cell motility and invasiveness, the extracellular portion of MUC16, through its interaction with galectin-1 and mesothelin, may thus play an important role in metastasis.

Role of MUC16 Mucin in Ovarian Cancer 75

relocalization from the cell membrane to the cytoplasm (Comamala *et al.,* 2011) and increases GKS3β activity (Comamala, unpublished data). It is thus possible that by regulating GKS3β activity, MUC16 regulates β-catenin subcellular localization/degradation. Importantly, β-catenin relocalization in MUC16 knockdown cells is associated with increase cell motility, migration and invasiveness *in vitro* (Comamala *et al.,* 2011). So far, there is no evidence that MUC16 cytoplasmic tail co-localizes with β-catenin in the cytoplasm or the nucleus. How does MUC16 regulates GKS3β activity is also not known. GKS3β has been shown to bind directly to MUC1 cytoplasmic tail and phosphorylates serine in a DRSP site adjacent to that for the β-catenin interaction (Li *et al.,* 1998). This GKS3β target motif is not present in MUC16 cytoplasmic tail and it has not been established yet whether MUC16 binds to GKS3β. The binding of GKS3β to MUC1 appears to be regulated by the phosphorylation of MUC1 by Src family members (Singh *et al.,* 2006). MUC16 cytoplasmic tail is phosphorylated by EGFR activation and its phosphorylation promotes the release of the extracellular domain. Other consequence of this phosphorylation event has not been

MUC1 and MUC4 have been implicated in the regulation of cell growth through their interaction with tyrosine kinase growth factor although these mucins act through different mechanisms (Bafna *et al.,* 2010). MUC1 interacts with ErbB1 through its cytoplasmic tail and increases cell proliferation via the activation of ERK pathway (Jepson *et al.,* 2002). MUC4 probably interacts with ErbB2 through its extracellular domain which leads to the activation of ERK and Akt pathways to promote cell growth (Carraway *et al.,* 2007). MUC16 was also recently shown to affect the growth characteristics of ovarian cancer cells (Thériault *et al.,* 2011). Although the OVCAR3 cell growth rate was not affected by MUC16 knockdown, knockdown cells reached a stationary growth phase in a shorter time. There was no appreciable difference in spontaneous apoptosis between the MUC16 knockdown cells and control cells. Conversely, stable expression of the C-terminal domain into MUC16 negative SKOV3 cells prolonged anchorage dependent growth before they reached the stationary phase. Deletion of the cytoplasmic tail completely abrogated the effect of the MUC16 Cterminal domain on cell growth. It is not known how MUC16 affects tumor cell growth. Stable expression of the MUC16 C-terminal domain in SKOV3 cells did not alter the expression or phosphorylation of EGFR. Although these observations do not rule out the involvement of receptor tyrosine kinase, other partners are probably required to modulate

A recent study showed that MUC16 confers protection against genotoxic agents such as cisplatin in p53 null ovarian cancer cells (Boivin *et al.,* 2009). Single-chain antibody-mediated downregulation of MUC16 sensitized the MUC16 overexpressing OVCAR3 cell line to cisplatin but not to taxol. Conversely, ectopic expression of MUC16 C-terminal domain increased SKOV3 cell line resistance to cisplatin. The downregulation of MUC16 in OVCAR3 cells activates the PI3K/Akt pathway (Comamala *et al.,* 2011). The authors also reported that MUC16 knockdown in these cells decreased FOXO3a nuclear localization. FOXO3a function is controlled in part by activation of the Akt pathway. Akt phosphorylates FOXO3a, resulting in binding of FOXO3a to 14-3-3 proteins and retention of FOXO3a in the cytoplasm. In contrast, dephosphorylation of FOXO3a induces its nuclear localization where it transactivates gene expression (Nemoto *et al.,* 2002). FOXO3a modulates the expression of several genes that regulate the cellular response to stress at the G2-M checkpoint. The growth arrest and DNA damage response gene Gadd54a is a target of FOXO3a that

yet reported.

cell growth.

MUC16 possesses immunosuppressive properties. Patankar *et al.,* reported that natural killer (NK) cells incubated with soluble MUC16 exhibited a 50–70% decrease in the lysis of tumor cells (Patankar *et al.,* 2005). MUC16-expressing EOC cells are also protected from lysis by primary NK cells (Gubbels *et al.,* 2010). Both soluble and membrane-bound MUC16 thus appear to be potent inhibitors of NK cells response *in vitro*. MUC16 downregulates CD16 expression in NK cells found in peritoneal fluids of patients with EOC (Patankar *et al.,* 2005; Belisle *et al.,* 2007). The secreted MUC16 binds to NK cells, B cells and monocytes via Siglec-9, a receptor found on immune cells that inhibits the NK cell response (Belisle *et al.,* 2010). The high levels of secreted MUC16 found in ascites may be one of the factors contributing to the immunosuppressive properties of ascites.

MUC1 and MUC4 mucins have been shown to promote the transformation of fibroblast cells. For example, when the C-terminal portion of MUC1 was stably transfected into 3Y1 fibroblast cells, soft agar colonies and subcutaneous tumors in nude mice were readily obtained (Li *et al.,* 2003). The transforming potential of MUC16 has not been reported yet but limited data from our laboratory showed that stable transfection of MUC16 C-terminal portion (extracellular unique region, transmembrane domain and full-length cytoplasmic tail) into normal ovarian cells failed to immortalized these cells as well as HFL-1 human fibroblast lung cells (Thériault, unpublished data). Recently, ectopic expression of MUC-16 C-terminal domain has been shown to increase tumorigenicity of SKOV3 ovarian cancer cell line in a xenograft mouse model (Thériault *et al.,* 2011). Deletion of the cytoplasmic tail completely abrogated this effect demonstrating that the enhanced tumorigenicity is mediated by interaction of the cytoplasmic tail with intracellular signaling molecules. Consistent with these results, single-chain antibody-mediated knockdown of cell surface MUC16 completely abrogated the formation of colonies in soft agar and subcutaneous tumors with OVCAR3 cells suggesting that MUC16 could be indeed an oncogene (Thériault *et al.,* 2011). Although MUC1 and MUC4 affect tumor progression through the interaction of their cytoplasmic tail with various intracellular signaling molecules (for review, see Bafna *et al.,* 2010), there is very limited data available on the signaling pathways activated by MUC16 cytoplasmic tail. Data from our laboratory suggest that the expression of anti-apoptotic proteins Bcl-2 and Bcl-XL and pro-apoptotic protein Bax is not affected by ectopic expression of MUC16 C-terminal domain (Matte *et al.,*, unpublished data). This observation contrasts with those of Raina *et al.,* which showed that stable transfection of MUC1 in rat 3Y1 fibroblast upregulates Bcl-XL but not Bcl-2 expression (Raina *et al.,* 2004). Growth factors induce tyrosine phosphorylation of MUC1 cytoplasmic tail (Ren *et al.,* 2006). This phosphorylation increases the binding of MUC1 to β-catenin and induces the translocation of MUC1 and β-catenin to the nucleus (Ren *et al.,* 2006). The dysregulation of β-catenin signaling contributes to the transformed phenotype of various cancers (Huang *et al.,* 2005). GSK3β phosphorylates β-catenin and targets it for ubiquitination and degradation (through β-Trcp, an E3 ubiquitin ligase) whereas the inhibition of GSK3β kinase activity results in the translocation of β-catenin from the cytosol to the nucleus where it acts as a transactivator of transcription. MUC1 increases the cytoplasmic and nuclear localization of β-catenin by inhibiting GSK3β-mediated phosphorylation and degradation of β-catenin (Huang *et al.,* 2005). Whether MUC16 could play a role similar to MUC1 is unknown. MUC16 cytoplasmic tail however lacks the β-catenin binding site. Nonetheless, MUC16 was shown to interact with β-catenin (Comamala *et al.,* 2011). In addition, MUC16 knockdown induces β-catenin

MUC16 possesses immunosuppressive properties. Patankar *et al.,* reported that natural killer (NK) cells incubated with soluble MUC16 exhibited a 50–70% decrease in the lysis of tumor cells (Patankar *et al.,* 2005). MUC16-expressing EOC cells are also protected from lysis by primary NK cells (Gubbels *et al.,* 2010). Both soluble and membrane-bound MUC16 thus appear to be potent inhibitors of NK cells response *in vitro*. MUC16 downregulates CD16 expression in NK cells found in peritoneal fluids of patients with EOC (Patankar *et al.,* 2005; Belisle *et al.,* 2007). The secreted MUC16 binds to NK cells, B cells and monocytes via Siglec-9, a receptor found on immune cells that inhibits the NK cell response (Belisle *et al.,* 2010). The high levels of secreted MUC16 found in ascites may be one of the factors contributing to

MUC1 and MUC4 mucins have been shown to promote the transformation of fibroblast cells. For example, when the C-terminal portion of MUC1 was stably transfected into 3Y1 fibroblast cells, soft agar colonies and subcutaneous tumors in nude mice were readily obtained (Li *et al.,* 2003). The transforming potential of MUC16 has not been reported yet but limited data from our laboratory showed that stable transfection of MUC16 C-terminal portion (extracellular unique region, transmembrane domain and full-length cytoplasmic tail) into normal ovarian cells failed to immortalized these cells as well as HFL-1 human fibroblast lung cells (Thériault, unpublished data). Recently, ectopic expression of MUC-16 C-terminal domain has been shown to increase tumorigenicity of SKOV3 ovarian cancer cell line in a xenograft mouse model (Thériault *et al.,* 2011). Deletion of the cytoplasmic tail completely abrogated this effect demonstrating that the enhanced tumorigenicity is mediated by interaction of the cytoplasmic tail with intracellular signaling molecules. Consistent with these results, single-chain antibody-mediated knockdown of cell surface MUC16 completely abrogated the formation of colonies in soft agar and subcutaneous tumors with OVCAR3 cells suggesting that MUC16 could be indeed an oncogene (Thériault *et al.,* 2011). Although MUC1 and MUC4 affect tumor progression through the interaction of their cytoplasmic tail with various intracellular signaling molecules (for review, see Bafna *et al.,* 2010), there is very limited data available on the signaling pathways activated by MUC16 cytoplasmic tail. Data from our laboratory suggest that the expression of anti-apoptotic proteins Bcl-2 and Bcl-XL and pro-apoptotic protein Bax is not affected by ectopic expression of MUC16 C-terminal domain (Matte *et al.,*, unpublished data). This observation contrasts with those of Raina *et al.,* which showed that stable transfection of MUC1 in rat 3Y1 fibroblast upregulates Bcl-XL but not Bcl-2 expression (Raina *et al.,* 2004). Growth factors induce tyrosine phosphorylation of MUC1 cytoplasmic tail (Ren *et al.,* 2006). This phosphorylation increases the binding of MUC1 to β-catenin and induces the translocation of MUC1 and β-catenin to the nucleus (Ren *et al.,* 2006). The dysregulation of β-catenin signaling contributes to the transformed phenotype of various cancers (Huang *et al.,* 2005). GSK3β phosphorylates β-catenin and targets it for ubiquitination and degradation (through β-Trcp, an E3 ubiquitin ligase) whereas the inhibition of GSK3β kinase activity results in the translocation of β-catenin from the cytosol to the nucleus where it acts as a transactivator of transcription. MUC1 increases the cytoplasmic and nuclear localization of β-catenin by inhibiting GSK3β-mediated phosphorylation and degradation of β-catenin (Huang *et al.,* 2005). Whether MUC16 could play a role similar to MUC1 is unknown. MUC16 cytoplasmic tail however lacks the β-catenin binding site. Nonetheless, MUC16 was shown to interact with β-catenin (Comamala *et al.,* 2011). In addition, MUC16 knockdown induces β-catenin

the immunosuppressive properties of ascites.

relocalization from the cell membrane to the cytoplasm (Comamala *et al.,* 2011) and increases GKS3β activity (Comamala, unpublished data). It is thus possible that by regulating GKS3β activity, MUC16 regulates β-catenin subcellular localization/degradation. Importantly, β-catenin relocalization in MUC16 knockdown cells is associated with increase cell motility, migration and invasiveness *in vitro* (Comamala *et al.,* 2011). So far, there is no evidence that MUC16 cytoplasmic tail co-localizes with β-catenin in the cytoplasm or the nucleus. How does MUC16 regulates GKS3β activity is also not known. GKS3β has been shown to bind directly to MUC1 cytoplasmic tail and phosphorylates serine in a DRSP site adjacent to that for the β-catenin interaction (Li *et al.,* 1998). This GKS3β target motif is not present in MUC16 cytoplasmic tail and it has not been established yet whether MUC16 binds to GKS3β. The binding of GKS3β to MUC1 appears to be regulated by the phosphorylation of MUC1 by Src family members (Singh *et al.,* 2006). MUC16 cytoplasmic tail is phosphorylated by EGFR activation and its phosphorylation promotes the release of the extracellular domain. Other consequence of this phosphorylation event has not been yet reported.

MUC1 and MUC4 have been implicated in the regulation of cell growth through their interaction with tyrosine kinase growth factor although these mucins act through different mechanisms (Bafna *et al.,* 2010). MUC1 interacts with ErbB1 through its cytoplasmic tail and increases cell proliferation via the activation of ERK pathway (Jepson *et al.,* 2002). MUC4 probably interacts with ErbB2 through its extracellular domain which leads to the activation of ERK and Akt pathways to promote cell growth (Carraway *et al.,* 2007). MUC16 was also recently shown to affect the growth characteristics of ovarian cancer cells (Thériault *et al.,* 2011). Although the OVCAR3 cell growth rate was not affected by MUC16 knockdown, knockdown cells reached a stationary growth phase in a shorter time. There was no appreciable difference in spontaneous apoptosis between the MUC16 knockdown cells and control cells. Conversely, stable expression of the C-terminal domain into MUC16 negative SKOV3 cells prolonged anchorage dependent growth before they reached the stationary phase. Deletion of the cytoplasmic tail completely abrogated the effect of the MUC16 Cterminal domain on cell growth. It is not known how MUC16 affects tumor cell growth. Stable expression of the MUC16 C-terminal domain in SKOV3 cells did not alter the expression or phosphorylation of EGFR. Although these observations do not rule out the involvement of receptor tyrosine kinase, other partners are probably required to modulate cell growth.

A recent study showed that MUC16 confers protection against genotoxic agents such as cisplatin in p53 null ovarian cancer cells (Boivin *et al.,* 2009). Single-chain antibody-mediated downregulation of MUC16 sensitized the MUC16 overexpressing OVCAR3 cell line to cisplatin but not to taxol. Conversely, ectopic expression of MUC16 C-terminal domain increased SKOV3 cell line resistance to cisplatin. The downregulation of MUC16 in OVCAR3 cells activates the PI3K/Akt pathway (Comamala *et al.,* 2011). The authors also reported that MUC16 knockdown in these cells decreased FOXO3a nuclear localization. FOXO3a function is controlled in part by activation of the Akt pathway. Akt phosphorylates FOXO3a, resulting in binding of FOXO3a to 14-3-3 proteins and retention of FOXO3a in the cytoplasm. In contrast, dephosphorylation of FOXO3a induces its nuclear localization where it transactivates gene expression (Nemoto *et al.,* 2002). FOXO3a modulates the expression of several genes that regulate the cellular response to stress at the G2-M checkpoint. The growth arrest and DNA damage response gene Gadd54a is a target of FOXO3a that

Role of MUC16 Mucin in Ovarian Cancer 77

adhesive properties. Following this EMT, floating tumor cells revert to an epithelial phenotype and express MUC16 leading to adhesion to mesothelial cells via MUC16/mesothelin interaction and the formation of tumor implants in the peritoneal

MUC16 has been shown to alter tumorigenicity and metastasis of EOC cells (Thériault *et al.,* 2011). MUC16 knockdown inhibited cell growth in soft agar and abolished the formation of subcutaneous tumor nodule. Conversely, the MUC16 C-terminal domain appears to be sufficient to enhance *in vitro* and *in vivo* tumorigenicity, and promote dissemination of tumor cells throughout the peritoneal cavity of SCID mice. Importantly, deletion of MUC16 cytoplasmic tail completely abolished these effects. Although the mechanism by which MUC16 affects tumorigenicity and metastasis is unknown, this study suggests that MUC16

Although MUC16's functions are beginning to be elucidated in EOC cells, the normal function of MUC16 is for the most part unknown. As mentioned previously, it is expressed by various tissues, notably the conjonctiva and the lachrymal glands, were it can play a protective role against bacterial infection. Its expression in fallopian tube and endometrium suggests a role in reproduction. However, knockout mice have been shown to display a normal phenotype by 1 year of age demonstrating that MUC16 is not required for mouse development and reproduction (Cheon *et al.,* 2009). Consistent with these data, MUC1 null mice have normal fertility and development (Spicer *et al.,* 1995). One explanation that has been evoked for the lack of phenotype for MUC16 and MUC1 knockout mice is that

Since its discovery in the late 1970s, MUC16 glycoprotein has been recognized as a useful clinical biomarker in advance diseases. However, accumulating evidence suggests that MUC16 is more than a biomarker for disease progression; MUC16 contributes to the pathogenesis and progression of EOC. MUC16 appears to regulate cell survival, cell motility, invasiveness and tumorigenicity in EOC cells. These phenotypic effects are also shared by other membrane-bounded mucins such as MUC1 and MUC4. However, the underlying mechanisms responsible for the biological functions are likely to differ between mucins because of their structural differences, notably in their cytoplasmic tail. Although progress has been made regarding the role of MUC16 in tumor progression, the signaling pathways activated by its cytoplasmic tail are mostly unknown. The functional role of MUC16/β-catenin and MUC16/E-cadherin interactions is not known. Further studies are needed to understand the contribution of these interactions in tumor progression. Identifying the signaling molecules activated by MUC16 and elucidating their contribution to EOC progression will be critical in the near future as MUC16 may represent a target for

Adams CL, Nelson WJ, Smith SJ. Quantitative analysis of cadherin-catenin-actin

reorganization during development of cell-cell adhesion. J Cell Biol 1996;135:1899-

cavity.

plays a critical role in the progression of EOC.

**6. Conclusions and future directions** 

EOC treatment.

**7. References** 

1911.

functional redundancy can compensate for the loss of other mucins.

mediates part of FOXO3a's effects on DNA repair (Tran *et al.,* 2002). Thus, preventing the nuclear localization of FOXO3a contributes to the apoptotic response to genotoxic drugs. These data suggest that MUC16 knockdown sensitizes tumor cells to genotoxic drugs by activating Akt which in turn prevents FOXO3a nuclear localization. The knockdown of MUC1 has also been reported to sensitize carcinoma cells to apoptosis induced by genotoxic agents (Yin *et al.,* 2004; Ren *et al.,* 2004).

EOC is a highly metastatic disease which primarily metastasizes to the serosal cavities while dissemination through the vasculature is unusual (Naora *et al.,* 2005). During the progression to a metastatic phenotype, carcinoma cells undergo morphological changes, become motile and acquire the ability to migrate and invade to establish secondary tumors at distant sites. This epithelial to mesenchymal transition (EMT) is characterized by coordinated molecular and cellular changes including a reduction in cell-cell adhesion, the loss of apical-basolateral polarity, the loss of epithelial markers and the gain of mesenchymal markers (Vergara *et al*, 2010; Hugo *et al*, 2007). EMT is an important physiological process during embryogenesis and wound healing, but also a key step in cancer metastasis (Radisky, 2005). EMT is a necessary step towards metastatic tumor progression during detachment of tumor cells from the primary tumor site and attachment to metastatic sites. A key feature of EMT is the switch from E-cadherin expression at the cell surface to N-cadherin which promotes the interaction with stromal components (Cavallaro *et al.,* 2004). EMT results in enhanced cell motility and invasion. MUC16 was recently shown to be an important regulator of EMT in OC cells (Comamala *et al.,* 2011). Using a MUC16 knockdown OC cell model, the authors showed that downregulation of MUC16 cell surface expression prevents homotypic cell aggregation, enhances disruption of cell-cell junctions and increases cell motility and invasiveness. These effects were associated with the loss of epithelial markers such as E-cadherin and cytokeratin-18 and gain of mesenchymal markers such as N-cadherin and vimentin in knockdown cells. These data suggest that MUC16 is involved in the metastatic process. As mentioned previously, MUC16 knockdown induces an intracellular relocalization of E-cadherin. It is possible that the binding of MUC16 to Ecadherin complexes results in the surface localization of E-cadherin, which mediates cell contact and suppression of cell migration. Conversely, in the absence of MUC16, E-cadherin relocalizes in the cytoplasm, which abolishes its ability to promote cell contact formation. The cytoplasmic domain of E-cadherin binds to β-catenin, which forms complexes with αcatenin (Ozawa *et al*, 1990), actin (Adams *et al*, 1996), p120 (Staddon *et al*, 1995), EGFR (Hoschuetzky *et al*, 1994), and other proteins. It is possible that by forming a complex with E-cadherin and/or β-catenin, MUC16 re-distributes EGFR and consequently modulates its signaling pathway. Although expression of MUC16 C-terminal domain in SKOV3 cells does not affect EGFR phosphorylation, MUC16 knockdown activates EGFR resulting in increased activation of Akt, ERK1/2 and MMP-2 and MMP-9 (Comamala *et al.,* 2011). Activation of the MAPK-ERK pathway has been shown to upregulate MMP-9 and to enhance cell migration (Suyama *et al*, 2002). Akt activation has been associated with induction of EMT in carcinoma cells (Grille *et al*, 2003; Yan *et al*, 2009). In summary, the early steps in ovarian tumor metastasis involve shedding of the primary tumor through alterations of cell adhesive properties into ascites to form free floating cells or multicellular aggregates. Tumor cells from the primary site express MUC16, display a more epithelial phenotype and express Ecadherin. Shedding from the primary tumor site involves the loss of MUC16 and E-cadherin expression and the gain of mesenchymal markers leading to increased motility and loss of

mediates part of FOXO3a's effects on DNA repair (Tran *et al.,* 2002). Thus, preventing the nuclear localization of FOXO3a contributes to the apoptotic response to genotoxic drugs. These data suggest that MUC16 knockdown sensitizes tumor cells to genotoxic drugs by activating Akt which in turn prevents FOXO3a nuclear localization. The knockdown of MUC1 has also been reported to sensitize carcinoma cells to apoptosis induced by genotoxic

EOC is a highly metastatic disease which primarily metastasizes to the serosal cavities while dissemination through the vasculature is unusual (Naora *et al.,* 2005). During the progression to a metastatic phenotype, carcinoma cells undergo morphological changes, become motile and acquire the ability to migrate and invade to establish secondary tumors at distant sites. This epithelial to mesenchymal transition (EMT) is characterized by coordinated molecular and cellular changes including a reduction in cell-cell adhesion, the loss of apical-basolateral polarity, the loss of epithelial markers and the gain of mesenchymal markers (Vergara *et al*, 2010; Hugo *et al*, 2007). EMT is an important physiological process during embryogenesis and wound healing, but also a key step in cancer metastasis (Radisky, 2005). EMT is a necessary step towards metastatic tumor progression during detachment of tumor cells from the primary tumor site and attachment to metastatic sites. A key feature of EMT is the switch from E-cadherin expression at the cell surface to N-cadherin which promotes the interaction with stromal components (Cavallaro *et al.,* 2004). EMT results in enhanced cell motility and invasion. MUC16 was recently shown to be an important regulator of EMT in OC cells (Comamala *et al.,* 2011). Using a MUC16 knockdown OC cell model, the authors showed that downregulation of MUC16 cell surface expression prevents homotypic cell aggregation, enhances disruption of cell-cell junctions and increases cell motility and invasiveness. These effects were associated with the loss of epithelial markers such as E-cadherin and cytokeratin-18 and gain of mesenchymal markers such as N-cadherin and vimentin in knockdown cells. These data suggest that MUC16 is involved in the metastatic process. As mentioned previously, MUC16 knockdown induces an intracellular relocalization of E-cadherin. It is possible that the binding of MUC16 to Ecadherin complexes results in the surface localization of E-cadherin, which mediates cell contact and suppression of cell migration. Conversely, in the absence of MUC16, E-cadherin relocalizes in the cytoplasm, which abolishes its ability to promote cell contact formation. The cytoplasmic domain of E-cadherin binds to β-catenin, which forms complexes with αcatenin (Ozawa *et al*, 1990), actin (Adams *et al*, 1996), p120 (Staddon *et al*, 1995), EGFR (Hoschuetzky *et al*, 1994), and other proteins. It is possible that by forming a complex with E-cadherin and/or β-catenin, MUC16 re-distributes EGFR and consequently modulates its signaling pathway. Although expression of MUC16 C-terminal domain in SKOV3 cells does not affect EGFR phosphorylation, MUC16 knockdown activates EGFR resulting in increased activation of Akt, ERK1/2 and MMP-2 and MMP-9 (Comamala *et al.,* 2011). Activation of the MAPK-ERK pathway has been shown to upregulate MMP-9 and to enhance cell migration (Suyama *et al*, 2002). Akt activation has been associated with induction of EMT in carcinoma cells (Grille *et al*, 2003; Yan *et al*, 2009). In summary, the early steps in ovarian tumor metastasis involve shedding of the primary tumor through alterations of cell adhesive properties into ascites to form free floating cells or multicellular aggregates. Tumor cells from the primary site express MUC16, display a more epithelial phenotype and express Ecadherin. Shedding from the primary tumor site involves the loss of MUC16 and E-cadherin expression and the gain of mesenchymal markers leading to increased motility and loss of

agents (Yin *et al.,* 2004; Ren *et al.,* 2004).

adhesive properties. Following this EMT, floating tumor cells revert to an epithelial phenotype and express MUC16 leading to adhesion to mesothelial cells via MUC16/mesothelin interaction and the formation of tumor implants in the peritoneal cavity.

MUC16 has been shown to alter tumorigenicity and metastasis of EOC cells (Thériault *et al.,* 2011). MUC16 knockdown inhibited cell growth in soft agar and abolished the formation of subcutaneous tumor nodule. Conversely, the MUC16 C-terminal domain appears to be sufficient to enhance *in vitro* and *in vivo* tumorigenicity, and promote dissemination of tumor cells throughout the peritoneal cavity of SCID mice. Importantly, deletion of MUC16 cytoplasmic tail completely abolished these effects. Although the mechanism by which MUC16 affects tumorigenicity and metastasis is unknown, this study suggests that MUC16 plays a critical role in the progression of EOC.

Although MUC16's functions are beginning to be elucidated in EOC cells, the normal function of MUC16 is for the most part unknown. As mentioned previously, it is expressed by various tissues, notably the conjonctiva and the lachrymal glands, were it can play a protective role against bacterial infection. Its expression in fallopian tube and endometrium suggests a role in reproduction. However, knockout mice have been shown to display a normal phenotype by 1 year of age demonstrating that MUC16 is not required for mouse development and reproduction (Cheon *et al.,* 2009). Consistent with these data, MUC1 null mice have normal fertility and development (Spicer *et al.,* 1995). One explanation that has been evoked for the lack of phenotype for MUC16 and MUC1 knockout mice is that functional redundancy can compensate for the loss of other mucins.

#### **6. Conclusions and future directions**

Since its discovery in the late 1970s, MUC16 glycoprotein has been recognized as a useful clinical biomarker in advance diseases. However, accumulating evidence suggests that MUC16 is more than a biomarker for disease progression; MUC16 contributes to the pathogenesis and progression of EOC. MUC16 appears to regulate cell survival, cell motility, invasiveness and tumorigenicity in EOC cells. These phenotypic effects are also shared by other membrane-bounded mucins such as MUC1 and MUC4. However, the underlying mechanisms responsible for the biological functions are likely to differ between mucins because of their structural differences, notably in their cytoplasmic tail. Although progress has been made regarding the role of MUC16 in tumor progression, the signaling pathways activated by its cytoplasmic tail are mostly unknown. The functional role of MUC16/β-catenin and MUC16/E-cadherin interactions is not known. Further studies are needed to understand the contribution of these interactions in tumor progression. Identifying the signaling molecules activated by MUC16 and elucidating their contribution to EOC progression will be critical in the near future as MUC16 may represent a target for EOC treatment.

#### **7. References**

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**5** 

*1Institute of Pathology,* 

*Germany* 

*University Medicine Charité, Berlin 2Institute of Toxicology and Genetics,* 

*Karlsruhe Institute of Technology, Karlsruhe* 

**Apoptosis Pathways in Ovarian Cancer** 

Tumour initiation and progression are driven by constitutively activated oncogenes mediating deregulation of the balance between cell death- and survival pathways. Among the most relevant signalling cascades activated in the majority of tumour types, the RAS/mitogen-activated protein kinase (Ras/MAPK), the phosphatidyl inositol-3 kinase/protein kinase B (PI3K/PKB) and the protein kinases C (PKC) signalling cascades were postulated (Weinstein, 1987; Nicosia et al., 2003; Roberts and Der, 2007; McCubrey et al., 2007; Breitkreutz et al., 2007). These cascades define individual characteristics of particular tumours and consequently their individual responsiveness to cancer

In this chapter, we will address the characteristics of the apoptotic signalling pathways in ovarian carcinomas. Particular attention will be given to the *HRS* family of tumour suppressor genes encoding proteins with phospholipase activity and suppressed in the majority of ovarian malignancies. We will describe signalling cascades down regulating two well-characterized members of this family H-REV107-1/HRLS3/PLA2G16 and TIG3/RARRES/RIG1 in tumour cells. Furthermore, potential therapeutic consequences of the re-expression of these genes defined as a class II tumour suppressors will be discussed.

**2. The HRS class II tumour suppressors are important mediators of IFN- and retinoid-dependent growth suppression and cell death in ovarian cancer** 

The *H-REV107*-related genes (*TIG3, H-REV107-1, HRSL2*) are known as inhibitors of proliferation of tumour cells in vivo and in vitro. While being almost ubiquitously expressed in normal tissues, down-regulation or complete loss of these genes in tumours and tumour cell lines have been reported. Expression can be reconstituted by different anti-proliferative signals such as interferons and retinoids, as well as by the inhibition of oncogenic pathways and interference with DNA methylation (Alessi et al., 1994; Husmann et al., 1998; Akiyama et al., 1999; Siegrist et al., 2001; Ito et al., 2001; Roder et al., 2002; Huang et al., 2002; Higuchi et al., 2003; Duvic et al., 2003). Re-activation of the H-REV107-1-related proteins and over-

expression of the genes induce apoptosis or differentiation of tumour cells.

**1. Introduction** 

therapy.

Christine Sers1, Reinhold Schafer1 and Irina Nazarenko2


### **Apoptosis Pathways in Ovarian Cancer**

Christine Sers1, Reinhold Schafer1 and Irina Nazarenko2

*1Institute of Pathology, University Medicine Charité, Berlin 2Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Karlsruhe Germany* 

#### **1. Introduction**

84 Ovarian Cancer – Basic Science Perspective

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> Tumour initiation and progression are driven by constitutively activated oncogenes mediating deregulation of the balance between cell death- and survival pathways. Among the most relevant signalling cascades activated in the majority of tumour types, the RAS/mitogen-activated protein kinase (Ras/MAPK), the phosphatidyl inositol-3 kinase/protein kinase B (PI3K/PKB) and the protein kinases C (PKC) signalling cascades were postulated (Weinstein, 1987; Nicosia et al., 2003; Roberts and Der, 2007; McCubrey et al., 2007; Breitkreutz et al., 2007). These cascades define individual characteristics of particular tumours and consequently their individual responsiveness to cancer therapy.

> In this chapter, we will address the characteristics of the apoptotic signalling pathways in ovarian carcinomas. Particular attention will be given to the *HRS* family of tumour suppressor genes encoding proteins with phospholipase activity and suppressed in the majority of ovarian malignancies. We will describe signalling cascades down regulating two well-characterized members of this family H-REV107-1/HRLS3/PLA2G16 and TIG3/RARRES/RIG1 in tumour cells. Furthermore, potential therapeutic consequences of the re-expression of these genes defined as a class II tumour suppressors will be discussed.

#### **2. The HRS class II tumour suppressors are important mediators of IFN- and retinoid-dependent growth suppression and cell death in ovarian cancer**

The *H-REV107*-related genes (*TIG3, H-REV107-1, HRSL2*) are known as inhibitors of proliferation of tumour cells in vivo and in vitro. While being almost ubiquitously expressed in normal tissues, down-regulation or complete loss of these genes in tumours and tumour cell lines have been reported. Expression can be reconstituted by different anti-proliferative signals such as interferons and retinoids, as well as by the inhibition of oncogenic pathways and interference with DNA methylation (Alessi et al., 1994; Husmann et al., 1998; Akiyama et al., 1999; Siegrist et al., 2001; Ito et al., 2001; Roder et al., 2002; Huang et al., 2002; Higuchi et al., 2003; Duvic et al., 2003). Re-activation of the H-REV107-1-related proteins and overexpression of the genes induce apoptosis or differentiation of tumour cells.

Apoptosis Pathways in Ovarian Cancer 87

Fig. 1. Phylogeny of HRS and the related LRAT, NSE and NCD protein families

NC proteins, the *C.elegans* Egl26 proteins, and the viral 2A proteins.

acid residues that corresponds the average length of the HRS proteins.

divergence.

A maximum parsimony tree was generated with the help of Tree-Puzzle (Strimmer and von Haeseler 1997) and includes the eukaryotic members of the HRS, LRAT, NSE families, plant

First, only HRS proteins were aligned using the ClustlW algorithm (EMBL-EBI), then LRAT and NSE families. From the *C.elegans*, the viral and the plant proteins only regions with a high similarity to the HRS proteins were compared. Additional upstream and downstream motifs were cut out. As a result, all sequences had a comparable length of about 160 amino

For the calculation of the phylogenetic relationships 1000 replicates were run. Branch support values are indicated at the nodes, distances are proportional to relative sequence

#### **2.1 HRS family members encode LRAT-related phospholipid-metabolizing enzymes**

Two independent groups (Hughes and Stanway, 2000; Anantharaman and Aravind, 2003) have unfolded the phylogenetic relationship between *H-REV107-1-*related genes, LRAT (Lecithin retinol acyltransferase) and viral and bacterial peptidases in previous works.

Here we aimed to identify and describe H-REV107-1 homologs in different organisms in order to follow their origin and development during the evolution.

For that purpose, we performed an *in silico* analysis, using PSI-Blast and Blast-p screening in the NCBI non-redundant database. This analysis revealed 62 homologous proteins in eukaryotic and prokaryotic organisms. To identify phylogenetic relationships Tree Puzzle (Strimmer and von Haeseler, 1997) was applied (Fig. 1). The analysis revealed five closely related proteins, suggesting their origin from the same ancestor protein during the evolution. These proteins, including H-REV107-1 comprise a new protein family, which we designated here as the HRS (H-REV107-1-related proteins) protein family (Table 1).

The novel HRS family is composed of tumour suppressors, which negatively regulate cell survival, control signal transduction and induce differentiation.


Table 1. Members of the HRS family

For that purpose, we performed an *in silico* analysis, using PSI-Blast and Blast-p screening in the NCBI non-redundant database. This analysis revealed 62 homologous proteins in eukaryotic and prokaryotic organisms. To identify phylogenetic relationships Tree Puzzle (Strimmer and von Haeseler, 1997) was applied (Fig. 1). The analysis revealed five closely related proteins, suggesting their origin from the same ancestor protein during the evolution. These proteins, including H-REV107-1 comprise a new protein family, which we

The novel HRS family is composed of tumour suppressors, which negatively regulate cell

designated here as the HRS (H-REV107-1-related proteins) protein family (Table 1).

**2.1 HRS family members encode LRAT-related phospholipid-metabolizing enzymes**  Two independent groups (Hughes and Stanway, 2000; Anantharaman and Aravind, 2003) have unfolded the phylogenetic relationship between *H-REV107-1-*related genes, LRAT (Lecithin retinol acyltransferase) and viral and bacterial peptidases in previous works. Here we aimed to identify and describe H-REV107-1 homologs in different organisms in

order to follow their origin and development during the evolution.

survival, control signal transduction and induce differentiation.

Table 1. Members of the HRS family

Fig. 1. Phylogeny of HRS and the related LRAT, NSE and NCD protein families A maximum parsimony tree was generated with the help of Tree-Puzzle (Strimmer and von Haeseler 1997) and includes the eukaryotic members of the HRS, LRAT, NSE families, plant NC proteins, the *C.elegans* Egl26 proteins, and the viral 2A proteins.

First, only HRS proteins were aligned using the ClustlW algorithm (EMBL-EBI), then LRAT and NSE families. From the *C.elegans*, the viral and the plant proteins only regions with a high similarity to the HRS proteins were compared. Additional upstream and downstream motifs were cut out. As a result, all sequences had a comparable length of about 160 amino acid residues that corresponds the average length of the HRS proteins.

For the calculation of the phylogenetic relationships 1000 replicates were run. Branch support values are indicated at the nodes, distances are proportional to relative sequence divergence.

Apoptosis Pathways in Ovarian Cancer 89

Fig. 2. HRS catalytic and protein-binding domains

Fourteen members of the HRS protein family found in the NCBI database were aligned using the Clustal W program as described in Figure 1B. Analysis, edition, and shading of

(http://www.psc.edu/biomed/genedoc/). The PSIPRED secondary structure prediction server was used to analyze potential secondary structures of the HRS protein sequences

The HRS proteins contain non-homologous proline-rich motifs on their N-termini (red line on the top of the alignment). The core parts of HRS proteins are highly conserved and contain the NlpC/P60 and NC domains (blue and green boxes, respectively). With a high prediction confidence of the PSIPRED standard analysis, three -strands were defined within the Nlpc/P60 domain (blue arrows). The first and the second strand contain the conserved GDL and HWXXY motifs; the VXXLAP motif comprises the third strand. The region downstream the third -strand with two conserved serine residues is likely to have a structure of -helix (green cylinder). The large NC domain (green box) contains a KALVK conserved motif of unknown function, two short stretches DXXG and NKXD, which are similar to conserved regions of GTPases (Akiyama et al., 1999; Bourne, Sanders, and McCormick 1990) and the NCEHFV conserved motif, characteristic for conventional NC domains. At the C-terminus, HRS proteins harbour a hydrophobic C-terminal -helix.

conserved domains were performed with the help of the GenDoc freeware

(http://bioinf.cs.ucl.ac.uk/psipred/) (McGuffin, Bryson, and Jones 2000).

#### **2.2 Clustered chromosomal localization of 4 human HRS genes on 11q13**

Four of the five members of the human HRS family, *HRLS2*, *H-REV107-1, TIG3* and *HRLS5*, are localized in one cluster on chromosome 11q13, supporting the hypothesis of their origin from the same ancestor (Fig. 3). The *HRLS2* and *H-REV107-1* genes, encoding most closely related family members (Fig. 1A), are located next to each other. The *H-REV107-1* gene spans between 63099K and 63138K on the chromosome 11q, and directly downstream of it, from 63077K to 63088K, the *HRLS2* gene is located. The *TIG3* gene is positioned on the opposite DNA strand directly downstream of *HRLS2*, the gene has a small non-coding region and spans between 63070K and 63079K. The *HRLS5* gene 62897K and 63015K is also located on chromosome 11q13, but separated by two genes encoding the thymosin-like 5 (TMSL5) and the lectin, galactoside-binding, soluble, 12 (galectin 12/LGAL12) proteins, from the other HRS genes.

Earlier findings suggested that chromosomal alterations resulting in *HRS* gene downregulation or loss are rather rare events in human carcinomas. Nevertheless, structural changes on 11q13 have been described in numerous cases and only recently methods such as array CGH and next generation sequencing (NGS) have improved the analysis such that the involvement of individual genes can now be analysed. Therefore, it cannot be excluded that future investigations might unravel smaller deletions influencing one of the clustered *HRS* genes on 11q13 in distinct tumour types.

#### **2.3 Domain structure and enzymatic activity of the HRS family members**

Phylogenetic analysis of HRS and HRS-related proteins revealed a high conservation within the so called NlpC/P60 domain (Anantharaman and Aravind, 2003). This sequence was indentified in LRAT proteins (lecithin retinol acyltransferase) as being essential for all-transretinol metabolism.

To analyse domain structure of other members of the HRS family, Clustl W alignment was performed. Using this program, 14 members of the HRS protein family found in the NCBI database, were analysed (Fig. 2).

The HRS proteins contain non-homologous proline-rich motifs on their N-termini (red line on the top of the alignment). The core parts of HRS proteins are highly conserved and contain the NlpC/P60 and NC domains ( Fig. 2, blue and green boxes, respectively). We predicted three strands within the Nlpc/P60 domain (Fig. 2, blue arrows). The first and the second strand contain the conserved GDL and HWXXY motifs; the VXXLAP motif comprises the third strand. The region downstream the third -strand with two conserved serine residues is likely to have the structure of -helix ( Fig. 2, green cylinder). The large NC domain depicted in Fig. 2 with a green box, contains a KALVK conserved motif of unknown function, two short stretches DXXG and NKXD, which are similar to conserved regions of GTPases (Akiyama et al., 1999; Bourne, Sanders, and McCormick 1990) and the NCEHFV conserved motif, characteristic for conventional NC domains. At the C-terminus, HRS proteins harbour a hydrophobic C-terminal -helix,described as a membrane-binding domain.

Recently, a crystal structure of the NlpC/P60 domain of H-REV107-1 has being resolved (Ren et al., 2010b). Within this domain, a phospholipase active site consisting of a Cys-His-His triad was identified. The residues H23 and C113 play a pivotal role for the H-REV107-1 enzymatic activity (Ren et al., 2010a). Meantime, the enzymatic activity of the H-REV107-1, TIG3, HRASLS2 and HRLP5 proteins has been characterized as PLA1/2- type hydrolysis, supporting a role of the HRS proteins in lipid metabolism.

Four of the five members of the human HRS family, *HRLS2*, *H-REV107-1, TIG3* and *HRLS5*, are localized in one cluster on chromosome 11q13, supporting the hypothesis of their origin from the same ancestor (Fig. 3). The *HRLS2* and *H-REV107-1* genes, encoding most closely related family members (Fig. 1A), are located next to each other. The *H-REV107-1* gene spans between 63099K and 63138K on the chromosome 11q, and directly downstream of it, from 63077K to 63088K, the *HRLS2* gene is located. The *TIG3* gene is positioned on the opposite DNA strand directly downstream of *HRLS2*, the gene has a small non-coding region and spans between 63070K and 63079K. The *HRLS5* gene 62897K and 63015K is also located on chromosome 11q13, but separated by two genes encoding the thymosin-like 5 (TMSL5) and the lectin, galactoside-binding, soluble, 12 (galectin 12/LGAL12) proteins,

Earlier findings suggested that chromosomal alterations resulting in *HRS* gene downregulation or loss are rather rare events in human carcinomas. Nevertheless, structural changes on 11q13 have been described in numerous cases and only recently methods such as array CGH and next generation sequencing (NGS) have improved the analysis such that the involvement of individual genes can now be analysed. Therefore, it cannot be excluded that future investigations might unravel smaller deletions influencing one of the clustered

Phylogenetic analysis of HRS and HRS-related proteins revealed a high conservation within the so called NlpC/P60 domain (Anantharaman and Aravind, 2003). This sequence was indentified in LRAT proteins (lecithin retinol acyltransferase) as being essential for all-trans-

To analyse domain structure of other members of the HRS family, Clustl W alignment was performed. Using this program, 14 members of the HRS protein family found in the NCBI

The HRS proteins contain non-homologous proline-rich motifs on their N-termini (red line on the top of the alignment). The core parts of HRS proteins are highly conserved and contain the NlpC/P60 and NC domains ( Fig. 2, blue and green boxes, respectively). We predicted three strands within the Nlpc/P60 domain (Fig. 2, blue arrows). The first and the second strand contain the conserved GDL and HWXXY motifs; the VXXLAP motif comprises the third strand. The region downstream the third -strand with two conserved serine residues is likely to have the structure of -helix ( Fig. 2, green cylinder). The large NC domain depicted in Fig. 2 with a green box, contains a KALVK conserved motif of unknown function, two short stretches DXXG and NKXD, which are similar to conserved regions of GTPases (Akiyama et al., 1999; Bourne, Sanders, and McCormick 1990) and the NCEHFV conserved motif, characteristic for conventional NC domains. At the C-terminus, HRS proteins harbour a

Recently, a crystal structure of the NlpC/P60 domain of H-REV107-1 has being resolved (Ren et al., 2010b). Within this domain, a phospholipase active site consisting of a Cys-His-His triad was identified. The residues H23 and C113 play a pivotal role for the H-REV107-1 enzymatic activity (Ren et al., 2010a). Meantime, the enzymatic activity of the H-REV107-1, TIG3, HRASLS2 and HRLP5 proteins has been characterized as PLA1/2- type hydrolysis,

**2.3 Domain structure and enzymatic activity of the HRS family members** 

hydrophobic C-terminal -helix,described as a membrane-binding domain.

supporting a role of the HRS proteins in lipid metabolism.

**2.2 Clustered chromosomal localization of 4 human HRS genes on 11q13** 

from the other HRS genes.

retinol metabolism.

database, were analysed (Fig. 2).

*HRS* genes on 11q13 in distinct tumour types.

Fig. 2. HRS catalytic and protein-binding domains

Fourteen members of the HRS protein family found in the NCBI database were aligned using the Clustal W program as described in Figure 1B. Analysis, edition, and shading of conserved domains were performed with the help of the GenDoc freeware (http://www.psc.edu/biomed/genedoc/). The PSIPRED secondary structure prediction server was used to analyze potential secondary structures of the HRS protein sequences (http://bioinf.cs.ucl.ac.uk/psipred/) (McGuffin, Bryson, and Jones 2000). The HRS proteins contain non-homologous proline-rich motifs on their N-termini (red line on the top of the alignment). The core parts of HRS proteins are highly conserved and contain the NlpC/P60 and NC domains (blue and green boxes, respectively). With a high prediction confidence of the PSIPRED standard analysis, three -strands were defined within the Nlpc/P60 domain (blue arrows). The first and the second strand contain the conserved GDL and HWXXY motifs; the VXXLAP motif comprises the third strand. The region downstream the third -strand with two conserved serine residues is likely to have a structure of -helix (green cylinder). The large NC domain (green box) contains a KALVK conserved motif of unknown function, two short stretches DXXG and NKXD, which are similar to conserved regions of GTPases (Akiyama et al., 1999; Bourne, Sanders, and McCormick 1990) and the NCEHFV conserved motif, characteristic for conventional NC domains. At the C-terminus, HRS proteins harbour a hydrophobic C-terminal -helix.

Apoptosis Pathways in Ovarian Cancer 91

hydrophobic C-terminal -helix, which directs and transiently binds the protein to intracellular membranes (Husmann et al., 1998; Nazarenko et al., 2007). Furthermore, a DXXG domain, also termed G3 motif, characteristic for RAS small GTPases, and mediating the binding of magnesium and -phosphate of GTP via the aspartic acid and glycine residue, respectively was identified (Kjeldgaard et al., 1996). However, a functional role of these

**2.4 Reversible Inhibition of the HRS genes H-REV107-1 and TIG3 by oncogenic** 

Members of the HRS gene family *H-REV107-1* and *TIG3* belong to the so called class II tumour suppressors. The major characteristic of this class, postulated in 1997 by Ruth Sager, is their down-regulation in tumours via reversible mechanisms, however not through mutations or deletions (Sager, 1997). Once re-expressed, these genes can exhibit their tumour-suppressive function and thereby contribute to the inhibition of tumour

**2.4.1 IFN-mediated re-expression of H-REV107-1 leads to the induction of apoptosis** 

Rat *H-Rev107-1*, the founder gene of the HRS family, was cloned from a subtractive cDNA library (Hajnal et al., 1994). The rat *H-Rev107-1* gene, expressed in immortalized rat fibroblasts, was identified as a gene suppressed in an HRAS-transformed derivative, but reexpressed in a revertant cell line. Further experiments suggested that repression of *H-Rev107-1* in HRAS-transformed cells was functionally involved in HRAS-dependent

Repression of *H-Rev107-1* was also detected in KRAS-transformed rat ovarian epithelial cells suggesting that in contrast to other HRS genes, *H-Rev107-1* suppression in response to RAS oncogenes is not associated with the RAS isoform. Most interestingly, *H-Rev107-1* downregulation upon KRAS-transformation appeared to be reversible and identified the *H-Rev107-1* gene as a target negatively regulated by the MEK-ERK pathway. The same observation was also made in PA1 human teratocarcinoma cells, which harbour an activated NRAS oncogene (Alessi et al., 1994) and suggested that *H-REV107-1* might be negatively

The human *H-REV107-1*, first described in 1998, was found ubiquitously expressed in normal human epithelial tissues (;Husmann et al., 1998). When compared to differentiated cells in situ, *H-REV107-1* is down-regulated in human tumour cell lines and tumour samples at the mRNA and at the protein level. Loss of *H-REV107-1* mRNA until now was detected in tumours derived from breast, lung, ovary, kidney and testis (Sers et al., 1997;Siegrist et al., 2001). In human ovarian carcinomas, we also demonstrated strongly diminished levels or complete loss of the H-REV107-1 protein. In ovarian carcinomas sequencing of the *H-REV107-1* coding region revealed no alterations within this region suggesting that *H-*

A functional involvement of H-REV107-1 inactivation in ovarian tumours was demonstrated by the finding that reactivation of endogenous H-REV107-1 in H-REV107-1-negative ovarian carcinoma cells induces apoptosis. In these cells, loss of *H-REV107-1* expression can be reconstituted upon administration of Interferon gamma (IFN), a finding reported earlier from rat astrocytoma cells (Bartel, 2004). Up-regulation of *H-REV107-1* in response to IFN

motifs in HRS proteins has not been defined yet.

transformation (Hajnal et al., 1994; Sers et al., 1997).

affected by RAS oncogene-dependent signalling in general.

*REV107-1* acts as class II tumour suppressor gene in these tissues.

**signalling cascades in tumours** 

progression.

**in ovarian cancer cells** 

Fig. 3. *HRS2/HRLS2, HRS3/H-REV107-1/ HRS4/TIG3* and *HRS5/HRLS5* genes are localized on chromosome 11q13 in one cluster. Gene orientation, length and mapping of the chromosome regions are directly obtained from the NCBI Map View server.

Additionally to the NlpC/P60 domain, HRS proteins contain a proline-rich N-terminal domain, responsible for establishing protein-protein interactions and a variable

Fig. 3. *HRS2/HRLS2, HRS3/H-REV107-1/ HRS4/TIG3* and *HRS5/HRLS5* genes are localized on chromosome 11q13 in one cluster. Gene orientation, length and mapping of the chromosome regions are directly obtained from the NCBI Map View server.

Additionally to the NlpC/P60 domain, HRS proteins contain a proline-rich N-terminal domain, responsible for establishing protein-protein interactions and a variable hydrophobic C-terminal -helix, which directs and transiently binds the protein to intracellular membranes (Husmann et al., 1998; Nazarenko et al., 2007). Furthermore, a DXXG domain, also termed G3 motif, characteristic for RAS small GTPases, and mediating the binding of magnesium and -phosphate of GTP via the aspartic acid and glycine residue, respectively was identified (Kjeldgaard et al., 1996). However, a functional role of these motifs in HRS proteins has not been defined yet.

#### **2.4 Reversible Inhibition of the HRS genes H-REV107-1 and TIG3 by oncogenic signalling cascades in tumours**

Members of the HRS gene family *H-REV107-1* and *TIG3* belong to the so called class II tumour suppressors. The major characteristic of this class, postulated in 1997 by Ruth Sager, is their down-regulation in tumours via reversible mechanisms, however not through mutations or deletions (Sager, 1997). Once re-expressed, these genes can exhibit their tumour-suppressive function and thereby contribute to the inhibition of tumour progression.

#### **2.4.1 IFN-mediated re-expression of H-REV107-1 leads to the induction of apoptosis in ovarian cancer cells**

Rat *H-Rev107-1*, the founder gene of the HRS family, was cloned from a subtractive cDNA library (Hajnal et al., 1994). The rat *H-Rev107-1* gene, expressed in immortalized rat fibroblasts, was identified as a gene suppressed in an HRAS-transformed derivative, but reexpressed in a revertant cell line. Further experiments suggested that repression of *H-Rev107-1* in HRAS-transformed cells was functionally involved in HRAS-dependent transformation (Hajnal et al., 1994; Sers et al., 1997).

Repression of *H-Rev107-1* was also detected in KRAS-transformed rat ovarian epithelial cells suggesting that in contrast to other HRS genes, *H-Rev107-1* suppression in response to RAS oncogenes is not associated with the RAS isoform. Most interestingly, *H-Rev107-1* downregulation upon KRAS-transformation appeared to be reversible and identified the *H-Rev107-1* gene as a target negatively regulated by the MEK-ERK pathway. The same observation was also made in PA1 human teratocarcinoma cells, which harbour an activated NRAS oncogene (Alessi et al., 1994) and suggested that *H-REV107-1* might be negatively affected by RAS oncogene-dependent signalling in general.

The human *H-REV107-1*, first described in 1998, was found ubiquitously expressed in normal human epithelial tissues (;Husmann et al., 1998). When compared to differentiated cells in situ, *H-REV107-1* is down-regulated in human tumour cell lines and tumour samples at the mRNA and at the protein level. Loss of *H-REV107-1* mRNA until now was detected in tumours derived from breast, lung, ovary, kidney and testis (Sers et al., 1997;Siegrist et al., 2001). In human ovarian carcinomas, we also demonstrated strongly diminished levels or complete loss of the H-REV107-1 protein. In ovarian carcinomas sequencing of the *H-REV107-1* coding region revealed no alterations within this region suggesting that *H-REV107-1* acts as class II tumour suppressor gene in these tissues.

A functional involvement of H-REV107-1 inactivation in ovarian tumours was demonstrated by the finding that reactivation of endogenous H-REV107-1 in H-REV107-1-negative ovarian carcinoma cells induces apoptosis. In these cells, loss of *H-REV107-1* expression can be reconstituted upon administration of Interferon gamma (IFN), a finding reported earlier from rat astrocytoma cells (Bartel, 2004). Up-regulation of *H-REV107-1* in response to IFN

Apoptosis Pathways in Ovarian Cancer 93

Fig. 4. Promoter sequence of the human *H-REV107-1* gene. The translatioal start site is indicated by +1, 997 base pairs of upstream sequence are shown. Individual sequence motifs

as identified by MatInspector are indicated.

works well at the mRNA level, yet only a small proportion of cells also express sufficient H-REV107-1 protein for detection. Most interestingly these cells undergo apoptosis (Sers et al., 2002). These observations made clear that H-REV107-1 is likely to interfere with the survival of ovarian cancer cells. Our work further supported this suggestion as we could show that H-REV107-1 is an inhibitor of PP2A whose function is required in ovarian carcinomas for cell survival (Nazarenko et al., 2007). This was the first hint indicating a role of the H-REV107-1 protein in the regulation of apoptotic intracellular signalling and will be discussed in part 3 of this chapter.

#### **2.4.2 Mechanisms of H-REV107-1 suppression in ovarian carcinomas via antiapoptotic pathways**

The reversible down-regulation of *H-REV107-1* in ovarian cancer has prompted the investigation of the mechanisms responsible for suppression. The human *H-REV107-1* promoter is located directly upstream of a 408bp 5'UT sequence. The sequence harbours several potential transcription factor binding sites including an Interferon-responsive IRSE motif, a CREB site, potential AP-1 and c-REL binding sites (Fig. 4).

The IRSE site, a DNA-sequence bound by the Interferon regulatory factors IRF-1 and IRF-2, provides the structural basis for the observed induction of *H-REV107-1* upon administration of IFN and conditionally expressed IRF-1 (Alessi et al., 1994). Comparison of *IRF-1* and *H-REV107-1* levels between human ovarian carcinoma cells and immortalized human ovarian epithelial cells, revealed strongly diminished *IRF-1 and H-REV1017-1* levels in the tumour cell lines. This suggested that loss of *IRF-1* expression might be one of the mechanisms of *H-REV107-1* suppression in human ovarian carcinomas (Sers at al., 2002).

Surprisingly, there is no conservation between the human *H-REV107-1* and the mouse or rat *H-REV107-1* promoter region, suggesting a different regulation of human *H-REV107-1* and the rodent homologues. More importantly, it was shown that murine *H-REV107-1* can be regulated via DNA methylation. In view of human tumours, next steps will include addressing the question, whether this methylation-dependent suppression of *H-REV107-1* is a tumour-related process, or a developmental process during which tissue-specific expression profiles are established.

#### **2.4.3 Physiological role of H-REV107-1 and its potential role in cancerogenesis**

Meantime, the enzymatic function of H-REV107-1 has been defined (Ueda et al., 2009). The protein acts as a cytosolic Ca2+-independent phospholipase Pla2G16, which catalyses esterolytic cleavage of glycerophospholipids to lysophospholipids. Supporting these data, a recent study in a knock-out model demonstrated that the H-rev107-1 physiological function is a major adipocyte phospholipase A2 (AdPLA). The protein inhibited lipolysis in adipocytes, regulating adiposity on systemic level (Jaworski et al., 2009). Ablation of the Hrev107-1 led to a significantly higher rate of lipolysis, accompanied by an increase in cyclic AMP levels (Jaworski et al., 2009). The knock-out animals were resistant to high-fat feeding and leptin-deficiency mediated obesity. Albeit, a direct impact of the H-rev107-1 ablation on tumourigenesis in vivo has not been tested yet, the observed increase in lipolysis and elevated levels of cAMP, also common in tumour cells, suggest a potentially higher susceptibility of the H-rev107-1 knockout animals to tumour growth as compared to their wild type littermates.

works well at the mRNA level, yet only a small proportion of cells also express sufficient H-REV107-1 protein for detection. Most interestingly these cells undergo apoptosis (Sers et al., 2002). These observations made clear that H-REV107-1 is likely to interfere with the survival of ovarian cancer cells. Our work further supported this suggestion as we could show that H-REV107-1 is an inhibitor of PP2A whose function is required in ovarian carcinomas for cell survival (Nazarenko et al., 2007). This was the first hint indicating a role of the H-REV107-1 protein in the regulation of apoptotic intracellular signalling and will be discussed

**2.4.2 Mechanisms of H-REV107-1 suppression in ovarian carcinomas via anti-**

motif, a CREB site, potential AP-1 and c-REL binding sites (Fig. 4).

*REV107-1* suppression in human ovarian carcinomas (Sers at al., 2002).

The reversible down-regulation of *H-REV107-1* in ovarian cancer has prompted the investigation of the mechanisms responsible for suppression. The human *H-REV107-1* promoter is located directly upstream of a 408bp 5'UT sequence. The sequence harbours several potential transcription factor binding sites including an Interferon-responsive IRSE

The IRSE site, a DNA-sequence bound by the Interferon regulatory factors IRF-1 and IRF-2, provides the structural basis for the observed induction of *H-REV107-1* upon administration of IFN and conditionally expressed IRF-1 (Alessi et al., 1994). Comparison of *IRF-1* and *H-REV107-1* levels between human ovarian carcinoma cells and immortalized human ovarian epithelial cells, revealed strongly diminished *IRF-1 and H-REV1017-1* levels in the tumour cell lines. This suggested that loss of *IRF-1* expression might be one of the mechanisms of *H-*

Surprisingly, there is no conservation between the human *H-REV107-1* and the mouse or rat *H-REV107-1* promoter region, suggesting a different regulation of human *H-REV107-1* and the rodent homologues. More importantly, it was shown that murine *H-REV107-1* can be regulated via DNA methylation. In view of human tumours, next steps will include addressing the question, whether this methylation-dependent suppression of *H-REV107-1* is a tumour-related process, or a developmental process during which tissue-specific

**2.4.3 Physiological role of H-REV107-1 and its potential role in cancerogenesis** 

Meantime, the enzymatic function of H-REV107-1 has been defined (Ueda et al., 2009). The protein acts as a cytosolic Ca2+-independent phospholipase Pla2G16, which catalyses esterolytic cleavage of glycerophospholipids to lysophospholipids. Supporting these data, a recent study in a knock-out model demonstrated that the H-rev107-1 physiological function is a major adipocyte phospholipase A2 (AdPLA). The protein inhibited lipolysis in adipocytes, regulating adiposity on systemic level (Jaworski et al., 2009). Ablation of the Hrev107-1 led to a significantly higher rate of lipolysis, accompanied by an increase in cyclic AMP levels (Jaworski et al., 2009). The knock-out animals were resistant to high-fat feeding and leptin-deficiency mediated obesity. Albeit, a direct impact of the H-rev107-1 ablation on tumourigenesis in vivo has not been tested yet, the observed increase in lipolysis and elevated levels of cAMP, also common in tumour cells, suggest a potentially higher susceptibility of the H-rev107-1 knockout animals to tumour growth as compared to their

in part 3 of this chapter.

**apoptotic pathways** 

expression profiles are established.

wild type littermates.


Fig. 4. Promoter sequence of the human *H-REV107-1* gene. The translatioal start site is indicated by +1, 997 base pairs of upstream sequence are shown. Individual sequence motifs as identified by MatInspector are indicated.

Apoptosis Pathways in Ovarian Cancer 95

Up-regulation of *TIG3* by IFN occurs in the same cells in which also *H-REV107-1* can be induced by this cytokine. Within the 5´ regulatory sequence of the *TIG3* gene an IRFresponsive element is present 84 base pairs upstream of the translational start site. However, compared to the related *H–REV107-1* gene, *TIG3* mRNA levels after IFN-administration follow a different kinetics suggesting that during the IFN-dependent apoptosis, these genes

Deregulation of retinoic acid receptors has been involved in ovarian tumours, indicating an essential role of genes targeted by retinoic acid signalling in the prevention of transformation (Benoit et al., 2001;Sun and Lotan, 2002). Furthermore, retinoids represent a promising alternative chemotherapeutic approach for the treatment of late stage ovarian cancer (Zhang et al., 2000;Fields et al., 2007) Consequently, TIG3, involved into retinoic signalling, is likely to be one of the potential mediators for a successful anti-cancer therapy

In addition to the retinoic acid responsiveness, we recently detected a negative regulation of *TIG3* via an activated MEK-ERK signalling pathway and a positive regulation via IFN in ovarian carcinoma cells (Lotz et al., 2005). Thus, like the related *H-REV107-1* gene, *TIG3* is a target of the oncogenic MEK-ERK signalling pathway. TIG3 itself can dampen the activity of ERK, which suggests an involvement of TIG3 in a negative feedback loop for the control of ERK activity. Inducible and constitutive overexpression of *TIG3* cDNA, resulted in growth suppression of A27/80 ovarian carcinoma cells indicating a functional role of the protein in cell growth control (DiSepio et al., 1998; Lotz et al., 2005). However, the mechanisms of ovarian cancer-specific MEK-ERK-dependent TIG3-suppression are

An important finding was reported by Ou et al., showing that TIG3 mediates IFN dependent down-regulation of HER-2 via regulation of the PI3-kinase pathway (Ou et al., 2008). Using human ovarian carcinoma cell lines OVCAR-3, SKOV-3, and TOV-21G, the group demonstrated an increase of the *TIG3* mRNA levels within 2 hours upon administration of IFN- to the cells. Up-regulation of *TIG3* correlated with the downregulation of p185 protein, which could be restored by the application of siRNA against *TIG3*. A promoter activity assays allowed to demonstrate that TIG3 acts in a HER-2 dependent manner, by a diminishment of the HER-2 activity. Abrogation of HER-2 signalling resulted in a down-regulation of the p185 subunit of the PI3-Kinase. Additionally, VEGF (vascular endothelial growth factor) secretion was regulated in a TIG3-HER-2 dependent manner in a model system. The anti-proliferative, HER-2-inhibiting effect of TIG3 could be abrogated by overexpression of HGR, a member of the neuregulin family activating epidermal growth factor receptor family members and restoring p185 expression

This work shows that TIG3 is an important regulator of survival signalling in ovarian carcinomas. Further experiments are necessary, verifying the in vitro observations in animal models of ovarian cancer. Additionally, examination of human ovarian carcinomas and a correlative analysis of TIG3, HER-2 and p185 expression will allow determining the general relevance the observed phenomenon.. Furthermore, due to the co-regulation of TIG3 and H-REV107-1 via IFN and MAPK signalling, a reactivation of both genes for therapeutic

purposes might exhibit an enhanced anti-apoptotic effect.

are involved at different stages of the process.

of ovarian carcinomas.

unknown.

(Ou et al., 2008).

The H-rev107-1 knockout model provides a first link between lipid metabolism and a tumour suppressive effect of phospholipases. Alterations in lipid metabolism, especially in phospholipids-related pathways and fatty acid biosynthesis are known to occur in ovarian carcinomas (Tania et al., 2010). . Thus, FAS (fatty acid synthase) is up-regulated in cancer cells and mediates activity of HER-2 (Gansler at al., 1997; Menendez et al., 2004). It has been suggested that HER-2 functions as a cellular energy sensor in response to the metabolic stress, supporting therapeutic advantages of combinatorial inhibition of HER-2 and FAS in HER-2-positive tumours. However, phospholipases PLA2 were known to function as positive regulators of cell proliferation and migration (Song et al., 2007), playing rather a tumour-promoting role. In contrast to that, we and other uncovered a tumour-suppressive function of H-REV107-1 and its related proteins functioning as PLA2 enzymes.

It is likely that these observations provide a new link between malignant transformation, tumour progression and alteration in lipid metabolism, which needs further investigations. An important aspect needs to be refurbished according to the latest findings, is a change of lipid metabolic in tumour-surrounding stroma. Recent data clearly demonstrate a key role of adipocytes in the preferential metastasis of ovarian cancer to omentum, indicating their function as an energy source for homing tumour cells (Nieman et al, 2011). These and other data support a significant role of metabolism regulation in tumours and tumour stroma, and suggesting that inclusion of metabolism-regulating agents in cancer therapy should be reexamined with respect to a potential pronounced beneficial effect on the efficacy of the treatment on a system level.

#### **2.5 TIG3, a target of the MAPK signalling pathway, acts as a tumour suppressor in ovarian cancer cells**

The *TIG3* gene was described independently by two groups (Husmann et al., 1998; DiSepio et al., 1998). DiSepio et al. had identified a close homologue of the rat *H-rev107-1* gene, named *RARRES/TIG3*, which was isolated from a differential display approach using Tazarotene-treated human keratinocytes. Tazarotene is a synthetic retinoid, developed for the treatment of psoriasis (Weinstein et al., 1997). Husmann et al. also described a gene closely related to the human *H-rev107-1*, named *H-REV107-2*, isolated during a sequencing project by Merck and the University of Washington. The H-REV107-2 protein differed from RARRES/TIG3 in a longer C-terminal region however; this was recently identified as an artefact (Lotz et al., 2005). Re-sequencing of the *H-REV107-2* cDNA construct revealed that the cDNA is identical to the *RARRES/TIG3* gene, referred further as *TIG3*. In addition, a similar sequence cloned from human gastric carcinoma cells was described as RIG1 (Huang et al., 2000). According to sequence comparisons, all proteins are identical except a difference of two amino acids between the proteins deduced from the TIG3 and the RIG1 sequence.

Expression analysis for *TIG3* performed on Multiple Tissue Northern Blots and Cancer Profiling Arrays suggested expression of the gene in normal ovary and in many other tissues. Similar to *H-REV107-1*, *TIG3* expression was down-regulated in human ovarian carcinomas and tumour-derived cell lines (DiSepio et al., 1998;Duvic et al., 2000;Shyu et al., 2003;Higuchi et al., 2003;Sturniolo et al., 2003;Lotz et al., 2005) and can be re-expressed upon treatment with IFN or retinoid and its analogous (Weinstein et al., 1997).

The H-rev107-1 knockout model provides a first link between lipid metabolism and a tumour suppressive effect of phospholipases. Alterations in lipid metabolism, especially in phospholipids-related pathways and fatty acid biosynthesis are known to occur in ovarian carcinomas (Tania et al., 2010). . Thus, FAS (fatty acid synthase) is up-regulated in cancer cells and mediates activity of HER-2 (Gansler at al., 1997; Menendez et al., 2004). It has been suggested that HER-2 functions as a cellular energy sensor in response to the metabolic stress, supporting therapeutic advantages of combinatorial inhibition of HER-2 and FAS in HER-2-positive tumours. However, phospholipases PLA2 were known to function as positive regulators of cell proliferation and migration (Song et al., 2007), playing rather a tumour-promoting role. In contrast to that, we and other uncovered a tumour-suppressive function of H-REV107-1 and its related proteins functioning as PLA2

It is likely that these observations provide a new link between malignant transformation, tumour progression and alteration in lipid metabolism, which needs further investigations. An important aspect needs to be refurbished according to the latest findings, is a change of lipid metabolic in tumour-surrounding stroma. Recent data clearly demonstrate a key role of adipocytes in the preferential metastasis of ovarian cancer to omentum, indicating their function as an energy source for homing tumour cells (Nieman et al, 2011). These and other data support a significant role of metabolism regulation in tumours and tumour stroma, and suggesting that inclusion of metabolism-regulating agents in cancer therapy should be reexamined with respect to a potential pronounced beneficial effect on the efficacy of the

**2.5 TIG3, a target of the MAPK signalling pathway, acts as a tumour suppressor in** 

The *TIG3* gene was described independently by two groups (Husmann et al., 1998; DiSepio et al., 1998). DiSepio et al. had identified a close homologue of the rat *H-rev107-1* gene, named *RARRES/TIG3*, which was isolated from a differential display approach using Tazarotene-treated human keratinocytes. Tazarotene is a synthetic retinoid, developed for the treatment of psoriasis (Weinstein et al., 1997). Husmann et al. also described a gene closely related to the human *H-rev107-1*, named *H-REV107-2*, isolated during a sequencing project by Merck and the University of Washington. The H-REV107-2 protein differed from RARRES/TIG3 in a longer C-terminal region however; this was recently identified as an artefact (Lotz et al., 2005). Re-sequencing of the *H-REV107-2* cDNA construct revealed that the cDNA is identical to the *RARRES/TIG3* gene, referred further as *TIG3*. In addition, a similar sequence cloned from human gastric carcinoma cells was described as RIG1 (Huang et al., 2000). According to sequence comparisons, all proteins are identical except a difference of two amino acids between the proteins deduced from the TIG3 and the RIG1

Expression analysis for *TIG3* performed on Multiple Tissue Northern Blots and Cancer Profiling Arrays suggested expression of the gene in normal ovary and in many other tissues. Similar to *H-REV107-1*, *TIG3* expression was down-regulated in human ovarian carcinomas and tumour-derived cell lines (DiSepio et al., 1998;Duvic et al., 2000;Shyu et al., 2003;Higuchi et al., 2003;Sturniolo et al., 2003;Lotz et al., 2005) and can be re-expressed upon

treatment with IFN or retinoid and its analogous (Weinstein et al., 1997).

enzymes.

treatment on a system level.

**ovarian cancer cells** 

sequence.

Up-regulation of *TIG3* by IFN occurs in the same cells in which also *H-REV107-1* can be induced by this cytokine. Within the 5´ regulatory sequence of the *TIG3* gene an IRFresponsive element is present 84 base pairs upstream of the translational start site. However, compared to the related *H–REV107-1* gene, *TIG3* mRNA levels after IFN-administration follow a different kinetics suggesting that during the IFN-dependent apoptosis, these genes are involved at different stages of the process.

Deregulation of retinoic acid receptors has been involved in ovarian tumours, indicating an essential role of genes targeted by retinoic acid signalling in the prevention of transformation (Benoit et al., 2001;Sun and Lotan, 2002). Furthermore, retinoids represent a promising alternative chemotherapeutic approach for the treatment of late stage ovarian cancer (Zhang et al., 2000;Fields et al., 2007) Consequently, TIG3, involved into retinoic signalling, is likely to be one of the potential mediators for a successful anti-cancer therapy of ovarian carcinomas.

In addition to the retinoic acid responsiveness, we recently detected a negative regulation of *TIG3* via an activated MEK-ERK signalling pathway and a positive regulation via IFN in ovarian carcinoma cells (Lotz et al., 2005). Thus, like the related *H-REV107-1* gene, *TIG3* is a target of the oncogenic MEK-ERK signalling pathway. TIG3 itself can dampen the activity of ERK, which suggests an involvement of TIG3 in a negative feedback loop for the control of ERK activity. Inducible and constitutive overexpression of *TIG3* cDNA, resulted in growth suppression of A27/80 ovarian carcinoma cells indicating a functional role of the protein in cell growth control (DiSepio et al., 1998; Lotz et al., 2005). However, the mechanisms of ovarian cancer-specific MEK-ERK-dependent TIG3-suppression are unknown.

An important finding was reported by Ou et al., showing that TIG3 mediates IFN dependent down-regulation of HER-2 via regulation of the PI3-kinase pathway (Ou et al., 2008). Using human ovarian carcinoma cell lines OVCAR-3, SKOV-3, and TOV-21G, the group demonstrated an increase of the *TIG3* mRNA levels within 2 hours upon administration of IFN- to the cells. Up-regulation of *TIG3* correlated with the downregulation of p185 protein, which could be restored by the application of siRNA against *TIG3*. A promoter activity assays allowed to demonstrate that TIG3 acts in a HER-2 dependent manner, by a diminishment of the HER-2 activity. Abrogation of HER-2 signalling resulted in a down-regulation of the p185 subunit of the PI3-Kinase. Additionally, VEGF (vascular endothelial growth factor) secretion was regulated in a TIG3-HER-2 dependent manner in a model system. The anti-proliferative, HER-2-inhibiting effect of TIG3 could be abrogated by overexpression of HGR, a member of the neuregulin family activating epidermal growth factor receptor family members and restoring p185 expression (Ou et al., 2008).

This work shows that TIG3 is an important regulator of survival signalling in ovarian carcinomas. Further experiments are necessary, verifying the in vitro observations in animal models of ovarian cancer. Additionally, examination of human ovarian carcinomas and a correlative analysis of TIG3, HER-2 and p185 expression will allow determining the general relevance the observed phenomenon.. Furthermore, due to the co-regulation of TIG3 and H-REV107-1 via IFN and MAPK signalling, a reactivation of both genes for therapeutic purposes might exhibit an enhanced anti-apoptotic effect.

Apoptosis Pathways in Ovarian Cancer 97

phosphorylation of Thr505 located within the activation loop of PKCδ increased already 15 minutes after the addition of okadaic acid or LY294002, indicating that PKCδ is directly inactivated by PP2A and PI3K. Although the levels of total PKCδ seemed to be slightly increased after long-term okadaic acid and LY294002 treatment, the phosphorylation was strongly diminished. H-REV107-1 negatively regulated the expression of PKCδ, supporting the finding that PKCδ is not involved in H-REV107-1-dependent cell death. Expression of atypical PKCι was increased following 48 hours of treatment with okadaic acid, but neither

To correlate phosphorylation of kinases in the activation site and their intracellular kinase activity, we applied in vitro kinase assay described in detail elsewhere (Nazarenko et al., 2010) and measured direct changes in the activity of PKCs upon okadaic acid treatment. A significant elevation of the PKCθ and PKCε activity was detected 24 hours after okadaic acid incubation, confirming that these PKCs, although not known to be direct PP2A targets, are

As inhibition of PP2A is required for H-REV107-1-dependent apoptosis, we next asked if these kinases might be involved in H-REV107-1-induced cell death and tested if the abrogation of PKCθ and PKCε activity impairs the proapoptotic function of H-REV107-1. OVCAR-3 cells were transfected either with the H-REV107-1 expression vector or with a control plasmid. Twelve hours later, the PKCθ- and PKCε-specific peptides were added. Caspase-3 cleavage was tested after 48 hours using Western blot analysis. H-REV107-1 expression resulted in the induction of caspase-3 cleavage, which was however not altered after peptide applications. Additionally, PKCθ-specific peptide treatment of control cells revealed a weak toxic effect. This result suggests that although PKCε and PKCθ are clearly activated in a PP2A and H-REV107-1-dependent manner, they are not essential for the H-

An important finding was that the atypical PKC is uncoupled from the PI3K pathway in ovarian cancer cells and is more likely to be a PP2A target. This is in contrast to the situation in the majority of normal and malignant tissues, in which PKCζ functions as an insulin-dependent PI3K effector. Importantly, overexpression of wild type H-REV107-1, but not of its PP2A interaction-deficient mutant, led to PKC phosphorylation, suggesting a direct link between the ability of H-REV107-1 to inhibit PP2A and the activation of

Electroporation of the ovarian carcinoma cells with PKCζ-expression plasmid demonstrated that high levels of this kinase are sufficient to induce apoptosis. In our work we demonstrated an increase of the sub-G1 cell population and caspase-3 cleavage. Molecular mechanisms by mean of which PKCζ induces apoptosis remained elusive and need further investigations. A recent work of Peng et al. might provide an additional hint for the mechanisms of PKCζ-dependent apoptosis (Chen et al., 2008). Using a mouse model, the authors demonstrated that PKCζ directly interacts with ERK1/2 in Kupffer cells, mediating a translocation of NF-kB into the nucleus and inducing its activity. The novelty of this finding is a direct link between PKCζ, EKR1/2 and NF-kB. Consistently, a cross-talk between NF-kB and PKCζ is well- characterised for many systems (Moscat et al., 2001;Moscat and az-Meco, 2011). Next, a potential interaction between PKCζ, ERK1/2, and NF-kB in ovarian cancer cells should be verified. A hypothetical scheme of PKC apoptotic

cascade and cross-talk with other pathways is represented on the Fig. 5.

phosphorylation nor total levels were affected by H-REV107-1.

negatively regulated by PP2A signalling in OVCAR-3 cells.

REV107-1 proapoptotic activity in OVCAR-3 cells.

PKCζ.

#### **3. H-REV107-1/HRLS3-driven interplay between PP2A and PKC signal transduction pathways in ovarian carcinomas**

In our previous work, we demonstrated that the class II tumour suppressor H-REV107-1 defined as an enzyme with a phospholipase activity (Jaworski et al., 2009) induces apoptosis in ovarian cancer cells by inhibition of a specific pool of serine/threonine phosphatase PP2A followed by the activation of the atypical PKC (Nazarenko et al., 2007;Nazarenko et al., 2010).

The PKC family comprises 3 groups of kinases that display very distinct modes of activation and function. The classical PKCs (,,) are activated in a calcium-dependent manner through phosphatidylserine (PS) and diacylglycerol (DAG). The novel PKCs (, , , ), are also regulated through PS and DAG, but are calcium-independent. Finally, there are the atypical PKCs (, /) that require neither calcium nor DAG, but in some cases PS, for activation (Parker and Murray-Rust, 2004;Mackay and Twelves, 2007;Breitkreutz et al., 2007). The different PKC isoenzymes are involved in the regulation of cell survival in normal organs and during tumourigenesis (Shayesteh et al., 1999;Leitges et al., 2001;Martin et al., 2002;Parker and Murray-Rust, 2004;Yin et al., 2005;Moscat et al., 2006). Among the classical PKCs, loss of PKC in ovarian carcinoma was found to be correlated with increased malignancy (Weichert et al., 2003). While the classical PKC is down-regulated in ovarian carcinomas, the novel PKC and PKC were found up-regulated in this tumour, yet no functional consequence has been inferred from this deregulation. In addition to the novel PKC and PKC, also the atypical PKC is highly expressed in ovarian carcinomas and acts as a cooperating oncogene with mutant RAS (Zhang et al., 2006).

Recently, we demonstrated that forced expression of H-REV107-1 in ovarian carcinoma cell lines resulted in the inhibition of PP2A activity, re-activation of PP2A target proteins, among them PKC, and induction of apoptosis (Nazarenko et al., 2007). Importantly, not only tumour cell lines, but also primary tumour cells isolated from the ascites of patients with ovarian carcinomas were sensitive to the treatment with okadaic acid, an inhibitor of PP2A. Induction of apoptosis after okadaic acid treatment was accompanied by the phosphorylation of PKC, confirming a survival role of PP2A in ovarian cancer, and a potential pro-apoptotic function of PKC. Based on the in vitro cell culture work we analyzed how different members of the PKC family are regulated by H-REV107-1 or by the inhibition of PP2A activity with okadaic acid. Additionally, we verified an impact of the PI3-kinase pathway, a major survival kinase in ovarian carcinoma, in the regulation of PKC.

Analysis of novel PKCs revealed differences at the level of expression and phosphorylation. Thus, treatment with okadaic acid for 48 hours and overexpression of H-REV107-1 led to an increased expression of PKCε. Additionally, H-REV107-1 indirectly induced phosphorylation of the COOH-terminal residue Ser729, shown to enhance the enzymatic activity of PKCε (Parekh et al., 2000). This suggests that PKCε activity might be partially regulated in an H-REV107-1-dependent manner. Phosphorylation of Thr538 within the activation loop of PKCθ was elevated after 48 hours of treatment with okadaic acid and the AKT inhibitor LY294002, suggesting a negative but indirect regulation through PP2A and PI-3K. Additionally, Thr538 phosphorylation of PKCθ was increased in cells expressing H-REV107-1, suggesting a potential role of this kinase in H-REV107-1 signalling. The

In our previous work, we demonstrated that the class II tumour suppressor H-REV107-1 defined as an enzyme with a phospholipase activity (Jaworski et al., 2009) induces apoptosis in ovarian cancer cells by inhibition of a specific pool of serine/threonine phosphatase PP2A followed by the activation of the atypical PKC (Nazarenko et al.,

The PKC family comprises 3 groups of kinases that display very distinct modes of activation and function. The classical PKCs (,,) are activated in a calcium-dependent manner through phosphatidylserine (PS) and diacylglycerol (DAG). The novel PKCs (, , , ), are also regulated through PS and DAG, but are calcium-independent. Finally, there are the atypical PKCs (, /) that require neither calcium nor DAG, but in some cases PS, for activation (Parker and Murray-Rust, 2004;Mackay and Twelves, 2007;Breitkreutz et al., 2007). The different PKC isoenzymes are involved in the regulation of cell survival in normal organs and during tumourigenesis (Shayesteh et al., 1999;Leitges et al., 2001;Martin et al., 2002;Parker and Murray-Rust, 2004;Yin et al., 2005;Moscat et al., 2006). Among the classical PKCs, loss of PKC in ovarian carcinoma was found to be correlated with increased malignancy (Weichert et al., 2003). While the classical PKC is down-regulated in ovarian carcinomas, the novel PKC and PKC were found up-regulated in this tumour, yet no functional consequence has been inferred from this deregulation. In addition to the novel PKC and PKC, also the atypical PKC is highly expressed in ovarian carcinomas and acts

Recently, we demonstrated that forced expression of H-REV107-1 in ovarian carcinoma cell lines resulted in the inhibition of PP2A activity, re-activation of PP2A target proteins, among them PKC, and induction of apoptosis (Nazarenko et al., 2007). Importantly, not only tumour cell lines, but also primary tumour cells isolated from the ascites of patients with ovarian carcinomas were sensitive to the treatment with okadaic acid, an inhibitor of PP2A. Induction of apoptosis after okadaic acid treatment was accompanied by the phosphorylation of PKC, confirming a survival role of PP2A in ovarian cancer, and a potential pro-apoptotic function of PKC. Based on the in vitro cell culture work we analyzed how different members of the PKC family are regulated by H-REV107-1 or by the inhibition of PP2A activity with okadaic acid. Additionally, we verified an impact of the PI3-kinase pathway, a major survival kinase in ovarian carcinoma, in the regulation of

Analysis of novel PKCs revealed differences at the level of expression and phosphorylation. Thus, treatment with okadaic acid for 48 hours and overexpression of H-REV107-1 led to an increased expression of PKCε. Additionally, H-REV107-1 indirectly induced phosphorylation of the COOH-terminal residue Ser729, shown to enhance the enzymatic activity of PKCε (Parekh et al., 2000). This suggests that PKCε activity might be partially regulated in an H-REV107-1-dependent manner. Phosphorylation of Thr538 within the activation loop of PKCθ was elevated after 48 hours of treatment with okadaic acid and the AKT inhibitor LY294002, suggesting a negative but indirect regulation through PP2A and PI-3K. Additionally, Thr538 phosphorylation of PKCθ was increased in cells expressing H-REV107-1, suggesting a potential role of this kinase in H-REV107-1 signalling. The

**3. H-REV107-1/HRLS3-driven interplay between PP2A and PKC signal** 

**transduction pathways in ovarian carcinomas** 

as a cooperating oncogene with mutant RAS (Zhang et al., 2006).

2007;Nazarenko et al., 2010).

PKC.

phosphorylation of Thr505 located within the activation loop of PKCδ increased already 15 minutes after the addition of okadaic acid or LY294002, indicating that PKCδ is directly inactivated by PP2A and PI3K. Although the levels of total PKCδ seemed to be slightly increased after long-term okadaic acid and LY294002 treatment, the phosphorylation was strongly diminished. H-REV107-1 negatively regulated the expression of PKCδ, supporting the finding that PKCδ is not involved in H-REV107-1-dependent cell death. Expression of atypical PKCι was increased following 48 hours of treatment with okadaic acid, but neither phosphorylation nor total levels were affected by H-REV107-1.

To correlate phosphorylation of kinases in the activation site and their intracellular kinase activity, we applied in vitro kinase assay described in detail elsewhere (Nazarenko et al., 2010) and measured direct changes in the activity of PKCs upon okadaic acid treatment. A significant elevation of the PKCθ and PKCε activity was detected 24 hours after okadaic acid incubation, confirming that these PKCs, although not known to be direct PP2A targets, are negatively regulated by PP2A signalling in OVCAR-3 cells.

As inhibition of PP2A is required for H-REV107-1-dependent apoptosis, we next asked if these kinases might be involved in H-REV107-1-induced cell death and tested if the abrogation of PKCθ and PKCε activity impairs the proapoptotic function of H-REV107-1. OVCAR-3 cells were transfected either with the H-REV107-1 expression vector or with a control plasmid. Twelve hours later, the PKCθ- and PKCε-specific peptides were added. Caspase-3 cleavage was tested after 48 hours using Western blot analysis. H-REV107-1 expression resulted in the induction of caspase-3 cleavage, which was however not altered after peptide applications. Additionally, PKCθ-specific peptide treatment of control cells revealed a weak toxic effect. This result suggests that although PKCε and PKCθ are clearly activated in a PP2A and H-REV107-1-dependent manner, they are not essential for the H-REV107-1 proapoptotic activity in OVCAR-3 cells.

An important finding was that the atypical PKC is uncoupled from the PI3K pathway in ovarian cancer cells and is more likely to be a PP2A target. This is in contrast to the situation in the majority of normal and malignant tissues, in which PKCζ functions as an insulin-dependent PI3K effector. Importantly, overexpression of wild type H-REV107-1, but not of its PP2A interaction-deficient mutant, led to PKC phosphorylation, suggesting a direct link between the ability of H-REV107-1 to inhibit PP2A and the activation of PKCζ.

Electroporation of the ovarian carcinoma cells with PKCζ-expression plasmid demonstrated that high levels of this kinase are sufficient to induce apoptosis. In our work we demonstrated an increase of the sub-G1 cell population and caspase-3 cleavage. Molecular mechanisms by mean of which PKCζ induces apoptosis remained elusive and need further investigations. A recent work of Peng et al. might provide an additional hint for the mechanisms of PKCζ-dependent apoptosis (Chen et al., 2008). Using a mouse model, the authors demonstrated that PKCζ directly interacts with ERK1/2 in Kupffer cells, mediating a translocation of NF-kB into the nucleus and inducing its activity. The novelty of this finding is a direct link between PKCζ, EKR1/2 and NF-kB. Consistently, a cross-talk between NF-kB and PKCζ is well- characterised for many systems (Moscat et al., 2001;Moscat and az-Meco, 2011). Next, a potential interaction between PKCζ, ERK1/2, and NF-kB in ovarian cancer cells should be verified. A hypothetical scheme of PKC apoptotic cascade and cross-talk with other pathways is represented on the Fig. 5.

Apoptosis Pathways in Ovarian Cancer 99

activation. Therefore, we asked whether PKC phosphorylation, which is necessary for apoptosis induction in ovarian cancer cells, might also be regulated by inhibitors and therapeutic agents that target mitogenic and survival pathways. The most prominent candidates for such an approach appeared to be the family of epidermal growth factor receptors (EGFR), whose members are frequently mutated and activated in human malignancies, and specific inhibitors are used for the treatment of ovarian carcinomas (De

We performed a reverse phase protein array analysis (RPPA) of OVCAR-3 cells treated with the EGFR inhibitors Cetuximab and Gefitinib/Iressa, and tested the expression and phosphorylation of PKC and the expression of PKC and PKC with antibodies established for this approach. To test if other signalling cascades are similarly affected following inhibition of EGFR signalling, we applied antibodies against phosphorylated AKT

This analysis showed an increased protein level of PKC and of its phosphorylated form 24 hours after the treatment with EGFR inhibitors. The RPPA analysis of ERK, AKT and Bad proteins revealed a moderate effect of EGFR inhibition on the phosphorylation status of these proteins and their expression. While total levels of Akt/PKB and phosphorylation of Bad Ser112 were unchanged, Akt/PKC and ERK phosphorylation were moderately increased after application of inhibitors. In addition to PKC, RPPA analysis also revealed elevated levels of PKC and PKC following incubation with the EGFR inhibitors,

Our experimental data obtained through profiling with reverse phase protein arrays revealed that application of Cetuximab or Gefitinib to OVCAR-3 cells induced only a moderate effect on MAPK and PI3K signalling, and had no effect onto cell growth. This suggests that specific targeting of EGFR is not sufficient to switch the survival program to an apoptotic program in these cells. In addition, EGFR inhibition led to a transient activation of PKC and to an up-regulation of PKC and PKC. Neither PKC nor PKC seem to play a crucial role in apoptosis induction in the cell lines tested, while we provided clear evidence for an involvement of PKC in the induction of apoptosis. The transient activation of PKC following EGFR interference was not sufficient to induce apoptosis. Therefore, the inhibition of oncogenic tyrosine kinase receptors might be a prerequisite for full or partial reconstitution of the players involved in apoptosis, but an additional trigger such as chemotherapy might be necessary to actually execute the death program

This chapter describes the impact of a family of tumour suppressor proteins, and the specific PKC-mediated signalling on apoptosis induction in ovarian cancer. The genes encoding H-REV107-1/HRSL3 and TIG3 both act as tumour suppressor genes. While the functional impact of TIG3 is still somewhat elusive, H-REV107-1 governs the decision between survival and apoptosis. Of major importance for the future research is the newly described function of H-REV107-1 and its related proteins, being phospholipases. This function indicates a specific role of lipid metabolism in the control of transformation and potentially tumour

Marinis et al., 2002;Blank et al., 2005).

(Nazarenko et al., 2010).

**5. Conclusion** 

progression.

and ERK proteins and against the Bad protein.

suggesting a role of these kinases in EGFR downstream signalling.

Fig. 5. Hypothetical scheme of the pro-apoptotic signal transduction network in ovarian cancer cells of two members of the HRS protein family, the H-REV107-1 (showed with red arrows) and TIG3 (showed with blue arrows). Both family members can be activated by IFNγ, whereas TIG3 can be additionally activated by ATRA. TIG3 mediates inhibition of HER-2, mediating herewith suppression of angiogenesis. H-REV107-1 inhibits PP2A, mediating activation of PKC. PKC functions as a major mediator of H-REV107-1-mediated cell death and is sufficient to induce apoptosis in a subset ovarian carcinoma cells, sensitive to the H-REV107-1--mediated apoptosis.

#### **4. Receptor kinase pathway profiling in ovarian cancer cells**

We applied the RPPA (reverse phase protein array) technique to define a potential regulation of PKCs by epidermal growth factor receptor inhibition. Earlier work on the H-REV107-1 tumour suppressor, an inhibitor of PP2A, has demonstrated that H-REV1071-1 is lost in a significant portion of ovarian tumours.

As a class II tumour suppressor gene, H-REV107-1 expression was reconstituted upon IFN treatment and MAPK inhibition and was able to induce specific phosphorylation of atypical PKC and the induction of apoptosis. These observations suggested to us that interference with oncogenic pathways might also have some impact on PKC isoform expression and/or

Fig. 5. Hypothetical scheme of the pro-apoptotic signal transduction network in ovarian cancer cells of two members of the HRS protein family, the H-REV107-1 (showed with red arrows) and TIG3 (showed with blue arrows). Both family members can be activated by IFNγ, whereas TIG3 can be additionally activated by ATRA. TIG3 mediates inhibition of HER-2, mediating herewith suppression of angiogenesis. H-REV107-1 inhibits PP2A,

mediating activation of PKC. PKC functions as a major mediator of H-REV107-1-mediated cell death and is sufficient to induce apoptosis in a subset ovarian carcinoma cells, sensitive

We applied the RPPA (reverse phase protein array) technique to define a potential regulation of PKCs by epidermal growth factor receptor inhibition. Earlier work on the H-REV107-1 tumour suppressor, an inhibitor of PP2A, has demonstrated that H-REV1071-1 is

As a class II tumour suppressor gene, H-REV107-1 expression was reconstituted upon IFN treatment and MAPK inhibition and was able to induce specific phosphorylation of atypical PKC and the induction of apoptosis. These observations suggested to us that interference with oncogenic pathways might also have some impact on PKC isoform expression and/or

**4. Receptor kinase pathway profiling in ovarian cancer cells** 

to the H-REV107-1--mediated apoptosis.

lost in a significant portion of ovarian tumours.

activation. Therefore, we asked whether PKC phosphorylation, which is necessary for apoptosis induction in ovarian cancer cells, might also be regulated by inhibitors and therapeutic agents that target mitogenic and survival pathways. The most prominent candidates for such an approach appeared to be the family of epidermal growth factor receptors (EGFR), whose members are frequently mutated and activated in human malignancies, and specific inhibitors are used for the treatment of ovarian carcinomas (De Marinis et al., 2002;Blank et al., 2005).

We performed a reverse phase protein array analysis (RPPA) of OVCAR-3 cells treated with the EGFR inhibitors Cetuximab and Gefitinib/Iressa, and tested the expression and phosphorylation of PKC and the expression of PKC and PKC with antibodies established for this approach. To test if other signalling cascades are similarly affected following inhibition of EGFR signalling, we applied antibodies against phosphorylated AKT and ERK proteins and against the Bad protein.

This analysis showed an increased protein level of PKC and of its phosphorylated form 24 hours after the treatment with EGFR inhibitors. The RPPA analysis of ERK, AKT and Bad proteins revealed a moderate effect of EGFR inhibition on the phosphorylation status of these proteins and their expression. While total levels of Akt/PKB and phosphorylation of Bad Ser112 were unchanged, Akt/PKC and ERK phosphorylation were moderately increased after application of inhibitors. In addition to PKC, RPPA analysis also revealed elevated levels of PKC and PKC following incubation with the EGFR inhibitors, suggesting a role of these kinases in EGFR downstream signalling.

Our experimental data obtained through profiling with reverse phase protein arrays revealed that application of Cetuximab or Gefitinib to OVCAR-3 cells induced only a moderate effect on MAPK and PI3K signalling, and had no effect onto cell growth. This suggests that specific targeting of EGFR is not sufficient to switch the survival program to an apoptotic program in these cells. In addition, EGFR inhibition led to a transient activation of PKC and to an up-regulation of PKC and PKC. Neither PKC nor PKC seem to play a crucial role in apoptosis induction in the cell lines tested, while we provided clear evidence for an involvement of PKC in the induction of apoptosis. The transient activation of PKC following EGFR interference was not sufficient to induce apoptosis. Therefore, the inhibition of oncogenic tyrosine kinase receptors might be a prerequisite for full or partial reconstitution of the players involved in apoptosis, but an additional trigger such as chemotherapy might be necessary to actually execute the death program (Nazarenko et al., 2010).

#### **5. Conclusion**

This chapter describes the impact of a family of tumour suppressor proteins, and the specific PKC-mediated signalling on apoptosis induction in ovarian cancer. The genes encoding H-REV107-1/HRSL3 and TIG3 both act as tumour suppressor genes. While the functional impact of TIG3 is still somewhat elusive, H-REV107-1 governs the decision between survival and apoptosis. Of major importance for the future research is the newly described function of H-REV107-1 and its related proteins, being phospholipases. This function indicates a specific role of lipid metabolism in the control of transformation and potentially tumour progression.

Apoptosis Pathways in Ovarian Cancer 101

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related to each other and to the H-rev107 family of proteins involved in the control

Furthermore, high expression levels of PKC and a correlation with poor prognosis were observed in human ovarian carcinoma samples and only the activation of endogenous PKC by okadaic acid or by the HRSL3 tumour suppressor, correlated with the induction of apoptosis in primary and immortalized ovarian carcinoma cells. This suggests a potentially inaccessible pro-apoptotic action of this kinase, which might be negatively regulated by activated tyrosine kinase receptors in ovarian cancer. In future research, identification of yet unknown substrates of the members of the HRS family will support current knowledge on the mechanisms of their pro-apoptotic function. Possibly, new aspects of functions, opening novel horizons in the therapy of ovarian cancer therapy, will be developed.

#### **6. Acknowledgment**

We thank Andreas Weihe and Uwe Richter (Institute of Genetics, Humboldt University, Berlin, Germany) for their help in the phylogenetic analysis of the HRS and HRS-related families. Steffen Reich (Institute of Pathology, Charité, Berlin, Germany, for the analysis of H-REV107-1 human promoter.

#### **7. References**


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**6. Acknowledgment** 

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**6** 

*Belgium* 

**Oncogenic Pathway Signatures** 

Xuan Bich Trinh1,2, Peter A. Van Dam1, Luc Y. Dirix1, Steven J. van Laere1 and Wiebren A. A. Tjalma2

*1Translational Cancer Research Unit, St Augustinus GZA Hospitals, Antwerp 2Department of Gynaecological Oncology, Antwerp University Hospital, Antwerp* 

Recent microarray technology and bioinformatics have shown the ability of analysing oncogenic cellular signalling pathways based upon gene signatures in cancers. (Bild et al., 2006; Dressman et al., 2007; Gatza et al., 2010) Epithelial ovarian cancer (EOC) is the most important cause of mortality among gynaecological cancers. Patients with EOC often present in an advanced stage. Treatment modalities consist in general of the sequence of surgical cytoreduction and platinum-taxane based chemotherapy. (Cannistra, 2004) Although the disease is relatively sensitive to cytotoxics, relapses occur in a majority of patients with advanced stage. (Cannistra, 2004) The emergence of resistance to conventional chemotherapeutics is an often-deadly event in the management of ovarian cancer patients. There is an urgent need for additional therapies that increase survival and/or quality of life

The objective of our study was to look for cellular pathways that have an effect on survival outcome by a bioinformatical approach. (Trinh et al., 2011) These pathways may guide us to find interesting targets in ovarian cancer. Survival can be used as a measure to quantify the biological relevance in this disease. Ideally, evaluation of survival outcome should be made in a homogenous population with a uniform treatment to avoid treatment-induced biases and uniform histology to find subtler differences independent from histology. Another methodology of estimating prognostic value may be the correlation with documented prognostic gene signatures that have shown to be of prognostic value in breast cancer and other types of cancer. The invasiveness gene signature (IGS) was generated using stem celllike or tumorigenic breast cancer cells.(Liu et al., 2007) This signature has shown prognostic value in lung cancer, medulloblastoma and prostate cancer. The Wound healing response (WHR) signature, based upon genes induced by wound healing, also has shown its prognostic value in breast cancer, NSLC and bladder cancer. (Chang et al., 2005; Lauss, Ringnér, & Höglund, 2010; Mostertz et al., 2010) The genomic grade index (GGI) is a signature that divides low-grade versus high-grade breast carcinomas. (Sotiriou et al., 2006) Interestingly, using this signature, histological intermediate-grade tumours could be classified as low- or high-grade tumours with the preservation of the gene signatures'

**1. Introduction** 

in these patients.

prognostic value.

**and Survival Outcome** 


### **Oncogenic Pathway Signatures and Survival Outcome**

Xuan Bich Trinh1,2, Peter A. Van Dam1, Luc Y. Dirix1, Steven J. van Laere1 and Wiebren A. A. Tjalma2 *1Translational Cancer Research Unit, St Augustinus GZA Hospitals, Antwerp 2Department of Gynaecological Oncology, Antwerp University Hospital, Antwerp Belgium* 

#### **1. Introduction**

104 Ovarian Cancer – Basic Science Perspective

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cells requires both a unique and a redundant protein kinase C signaling pathway:

identifies PKCiota as a biomarker and potential oncogene in ovarian carcinoma:

Recent microarray technology and bioinformatics have shown the ability of analysing oncogenic cellular signalling pathways based upon gene signatures in cancers. (Bild et al., 2006; Dressman et al., 2007; Gatza et al., 2010) Epithelial ovarian cancer (EOC) is the most important cause of mortality among gynaecological cancers. Patients with EOC often present in an advanced stage. Treatment modalities consist in general of the sequence of surgical cytoreduction and platinum-taxane based chemotherapy. (Cannistra, 2004) Although the disease is relatively sensitive to cytotoxics, relapses occur in a majority of patients with advanced stage. (Cannistra, 2004) The emergence of resistance to conventional chemotherapeutics is an often-deadly event in the management of ovarian cancer patients. There is an urgent need for additional therapies that increase survival and/or quality of life in these patients.

The objective of our study was to look for cellular pathways that have an effect on survival outcome by a bioinformatical approach. (Trinh et al., 2011) These pathways may guide us to find interesting targets in ovarian cancer. Survival can be used as a measure to quantify the biological relevance in this disease. Ideally, evaluation of survival outcome should be made in a homogenous population with a uniform treatment to avoid treatment-induced biases and uniform histology to find subtler differences independent from histology. Another methodology of estimating prognostic value may be the correlation with documented prognostic gene signatures that have shown to be of prognostic value in breast cancer and other types of cancer. The invasiveness gene signature (IGS) was generated using stem celllike or tumorigenic breast cancer cells.(Liu et al., 2007) This signature has shown prognostic value in lung cancer, medulloblastoma and prostate cancer. The Wound healing response (WHR) signature, based upon genes induced by wound healing, also has shown its prognostic value in breast cancer, NSLC and bladder cancer. (Chang et al., 2005; Lauss, Ringnér, & Höglund, 2010; Mostertz et al., 2010) The genomic grade index (GGI) is a signature that divides low-grade versus high-grade breast carcinomas. (Sotiriou et al., 2006) Interestingly, using this signature, histological intermediate-grade tumours could be classified as low- or high-grade tumours with the preservation of the gene signatures' prognostic value.

Oncogenic Pathway Signatures and Survival Outcome 107

naïve HUVEC's: 4.276, P<0.0001). The boxplot representation is provided in Figure 1B. In addition, we observed a strong correlation between the VEGF-A activation probability scores and the time of VEGF-A incubation of HUVEC's (Correlation coefficient = 0.762; P=0.038). (Figure 1C). To validate our procedure, we applied our algorithm on the samples

Fig. 1. Principal Component Analysis Plot (A) segregates VEGF-A treated cells versus untreated cells. The calculated activation scores were higher in treated cells versus untreated

cells in an apparent time dependent way. (B+C)

in gene expression data sets GSE10778 and GSE15464.

#### **2. Oncogenic pathways**

The oncogenic gene signatures were derived from a recent paper by Gatza and colleagues and applied similarly. (Gatza et al., 2010) These pathway signatures were mainly generated by activating or silencing specific genes in cell lines experiments. The signatures were robustly validated afterwards. For each pathway, a pathway activation score was calculated based upon the gene signature to quantify the activation by a score.

Briefly, for each array-sample the pathway-specific informative genes were identified. Next a pathway score was calculated by adding up the products of the gene expression for each gene and its corresponding regression coefficient, which indicates the weight (amplitude of regression coefficient) and the effect (sign of regression coefficient) of the corresponding gene for activation of the corresponding pathway. Finally, the pathway scores were scaled using the intercept values provided in the original manuscript and standardized for comparability by median-centering and setting the standard deviation to 1. Pathways included in the analysis were AKT, β-Catenin, E2F1, EGFR, ER, HER2, INFα, INFγ, MYC, p53, p63, PI3K, PR, RAS, SRC, STAT3, TNFα, and TGFβ.

Since PARP inhibitors and VEGF-A inhibitors have shown promising results in ovarian cancer, the BRCA pathway and VEGF-A pathway was also studied. (Audeh et al., 2010; Burger et al., 2010; Fong et al., 2009; 2010) For the BRCA signature, we used one that was published by Konstantinopoulos and colleagues. (Konstantinopoulos et al., 2010) For a VEGF-A signature we have used and validated genes that were reported by Hu et al. (Hu et al., 2009) A BRCA activation score was applied using the same methodology with 60 genes, their weight and sign. (Konstantinopoulos et al., 2010) Prognostic gene signatures (IGS, GGI and WHR) were also applied by previously described methodology. (Chang et al., 2005; Liu et al., 2007; Sotiriou et al., 2006) All gene signature activation scores were handled as a continuous variable. The same standardisation (Median=0; SD=1) was applied for each gene signature.

For the VEGF-A activation signature we used the 13 genes reported by Hu and colleagues. (Hu et al., 2009) To validate and transform this gene signature into a VEGF-A activation probability score we performed subsequent analysis using publicly available gene expression data sets on naïve and VEGF-A treated HUVEC cell lines (GSE18913 (N=21), GSE10778 (N=9; only the HGU133A samples were used) and GSE15464 (N=4)). Each data set was normalised using the GC-RMA algorithm and informative genes (above log 2(100) in at least 25% of the genes) were filtered in. First, we applied a principal component analysis on the GSE18913 data set using the informative VEGF-A signatures genes only (N=10). Only 10 out of 13 genes (*FABP5, UCHL1, PLOD, DDIT4, VEGF, ADM, ANGPTL4, NDRG1, NP* and *SLC16A3*) were reliably measured (high signal-to-noise ratio). Using these 10 genes in a principal component analysis (PCA) we were able to demonstrate a significant segregation of VEGF-A treated and naïve HUVEC's along the first principal component. Class label permutation analysis revealed that the observed Euclidean distance between the centroids of the VEGF-A treated and naïve HUVEC's on the 2D scatterplot representation of the PCA was significantly different from the expected Euclidean distance (Figure 1A; Observed Euclidean distance=2.185, Expected Euclidean distance=0.682, P<0.0001).

Next, we transformed the VEGF-A signature into a VEGF-A activation probability score adopting the methodology described by Gatza and his colleagues. (Gatza et al., 2010). Therefore, we used the regression coefficients that define the first principal component and multiplied these with the gene expression values of their corresponding genes. The products were summed and the resulting score was compared between VEGF-A treated and naïve HUVEC's using a Mann-Whitney U-test (Median VEGF-A treated HUVEC's: 6.416, Median

The oncogenic gene signatures were derived from a recent paper by Gatza and colleagues and applied similarly. (Gatza et al., 2010) These pathway signatures were mainly generated by activating or silencing specific genes in cell lines experiments. The signatures were robustly validated afterwards. For each pathway, a pathway activation score was calculated

Briefly, for each array-sample the pathway-specific informative genes were identified. Next a pathway score was calculated by adding up the products of the gene expression for each gene and its corresponding regression coefficient, which indicates the weight (amplitude of regression coefficient) and the effect (sign of regression coefficient) of the corresponding gene for activation of the corresponding pathway. Finally, the pathway scores were scaled using the intercept values provided in the original manuscript and standardized for comparability by median-centering and setting the standard deviation to 1. Pathways included in the analysis were AKT, β-Catenin, E2F1, EGFR, ER, HER2, INFα, INFγ, MYC,

Since PARP inhibitors and VEGF-A inhibitors have shown promising results in ovarian cancer, the BRCA pathway and VEGF-A pathway was also studied. (Audeh et al., 2010; Burger et al., 2010; Fong et al., 2009; 2010) For the BRCA signature, we used one that was published by Konstantinopoulos and colleagues. (Konstantinopoulos et al., 2010) For a VEGF-A signature we have used and validated genes that were reported by Hu et al. (Hu et al., 2009) A BRCA activation score was applied using the same methodology with 60 genes, their weight and sign. (Konstantinopoulos et al., 2010) Prognostic gene signatures (IGS, GGI and WHR) were also applied by previously described methodology. (Chang et al., 2005; Liu et al., 2007; Sotiriou et al., 2006) All gene signature activation scores were handled as a continuous variable. The

For the VEGF-A activation signature we used the 13 genes reported by Hu and colleagues. (Hu et al., 2009) To validate and transform this gene signature into a VEGF-A activation probability score we performed subsequent analysis using publicly available gene expression data sets on naïve and VEGF-A treated HUVEC cell lines (GSE18913 (N=21), GSE10778 (N=9; only the HGU133A samples were used) and GSE15464 (N=4)). Each data set was normalised using the GC-RMA algorithm and informative genes (above log 2(100) in at least 25% of the genes) were filtered in. First, we applied a principal component analysis on the GSE18913 data set using the informative VEGF-A signatures genes only (N=10). Only 10 out of 13 genes (*FABP5, UCHL1, PLOD, DDIT4, VEGF, ADM, ANGPTL4, NDRG1, NP* and *SLC16A3*) were reliably measured (high signal-to-noise ratio). Using these 10 genes in a principal component analysis (PCA) we were able to demonstrate a significant segregation of VEGF-A treated and naïve HUVEC's along the first principal component. Class label permutation analysis revealed that the observed Euclidean distance between the centroids of the VEGF-A treated and naïve HUVEC's on the 2D scatterplot representation of the PCA was significantly different from the expected Euclidean distance (Figure 1A;

same standardisation (Median=0; SD=1) was applied for each gene signature.

Observed Euclidean distance=2.185, Expected Euclidean distance=0.682, P<0.0001).

Next, we transformed the VEGF-A signature into a VEGF-A activation probability score adopting the methodology described by Gatza and his colleagues. (Gatza et al., 2010). Therefore, we used the regression coefficients that define the first principal component and multiplied these with the gene expression values of their corresponding genes. The products were summed and the resulting score was compared between VEGF-A treated and naïve HUVEC's using a Mann-Whitney U-test (Median VEGF-A treated HUVEC's: 6.416, Median

based upon the gene signature to quantify the activation by a score.

p53, p63, PI3K, PR, RAS, SRC, STAT3, TNFα, and TGFβ.

**2. Oncogenic pathways** 

naïve HUVEC's: 4.276, P<0.0001). The boxplot representation is provided in Figure 1B. In addition, we observed a strong correlation between the VEGF-A activation probability scores and the time of VEGF-A incubation of HUVEC's (Correlation coefficient = 0.762; P=0.038). (Figure 1C). To validate our procedure, we applied our algorithm on the samples in gene expression data sets GSE10778 and GSE15464.

Fig. 1. Principal Component Analysis Plot (A) segregates VEGF-A treated cells versus untreated cells. The calculated activation scores were higher in treated cells versus untreated cells in an apparent time dependent way. (B+C)

Oncogenic Pathway Signatures and Survival Outcome 109

A dataset of 285 patients (Melbourne dataset) was obtained though the Gene Expression Omnibus GEO database (GSE 9891) together with the clinical annotation data file. Only patients that had carcinomas of serous histology in advanced stages (III/IV) were included for analysis. Patients were selected that received platinum and taxane based chemotherapy. Other patients who did not receive chemotherapy or received only one agent, platinum or taxane, were also excluded. After this selection N=165 patients were eligible for further analysis. This dataset contained gene expression data derived from the Affymetrix U133\_plus2 platform, which already underwent normalisation using the Robust Multiarray Averaging (RMA) method and subsequent filtering by excluding log expression values of <7 and a variance of <0.5. After filtering there were 8,732 probe sets left that are considered informative. Progression free survival was used in further

A second dataset GSE3149 N=153 (North Carolina dataset) with clinical data was also obtained from the GEO website. Here, the same criteria for patient selection were used. After selection N=107 were further analysed. The North Carolina dataset used the same Affymetrix U133\_plus2 platform. The raw data were processed in Bioconductor in R software packages. Filtering was done by selecting expressions below a threshold (log 2 of 100) that are present in at least 25% of the arrayed samples. Normalisation was done using GC-Robust Multiarray Averaging. The number of probe sets that were informative was 7,741. Overall survival data was used, as there was no progression free survival data available. (Bild et al., 2006) A third dataset (Québec dataset) were patients (N=20) that were selected to be either chemoresistant versus chemosensitive. Here, raw microarray data based upon the Agilent platform Human 1A (v2) oligonucleotide microarray were normalised using the Lowess normalisation method. Hereafter, 16,096 genes were eligible for further analysis. Progression free survival data were used. RAW gene expression data is publicly available according to MIAME guidelines through the GEO database (Accession number: GSE 28739). (Bachvarov et al., 2006) A fourth dataset (Niigata Dataset-GSE 17260) contained samples that originated from patients who met the inclusion criteria from present study. Progression free survival data were available. The authors used the Agilent Whole Human Genome Oligo Microarray platform and normalised the data using upper quartile normalisation. 28,446 genes were found to be informative. (Yoshihara et al., 2010) A fifth and sixth dataset (Boston dataset A +B - GSE19829) were derived from a report studying BRCAness in ovarian cancer. (Konstantinopoulos et al., 2010) Progression free survival data was used. After selection, (N=26) and (N=36) patients were eligible. These datasets were RMA-normalised. 35252 and 5626 probe set ID's were informative after filtering. Gene expression data was derived from two platforms: the Affymetrix U133\_plus2 platform and

**4. Correlation of pathway activation scores with prognostic signatures** 

We applied the oncogenic pathways on the six datasets. These datasets together represent a total of N=464 advanced serous papillary carcinomas. A summary of these 6 datasets is listed in Table 1. Since these are selected oncogenic pathways, it is plausible that many significant correlations were found between pathway activations and the 3 prognostic

**3. Patient datasets** 

analysis. (Tothill et al., 2008)

the Affymetrix 95UAv2.

Fig. 2. Validation of the VEGF-A activation score methodology in data derived from two other experiments. The activation scores of VEGF-A treated cells were higher than the untreated condition (red dot). The higher activation scores were observed for VEGF-A treated cells but not for EGF treated cells, suggesting he specificity of the activation score for VEGF-A.

#### **3. Patient datasets**

108 Ovarian Cancer – Basic Science Perspective

A

 C Fig. 2. Validation of the VEGF-A activation score methodology in data derived from two other experiments. The activation scores of VEGF-A treated cells were higher than the untreated condition (red dot). The higher activation scores were observed for VEGF-A treated cells but not for EGF treated cells, suggesting he specificity of the activation score for VEGF-A.

B

A dataset of 285 patients (Melbourne dataset) was obtained though the Gene Expression Omnibus GEO database (GSE 9891) together with the clinical annotation data file. Only patients that had carcinomas of serous histology in advanced stages (III/IV) were included for analysis. Patients were selected that received platinum and taxane based chemotherapy. Other patients who did not receive chemotherapy or received only one agent, platinum or taxane, were also excluded. After this selection N=165 patients were eligible for further analysis. This dataset contained gene expression data derived from the Affymetrix U133\_plus2 platform, which already underwent normalisation using the Robust Multiarray Averaging (RMA) method and subsequent filtering by excluding log expression values of <7 and a variance of <0.5. After filtering there were 8,732 probe sets left that are considered informative. Progression free survival was used in further analysis. (Tothill et al., 2008)

A second dataset GSE3149 N=153 (North Carolina dataset) with clinical data was also obtained from the GEO website. Here, the same criteria for patient selection were used. After selection N=107 were further analysed. The North Carolina dataset used the same Affymetrix U133\_plus2 platform. The raw data were processed in Bioconductor in R software packages. Filtering was done by selecting expressions below a threshold (log 2 of 100) that are present in at least 25% of the arrayed samples. Normalisation was done using GC-Robust Multiarray Averaging. The number of probe sets that were informative was 7,741. Overall survival data was used, as there was no progression free survival data available. (Bild et al., 2006) A third dataset (Québec dataset) were patients (N=20) that were selected to be either chemoresistant versus chemosensitive. Here, raw microarray data based upon the Agilent platform Human 1A (v2) oligonucleotide microarray were normalised using the Lowess normalisation method. Hereafter, 16,096 genes were eligible for further analysis. Progression free survival data were used. RAW gene expression data is publicly available according to MIAME guidelines through the GEO database (Accession number: GSE 28739). (Bachvarov et al., 2006) A fourth dataset (Niigata Dataset-GSE 17260) contained samples that originated from patients who met the inclusion criteria from present study. Progression free survival data were available. The authors used the Agilent Whole Human Genome Oligo Microarray platform and normalised the data using upper quartile normalisation. 28,446 genes were found to be informative. (Yoshihara et al., 2010) A fifth and sixth dataset (Boston dataset A +B - GSE19829) were derived from a report studying BRCAness in ovarian cancer. (Konstantinopoulos et al., 2010) Progression free survival data was used. After selection, (N=26) and (N=36) patients were eligible. These datasets were RMA-normalised. 35252 and 5626 probe set ID's were informative after filtering. Gene expression data was derived from two platforms: the Affymetrix U133\_plus2 platform and the Affymetrix 95UAv2.

#### **4. Correlation of pathway activation scores with prognostic signatures**

We applied the oncogenic pathways on the six datasets. These datasets together represent a total of N=464 advanced serous papillary carcinomas. A summary of these 6 datasets is listed in Table 1. Since these are selected oncogenic pathways, it is plausible that many significant correlations were found between pathway activations and the 3 prognostic

Oncogenic Pathway Signatures and Survival Outcome 111

signatures (IGS, WHR and GGI). The β-Catenin pathway showed consistent and strong correlations. (Table 2) Since the six datasets were generated on different platforms with different methodologies, we estimated the overall effect of a pathway activation score by using a meta-analysis approach (Table 2). Similar meta-analysis of correlation coefficients showed that the BRCA, E2F1, EGFR, HER2, MYC, p53, p63 and PI3K showed steady correlations with the WHR, GGI and IGS. The RAS pathway and TGFβ pathway showed significant correlations with 2/3 prognostic signatures. Table 3 shows the overall correlation estimates, which were the most significant. While most pathway activation scores showed a positive correlation, the

**Rho estimates WHR IGS GGI** 


BRCA 0.43 0.36 0.36

E2F1 0.51 0.42 0.54

EGFR -0.52 -0.43 -0.42

HER2 -0.45 -0.5 -0.26

MYC 0.69 0.53 0.4

p53 -0.59 -0.42 -0.72

p63 0.46 0.29 0.36

PI3K 0.43 0.33 0.29

RAS 0.51 0.2 0.4

TGF -0.23 -0.3 -0.13

Table 3. Estimates of Pearson rho correlation coefficients after meta-analysis of six datasets between pathway activation scores and prognostic gene signatures: wound healing response signature (WHR)/ Invasiveness gene signature IGS and Genomic grade Index (GGI). Most significant correlations are shown. (Threshold p-value adjusted for multiple testing=0.0025)

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p<0.0001 p<0.0001

p<0.0001 p=0.001 p<0.0001

p<0.0001 p<0.0001 p=0.002

p<0.0001 p=0.017 p<0.0001

p=0.0001 p<0.0001 p=0.004

EGFR, HER2, p53 and TGFβ pathway showed a negative correlation.


Table 1. A summary of datasets that were used in the meta-analysis.


Table 2. This shows the consistent correlations of the β-Catenin activation scores and WHR/IGS/GGI in each separate dataset (Québec, North Carolina, Melbourne, Niigata, Boston A and Boston B dataset). Overall Rho Coefficients were estimated by a meta-analysis approach using random models effects.

**lisation** 

GC-

Upper

**Clinical outcome** 

U133\_plus2 RMA PFS yes yes

U133\_plus2 RMA PFS yes yes

U95\_A2 RMA PFS yes yes

p=3.4 E-4 p=0.001 p=2.0 E-4

p=7.7 E-40 p=9.9 E-59 p=8.0 E-18

p=2.8 E-22 p=6.9 E-11 p=5.6 E-28

p=2.4 E-19 1.0 E-22 p=4.5 E-25

p=1.2 E-7 p=0.013 p=5.5 E-9

p=1.8 E-7 p=3.7 E-4 p=0.13

p<0.0001 p<0.0001 p<0.0001

**Uniform treatment** 

Lowess PFS yes yes

RMA OS yes yes

quartile PFS yes yes

**Advanced stage/ serous papillary histology** 

**Dataset N=464 Platform Norma-**

Agilent Human 1A (v2)

U133\_plus2

Agilent Whole Human Genome Oligo Microarray

Table 1. A summary of datasets that were used in the meta-analysis.

**Pearson Rho WHR IGS GGI** 

Québec 0.65 0.62 0.67

North Carol 0.81 0.89 0.6

Melbourne 0.73 0.54 0.79

Niigata 0.77 0.73 0.79

Boston A 0.83 0.48 0.87

Boston B 0.75 0.56 0.26

Meta Analysis 0.73 0.62 0.79

Table 2. This shows the consistent correlations of the β-Catenin activation scores and WHR/IGS/GGI in each separate dataset (Québec, North Carolina, Melbourne, Niigata, Boston A and Boston B dataset). Overall Rho Coefficients were estimated by a meta-analysis

<sup>107</sup>Affymetrix

2008 165 Affymetrix

2010 26 Affymetrix

2010 36 Affymetrix

approach using random models effects.

Québec 2006 20

Niigata 2010 110

North Carolina 2006

Melbourne

Boston A

Boston B

signatures (IGS, WHR and GGI). The β-Catenin pathway showed consistent and strong correlations. (Table 2) Since the six datasets were generated on different platforms with different methodologies, we estimated the overall effect of a pathway activation score by using a meta-analysis approach (Table 2). Similar meta-analysis of correlation coefficients showed that the BRCA, E2F1, EGFR, HER2, MYC, p53, p63 and PI3K showed steady correlations with the WHR, GGI and IGS. The RAS pathway and TGFβ pathway showed significant correlations with 2/3 prognostic signatures. Table 3 shows the overall correlation estimates, which were the most significant. While most pathway activation scores showed a positive correlation, the EGFR, HER2, p53 and TGFβ pathway showed a negative correlation.


Table 3. Estimates of Pearson rho correlation coefficients after meta-analysis of six datasets between pathway activation scores and prognostic gene signatures: wound healing response signature (WHR)/ Invasiveness gene signature IGS and Genomic grade Index (GGI). Most significant correlations are shown. (Threshold p-value adjusted for multiple testing=0.0025)

Oncogenic Pathway Signatures and Survival Outcome 113

effect of pathway activation using a random effects model. After this analysis, the β-Catenin, E2F1, PR, p63 PI3K and RAS pathway activation showed a significant association with clinical outcome. Considering the overall effect by means of Hazard Ratios, the β-Catenin pathway showed the most prominent effect after meta-analysis (HR= 0.74; 95%CI [0.62- 0.88]). The survival analysis showed that the higher the activation of the β-Catenin pathway, the better the outcome was. Also for PR, E2F1, RAS, PI3K and p63 increased activation of

Because of these rather unexpected results, we calculated the activation scores of selected discovered pathways in other independent datasets as additional quality control to confirm whether the directions of the activation scores were certainly correct. For β-Catenin the For the 3 prognostic signatures there was a tendency that a prognostic worse outcome predicted by IGS, WHR and GGI showed an unexpected higher probability of better clinical survival outcome. Further analysis in the Québec dataset showed that chemoresistant patients showed significant lower scores than chemosensitive patients and therefore may

Fig. 4. In the Québec dataset sensitive (S) patients showed a higher genomic grade index (GGI) compared to chemoresistant patients (R) (p=0.002). Similarly chemosensitive patients showed a higher wound healing response score (p=0.02) and a higher invasiveness gene

Our initial analysis consisted of two datasets. The initial design was to use one dataset, as a discovery dataset while the other one would serve as a validation set. Since bioinformatical mislabelling errors/reproducibility issues have lead to withdrawal of papers of the same research group from which one dataset originated, we sought additional datasets to confirm our findings and render more power and reliability. (Bonnefoi et al., 2011; Potti et al., 2011) Furthermore, this research group and critical review by another research group have confirmed that the dataset that was used in the present meta-analysis was indeed correctly annotated. (Baggerly, Coombes, & Neeley, 2008; Dressman et al., 2007) With the availability of more datasets, we noticed variation among pathway's association with survival outcome. We therefore used a meta-analysis approach to estimate the overall effect. The advantage is that several studies can be combined despite differences in platforms and methodologies. This overall effect estimation takes into account the number of patients of each separate dataset and confidence interval in the estimation of correlation coefficient of survival hazard ratios. The heterogeneity among datasets (e.g. different patient selection criteria) may partly explain some opposite findings. The Québec dataset is different from others because this

respective pathway was associated with more favourable survival.

explain this finding.

signature score (IGS) (p=0.06).

**6. Discussion** 

#### **5. Association of pathway activation scores with survival outcome**

While some pathways were associated with survival outcome in one or more datasets, they showed no or opposite result in another dataset. To estimate the overall survival effect of a given pathway, a similar meta-analysis approach was performed to estimate the overall

Fig. 3. Forest plots of meta-analysis using a random effects model of the β-Catenin BRCA, E2F1, p63, PR, PI3K, RAS and VEGF pathway.

While some pathways were associated with survival outcome in one or more datasets, they showed no or opposite result in another dataset. To estimate the overall survival effect of a given pathway, a similar meta-analysis approach was performed to estimate the overall

Fig. 3. Forest plots of meta-analysis using a random effects model of the β-Catenin BRCA,

E2F1, p63, PR, PI3K, RAS and VEGF pathway.

**5. Association of pathway activation scores with survival outcome** 

effect of pathway activation using a random effects model. After this analysis, the β-Catenin, E2F1, PR, p63 PI3K and RAS pathway activation showed a significant association with clinical outcome. Considering the overall effect by means of Hazard Ratios, the β-Catenin pathway showed the most prominent effect after meta-analysis (HR= 0.74; 95%CI [0.62- 0.88]). The survival analysis showed that the higher the activation of the β-Catenin pathway, the better the outcome was. Also for PR, E2F1, RAS, PI3K and p63 increased activation of respective pathway was associated with more favourable survival.

Because of these rather unexpected results, we calculated the activation scores of selected discovered pathways in other independent datasets as additional quality control to confirm whether the directions of the activation scores were certainly correct. For β-Catenin the

For the 3 prognostic signatures there was a tendency that a prognostic worse outcome predicted by IGS, WHR and GGI showed an unexpected higher probability of better clinical survival outcome. Further analysis in the Québec dataset showed that chemoresistant patients showed significant lower scores than chemosensitive patients and therefore may explain this finding.

Fig. 4. In the Québec dataset sensitive (S) patients showed a higher genomic grade index (GGI) compared to chemoresistant patients (R) (p=0.002). Similarly chemosensitive patients showed a higher wound healing response score (p=0.02) and a higher invasiveness gene signature score (IGS) (p=0.06).

#### **6. Discussion**

Our initial analysis consisted of two datasets. The initial design was to use one dataset, as a discovery dataset while the other one would serve as a validation set. Since bioinformatical mislabelling errors/reproducibility issues have lead to withdrawal of papers of the same research group from which one dataset originated, we sought additional datasets to confirm our findings and render more power and reliability. (Bonnefoi et al., 2011; Potti et al., 2011) Furthermore, this research group and critical review by another research group have confirmed that the dataset that was used in the present meta-analysis was indeed correctly annotated. (Baggerly, Coombes, & Neeley, 2008; Dressman et al., 2007) With the availability of more datasets, we noticed variation among pathway's association with survival outcome. We therefore used a meta-analysis approach to estimate the overall effect. The advantage is that several studies can be combined despite differences in platforms and methodologies. This overall effect estimation takes into account the number of patients of each separate dataset and confidence interval in the estimation of correlation coefficient of survival hazard ratios. The heterogeneity among datasets (e.g. different patient selection criteria) may partly explain some opposite findings. The Québec dataset is different from others because this

Oncogenic Pathway Signatures and Survival Outcome 115

decreased *E2F1* gene expression by RT-PCR. (Reimer et al., 2006; 2007) It must be remarked that the latter study included an overrepresentation of patients with clear cell carcinomas

The β-Catenin protein is a multifunctional protein. It was originally discovered as a protein that is associated with the cytoplasmatic region of E-cadherin. E-cadherin is a transmembrane protein that is involved in cell-cell contact and cell's adhesive functions. Furthermore, β-Catenin is involved in Wnt signalling as a nuclear transcription factor and is believed to play a role in cancer stem cells. (Nusse, 2008) Loss of its membranous function or a higher nuclear presence has been linked with poor survival in several studies in ovarian cancer based upon immunohistochemical studies. (Faleiro-Rodrigues et al., 2004; Faleiro-Rodrigues, Macedo-Pinto, Pereira, & Lopes, 2004; Irving et al., 2005; 2005; Rosen et al., 2010; 2010; Stawerski et al., 2008; Stawerski, Wagrowska-Danilewicz, Stasikowska, Gottwald, & Danilewicz, 2008; Voutilainen et al., 2006; 2006) In addition, a correlation of β-Catenin protein expression has been described with tumour grade and Ki-67 expression. (Stawerski et al., 2008; Voutilainen et al., 2006) Present results are thus confirmative of earlier findings that β-Catenin is associated with survival outcome. The consideration must be made whether this effect is not attributed to its predictive value to platinum-taxane chemotherapy rather than its prognostic value. In present study, β-Catenin had strong and consistent correlation with IGS/WHR/GGI. Although these signatures were constructed based upon different oncogenic biological processes (wound healing, stem cell phenotype, grade), their major common force has been proven to be cell proliferation. (Wirapati et al., 2008) The observation that chemosensitive patients in present analysis showed significantly higher

Similarly, the unexpected findings that increased activation of PI3K-, and RAS- pathways are more favourable for survival may be explained by their predictive value for chemotherapy. This hypothetically may have clinical consequences. Several compounds target the PI3K pathway or downstream effectors (e.g. mTOR) and are under early clinical development in epithelial ovarian cancer. Other compounds have inhibitory effects on the RAS pathway, e.g lonafarnib (a farnesyltransferase inhibitor). Recent findings of a randomised phase II trial (IGCS meeting 2010, W. Meier et al.) showed that the concomitant addition to standard chemotherapy (first line) and 6-month continuation of lonafarnib in primary epithelial ovarian cancer stage IIB-IV (n=105) resulted in borderline poorer outcome for the experimental- lonafarnib arm (overall survival HR=0.62 95CI%(0.36-1.06) p=0.08) or even resulted in significant unexpected worse outcome (p=0.01) in the experimental stratum of patients with suboptimal debulking. This finding may be relevant in the context of our results. Since increased activation of pathways as RAS and PI3K have been found to be favourable for survival outcome, the question should be asked whether inhibition of one of these pathways concomitant with chemotherapy is desirable. These pathways are driving forces of proliferation, which is an important factor in the efficacy of standard chemotherapeutics. We hypothesize that inhibition of these pathways may therefore also negatively affect the efficacy of these chemotherapies and theoretically induce chemoresistance. This would possibly be an explanation for the recent unexpected findings of lonafarnib in ovarian cancer. Hence, we theorize that these agents may have their potential in ovarian cancer in a sequential adjuvant setting rather than its concomitant

The PR pathway did not show any relevant association with IGS or GGI. It did show high significant association with survival outcome and WHR. Other immunohistochemical

(42.9%) and may be less informative here.

combination with chemotherapy.

values of GGI, WHR and IGS renders credibility to this statement.

specifically selected patients to study differential expression between chemosensitive versus chemoresistant tumours. (Bachvarov et al., 2006) This dataset therefore may represent the extremities of this disease. Interestingly this dataset showed clearly that chemosensitive patients had tumours that were more likely to be of unfavourable outcome estimated by WHR/IGS/GGI. This contradictory finding may be explained by the finding that these three prognostic signatures are all primarily associated with increased proliferation. (Wirapati et al., 2008) It is known that chemosensitive tumours have higher tumour cell proliferation indexes in serous ovarian cancer. (Itamochi et al., 2002; Têtu et al., 2008) The estimated prognostic values in this survival analysis therefore seems strongly oppositely confounded by the predictive value for platinum/taxane-based chemotherapy.

Despite the heterogeneity in datasets and confounding of predictive value versus prognostic value, the E2F1, β-Catenin and the PI3K activation scores showed overall association with survival outcome (p<0.01) and consistent significant correlations with three prognostic signatures.

Fig. 5. A Venn diagram is showing combined results of the meta-analysis: β-Catenin, E2F1, p63 and PI3K activation scores showed significant association with survival and were significantly correlated with all three prognostic signatures (WHR/IGS/GGI) after metaanalysis. PR and RAS activation scores were associated with clinical outcome, but did not consistently correlate with prognostic signatures. \*Negative correlation coefficient \*\*borderline significance with clinical outcome

The E2F1 pathway a critical role in proliferation and apoptosis. It has been shown that transcription factor E2F1 interacts with the p53 and PI3K pathway. (Hallstrom, Mori, & Nevins, 2008; Reimer et al., 2006; 2007) Its role in ovarian cancer has been unclear, as other research groups have found similar favourable survival with increased E2F1 pathway activation (Hallstrom et al., 2008), while other findings have shown favourable survival with

specifically selected patients to study differential expression between chemosensitive versus chemoresistant tumours. (Bachvarov et al., 2006) This dataset therefore may represent the extremities of this disease. Interestingly this dataset showed clearly that chemosensitive patients had tumours that were more likely to be of unfavourable outcome estimated by WHR/IGS/GGI. This contradictory finding may be explained by the finding that these three prognostic signatures are all primarily associated with increased proliferation. (Wirapati et al., 2008) It is known that chemosensitive tumours have higher tumour cell proliferation indexes in serous ovarian cancer. (Itamochi et al., 2002; Têtu et al., 2008) The estimated prognostic values in this survival analysis therefore seems strongly oppositely confounded

Despite the heterogeneity in datasets and confounding of predictive value versus prognostic value, the E2F1, β-Catenin and the PI3K activation scores showed overall association with survival outcome (p<0.01) and consistent significant correlations with three prognostic

Fig. 5. A Venn diagram is showing combined results of the meta-analysis: β-Catenin, E2F1, p63 and PI3K activation scores showed significant association with survival and were significantly correlated with all three prognostic signatures (WHR/IGS/GGI) after metaanalysis. PR and RAS activation scores were associated with clinical outcome, but did not consistently correlate with prognostic signatures. \*Negative correlation coefficient

The E2F1 pathway a critical role in proliferation and apoptosis. It has been shown that transcription factor E2F1 interacts with the p53 and PI3K pathway. (Hallstrom, Mori, & Nevins, 2008; Reimer et al., 2006; 2007) Its role in ovarian cancer has been unclear, as other research groups have found similar favourable survival with increased E2F1 pathway activation (Hallstrom et al., 2008), while other findings have shown favourable survival with

\*\*borderline significance with clinical outcome

by the predictive value for platinum/taxane-based chemotherapy.

signatures.

decreased *E2F1* gene expression by RT-PCR. (Reimer et al., 2006; 2007) It must be remarked that the latter study included an overrepresentation of patients with clear cell carcinomas (42.9%) and may be less informative here.

The β-Catenin protein is a multifunctional protein. It was originally discovered as a protein that is associated with the cytoplasmatic region of E-cadherin. E-cadherin is a transmembrane protein that is involved in cell-cell contact and cell's adhesive functions. Furthermore, β-Catenin is involved in Wnt signalling as a nuclear transcription factor and is believed to play a role in cancer stem cells. (Nusse, 2008) Loss of its membranous function or a higher nuclear presence has been linked with poor survival in several studies in ovarian cancer based upon immunohistochemical studies. (Faleiro-Rodrigues et al., 2004; Faleiro-Rodrigues, Macedo-Pinto, Pereira, & Lopes, 2004; Irving et al., 2005; 2005; Rosen et al., 2010; 2010; Stawerski et al., 2008; Stawerski, Wagrowska-Danilewicz, Stasikowska, Gottwald, & Danilewicz, 2008; Voutilainen et al., 2006; 2006) In addition, a correlation of β-Catenin protein expression has been described with tumour grade and Ki-67 expression. (Stawerski et al., 2008; Voutilainen et al., 2006) Present results are thus confirmative of earlier findings that β-Catenin is associated with survival outcome. The consideration must be made whether this effect is not attributed to its predictive value to platinum-taxane chemotherapy rather than its prognostic value. In present study, β-Catenin had strong and consistent correlation with IGS/WHR/GGI. Although these signatures were constructed based upon different oncogenic biological processes (wound healing, stem cell phenotype, grade), their major common force has been proven to be cell proliferation. (Wirapati et al., 2008) The observation that chemosensitive patients in present analysis showed significantly higher values of GGI, WHR and IGS renders credibility to this statement.

Similarly, the unexpected findings that increased activation of PI3K-, and RAS- pathways are more favourable for survival may be explained by their predictive value for chemotherapy. This hypothetically may have clinical consequences. Several compounds target the PI3K pathway or downstream effectors (e.g. mTOR) and are under early clinical development in epithelial ovarian cancer. Other compounds have inhibitory effects on the RAS pathway, e.g lonafarnib (a farnesyltransferase inhibitor). Recent findings of a randomised phase II trial (IGCS meeting 2010, W. Meier et al.) showed that the concomitant addition to standard chemotherapy (first line) and 6-month continuation of lonafarnib in primary epithelial ovarian cancer stage IIB-IV (n=105) resulted in borderline poorer outcome for the experimental- lonafarnib arm (overall survival HR=0.62 95CI%(0.36-1.06) p=0.08) or even resulted in significant unexpected worse outcome (p=0.01) in the experimental stratum of patients with suboptimal debulking. This finding may be relevant in the context of our results. Since increased activation of pathways as RAS and PI3K have been found to be favourable for survival outcome, the question should be asked whether inhibition of one of these pathways concomitant with chemotherapy is desirable. These pathways are driving forces of proliferation, which is an important factor in the efficacy of standard chemotherapeutics. We hypothesize that inhibition of these pathways may therefore also negatively affect the efficacy of these chemotherapies and theoretically induce chemoresistance. This would possibly be an explanation for the recent unexpected findings of lonafarnib in ovarian cancer. Hence, we theorize that these agents may have their potential in ovarian cancer in a sequential adjuvant setting rather than its concomitant combination with chemotherapy.

The PR pathway did not show any relevant association with IGS or GGI. It did show high significant association with survival outcome and WHR. Other immunohistochemical

Oncogenic Pathway Signatures and Survival Outcome 117

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#### **7. Conclusions**

To conclude, oncogenic pathway profiling of advanced serous ovarian tumours revealed that it is difficult to estimate the true prognostic value of a pathway since there seems confounding of predictive factors. Despite these biases, with a meta-analysis approach of 6 independent datasets generated on different micro-array platforms, we found that a PR and RAS activation score was associated with clinical outcome. Activation scores for β-Catenin, p63, E2F1 and PI3K were also associated with survival and were consistently correlated with three prognostic gene signatures.

#### **8. References**


studies have shown that the PR protein expression has predictive of prognostic value, more than the expression of ER. (Hah et al., 2011; Høgdall et al., 2007; Tangjitgamol, Manusirivithaya, Khunnarong, Jesadapatarakul, & Tanwanich, 2009; X.-Y. Yang, Xi, K.-X. Yang, & Yu, 2009) Since PR expression is a downstream target of the ER pathway, this finding may indicate that an active ER pathway, rather than the expression of ER by itself may be of importance. Anti-hormonal therapies have shown anti-tumoural activity in relapsed/refractory ovarian cancer in phase II studies. (del Carmen et al., 2003; Papadimitriou et al., 2004; Smyth et al., 2007; C. J. Williams, 2001; C. Williams, Simera, & Bryant, 2010) Biomarker studies have shown that increasing ER expression was associated with increasing CA125 response rate. (Smyth et al., 2007) We suggest that further studies are needed to study if PR expression may add value as a suitable biomarker to select patients

To conclude, oncogenic pathway profiling of advanced serous ovarian tumours revealed that it is difficult to estimate the true prognostic value of a pathway since there seems confounding of predictive factors. Despite these biases, with a meta-analysis approach of 6 independent datasets generated on different micro-array platforms, we found that a PR and RAS activation score was associated with clinical outcome. Activation scores for β-Catenin, p63, E2F1 and PI3K were also associated with survival and were consistently correlated with

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**7** 

*USA* 

**Dysregulated TGF Signaling in Ovarian Cancer** 

Ovarian cancer is one of the most lethal gynecological cancers in the United States. NCI estimates ~21,880 new cases with ~13,850 deaths in 2011 (http://www.cancer.gov/ cancertopics/ types/ovariancancer). Unfortunately, the majority of these cases are only discovered at advanced stages (stage III or IV) due to the cancer's asymptomatic nature which has an overall survival rate between 5-25% (Bast et al., 2009; Hennessy et al., 2008). Hence, the inability to detect this disease during early stages has led to poor prognosis. Despite improvements in medicine and patient care, reasonable screening measures for detecting early stage ovarian cancers are presently lacking. Thus, a better understanding of the molecular

The current strategy for treatment of ovarian cancer is surgical debulking followed by chemotherapy (Bast et al., 2009; Hennessy et al., 2008). Although ~70% of ovarian cancers respond to a combination of platinum and taxane-based chemotherapy administered after surgery, current treatments are of limited efficacy in preventing tumor recurrence and progression (Bast et al., 2009; Hennessy et al., 2008). Thus, new anti-neoplastic agents are

Recently, evidence has emerged revealing the importance of genomic aberrations in the progression of ovarian cancer (Gorringe & Campbell, 2009; Gray et al., 2003). Through the use of high throughput technologies (i.e. array comparative genomic hybridization (aCGH), microarray, and SNP arrays), specific genomic regions have been identified to be either amplified or silenced in tumor progression (Gorringe & Campbell, 2009; Gray et al., 2003). One such region which we and others (Nanjundan et al., 2007; Osterberg et al., 2009) have previously identified to be frequently amplified early in serous epithelial ovarian cancer development is the 3q26.2 region which harbors Transforming Growth Factor pathway (TGF) co-repressors, ecotropic viral integration site-1 (EVI1) (Nanjundan et al., 2007) and SnoN/SkiL (Nanjundan et al., 2008). A large amount of work has recently emerged involving the intricacies of TGF signaling and its role in cancer progression. Importantly,

There exist three isoforms of TGF, namely TGF1, TGF2, and TGF3, which are initially present in the inactive latent form (L-TGF) (Elliott & Blobe, 2005; Meulmeester & Ten Dijke,

urgently needed to increase the chemotherapeutic sensitivity of ovarian cancer cells.

events that underlie ovarian cancer development are needed.

this signaling pathway is dysregulated in ovarian carcinomas.

**2. Dual functionality of TGF signaling in cancer** 

**1. Introduction** 

Kyle Bauckman2, Christie Campla1 and Meera Nanjundan1,2

*1University of South Florida, Department of Cell Biology,* 

*2Moffitt Cancer Center, Cancer Biology Program, Tampa, Florida* 

*Microbiology, and Molecular Biology,* 

unified understanding of breast cancer subtyping and prognosis signatures *Breast cancer research : BCR*, *10*(4), R65. doi:10.1186/bcr2124


### **Dysregulated TGF Signaling in Ovarian Cancer**

Kyle Bauckman2, Christie Campla1 and Meera Nanjundan1,2 *1University of South Florida, Department of Cell Biology,* 

*Microbiology, and Molecular Biology, 2Moffitt Cancer Center, Cancer Biology Program, Tampa, Florida USA* 

#### **1. Introduction**

120 Ovarian Cancer – Basic Science Perspective

Yang, X.-Y., Xi, M.-R., Yang, K.-X., & Yu, H. (2009). Prognostic value of estrogen receptor

*Gynecologic oncology*, *113*(1), 99–104. doi:10.1016/j.ygyno.2008.12.018 Yoshihara, K., Tajima, A., Yahata, T., Kodama, S., Fujiwara, H., Suzuki, M., Onishi, Y., et al.

*cancer research : BCR*, *10*(4), R65. doi:10.1186/bcr2124

doi:10.1371/journal.pone.0009615

unified understanding of breast cancer subtyping and prognosis signatures *Breast* 

and progesterone receptor status in young Chinese ovarian carcinoma patients

(2010). Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets. *PLoS ONE*, *5*(3), e9615.

> Ovarian cancer is one of the most lethal gynecological cancers in the United States. NCI estimates ~21,880 new cases with ~13,850 deaths in 2011 (http://www.cancer.gov/ cancertopics/ types/ovariancancer). Unfortunately, the majority of these cases are only discovered at advanced stages (stage III or IV) due to the cancer's asymptomatic nature which has an overall survival rate between 5-25% (Bast et al., 2009; Hennessy et al., 2008). Hence, the inability to detect this disease during early stages has led to poor prognosis. Despite improvements in medicine and patient care, reasonable screening measures for detecting early stage ovarian cancers are presently lacking. Thus, a better understanding of the molecular events that underlie ovarian cancer development are needed.

> The current strategy for treatment of ovarian cancer is surgical debulking followed by chemotherapy (Bast et al., 2009; Hennessy et al., 2008). Although ~70% of ovarian cancers respond to a combination of platinum and taxane-based chemotherapy administered after surgery, current treatments are of limited efficacy in preventing tumor recurrence and progression (Bast et al., 2009; Hennessy et al., 2008). Thus, new anti-neoplastic agents are urgently needed to increase the chemotherapeutic sensitivity of ovarian cancer cells.

> Recently, evidence has emerged revealing the importance of genomic aberrations in the progression of ovarian cancer (Gorringe & Campbell, 2009; Gray et al., 2003). Through the use of high throughput technologies (i.e. array comparative genomic hybridization (aCGH), microarray, and SNP arrays), specific genomic regions have been identified to be either amplified or silenced in tumor progression (Gorringe & Campbell, 2009; Gray et al., 2003). One such region which we and others (Nanjundan et al., 2007; Osterberg et al., 2009) have previously identified to be frequently amplified early in serous epithelial ovarian cancer development is the 3q26.2 region which harbors Transforming Growth Factor pathway (TGF) co-repressors, ecotropic viral integration site-1 (EVI1) (Nanjundan et al., 2007) and SnoN/SkiL (Nanjundan et al., 2008). A large amount of work has recently emerged involving the intricacies of TGF signaling and its role in cancer progression. Importantly, this signaling pathway is dysregulated in ovarian carcinomas.

#### **2. Dual functionality of TGF signaling in cancer**

There exist three isoforms of TGF, namely TGF1, TGF2, and TGF3, which are initially present in the inactive latent form (L-TGF) (Elliott & Blobe, 2005; Meulmeester & Ten Dijke,

Dysregulated TGF Signaling in Ovarian Cancer 123

The TGF signaling pathway has the ability to transition from a tumor suppressor (in normal or early stage cancers) to a tumor promoter (late stages of cancer) (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011) (Figure 2). During the early stages of epithelial tumorigenesis, TGFβ inhibits tumor development and growth by inducing cell cycle arrest, senescence, and apoptosis; this aids in maintaining cellular homeostasis critical for prevention of continuous cell proliferation and thus tumor formation (Elliott & Blobe, 2005). This functionality is elicited via induction of cyclin-dependent kinase inhibitors (CDK), namely p15, p21, and p27. TGF also represses expression of the c-myc oncogene which leads to activation of these CDK inhibitors (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011). Additional molecules that are involved in the TGF apoptotic functional response include the death receptor FAS, GADD45b, BIM, and DAPK (Elliott & Blobe, 2005;

Fig. 2. The TGF signaling pathway elicits dual functionality. The pathway can transition from a tumor suppressive (normal or early stages of cancer) role to a tumor promoting role

During the progression of cancer, mutations in these components may lead to disruption of TGF mediated control of cell proliferation. In late stages of tumor progression, tumor cells become resistant to growth inhibition due to inactivation of the TGFβ signaling pathway thus leading to altered cell cycle control (Elliott & Blobe, 2005). TGF becomes capable of inducing metastatic functions via increased cellular migration, invasiveness, loss of epithelial markers, and a corresponding acquisition of mesenchymal

Meulmeester & Ten Dijke, 2011).

(late stages of cancer).

characteristics.

2011). In its active dimeric form, the TGF ligand binds to the TGF receptor type II (TGFRII) leading to heterotetrameric receptor complex formation with TGFRI. In addition, the coreceptor, TGFRIII or proteoglycan (a.k.a. endoglin), aids binding of the ligand to the TGFRII (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011). The activated receptors then recruit receptor regulated SMADs (R-SMADs) such as SMAD2/3 which form a complex with a Co-SMAD, SMAD4, and then shuttles into the nucleus. These activated SMADs associate with DNA binding transcription factors to enhance DNA binding to regulate transcription of TGFβ target genes such as cyclin-dependent kinase inhibitors (i.e. p21, involved in regulating cell survival) (Elliott & Blobe, 2005) (Figure 1). The TGF pathway is regulated via several mechanisms including (1) phosphorylation, (2) ubiquitination, (3) inhibitory SMADs (i.e. SMAD6 and SMAD7), and (4) transcriptional co-repressors (i.e. SnoN/SkiL and EVI1) (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011). In addition to the canonical SMAD dependent pathway, there exists the non-canonical pathway involving (1) TRAF5/TAK1/p38-JNK, (2) RhoA/ROCK, and (3) ERK/MAPK (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011).

Fig. 1. The TGF signaling pathway. Active dimeric TGF ligand binds to TGFRII on the cell surface leading to complex formation with TGFRI. Endoglin (TGFRIII) assists in recruitment of the active TGF ligand to bind to the cell surface receptors. Following receptor activation, receptor SMADs (SMAD2/3) become phosphorylated and form a complex with the Co-SMAD (SMAD4) which then translocate into the nuclear compartment to regulate transcription of various TGF target genes.

2011). In its active dimeric form, the TGF ligand binds to the TGF receptor type II (TGFRII) leading to heterotetrameric receptor complex formation with TGFRI. In addition, the coreceptor, TGFRIII or proteoglycan (a.k.a. endoglin), aids binding of the ligand to the TGFRII (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011). The activated receptors then recruit receptor regulated SMADs (R-SMADs) such as SMAD2/3 which form a complex with a Co-SMAD, SMAD4, and then shuttles into the nucleus. These activated SMADs associate with DNA binding transcription factors to enhance DNA binding to regulate transcription of TGFβ target genes such as cyclin-dependent kinase inhibitors (i.e. p21, involved in regulating cell survival) (Elliott & Blobe, 2005) (Figure 1). The TGF pathway is regulated via several mechanisms including (1) phosphorylation, (2) ubiquitination, (3) inhibitory SMADs (i.e. SMAD6 and SMAD7), and (4) transcriptional co-repressors (i.e. SnoN/SkiL and EVI1) (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011). In addition to the canonical SMAD dependent pathway, there exists the non-canonical pathway involving (1) TRAF5/TAK1/p38-JNK, (2) RhoA/ROCK, and (3) ERK/MAPK (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011).

Fig. 1. The TGF signaling pathway. Active dimeric TGF ligand binds to TGFRII on the cell surface leading to complex formation with TGFRI. Endoglin (TGFRIII) assists in recruitment of the active TGF ligand to bind to the cell surface receptors. Following receptor activation, receptor SMADs (SMAD2/3) become phosphorylated and form a complex with the Co-SMAD (SMAD4) which then translocate into the nuclear compartment

to regulate transcription of various TGF target genes.

The TGF signaling pathway has the ability to transition from a tumor suppressor (in normal or early stage cancers) to a tumor promoter (late stages of cancer) (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011) (Figure 2). During the early stages of epithelial tumorigenesis, TGFβ inhibits tumor development and growth by inducing cell cycle arrest, senescence, and apoptosis; this aids in maintaining cellular homeostasis critical for prevention of continuous cell proliferation and thus tumor formation (Elliott & Blobe, 2005). This functionality is elicited via induction of cyclin-dependent kinase inhibitors (CDK), namely p15, p21, and p27. TGF also represses expression of the c-myc oncogene which leads to activation of these CDK inhibitors (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011). Additional molecules that are involved in the TGF apoptotic functional response include the death receptor FAS, GADD45b, BIM, and DAPK (Elliott & Blobe, 2005; Meulmeester & Ten Dijke, 2011).

Fig. 2. The TGF signaling pathway elicits dual functionality. The pathway can transition from a tumor suppressive (normal or early stages of cancer) role to a tumor promoting role (late stages of cancer).

During the progression of cancer, mutations in these components may lead to disruption of TGF mediated control of cell proliferation. In late stages of tumor progression, tumor cells become resistant to growth inhibition due to inactivation of the TGFβ signaling pathway thus leading to altered cell cycle control (Elliott & Blobe, 2005). TGF becomes capable of inducing metastatic functions via increased cellular migration, invasiveness, loss of epithelial markers, and a corresponding acquisition of mesenchymal characteristics.

Dysregulated TGF Signaling in Ovarian Cancer 125

Decreased expression of SMAD4 has been described in several ovarian cancer cell lines (Hu et al., 2000) which appears to correlate with dysregulated expression of p21 and c-myc

Unlike pancreatic cancer in which ~50% of SMAD4 is mutated (Elliott & Blobe, 2005), reports of the presence of SMAD4 variants in ovarian cancers are lacking (Wang et al., 2000b). However, mutational analysis of additional SMAD family members showed that 35% and 23%, respectively, of ovarian tumor specimens contained a polymorphism in intron 2 of SMAD6 and a polymorphism at codon 208 in SMAD7. Neither of these mutations were associated with amino acid changes and thus, are unlikely to be important in ovarian cancer development (Wang et al., 2000a). Similarly, 42% of ovarian tumor specimens had a polymorphism for SMAD2 which was not associated with an amino acid change; thus it is also unlikely that this mutation is significant in the development of ovarian cancer (Wang et

**3.4 TGF transcriptional co-regulator/co-factor expression in ovarian cancer** 

transcriptional regulator, is upregulated in ovarian cancers (Garte, 1993).

**3.5 Other TGF mediator expression in ovarian cancer** 

and is repressed by histone modifications (Yeh et al., 2011).

**4. SnoN/SkiL, a TGF transcriptional modulator, in ovarian cancer** 

SnoN/SkiL belongs to the Ski family (i.e. Ski, SnoN, Fussel-15, and Fussel-18), a group of proto-oncogenes involved in early developmental processes sharing structural and

Ecotropic viral integration site-1 (EVI1), a TGF corepressor, was elevated up to 40-fold in ovarian carcinoma cells via RNAse protection assay (Brooks et al., 1996). Similarly, via microarray analysis and qPCR validation, EVI1 was found to be upregulated in advanced stage ovarian cancers (Sunde et al., 2006). In our analysis, we identified that EVI1 and MDS1/EVI1 are amplified in advanced stage serous epithelial ovarian cancers at the DNA, RNA, and protein levels via aCGH, transcriptional profiling/qPCR analysis, and western blot analysis (Nanjundan et al., 2007). Further, SnoN/SkiL, another TGF corepressor, is likewise increased at the DNA and RNA levels (cCGH and qPCR) in advanced stage serous epithelial ovarian cancers (Nanjundan et al., 2008). In addition, c-myc, an oncogenic

Other TGF mediators whose expression is altered in ovarian cancers include DACH1 and BMP7 which are both upregulated and inhibit TGF signaling (Sunde et al., 2006). Mediators in ovarian cancers that are downregulated include PCAF and TFE3 (which enhance TGF signaling) (Sunde et al., 2006). Other molecules that could attenuate TGFproliferative control include FOXG1 which is overexpressed in high-grade ovarian cancers and suppresses p21 WAF1/CIP1 transcription (Chan et al., 2009). BAMBI (BMP and activin membrane-bound inhibitor) is overexpressed in ovarian cancers promoting resistance to TGF mediated apoptosis by shuttling into the nuclear compartment with SMAD2/3 in a TGF dependent manner (Pils et al., 2011). EZH2 is increased in ovarian cancers and appears to be involved in altering metastatic potential by upregulating TGF1 (Rao et al., 2010). A SMAD4 target gene, RunX1T1, is a tumor suppressor in ovarian cancers

**3.3 SMAD expression in ovarian cancer** 

(Antony et al., 2010).

al., 1999).

#### **3. Dysregulated TGF signaling in ovarian cancer**

Several components of the TGFsignaling pathway have been reported to be dysregulated in ovarian cancers and are summarized in the subsections below.

#### **3.1 TGF ligand expression in ovarian cancer**

By immunohistochemical and in situ hybridization approaches, all of the three TGF ligands (TGF1, TGF2, and TGF3) are markedly elevated in ovarian cancer cells (Henriksen et al., 1995). Similar results were obtained via RNAse protection assay in primary ovarian cancer specimens (Bartlett et al., 1997). Northern blot analysis indicated that mRNA levels of both TGF1 and TGF3 are increased in recurrent ovarian cancers (Bristow et al., 1999). Using enzyme-linked immunosorbant assay (ELISA), TGF1 levels are increased in plasma and peritoneal fluid of advanced stage ovarian cancer patients (Santin et al., 2001). Increased expression of TGF appears to be correlated with a poor patient survival outcome which is associated with peritoneal metastasis, expression of vascular endothelial growth factor (VEGF), and microvessel density (markers of angiogenesis) (Nakanishi et al., 1997).

Mutational analysis of TGF1 assessed by PCR-SSCP (polymerase chain reaction singlestrand conformational polymorphism) uncovered defects in the coding region of exons 5, 6, and 7 (Cardillo et al., 1997). However, these alterations were not associated with histological type of the tumor or its transcript/protein expression levels (Cardillo et al., 1997).

#### **3.2 TGF receptor expression in ovarian cancer**

There appears to be some discrepancy in the reported levels of TGF receptors in ovarian cancer which may be due to the nature of the cell lines and tumor specimens assessed. In one study, the proximal components of the TGF signaling pathway (receptor expression and its phosphorylation status) appeared to be intact in primary ovarian cancer cell cultures; this indicated that downstream mechanisms could be responsible for growth resistance to TGF such as increased matrix metalloproteinase-2 (MMP2) expression (Yamada et al., 1999). Yet in another report, TGFRII transcripts were undetectable in TGF resistant ovarian cancer cell lines (AZ224 and AZ547) whereas SKOV3 cells were positive for TGFRII expression (Zeinoun et al., 1999). TGFRII was also detectable in an additional 14 ovarian cancer cell lines (Hu et al., 2000) (Xi et al., 2004). A more recent study reported reduced TGFRII levels which was determined via microarray analysis and validated via real-time PCR (qPCR) (Sunde et al., 2006).

Via northern blot analysis, expression of TGFRI and TGFRIII was markedly reduced in recurrent ovarian tumors (Bristow et al., 1999). In an independent study, TGFRIII was notably decreased or absent in ovarian cancers at the RNA and protein levels (Hempel et al., 2007).

Mutational analysis of TGFRI and TGFRII uncovered mutations in a minority of ovarian cancers (Ding et al., 2005). Specifically, a frameshift mutation has been identified in Exon 5 of TGFRI in 31% of ovarian tumors (Wang et al., 2000b), in exons 2, 3, 4, and 6 of TGFRI (catalytic domain of the kinase) in 33% primary ovarian cancers (Chen et al., 2001), and deletions in exon 1 of TGFRI in <30% of ovarian tumors (Antony et al., 2010). Likewise, missense mutations have been identified in TGFRII (Francis-Thickpenny et al., 2001) and deletions in exon 3 of TGFRII in ovarian tumors (Antony et al., 2010).

Several components of the TGFsignaling pathway have been reported to be dysregulated

By immunohistochemical and in situ hybridization approaches, all of the three TGF ligands (TGF1, TGF2, and TGF3) are markedly elevated in ovarian cancer cells (Henriksen et al., 1995). Similar results were obtained via RNAse protection assay in primary ovarian cancer specimens (Bartlett et al., 1997). Northern blot analysis indicated that mRNA levels of both TGF1 and TGF3 are increased in recurrent ovarian cancers (Bristow et al., 1999). Using enzyme-linked immunosorbant assay (ELISA), TGF1 levels are increased in plasma and peritoneal fluid of advanced stage ovarian cancer patients (Santin et al., 2001). Increased expression of TGF appears to be correlated with a poor patient survival outcome which is associated with peritoneal metastasis, expression of vascular endothelial growth factor (VEGF), and microvessel density (markers of

Mutational analysis of TGF1 assessed by PCR-SSCP (polymerase chain reaction singlestrand conformational polymorphism) uncovered defects in the coding region of exons 5, 6, and 7 (Cardillo et al., 1997). However, these alterations were not associated with histological

There appears to be some discrepancy in the reported levels of TGF receptors in ovarian cancer which may be due to the nature of the cell lines and tumor specimens assessed. In one study, the proximal components of the TGF signaling pathway (receptor expression and its phosphorylation status) appeared to be intact in primary ovarian cancer cell cultures; this indicated that downstream mechanisms could be responsible for growth resistance to TGF such as increased matrix metalloproteinase-2 (MMP2) expression (Yamada et al., 1999). Yet in another report, TGFRII transcripts were undetectable in TGF resistant ovarian cancer cell lines (AZ224 and AZ547) whereas SKOV3 cells were positive for TGFRII expression (Zeinoun et al., 1999). TGFRII was also detectable in an additional 14 ovarian cancer cell lines (Hu et al., 2000) (Xi et al., 2004). A more recent study reported reduced TGFRII levels which was determined via microarray analysis and validated via

Via northern blot analysis, expression of TGFRI and TGFRIII was markedly reduced in recurrent ovarian tumors (Bristow et al., 1999). In an independent study, TGFRIII was notably decreased or absent in ovarian cancers at the RNA and protein levels (Hempel et al.,

Mutational analysis of TGFRI and TGFRII uncovered mutations in a minority of ovarian cancers (Ding et al., 2005). Specifically, a frameshift mutation has been identified in Exon 5 of TGFRI in 31% of ovarian tumors (Wang et al., 2000b), in exons 2, 3, 4, and 6 of TGFRI (catalytic domain of the kinase) in 33% primary ovarian cancers (Chen et al., 2001), and deletions in exon 1 of TGFRI in <30% of ovarian tumors (Antony et al., 2010). Likewise, missense mutations have been identified in TGFRII (Francis-Thickpenny et al., 2001) and

deletions in exon 3 of TGFRII in ovarian tumors (Antony et al., 2010).

type of the tumor or its transcript/protein expression levels (Cardillo et al., 1997).

**3. Dysregulated TGF signaling in ovarian cancer** 

**3.1 TGF ligand expression in ovarian cancer** 

angiogenesis) (Nakanishi et al., 1997).

**3.2 TGF receptor expression in ovarian cancer** 

real-time PCR (qPCR) (Sunde et al., 2006).

2007).

in ovarian cancers and are summarized in the subsections below.

#### **3.3 SMAD expression in ovarian cancer**

Decreased expression of SMAD4 has been described in several ovarian cancer cell lines (Hu et al., 2000) which appears to correlate with dysregulated expression of p21 and c-myc (Antony et al., 2010).

Unlike pancreatic cancer in which ~50% of SMAD4 is mutated (Elliott & Blobe, 2005), reports of the presence of SMAD4 variants in ovarian cancers are lacking (Wang et al., 2000b). However, mutational analysis of additional SMAD family members showed that 35% and 23%, respectively, of ovarian tumor specimens contained a polymorphism in intron 2 of SMAD6 and a polymorphism at codon 208 in SMAD7. Neither of these mutations were associated with amino acid changes and thus, are unlikely to be important in ovarian cancer development (Wang et al., 2000a). Similarly, 42% of ovarian tumor specimens had a polymorphism for SMAD2 which was not associated with an amino acid change; thus it is also unlikely that this mutation is significant in the development of ovarian cancer (Wang et al., 1999).

#### **3.4 TGF transcriptional co-regulator/co-factor expression in ovarian cancer**

Ecotropic viral integration site-1 (EVI1), a TGF corepressor, was elevated up to 40-fold in ovarian carcinoma cells via RNAse protection assay (Brooks et al., 1996). Similarly, via microarray analysis and qPCR validation, EVI1 was found to be upregulated in advanced stage ovarian cancers (Sunde et al., 2006). In our analysis, we identified that EVI1 and MDS1/EVI1 are amplified in advanced stage serous epithelial ovarian cancers at the DNA, RNA, and protein levels via aCGH, transcriptional profiling/qPCR analysis, and western blot analysis (Nanjundan et al., 2007). Further, SnoN/SkiL, another TGF corepressor, is likewise increased at the DNA and RNA levels (cCGH and qPCR) in advanced stage serous epithelial ovarian cancers (Nanjundan et al., 2008). In addition, c-myc, an oncogenic transcriptional regulator, is upregulated in ovarian cancers (Garte, 1993).

#### **3.5 Other TGF mediator expression in ovarian cancer**

Other TGF mediators whose expression is altered in ovarian cancers include DACH1 and BMP7 which are both upregulated and inhibit TGF signaling (Sunde et al., 2006). Mediators in ovarian cancers that are downregulated include PCAF and TFE3 (which enhance TGF signaling) (Sunde et al., 2006). Other molecules that could attenuate TGFproliferative control include FOXG1 which is overexpressed in high-grade ovarian cancers and suppresses p21 WAF1/CIP1 transcription (Chan et al., 2009). BAMBI (BMP and activin membrane-bound inhibitor) is overexpressed in ovarian cancers promoting resistance to TGF mediated apoptosis by shuttling into the nuclear compartment with SMAD2/3 in a TGF dependent manner (Pils et al., 2011). EZH2 is increased in ovarian cancers and appears to be involved in altering metastatic potential by upregulating TGF1 (Rao et al., 2010). A SMAD4 target gene, RunX1T1, is a tumor suppressor in ovarian cancers and is repressed by histone modifications (Yeh et al., 2011).

#### **4. SnoN/SkiL, a TGF transcriptional modulator, in ovarian cancer**

SnoN/SkiL belongs to the Ski family (i.e. Ski, SnoN, Fussel-15, and Fussel-18), a group of proto-oncogenes involved in early developmental processes sharing structural and

Dysregulated TGF Signaling in Ovarian Cancer 127

in mouse cancer models; it has been well studied as an oncogene in acute myeloid leukemia (AML) and in myelodysplastic syndrome (MDS) (Levy et al., 1994; Morishita et al., 1992b). Functions of EVI1 include (1) proliferation of leukemic cells (Tanaka et al., 1995), (2) cellular transformation (Kilbey & Bartholomew, 1998), (3) inhibition of growth factor mediated differentiation and survival (Morishita et al., 1992a), (4) induction of neural and megakaryocyte differentiation, and (5) inhibition of interferon (Buonamici et al., 2005) and TGF signaling (Izutsu et al., 2001; Soderholm et al., 1997; Sood et al., 1999; Vinatzer et al., 2003; Vinatzer et al., 2001). Notably, EVI1 represses transcription via binding to SMADs and recruiting CtBP1/HDAC (Izutsu et al., 2001; Palmer et al., 2001; Senyuk et al., 2002) to target promoter elements, increasing AP-1 activity (Tanaka et al., 1994), disrupting JNK induced apoptosis (Maki et al., 2008), inhibiting PML function (Buonamici et al., 2005), binding to BRG1 (Chi et al., 2003), and activating PI3K by reducing TGF and drug induced apoptosis (Liu et al., 2006; Yoshimi et al., 2011). Supporting its role as an inducer of cellular proliferation, EVI1 knockout mice are embryonically lethal due to hypocellularity across multiple organ sites (Hoyt et al., 1997). There exist multiple splice variants of EVI1 whose functions are presently unclear (Alzuherri et al., 2006; Jazaeri et al., 2010; Vinatzer et al., 2003). In particular, the MDS1/EVI1 is a read-through splice form which contains a novel PR (PRD1-BF1-RIZ homology) domain; its functionality is unclear and is suggested to be context or cell type dependent (either eliciting functionality similar or antagonistic to EVI1 (Vinatzer et al., 2003). Structurally, EVI1 contains 2 zinc finger domains, an intervening region required for transformation, and a repressor domain necessary for binding to

In ovarian cancer, the first report of altered EVI1 expression in ovarian carcinoma cells demonstrated up to a 40-fold increase in its mRNA levels via RNAse protection assay compared to the normal ovary; these initial findings implicate a novel role for EVI1 in solid tumor carcinogenesis (Brooks et al., 1996). A decade later, increased EVI1 levels in advanced stage ovarian cancers supported these initial findings via oligonucleotide arrays profiling and validation via qPCR analysis (Sunde et al., 2006). The same researchers also found that the EVI1 gene locus was amplified in 43% of the tumors with a significant correlation between gene copy and EVI1 gene expression levels (Sunde et al., 2006). They also reported that EVI1 inhibited TGF signaling in normal immortalized ovarian epithelial cells (Sunde et al., 2006). Our research has also uncovered increased copy number at the EVI1 locus in advanced stage serous epithelial ovarian carcinomas via aCGH analysis (Nanjundan et al., 2007). We found that EVI1 DNA copy number increases were associated with at least a 5-fold increase in RNA transcript levels in the majority of advanced ovarian cancers (Nanjundan et al., 2007). More recent whole genome aCGH analysis of stage III ovarian serous carcinomas also identified a gain at 3q26.2 with their gene expression analysis demonstrating elevated EVI1 expression (Osterberg et al., 2009). Protein level determination via western blotting analysis showed a corresponding increase in MDS1/EVI1 and EVI1 expression in ovarian cancers and multiple ovarian cancer cell lines (Nanjundan et al., 2007). Interestingly, functional studies by transient transfection into normal immortalized epithelial cells demonstrated that EVI1 and MDS1/EVI1 increased cell proliferation, migration, and decreased TGF-mediated plasminogen activator inhibitor-1 (PAI-1) promoter activity (Nanjundan et al., 2007). In yet another recent study, highest expression of EVI1 and a splice variant, Del324 (EVI1s), was observed in ovarian cancer specimens with a constant ratio between the two splice

CtBP1/HDAC (Nanjundan et al., 2007).

functional features characteristic of winged-helix/forkhead class of DNA binding proteins (Deheuninck & Luo, 2009). However, these proteins do not directly bind to DNA but associate to DNA via interaction via nuclear proteins (i.e. SMADs) (Deheuninck & Luo, 2009). Thus, the mechanism of repression of TGF signaling occurs by transcriptional modulation by recruitment of nuclear corepressors (i.e. N-CoR), histone deacetylase complex (HDAC), and interference of SMAD-mediated binding to the transcriptional coactivator, p300/CBP (Deheuninck & Luo, 2009). Ski and SnoN genes have an overall homology of 50% and both are tightly regulated at multiple levels via: (1) transcriptional regulation, (2) protein degradation, (3) post-translational modifications, and (4) subcellular localization (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). SnoN is not only phosphorylated by TGF activating kinase (TAK1) but can also physically associate with TAK1 leading to SnoN degradation (Kajino et al., 2007). SnoN is degraded rapidly via proteasome-mediated degradation upon TGF stimulation via SMURF2, APC, and Arkadia (RNF111) E3 ubiquitin ligases (Inoue & Imamura, 2008; Izzi & Attisano, 2004; Levy et al., 2007). SnoN can also interact with promyelocytic leukemia protein (PML) which promotes its association with PML nuclear bodies to stabilize p53 leading to induction of premature senescence (Lamouille & Derynck, 2009; Pan et al., 2009). SnoN can be sumoylated via SUMO E3 ligase PIAS independently of TGF signaling and its ubiquitination status (Hsu et al., 2006; Wrighton et al., 2007). Although sumoylation does not alter its stability or subcellular localization, it may augment SnoN-mediated repression of TGF signaling on specific promoters such as the myogenin promoter (Hsu et al., 2006). Although SnoN is predominantly nuclear localized, it can be cytoplasmically localized in normal cells under non-pathological conditions (Krakowski et al., 2005).

Both Ski and SnoN are expressed in all adult tissues at low levels and are involved in differentiation of neural and muscle cells (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). Expression of Ski and SnoN are altered in numerous disease states including cancer (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). Our research indicates that SnoN levels are upregulated in serous epithelial ovarian cancers via different mechanisms including gene amplification, altered protein stability, and transcriptional activation (Nanjundan et al., 2008). Further, siRNA targeting SnoN leads to reduction in ovarian cancer cell proliferation implicating a pro-oncogenic function (Nanjundan et al., 2008; Smith et al., 2010). In addition, attenuated SnoN protein via siRNA is detrimental to breast and lung cancer cellular transformation in both *in vitro* and *in vivo* mouse xenograft models (Zhu et al., 2007). Strikingly, SnoN has also been implicated in a tumor suppressive function. Deletion of one copy of SnoN leads to increased susceptibility to carcinogen-induced tumor development (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). Furthermore, long-term stable expression of SnoN in an ovarian cell line leads to induction of senescence (i.e. oncogene-induced senescence similar to that described for Ras) (Nanjundan et al., 2008). In another study, SnoN induces premature senescence in a PML and p53–dependent fashion; it also inhibits epithelial-mesenchymal transition (EMT) and tumor metastasis in breast and lung cancer cells (Pan et al., 2009; Zhu et al., 2007). Collectively, these findings suggest that SnoN elicits multiple roles in cancer development.

#### **5. EVI1, a TGF transcriptional modulator, in ovarian cancer**

EVI1, ecotropic viral integration site-1 protein, now called MECOM (MDS1 and EVI1 complex) is located at the 3q26.2 locus. It was initially identified as a site for viral integration

functional features characteristic of winged-helix/forkhead class of DNA binding proteins (Deheuninck & Luo, 2009). However, these proteins do not directly bind to DNA but associate to DNA via interaction via nuclear proteins (i.e. SMADs) (Deheuninck & Luo, 2009). Thus, the mechanism of repression of TGF signaling occurs by transcriptional modulation by recruitment of nuclear corepressors (i.e. N-CoR), histone deacetylase complex (HDAC), and interference of SMAD-mediated binding to the transcriptional coactivator, p300/CBP (Deheuninck & Luo, 2009). Ski and SnoN genes have an overall homology of 50% and both are tightly regulated at multiple levels via: (1) transcriptional regulation, (2) protein degradation, (3) post-translational modifications, and (4) subcellular localization (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). SnoN is not only phosphorylated by TGF activating kinase (TAK1) but can also physically associate with TAK1 leading to SnoN degradation (Kajino et al., 2007). SnoN is degraded rapidly via proteasome-mediated degradation upon TGF stimulation via SMURF2, APC, and Arkadia (RNF111) E3 ubiquitin ligases (Inoue & Imamura, 2008; Izzi & Attisano, 2004; Levy et al., 2007). SnoN can also interact with promyelocytic leukemia protein (PML) which promotes its association with PML nuclear bodies to stabilize p53 leading to induction of premature senescence (Lamouille & Derynck, 2009; Pan et al., 2009). SnoN can be sumoylated via SUMO E3 ligase PIAS independently of TGF signaling and its ubiquitination status (Hsu et al., 2006; Wrighton et al., 2007). Although sumoylation does not alter its stability or subcellular localization, it may augment SnoN-mediated repression of TGF signaling on specific promoters such as the myogenin promoter (Hsu et al., 2006). Although SnoN is predominantly nuclear localized, it can be cytoplasmically localized in normal cells under

Both Ski and SnoN are expressed in all adult tissues at low levels and are involved in differentiation of neural and muscle cells (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). Expression of Ski and SnoN are altered in numerous disease states including cancer (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). Our research indicates that SnoN levels are upregulated in serous epithelial ovarian cancers via different mechanisms including gene amplification, altered protein stability, and transcriptional activation (Nanjundan et al., 2008). Further, siRNA targeting SnoN leads to reduction in ovarian cancer cell proliferation implicating a pro-oncogenic function (Nanjundan et al., 2008; Smith et al., 2010). In addition, attenuated SnoN protein via siRNA is detrimental to breast and lung cancer cellular transformation in both *in vitro* and *in vivo* mouse xenograft models (Zhu et al., 2007). Strikingly, SnoN has also been implicated in a tumor suppressive function. Deletion of one copy of SnoN leads to increased susceptibility to carcinogen-induced tumor development (Deheuninck & Luo, 2009; Luo, 2004; Pan et al., 2009). Furthermore, long-term stable expression of SnoN in an ovarian cell line leads to induction of senescence (i.e. oncogene-induced senescence similar to that described for Ras) (Nanjundan et al., 2008). In another study, SnoN induces premature senescence in a PML and p53–dependent fashion; it also inhibits epithelial-mesenchymal transition (EMT) and tumor metastasis in breast and lung cancer cells (Pan et al., 2009; Zhu et al., 2007). Collectively, these findings suggest that

non-pathological conditions (Krakowski et al., 2005).

SnoN elicits multiple roles in cancer development.

**5. EVI1, a TGF transcriptional modulator, in ovarian cancer** 

EVI1, ecotropic viral integration site-1 protein, now called MECOM (MDS1 and EVI1 complex) is located at the 3q26.2 locus. It was initially identified as a site for viral integration in mouse cancer models; it has been well studied as an oncogene in acute myeloid leukemia (AML) and in myelodysplastic syndrome (MDS) (Levy et al., 1994; Morishita et al., 1992b). Functions of EVI1 include (1) proliferation of leukemic cells (Tanaka et al., 1995), (2) cellular transformation (Kilbey & Bartholomew, 1998), (3) inhibition of growth factor mediated differentiation and survival (Morishita et al., 1992a), (4) induction of neural and megakaryocyte differentiation, and (5) inhibition of interferon (Buonamici et al., 2005) and TGF signaling (Izutsu et al., 2001; Soderholm et al., 1997; Sood et al., 1999; Vinatzer et al., 2003; Vinatzer et al., 2001). Notably, EVI1 represses transcription via binding to SMADs and recruiting CtBP1/HDAC (Izutsu et al., 2001; Palmer et al., 2001; Senyuk et al., 2002) to target promoter elements, increasing AP-1 activity (Tanaka et al., 1994), disrupting JNK induced apoptosis (Maki et al., 2008), inhibiting PML function (Buonamici et al., 2005), binding to BRG1 (Chi et al., 2003), and activating PI3K by reducing TGF and drug induced apoptosis (Liu et al., 2006; Yoshimi et al., 2011). Supporting its role as an inducer of cellular proliferation, EVI1 knockout mice are embryonically lethal due to hypocellularity across multiple organ sites (Hoyt et al., 1997). There exist multiple splice variants of EVI1 whose functions are presently unclear (Alzuherri et al., 2006; Jazaeri et al., 2010; Vinatzer et al., 2003). In particular, the MDS1/EVI1 is a read-through splice form which contains a novel PR (PRD1-BF1-RIZ homology) domain; its functionality is unclear and is suggested to be context or cell type dependent (either eliciting functionality similar or antagonistic to EVI1 (Vinatzer et al., 2003). Structurally, EVI1 contains 2 zinc finger domains, an intervening region required for transformation, and a repressor domain necessary for binding to CtBP1/HDAC (Nanjundan et al., 2007).

In ovarian cancer, the first report of altered EVI1 expression in ovarian carcinoma cells demonstrated up to a 40-fold increase in its mRNA levels via RNAse protection assay compared to the normal ovary; these initial findings implicate a novel role for EVI1 in solid tumor carcinogenesis (Brooks et al., 1996). A decade later, increased EVI1 levels in advanced stage ovarian cancers supported these initial findings via oligonucleotide arrays profiling and validation via qPCR analysis (Sunde et al., 2006). The same researchers also found that the EVI1 gene locus was amplified in 43% of the tumors with a significant correlation between gene copy and EVI1 gene expression levels (Sunde et al., 2006). They also reported that EVI1 inhibited TGF signaling in normal immortalized ovarian epithelial cells (Sunde et al., 2006). Our research has also uncovered increased copy number at the EVI1 locus in advanced stage serous epithelial ovarian carcinomas via aCGH analysis (Nanjundan et al., 2007). We found that EVI1 DNA copy number increases were associated with at least a 5-fold increase in RNA transcript levels in the majority of advanced ovarian cancers (Nanjundan et al., 2007). More recent whole genome aCGH analysis of stage III ovarian serous carcinomas also identified a gain at 3q26.2 with their gene expression analysis demonstrating elevated EVI1 expression (Osterberg et al., 2009). Protein level determination via western blotting analysis showed a corresponding increase in MDS1/EVI1 and EVI1 expression in ovarian cancers and multiple ovarian cancer cell lines (Nanjundan et al., 2007). Interestingly, functional studies by transient transfection into normal immortalized epithelial cells demonstrated that EVI1 and MDS1/EVI1 increased cell proliferation, migration, and decreased TGF-mediated plasminogen activator inhibitor-1 (PAI-1) promoter activity (Nanjundan et al., 2007). In yet another recent study, highest expression of EVI1 and a splice variant, Del324 (EVI1s), was observed in ovarian cancer specimens with a constant ratio between the two splice

Dysregulated TGF Signaling in Ovarian Cancer 129

regulate its expression (De Weer et al., 2011; Dickstein et al., 2010). Of further interest is the recent identification of the physical interaction between EVI1 and SIRT1, a histone deacetylase which is itself a direct target of EVI1. Interaction between SIRT1 and EVI1 leads to EVI1 degradation (Pradhan et al., 2011). SIRT1 is increased in AML patient samples where EVI1 is elevated (Pradhan et al., 2011). In addition, EVI1 interacts directly with SUV39H1 and G9a, both histone methyltransferases, which elicit methyltransferase activities and enhance the repressive activity of EVI1 (Cattaneo & Nucifora, 2008; Spensberger & Delwel, 2008). Thus, the oncogenic activity of EVI1 may be involved in deacetylation and methylation events which would lead to altered chromatin structure and, thus,

More recently, TGF has been implicated in regulating autophagy (Gajewska et al., 2005; Kiyono et al., 2009), a self eating process whereby damaged cellular organelles and other cellular material are sequestered within autophagosomes. These double-membrane structures eventually fuse with single-membrane lysosomes leading to degradation of the inner contents (Huang & Klionsky, 2007; Yang & Klionsky, 2009) (Figure 3). Autophagy is activated in response to multiple stresses during cancer progression including nutrient starvation, the unfolded protein response (UPR), hypoxia, and cellular treatment with cytotoxic chemotherapeutic agents (Huang & Klionsky, 2007; Yang & Klionsky, 2009). It has been suggested that autophagy promotes tumorigenic development; thus, it would be an ideal target for tumor ablation. Indeed, increased levels of autophagy are observed in tumor cells following treatment of cells with chemotherapeutic agents (Kondo et al., 2005; Kondo &

The isolation membrane of the autophagosome arises due to complex formation between beclin-1 and hVps34 (Geng & Klionsky, 2008; Klionsky, 2005; Wang & Klionsky, 2003; Yorimitsu & Klionsky, 2005). The membrane elongates via activation of ubiquitin-like conjugation system. ATG12 is activated by ATG7 which is then transferred to ATG10 and finally covalently attached to ATG5 (Geng & Klionsky, 2008). The ATG12-ATG5 conjugate localizes to autophagosome precursors and dissociates prior to or following completion of formation of the autophagic vacuole. Another ubiquitin-like modification system involving LC3 (microtubule associated protein 1 light chain 3) completes autophagosome formation (Geng & Klionsky, 2008). The cytosolic precursor of LC3 (LC3-I) becomes cleaved at its Cterminus by ATG4 and is conjugated to phosphatidylethanolamine (PE) to generate the membrane bound LC3-II form; this process requires ATG7 and ATG3 activities (Geng & Klionsky, 2008; Klionsky, 2005; Wang & Klionsky, 2003; Yorimitsu & Klionsky, 2005). LC3-II is specifically targeted to ATG12-ATG5 associated autophagosomal precursor membranes. Following fusion of autophagosomes with lysosomes, LC3-II becomes delipidated and returns to the cytosolic pool to be recycled (Geng & Klionsky, 2008; Klionsky, 2005; Wang &

The initial finding that TGF induces autophagy was observed in bovine mammary epithelial BME-UV1 cells; both LC3 and beclin-1 expression were induced following TGF1 treatment leading to cell death (Gajewska et al., 2005). Following reports support this finding in a number of cell lines including hepatocellular and breast carcinoma cell lines

**6. Novel perspective into the functionality of TGF: Autophagy** 

transcriptional events.

Kondo, 2006).

Klionsky, 2003; Yorimitsu & Klionsky, 2005).

variants across all specimens assessed (Jazaeri et al., 2010). However, their analysis did not identify an altered expression protein pattern between serous ovarian cancers and fallopian tube fimbria or benign neoplasms (Jazaeri et al., 2010). In support of our functional studies in OVCAR8 cells (Nanjundan et al., 2007), when EVI1 was expressed exogenously in this ovarian carcinoma cell line (which harbors a deletion at the EVI1 locus), there was no altered proliferation (Jazaeri et al., 2010). Furthermore, with knockdown of specific EVI1 forms (via siRNA and shRNA) in ovarian cancer cells, there was no alteration in functionality (Jazaeri et al., 2010). Although their data do not support a role for EVI1 in ovarian cancer cell proliferation (Jazaeri et al., 2010), further investigations are warranted to determine the functional relevance of disrupted TGF signaling via EVI1 in ovarian cancer.

#### **5.1 Epigenetic aberrations, EVI1, and ovarian cancer**

Epigenetic modifications refers to changes in gene expression as a result of DNA methylation, histone modification, nucleosome repositioning, and post-transcriptional gene regulation by micro-RNAs (Balch et al., 2009). DNA methyltransferases are involved in adding methyl groups to the cytosine-5 position within CpG dinucleotides (Balch et al., 2009). CpG dense regions, however, are normally unmethylated in normal specimens (Balch et al., 2009). Histone modifications are extensive and can regulate transcription in an open or closed conformation on the chromatin structure (Balch et al., 2009). These regions can be extensively altered in disease states such as cancer with a general DNA hypomethylation status and localized hypermethylation of promoter associated CpG islands in cases of tumor suppressor genes (Balch et al., 2009). Further, dysregulation of miRNA expression has been also linked to cancer development (Balch et al., 2009). A number of epigenetic aberrations are well noted in ovarian cancer (Balch et al., 2009).

Based on homology to proteins with PR domains, MDS1/EVI1 (which contains such a domain) has the potential to elicit protein methyltransferase activity (Vinatzer et al., 2003; Vinatzer et al., 2001). However, we did not detect any such activity associated with MDS1/EVI1 via *in vitro* methyltransferase activity assays using free histones as substrate (Nanjundan et al., 2007). There was some weak associated activity which we suggested to be due to co-immunoprecipitating molecules, possibly SWI/SNF components or proteins associated with methyltransferase activity (Nanjundan et al., 2007). Indeed, EVI1 has recently been shown to physically interact with molecules which have such activities (Cattaneo & Nucifora, 2008; Lugthart et al., 2011; Pradhan et al., 2011; Senyuk et al., 2011; Spensberger & Delwel, 2008).

Indeed, links between DNA hypermethylation and EVI1 are observed in AML (Lugthart et al., 2011); further, EVI1 physically interacts with DNA methyltransferase 3A/3B (DNMT3A/3B) (Senyuk et al., 2011). Thus, EVI1 is likely involved in promoter DNA methylation in leukemia and possibly in other solid tumors such as ovarian cancers. EVI1 regulates the expression of microRNA-124 which is involved in regulation of differentiation and cycling of hematopoietic cells (De Weer et al., 2011; Dickstein et al., 2010). This was demonstrated to occur via methylation of CpG dinucleotides upstream of the miRNA leading to its repression and hence, increased expression of genes involved in cell division such as Bmi1 and cyclin D3 (De Weer et al., 2011; Dickstein et al., 2010). Through its interaction with DNMT3, the EVI1 complex binds to regulatory regions of the miRNA to

variants across all specimens assessed (Jazaeri et al., 2010). However, their analysis did not identify an altered expression protein pattern between serous ovarian cancers and fallopian tube fimbria or benign neoplasms (Jazaeri et al., 2010). In support of our functional studies in OVCAR8 cells (Nanjundan et al., 2007), when EVI1 was expressed exogenously in this ovarian carcinoma cell line (which harbors a deletion at the EVI1 locus), there was no altered proliferation (Jazaeri et al., 2010). Furthermore, with knockdown of specific EVI1 forms (via siRNA and shRNA) in ovarian cancer cells, there was no alteration in functionality (Jazaeri et al., 2010). Although their data do not support a role for EVI1 in ovarian cancer cell proliferation (Jazaeri et al., 2010), further investigations are warranted to determine the functional relevance of disrupted TGF

Epigenetic modifications refers to changes in gene expression as a result of DNA methylation, histone modification, nucleosome repositioning, and post-transcriptional gene regulation by micro-RNAs (Balch et al., 2009). DNA methyltransferases are involved in adding methyl groups to the cytosine-5 position within CpG dinucleotides (Balch et al., 2009). CpG dense regions, however, are normally unmethylated in normal specimens (Balch et al., 2009). Histone modifications are extensive and can regulate transcription in an open or closed conformation on the chromatin structure (Balch et al., 2009). These regions can be extensively altered in disease states such as cancer with a general DNA hypomethylation status and localized hypermethylation of promoter associated CpG islands in cases of tumor suppressor genes (Balch et al., 2009). Further, dysregulation of miRNA expression has been also linked to cancer development (Balch et al., 2009). A number of epigenetic aberrations

Based on homology to proteins with PR domains, MDS1/EVI1 (which contains such a domain) has the potential to elicit protein methyltransferase activity (Vinatzer et al., 2003; Vinatzer et al., 2001). However, we did not detect any such activity associated with MDS1/EVI1 via *in vitro* methyltransferase activity assays using free histones as substrate (Nanjundan et al., 2007). There was some weak associated activity which we suggested to be due to co-immunoprecipitating molecules, possibly SWI/SNF components or proteins associated with methyltransferase activity (Nanjundan et al., 2007). Indeed, EVI1 has recently been shown to physically interact with molecules which have such activities (Cattaneo & Nucifora, 2008; Lugthart et al., 2011; Pradhan et al., 2011; Senyuk et al., 2011;

Indeed, links between DNA hypermethylation and EVI1 are observed in AML (Lugthart et al., 2011); further, EVI1 physically interacts with DNA methyltransferase 3A/3B (DNMT3A/3B) (Senyuk et al., 2011). Thus, EVI1 is likely involved in promoter DNA methylation in leukemia and possibly in other solid tumors such as ovarian cancers. EVI1 regulates the expression of microRNA-124 which is involved in regulation of differentiation and cycling of hematopoietic cells (De Weer et al., 2011; Dickstein et al., 2010). This was demonstrated to occur via methylation of CpG dinucleotides upstream of the miRNA leading to its repression and hence, increased expression of genes involved in cell division such as Bmi1 and cyclin D3 (De Weer et al., 2011; Dickstein et al., 2010). Through its interaction with DNMT3, the EVI1 complex binds to regulatory regions of the miRNA to

signaling via EVI1 in ovarian cancer.

**5.1 Epigenetic aberrations, EVI1, and ovarian cancer** 

are well noted in ovarian cancer (Balch et al., 2009).

Spensberger & Delwel, 2008).

regulate its expression (De Weer et al., 2011; Dickstein et al., 2010). Of further interest is the recent identification of the physical interaction between EVI1 and SIRT1, a histone deacetylase which is itself a direct target of EVI1. Interaction between SIRT1 and EVI1 leads to EVI1 degradation (Pradhan et al., 2011). SIRT1 is increased in AML patient samples where EVI1 is elevated (Pradhan et al., 2011). In addition, EVI1 interacts directly with SUV39H1 and G9a, both histone methyltransferases, which elicit methyltransferase activities and enhance the repressive activity of EVI1 (Cattaneo & Nucifora, 2008; Spensberger & Delwel, 2008). Thus, the oncogenic activity of EVI1 may be involved in deacetylation and methylation events which would lead to altered chromatin structure and, thus, transcriptional events.

#### **6. Novel perspective into the functionality of TGF: Autophagy**

More recently, TGF has been implicated in regulating autophagy (Gajewska et al., 2005; Kiyono et al., 2009), a self eating process whereby damaged cellular organelles and other cellular material are sequestered within autophagosomes. These double-membrane structures eventually fuse with single-membrane lysosomes leading to degradation of the inner contents (Huang & Klionsky, 2007; Yang & Klionsky, 2009) (Figure 3). Autophagy is activated in response to multiple stresses during cancer progression including nutrient starvation, the unfolded protein response (UPR), hypoxia, and cellular treatment with cytotoxic chemotherapeutic agents (Huang & Klionsky, 2007; Yang & Klionsky, 2009). It has been suggested that autophagy promotes tumorigenic development; thus, it would be an ideal target for tumor ablation. Indeed, increased levels of autophagy are observed in tumor cells following treatment of cells with chemotherapeutic agents (Kondo et al., 2005; Kondo & Kondo, 2006).

The isolation membrane of the autophagosome arises due to complex formation between beclin-1 and hVps34 (Geng & Klionsky, 2008; Klionsky, 2005; Wang & Klionsky, 2003; Yorimitsu & Klionsky, 2005). The membrane elongates via activation of ubiquitin-like conjugation system. ATG12 is activated by ATG7 which is then transferred to ATG10 and finally covalently attached to ATG5 (Geng & Klionsky, 2008). The ATG12-ATG5 conjugate localizes to autophagosome precursors and dissociates prior to or following completion of formation of the autophagic vacuole. Another ubiquitin-like modification system involving LC3 (microtubule associated protein 1 light chain 3) completes autophagosome formation (Geng & Klionsky, 2008). The cytosolic precursor of LC3 (LC3-I) becomes cleaved at its Cterminus by ATG4 and is conjugated to phosphatidylethanolamine (PE) to generate the membrane bound LC3-II form; this process requires ATG7 and ATG3 activities (Geng & Klionsky, 2008; Klionsky, 2005; Wang & Klionsky, 2003; Yorimitsu & Klionsky, 2005). LC3-II is specifically targeted to ATG12-ATG5 associated autophagosomal precursor membranes. Following fusion of autophagosomes with lysosomes, LC3-II becomes delipidated and returns to the cytosolic pool to be recycled (Geng & Klionsky, 2008; Klionsky, 2005; Wang & Klionsky, 2003; Yorimitsu & Klionsky, 2005).

The initial finding that TGF induces autophagy was observed in bovine mammary epithelial BME-UV1 cells; both LC3 and beclin-1 expression were induced following TGF1 treatment leading to cell death (Gajewska et al., 2005). Following reports support this finding in a number of cell lines including hepatocellular and breast carcinoma cell lines

Dysregulated TGF Signaling in Ovarian Cancer 131

Our recent work has shown that upon exposure to reactive oxygen generating conditions (i.e. arsenic trioxide (As2O3) which is used to treat patients with acute promyelocytic leukemia (APL)), SnoN protein levels increase which coincides with induction of autophagy in a beclin-1 independent manner (Smith et al., 2010). Other TGF signaling mediators were examined and As2O3 was found to reduce the protein expression of EVI1, TAK1, SMAD2/3, and TGFRII while increasing SnoN (Smith et al., 2010). Knockdown of SnoN via siRNA markedly reduced autophagy with a corresponding increase in apoptosis (Smith et al., 2010). Thus, disruption of induction of autophagy may be a novel therapeutic strategy to re-

Strategies need to be carefully designed for successful treatment of ovarian cancer patients via inhibition of TGF signaling pathway due to the apparent bifunctionality of TGF signaling. In particular, TGF levels, TGF receptor expression, and tumor stage/progression need to be assessed. There are in essence three major groups of TGF signaling therapeutics: (1) ligand traps including monoclonal TGF neutralizing antibodies and soluble TGFRI/RII; (2) antisense molecule mediated silencing strategies for targeting TGF ligands; and (3) small molecule inhibitors targeting TGFRI/RII and downstream mediators (Chou et al., 2010; Iyer et al., 2005; Korpal & Kang, 2010; Nagaraj & Datta, 2010). Neutralizing antibodies are designed to disrupt the interactions between TGF ligands and their cell-surface receptors (Chou et al., 2010). Some of these include 2G7 and 1D11 monoclonal antibodies which hinder the activity of all three TGF ligands to reduce tumor growth and metastasis (Chou et al., 2010). GC1008 is yet another neutralizing antibody which entered a Phase I/II clinical trial for advanced malignant melanoma and renal cell carcinoma patients (Chou et al., 2010). Soluble ligand traps include soluble TGFRII/III which hinder TGF interaction with its cognate cell surface receptors leading to inhibition of tumor growth and metastasis in athymic murine models (Chou et al., 2010). Antisense oligonucleotides are yet another route to block TGF signaling, specifically against TGF1 gene expression which reduced tumor survival and metastasis in mouse models (Chou et al., 2010). In particular, AP12008, an antisense molecule which targets TGF2, effectively targets pancreatic and melanoma cell lines; it entered a Phase IIb clinical trial for patients with high grade gliomas with successful outcomes (Chou et al., 2010). However, the effectiveness of these large molecule inhibitors has limitations including adequately targeting a solid tumor due to physical barriers (Chou et al., 2010). Thus, small molecule inhibitors may be more effective and have been developed to initially target TGFRI kinase activity with specificity (i.e. SB0431542, SD-208, LY580276, etc.). These act as competitive inhibitors of the ATP binding site of TGFRI kinase (Chou et al., 2010). In addition, there now exists a dual inhibitor of TGFRI and TGFRII (LY2109761) which hinders metastatic process effectively (Chou et al., 2010). Other strategies that are being developed include small molecule inhibitors to directly inhibit SMAD-specific pathways as opposed to the non-

In addition to the above targeting strategies, epigenetic therapy may also be another valuable therapeutic strategy for ovarian cancers with respect to targeting TGF transcriptional co-regulators such as EVI1. This strategy could potentially alter the epigenetic status leading to restoration of the expression of tumor suppressor genes with a

establish or increase sensitivity to therapeutic agents.

canonical pathways (Chou et al., 2010).

**7. Targeting the TGF signaling pathway for therapy** 

(Kiyono et al., 2009). TGF was noted to induce autophagosome formation with a corresponding conversion of LC3-I to LC3-II and increased expression of autophagic markers including beclin-1, ATG5, ATG7, and DAPK (Kiyono et al., 2009). In addition, knockdown of SMADs and other targets in the non-canonical SMAD pathways decreased TGF mediated autophagy (Kiyono et al., 2009). Autophagy induction led to induction of BIM and BMF (proapoptotic markers) which occurred prior to initiation of apoptosis (Kiyono et al., 2009).

Fig. 3. Activation of the TGF signaling pathway induces autophagy. TGF can induce autophagosome formation in cancer cell lines via enhanced expression of autophagic markers (i.e. beclin-1, ATG5, and ATG7) and enhanced conversion of LC3-I to LC3-II. Induction of TGF -mediated autophagy occurs prior to apoptosis.

Supporting reports of TGF-induced autophagy arise from studies of renal epithelial cells which is involved in induction of peritubular fibrosis and degeneration of nephrons (Koesters et al., 2010). Opposing the concept that TGF leads to autophagic mediated cell death, TGF was reported to protect mesangial cells from apoptosis as a protective mechanism for survival during serum starvation via a TAK1 and AKT dependent pathway (Ding et al., 2010).

(Kiyono et al., 2009). TGF was noted to induce autophagosome formation with a corresponding conversion of LC3-I to LC3-II and increased expression of autophagic markers including beclin-1, ATG5, ATG7, and DAPK (Kiyono et al., 2009). In addition, knockdown of SMADs and other targets in the non-canonical SMAD pathways decreased TGF mediated autophagy (Kiyono et al., 2009). Autophagy induction led to induction of BIM and BMF (proapoptotic markers) which occurred prior to initiation of apoptosis

Fig. 3. Activation of the TGF signaling pathway induces autophagy. TGF can induce autophagosome formation in cancer cell lines via enhanced expression of autophagic markers (i.e. beclin-1, ATG5, and ATG7) and enhanced conversion of LC3-I to LC3-II.

Supporting reports of TGF-induced autophagy arise from studies of renal epithelial cells which is involved in induction of peritubular fibrosis and degeneration of nephrons (Koesters et al., 2010). Opposing the concept that TGF leads to autophagic mediated cell death, TGF was reported to protect mesangial cells from apoptosis as a protective mechanism for survival during serum starvation via a TAK1 and AKT dependent pathway

Induction of TGF -mediated autophagy occurs prior to apoptosis.

(Kiyono et al., 2009).

(Ding et al., 2010).

Our recent work has shown that upon exposure to reactive oxygen generating conditions (i.e. arsenic trioxide (As2O3) which is used to treat patients with acute promyelocytic leukemia (APL)), SnoN protein levels increase which coincides with induction of autophagy in a beclin-1 independent manner (Smith et al., 2010). Other TGF signaling mediators were examined and As2O3 was found to reduce the protein expression of EVI1, TAK1, SMAD2/3, and TGFRII while increasing SnoN (Smith et al., 2010). Knockdown of SnoN via siRNA markedly reduced autophagy with a corresponding increase in apoptosis (Smith et al., 2010). Thus, disruption of induction of autophagy may be a novel therapeutic strategy to reestablish or increase sensitivity to therapeutic agents.

#### **7. Targeting the TGF signaling pathway for therapy**

Strategies need to be carefully designed for successful treatment of ovarian cancer patients via inhibition of TGF signaling pathway due to the apparent bifunctionality of TGF signaling. In particular, TGF levels, TGF receptor expression, and tumor stage/progression need to be assessed. There are in essence three major groups of TGF signaling therapeutics: (1) ligand traps including monoclonal TGF neutralizing antibodies and soluble TGFRI/RII; (2) antisense molecule mediated silencing strategies for targeting TGF ligands; and (3) small molecule inhibitors targeting TGFRI/RII and downstream mediators (Chou et al., 2010; Iyer et al., 2005; Korpal & Kang, 2010; Nagaraj & Datta, 2010). Neutralizing antibodies are designed to disrupt the interactions between TGF ligands and their cell-surface receptors (Chou et al., 2010). Some of these include 2G7 and 1D11 monoclonal antibodies which hinder the activity of all three TGF ligands to reduce tumor growth and metastasis (Chou et al., 2010). GC1008 is yet another neutralizing antibody which entered a Phase I/II clinical trial for advanced malignant melanoma and renal cell carcinoma patients (Chou et al., 2010). Soluble ligand traps include soluble TGFRII/III which hinder TGF interaction with its cognate cell surface receptors leading to inhibition of tumor growth and metastasis in athymic murine models (Chou et al., 2010). Antisense oligonucleotides are yet another route to block TGF signaling, specifically against TGF1 gene expression which reduced tumor survival and metastasis in mouse models (Chou et al., 2010). In particular, AP12008, an antisense molecule which targets TGF2, effectively targets pancreatic and melanoma cell lines; it entered a Phase IIb clinical trial for patients with high grade gliomas with successful outcomes (Chou et al., 2010). However, the effectiveness of these large molecule inhibitors has limitations including adequately targeting a solid tumor due to physical barriers (Chou et al., 2010). Thus, small molecule inhibitors may be more effective and have been developed to initially target TGFRI kinase activity with specificity (i.e. SB0431542, SD-208, LY580276, etc.). These act as competitive inhibitors of the ATP binding site of TGFRI kinase (Chou et al., 2010). In addition, there now exists a dual inhibitor of TGFRI and TGFRII (LY2109761) which hinders metastatic process effectively (Chou et al., 2010). Other strategies that are being developed include small molecule inhibitors to directly inhibit SMAD-specific pathways as opposed to the noncanonical pathways (Chou et al., 2010).

In addition to the above targeting strategies, epigenetic therapy may also be another valuable therapeutic strategy for ovarian cancers with respect to targeting TGF transcriptional co-regulators such as EVI1. This strategy could potentially alter the epigenetic status leading to restoration of the expression of tumor suppressor genes with a

Dysregulated TGF Signaling in Ovarian Cancer 133

We acknowledge RO1 CA123219 which supported our ovarian cancer studies we have

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**9. Acknowledgements** 

reported in this review.

1443.

**10. References** 

corresponding reduction in the expression of genes involved in mestastasis. For example, ADAM19, FBXO32, and RunX1T1 (tumor suppressors) are reduced in ovarian cancers but are normally increased in response to TGFthese genes are epigenetically silenced by promoter hypermethylation or histone modification (Balch et al., 2009).

With respect to SnoN/SkiL (Figure 4), based on our results with As2O3 in ovarian carcinoma cells, targeting of this TGF transcriptional co-regulator in ovarian cancers may lead to increased sensitivity to various chemotherapeutic agents.

Fig. 4. Targeting of SnoN/SkiL to increase the sensitivity of ovarian cancer cells to chemotherapeutics. SnoN/SkiL levels increase following As2O3 treatment leading to induction of autophagy and increased resistance to the agent. Targeting of SnoN/SkiL with specific inhibitors may be a strategy to improve the sensitivity of the chemotherapeutic agents in ovarian cancer patients.

### **8. Conclusion**

Although significant progress has been made in improving our understanding of the TGFsignalling pathway, there remain numerous areas for further investigation to improve our understanding of the regulation of the TGFpathway. Thus, future research could possibly lead to development of novel and improved strategies for treatment of ovarian cancer patients.

#### **9. Acknowledgements**

We acknowledge RO1 CA123219 which supported our ovarian cancer studies we have reported in this review.

#### **10. References**

132 Ovarian Cancer – Basic Science Perspective

corresponding reduction in the expression of genes involved in mestastasis. For example, ADAM19, FBXO32, and RunX1T1 (tumor suppressors) are reduced in ovarian cancers but are normally increased in response to TGFthese genes are epigenetically silenced by

With respect to SnoN/SkiL (Figure 4), based on our results with As2O3 in ovarian carcinoma cells, targeting of this TGF transcriptional co-regulator in ovarian cancers may lead to

Fig. 4. Targeting of SnoN/SkiL to increase the sensitivity of ovarian cancer cells to chemotherapeutics. SnoN/SkiL levels increase following As2O3 treatment leading to induction of autophagy and increased resistance to the agent. Targeting of SnoN/SkiL with specific inhibitors may be a strategy to improve the sensitivity of the chemotherapeutic

Although significant progress has been made in improving our understanding of the TGFsignalling pathway, there remain numerous areas for further investigation to improve our understanding of the regulation of the TGFpathway. Thus, future research could possibly lead to development of novel and improved strategies for treatment of ovarian

agents in ovarian cancer patients.

**8. Conclusion** 

cancer patients.

promoter hypermethylation or histone modification (Balch et al., 2009).

increased sensitivity to various chemotherapeutic agents.


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**8** 

*Germany* 

**New Tumor Biomarkers in Ovarian Cancer and** 

In Europe and the United States, ovarian cancer is currently the major cause of death from gynecological malignancy. Up to 60% ovarian cancer patients die from locally advanced disease. Nonetheless, even patients treated with optimal cytoreduction may subsequently suffer from metastatic disease. Since treatment strategies are developed to control locoregional cancer growth, it may be anticipated that more patients will die of distant metastases. The lack of early disease signals further contributes to the fact that only onefourth of ovarian cancers are identified at stage I. Ovarian cancer remains thus undetected due to late symptoms and the lack of reliable and clinically applicable screening tools. Further, the identification of new biomarkers could optimize prediction and monitoring of

Presence of disseminated tumor cells (DTC) in bone marrow is a common phenomenon observed in solid epithelial tumors. As shown by a multi-center analysis of bone marrow (BM) specimens from more than 4,700 patients, DTC detection at the time of breast cancer diagnosis is strongly correlated with poor clinical outcome (level-I-evidence) [1]. There is growing evidence that hematogenous tumor cell dissemination may occur in other tumors, such as prostate, colon and gynecologic malignancies. DTC, as surrogate parameter for occult hematogenous spread, are routinely detected in 22-51% ovarian cancer patients. Interestingly, ovarian metastases to the bone are only rarely observed [2], [3], [4]. Whether bone marrow serves in these patients as a temporary compartment from where persistent DTC are able to migrate, remains unclear. It has been demonstrated that dissemination of isolated tumor cells to secondary sites occurs as early as in FIGO stage I disease [4]. Single tumor cells acquire thus the potential to disseminate to extraperitoneal compartments early

Of all prognostic factors, monitoring of minimal residual disease is the only one available after the tumor has been removed. Beside monitoring of tumor markers, there is currently a major effort to identify other biological markers which can be assessed with minimally invasive methods and persist beyond surgery. We previously reported on a significant correlation of positive bone marrow status with shortened relapse-free survival in ovarian cancer patients [5]. DTC persistence after completion of platinum-based chemotherapy was also found to be prognostically relevant [6]. Further, the identification of molecular biomarkers may represent excellent targets for new treatment strategies for chemoresistant

anticancer therapies and provide insights into ovarian cancer progression.

**1. Introduction** 

in the process of the disease.

*1Department of Obstetrics and Gynecology, University of Tuebingen, Tuebingen, 2Department of Obstetrics and Gynecology, Marienkrankenhaus Hamburg, Hamburg* 

**Its Prognostic and Clinical Relevance** 

Malgorzata Banys1,2, Natalia Krawczyk1 and Tanja Fehm1

presumptive antagonist, MDS1/EVI1, in patients with myeloid leukemia. Genes Chromosomes Cancer, 36**,** 80-89.


### **New Tumor Biomarkers in Ovarian Cancer and Its Prognostic and Clinical Relevance**

Malgorzata Banys1,2, Natalia Krawczyk1 and Tanja Fehm1 *1Department of Obstetrics and Gynecology, University of Tuebingen, Tuebingen, 2Department of Obstetrics and Gynecology, Marienkrankenhaus Hamburg, Hamburg Germany* 

#### **1. Introduction**

138 Ovarian Cancer – Basic Science Perspective

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In Europe and the United States, ovarian cancer is currently the major cause of death from gynecological malignancy. Up to 60% ovarian cancer patients die from locally advanced disease. Nonetheless, even patients treated with optimal cytoreduction may subsequently suffer from metastatic disease. Since treatment strategies are developed to control locoregional cancer growth, it may be anticipated that more patients will die of distant metastases. The lack of early disease signals further contributes to the fact that only onefourth of ovarian cancers are identified at stage I. Ovarian cancer remains thus undetected due to late symptoms and the lack of reliable and clinically applicable screening tools. Further, the identification of new biomarkers could optimize prediction and monitoring of anticancer therapies and provide insights into ovarian cancer progression.

Presence of disseminated tumor cells (DTC) in bone marrow is a common phenomenon observed in solid epithelial tumors. As shown by a multi-center analysis of bone marrow (BM) specimens from more than 4,700 patients, DTC detection at the time of breast cancer diagnosis is strongly correlated with poor clinical outcome (level-I-evidence) [1]. There is growing evidence that hematogenous tumor cell dissemination may occur in other tumors, such as prostate, colon and gynecologic malignancies. DTC, as surrogate parameter for occult hematogenous spread, are routinely detected in 22-51% ovarian cancer patients. Interestingly, ovarian metastases to the bone are only rarely observed [2], [3], [4]. Whether bone marrow serves in these patients as a temporary compartment from where persistent DTC are able to migrate, remains unclear. It has been demonstrated that dissemination of isolated tumor cells to secondary sites occurs as early as in FIGO stage I disease [4]. Single tumor cells acquire thus the potential to disseminate to extraperitoneal compartments early in the process of the disease.

Of all prognostic factors, monitoring of minimal residual disease is the only one available after the tumor has been removed. Beside monitoring of tumor markers, there is currently a major effort to identify other biological markers which can be assessed with minimally invasive methods and persist beyond surgery. We previously reported on a significant correlation of positive bone marrow status with shortened relapse-free survival in ovarian cancer patients [5]. DTC persistence after completion of platinum-based chemotherapy was also found to be prognostically relevant [6]. Further, the identification of molecular biomarkers may represent excellent targets for new treatment strategies for chemoresistant

New Tumor Biomarkers in Ovarian Cancer and Its Prognostic and Clinical Relevance 141

Disseminated tumor cells can be detected in 22-51% of ovarian cancer patients stage FIGO I-III [2, 4, 6, 11]. This relatively high incidence suggests that single tumor cells acquire the potential to disseminate to secondary sites outside the peritoneum early in the course of the disease, and that blood-borne dissemination in ovarian malignancy is a common rather than random occurrence. The number of detected cytokeratin-positive cells generally ranges in

The influence of primary tumor's characteristics on DTC presence is unclear. Based on a group of 108 primary ovarian cancer patients, Braun et al. reported no correlation between classical prognostic factors, such as FIGO stage, tumor type, residual intraperitoneal tumor, the presence of ascites, peritoneal metastasis or lymph node involvement, and DTC status [4]. The only parameter associated with BM positivity was grading (p = 0.02). In a study collective of 112 patients, we could confirm this observation [5]. Similar results were

For ovarian cancer, there is only limited data on prognostic value of DTC detection (Table 1). Braun et al. reported reduced distant disease-free survival in patients with detectable DTC at the time of diagnosis [4]. This correlation was confirmed in a subgroup of 64 optimally debulked patients, which indicated the importance of bone marrow status in patients who received successful surgical cytoreduction. We previously demonstrated that DTC positivity affects disease-free survival in a group of 112 ovarian cancer patients stage FIGO I-III [5]. Interestingly, positive DTC status was also an indicator for early local recurrence which is mostly due to suboptimal tumor debulking surgery and abdominal spread. Therefore, it might be speculated that DTC are indicators of a more aggressive phenotype of the primary disease that is likely to cause local recurrence. In contrast, other authors reported no

Banys [5] 112 DTC (ICC) 12 DFS Braun [4] 108 DTC (ICC) 45 DFS Aktas [13] 95 DTC (ICC) 28 n.s. Fehm [2] 69 DTC (ICC) 5 n.s. Schindlbeck [11] 90 DTC (ICC) 28 DDFS Marth [10] 73 DTC (immunobeads) 25 n.s. Wimberger [16] 62 DTC (ICC) 18 DFS 1 Cain [14] 50 DTC (ICC) n.s. Wimberger et al. [6] 30 DTC (ICC) 18 2 PFS *Abbreviations:* DFS – disease-free survival, DDFS – distant disease-free survival, DTC – disseminated tumor cells in bone marrow, ICC – immunocytochemistry, n.s. – not significant, PFS – progression-free

Table 1. Prognostic relevance of disseminated tumor cells and other biomarkers in ovarian

**Median follow-up [months]** 

**Prognostic significance** 

bone marrow from 1 to 30 per 2x106 mononuclear cells [5, 6].

**2.1 Prognostic relevance of DTC/CTC in ovarian cancer** 

**Author N Method** 

reported by others [6, 10, 11, 13].

survival

2 Mean

cancer.

1 DTC detected after chemotherapy

ovarian cancer patients. Recently, attempts have been made to target DTC by using antibody-based therapy with catumaxomab [7]. However, data on DTC detection in gynecological malignancies are so far limited [4], [8], [9], [10], [11].

In the following chapter, we discuss new biomarkers and circulating tumor cells. Data on prognostic and clinical relevance are presented.

#### **2. Disseminated and circulating tumor cells in ovarian cancer**

High mortality in patients with ovarian malignancies is mostly due to their locally advanced tumor rather than to distant metastatic disease. According to autopsy studies, however, occult metastatic tumors are encountered frequently at distant sites (e.g. liver, lung, bone, central nervous system) [3]. Up to 38% of patients with ovarian cancer developed metastases consistent with Stage IV disease at some time during the natural history of their disease. These results suggest that hematogenous dissemination of single tumor cells is a phenomenon much more common than would be expected from the clinical course of the disease.

Numerous techniques have been developed to isolate and quantify disseminated tumor cells in epithelial carcinomas. So far, no specific antigen or marker gene has been described for ovarian cancer. Therefore, the most widely used DTC detection assays rely on antibodybased capture of tumor cells, which express epithelial markers that are absent from normal leukocytes (Figure 1). Commonly targeted antigens are cytokeratin and EpCAM because their expression is relatively constant and universal in cells of epithelial origin [2, 6, 11].

Fig. 1. Disseminated tumor cell from ovarian cancer patient with typical cytomorphology and immunophenotype (positive cytokeratin-staining, large nucleus, high nuclear to cytoplasmic ratio, nucleus partially covered by CK-staining, nucleus granular [12].

Disseminated tumor cells can be detected in 22-51% of ovarian cancer patients stage FIGO I-III [2, 4, 6, 11]. This relatively high incidence suggests that single tumor cells acquire the potential to disseminate to secondary sites outside the peritoneum early in the course of the disease, and that blood-borne dissemination in ovarian malignancy is a common rather than random occurrence. The number of detected cytokeratin-positive cells generally ranges in bone marrow from 1 to 30 per 2x106 mononuclear cells [5, 6].

The influence of primary tumor's characteristics on DTC presence is unclear. Based on a group of 108 primary ovarian cancer patients, Braun et al. reported no correlation between classical prognostic factors, such as FIGO stage, tumor type, residual intraperitoneal tumor, the presence of ascites, peritoneal metastasis or lymph node involvement, and DTC status [4]. The only parameter associated with BM positivity was grading (p = 0.02). In a study collective of 112 patients, we could confirm this observation [5]. Similar results were reported by others [6, 10, 11, 13].

#### **2.1 Prognostic relevance of DTC/CTC in ovarian cancer**

For ovarian cancer, there is only limited data on prognostic value of DTC detection (Table 1). Braun et al. reported reduced distant disease-free survival in patients with detectable DTC at the time of diagnosis [4]. This correlation was confirmed in a subgroup of 64 optimally debulked patients, which indicated the importance of bone marrow status in patients who received successful surgical cytoreduction. We previously demonstrated that DTC positivity affects disease-free survival in a group of 112 ovarian cancer patients stage FIGO I-III [5]. Interestingly, positive DTC status was also an indicator for early local recurrence which is mostly due to suboptimal tumor debulking surgery and abdominal spread. Therefore, it might be speculated that DTC are indicators of a more aggressive phenotype of the primary disease that is likely to cause local recurrence. In contrast, other authors reported no


*Abbreviations:* DFS – disease-free survival, DDFS – distant disease-free survival, DTC – disseminated tumor cells in bone marrow, ICC – immunocytochemistry, n.s. – not significant, PFS – progression-free survival

1 DTC detected after chemotherapy

2 Mean

140 Ovarian Cancer – Basic Science Perspective

ovarian cancer patients. Recently, attempts have been made to target DTC by using antibody-based therapy with catumaxomab [7]. However, data on DTC detection in

In the following chapter, we discuss new biomarkers and circulating tumor cells. Data on

High mortality in patients with ovarian malignancies is mostly due to their locally advanced tumor rather than to distant metastatic disease. According to autopsy studies, however, occult metastatic tumors are encountered frequently at distant sites (e.g. liver, lung, bone, central nervous system) [3]. Up to 38% of patients with ovarian cancer developed metastases consistent with Stage IV disease at some time during the natural history of their disease. These results suggest that hematogenous dissemination of single tumor cells is a phenomenon much

Numerous techniques have been developed to isolate and quantify disseminated tumor cells in epithelial carcinomas. So far, no specific antigen or marker gene has been described for ovarian cancer. Therefore, the most widely used DTC detection assays rely on antibodybased capture of tumor cells, which express epithelial markers that are absent from normal leukocytes (Figure 1). Commonly targeted antigens are cytokeratin and EpCAM because their expression is relatively constant and universal in cells of epithelial origin [2, 6, 11].

Fig. 1. Disseminated tumor cell from ovarian cancer patient with typical cytomorphology and immunophenotype (positive cytokeratin-staining, large nucleus, high nuclear to cytoplasmic ratio, nucleus partially covered by CK-staining, nucleus granular [12].

gynecological malignancies are so far limited [4], [8], [9], [10], [11].

**2. Disseminated and circulating tumor cells in ovarian cancer** 

more common than would be expected from the clinical course of the disease.

prognostic and clinical relevance are presented.

Table 1. Prognostic relevance of disseminated tumor cells and other biomarkers in ovarian cancer.

New Tumor Biomarkers in Ovarian Cancer and Its Prognostic and Clinical Relevance 143

[20]. In contrast, Marth et al. reported a 12% incidence throughout all tumor stages but observed no correlation with clinical outcome [10]. Interestingly, positive finding in the blood was highly associated with DTC detection in bone marrow. Smaller studies showed

Beyond the prognostic value of DTC detection, monitoring of minimal residual disease following treatment represent a promising parameter for the assessment of residual risk of relapse. Tumor markers such as CA125 are clinically accepted tools for therapy monitoring in advanced ovarian cancer. Nevertheless, CA125 levels generally decline rapidly during chemotherapy and are mostly below cut-off level at the end of treatment even though a significant proportion of patients will face a relapse of the disease within five years. Moreover, the clinical utility of serial CA125 measurements for early therapy of a relapse is currently controversially debated [25]. In this context, the presence of isolated tumor cells in bone marrow and possibly in peripheral blood, might indicate occult tumor load after first line therapy and serve as a parameter for suboptimal response to therapy. For other tumor entities, such as breast cancer, DTC persistence after treatment is an independent indicator of reduced clinical outcome [26]. Whether therapy-resistant DTC also affect survival in ovarian cancer, is a subject of current studies. Wimberger et al. correlated changes in DTC counts before and after first-line chemotherapy to clinical course of disease in 30 ovarian cancer patients. DTC persistence was observed in half of the patients after chemotherapy. Patients with marked increase in DTC numbers had significantly shortened progression-free

So far, assessment of therapy efficacy in asymptomatic ovarian cancer patients after completion of standard chemotherapy has not been possible until patient's eventual relapse. A reliable therapy monitoring tool could identify high-risk patients in need of additional therapy. Whether persistent DTC, as surrogate parameter of minimal residual disease, may be targeted by secondary adjuvant therapy is currently under investigation and should be

As to progression of ovarian cancer, an interesting hypothesis has been introduced recently. According to 'classical' model of carcinogenesis, any cell may be source of malignant transformation and lead to tumor growth. However, emerging evidence has suggested that the capability of cancer to grow, proliferate and eventually relapse is dependent on a small subpopulation of tumor cells, called cancer stem cells (CSC). These cells are considered especially significant on the background of drug resistance, tumor dormancy, minimal residual disease, and disease recurrence. Several cancer entities, such as ovarian cancer, retinoblastoma, gastrointestinal and breast cancer might arise from a small population of cells with stem cell properties that sustain tumor formation and growth [27]. This 'stem cell hypothesis' assumes an important role of tumor-initiating progenitor cells in tumor progression. Accordingly, cancer stem cells, but not the remaining cells in the primary tumor, have the ability to self-renew, propagate tumorigenesis and are drug-resistant [28]. Ovarian cancer cell lines were demonstrated to feature "side population" cells with ability to differentiate into cancers with different histologies, similar to the assumed pluripotent

varying CTC incidence, depending on methodology [21, 22].

**2.3 Therapy monitoring** 

survival [6].

further studied.

**2.4 Stem cell hypothesis** 

significant correlation between bone marrow status and survival in ovarian cancer patients [10, 14]. One possible explanation for this discrepancy might be the time point of bone marrow aspiration. For instance, Marth et al. showed no association between the presence of tumor cells in BM and survival [10]. However, all samples were collected after surgery, whereas other authors aspirated BM immediately preoperatively [4, 5]. A transient dissemination of cancer cells from the primary tumor due to intraoperative manipulation could contribute to false-positive results and therefore affect further analysis [15].

#### **2.2 Circulating tumor cells**

One limitation of bone marrow sampling is its invasiveness. Since BM biopsy is not well tolerated by many patients, translational research have focused increasingly on circulating tumor cell (CTC) detection in the blood. In breast cancer, a significant impact of CTC detection on survival has already been established both in primary and metastatic situation [17, 18]. Currently, two commercially available kits for CTC detection in breast cancer are in use: antibody-based CellSearch and Multiplex-RT-PCR AdnaTest. Both tests were modified and validated in ovarian cancer patients (Table 2). The largest trial so far is the recently published study by Poveda et al. including 216 patients diagnosed with relapsed ovarian cancer [19]. Elevated numbers of CTC (> 1 cell / 7.5 ml blood) detected by the CellSearch assay before start of therapy predicted unfavorable prognosis. Aktas et al. used a modified AdnaTest assay to isolate cells expressing EpCAM, MUC-1, HER-2 or CA 125 transcripts [13]. Patients with detectable CTC has significantly shorter survival, irrespective of time point of blood sampling (before surgery or after chemotherapy). Further, Fan et al. examined 66 primary ovarian patients using a cell invasion assay and reported a significant decrease in disease-free survival in CTC-positive ovarian patients


*Abbreviations:* CTC – circulating tumor cells in peripheral blood, DFS – disease-free survival, ICC – immunocytochemistry, n.s. – not significant, PFS – progression-free survival 1 Relapsed ovarian cancer

2 Both before and after chemotherapy

3 Mean

Table 2. Prognostic relevance of circulating tumor cells in ovarian cancer.

[20]. In contrast, Marth et al. reported a 12% incidence throughout all tumor stages but observed no correlation with clinical outcome [10]. Interestingly, positive finding in the blood was highly associated with DTC detection in bone marrow. Smaller studies showed varying CTC incidence, depending on methodology [21, 22].

#### **2.3 Therapy monitoring**

142 Ovarian Cancer – Basic Science Perspective

significant correlation between bone marrow status and survival in ovarian cancer patients [10, 14]. One possible explanation for this discrepancy might be the time point of bone marrow aspiration. For instance, Marth et al. showed no association between the presence of tumor cells in BM and survival [10]. However, all samples were collected after surgery, whereas other authors aspirated BM immediately preoperatively [4, 5]. A transient dissemination of cancer cells from the primary tumor due to intraoperative manipulation

One limitation of bone marrow sampling is its invasiveness. Since BM biopsy is not well tolerated by many patients, translational research have focused increasingly on circulating tumor cell (CTC) detection in the blood. In breast cancer, a significant impact of CTC detection on survival has already been established both in primary and metastatic situation [17, 18]. Currently, two commercially available kits for CTC detection in breast cancer are in use: antibody-based CellSearch and Multiplex-RT-PCR AdnaTest. Both tests were modified and validated in ovarian cancer patients (Table 2). The largest trial so far is the recently published study by Poveda et al. including 216 patients diagnosed with relapsed ovarian cancer [19]. Elevated numbers of CTC (> 1 cell / 7.5 ml blood) detected by the CellSearch assay before start of therapy predicted unfavorable prognosis. Aktas et al. used a modified AdnaTest assay to isolate cells expressing EpCAM, MUC-1, HER-2 or CA 125 transcripts [13]. Patients with detectable CTC has significantly shorter survival, irrespective of time point of blood sampling (before surgery or after chemotherapy). Further, Fan et al. examined 66 primary ovarian patients using a cell invasion assay and reported a significant decrease in disease-free survival in CTC-positive ovarian patients

Poveda [19] 216 CTC (ICC: CellSearch) 1 PFS, OS Sehouli [8] 167 CTC (ICC) 46 n.s. Marth [10] 90 CTC (immunomagnetic beads) 25 n.s. Aktas [13] 86 CTC (Multiplex-RT-PCR: AdnaTest) 28 OS 2 Heubner [23] 68 Circulating 20S-proteasomes 19 OS

Judson [24] 53 CTC (ICC) 19 3 n.s. *Abbreviations:* CTC – circulating tumor cells in peripheral blood, DFS – disease-free survival, ICC –

**Median follow-up [months]** 

invasion assay) 18 DFS

protease and caspase activity 18 DFS, OS

**Prognostic significance** 

could contribute to false-positive results and therefore affect further analysis [15].

**2.2 Circulating tumor cells** 

**Author N Method** 

Fan [20] 66 CTC (immunofluorescence, cell

Wimberger [16] 62 Circulating nucleosomes, DNA,

2 Both before and after chemotherapy

3 Mean

immunocytochemistry, n.s. – not significant, PFS – progression-free survival 1 Relapsed ovarian cancer

Table 2. Prognostic relevance of circulating tumor cells in ovarian cancer.

Beyond the prognostic value of DTC detection, monitoring of minimal residual disease following treatment represent a promising parameter for the assessment of residual risk of relapse. Tumor markers such as CA125 are clinically accepted tools for therapy monitoring in advanced ovarian cancer. Nevertheless, CA125 levels generally decline rapidly during chemotherapy and are mostly below cut-off level at the end of treatment even though a significant proportion of patients will face a relapse of the disease within five years. Moreover, the clinical utility of serial CA125 measurements for early therapy of a relapse is currently controversially debated [25]. In this context, the presence of isolated tumor cells in bone marrow and possibly in peripheral blood, might indicate occult tumor load after first line therapy and serve as a parameter for suboptimal response to therapy. For other tumor entities, such as breast cancer, DTC persistence after treatment is an independent indicator of reduced clinical outcome [26]. Whether therapy-resistant DTC also affect survival in ovarian cancer, is a subject of current studies. Wimberger et al. correlated changes in DTC counts before and after first-line chemotherapy to clinical course of disease in 30 ovarian cancer patients. DTC persistence was observed in half of the patients after chemotherapy. Patients with marked increase in DTC numbers had significantly shortened progression-free survival [6].

So far, assessment of therapy efficacy in asymptomatic ovarian cancer patients after completion of standard chemotherapy has not been possible until patient's eventual relapse. A reliable therapy monitoring tool could identify high-risk patients in need of additional therapy. Whether persistent DTC, as surrogate parameter of minimal residual disease, may be targeted by secondary adjuvant therapy is currently under investigation and should be further studied.

#### **2.4 Stem cell hypothesis**

As to progression of ovarian cancer, an interesting hypothesis has been introduced recently. According to 'classical' model of carcinogenesis, any cell may be source of malignant transformation and lead to tumor growth. However, emerging evidence has suggested that the capability of cancer to grow, proliferate and eventually relapse is dependent on a small subpopulation of tumor cells, called cancer stem cells (CSC). These cells are considered especially significant on the background of drug resistance, tumor dormancy, minimal residual disease, and disease recurrence. Several cancer entities, such as ovarian cancer, retinoblastoma, gastrointestinal and breast cancer might arise from a small population of cells with stem cell properties that sustain tumor formation and growth [27]. This 'stem cell hypothesis' assumes an important role of tumor-initiating progenitor cells in tumor progression. Accordingly, cancer stem cells, but not the remaining cells in the primary tumor, have the ability to self-renew, propagate tumorigenesis and are drug-resistant [28]. Ovarian cancer cell lines were demonstrated to feature "side population" cells with ability to differentiate into cancers with different histologies, similar to the assumed pluripotent

New Tumor Biomarkers in Ovarian Cancer and Its Prognostic and Clinical Relevance 145

proteins, OPN may serve as a potential target for the antibody-based therapies. *In vitro*, humanized anti-osteopontin antibody, hu1A12, was effective in inhibiting the cell adhesion, migration, invasion and colony formation and may be a promising therapeutic agent in breast cancer, and possibly other tumor entities, including ovarian cancer, as well [44].

In a manner similar to other acute phase proteins, increased levels of haptoglobin are observed in inflammatory processes, infections and various cancers, including breast, lung and bladder cancers [45]. Elevation of haptoglobin in blood of ovarian cancer patients has been reported in several studies [46-48]. Zhao et al. has shown that elevated haptoglobin at the time of diagnosis is associated with reduced overall survival in a multivariate analysis

Human Epididymis Protein 4 is a stable disulfide core protein associated with the *WFDC2*  gene that is overexpressed in ovarian cancer, particularly serous and endometrioid histologies. Serum HE4 levels were found to be elevated in ovarian cancer patients in numerous studies [49]. Conclusive data on the feasibility as a screening assay is pending. Holcomb et al. reported a superior specificity of HE4 compared to CA125 for the identification of malignant adnexal masses [50]. An evaluation of a marker panel ROMA (Risk of Ovarian Malignancy Algorithm) utilizing CA125 and HE4 initially yielded promising results [51]. However, subsequent validation in clinical setting did not confirm any benefit compared to use of CA125 alone [52, 53]. As to prognostic relevance, Peak et al. have shown that an elevated serum HE4 level was associated with reduced progression-free

Mesothelin is a cell surface glycoprotein that is present on normal mesothelial cells and overexpressed in mesothelioma, ovarian cancer and other malignant tumors [55-57]. As a screening tool, mesothelin was shown to perform comparably to CA125 and might improve

B7-H4 is one of the B7 family members that serve as negative regulators of T cell function. Its overexpression promotes cellular transformation and has been shown in a variety of

Numerous other biomarkers, such as prostasin, VEGF, macrophage colony stimulating factor, kallikrein 6 and 10, mucin 1, interleukins 6 and 8, apolipoprotein A1, OVX1 and many others, have been identified and yielded promising results [60, 61]. In 2009, the U.S. Food and Drug Administration approved the blood test panel OVA1 for the preoperative assessment of pelvic masses [62]. OVA1 incorporates five markers: CA125-II, transferrin, transthyretin, apolipoprotein AI, and beta 2 microglobulin. To date, none of the currently

cancers. Elevated levels of B7-H4 are detected in early-stage ovarian cancer [59].

discussed novel markers has a real potential to replace CA125 in clinical routine.

**3.2 Haptoglobin** 

**3.3 Human Epididymis Protein 4 (HE4)** 

cancer detection as a combined marker panel [58].

[45].

survival [54].

**3.4 Mesothelin** 

**3.5 B7-H4** 

**3.6 Additional markers** 

character of stem cells [29]. Such cells has been detected in various solid tumors, such as colon [30], breast [31] and ovarian cancer [32-34]. Based on animal models and natural course of the disease (e.g. high recurrence rates, multidrug resistance), it has been postulated that cancer stem cells play a crucial role in ovarian cancer [32, 35]. Szotek et al. detected side population (SP) not only in human ovarian cancer cell lines, but also in primary ascites cancer cells [32]. Hosonuma et al. analyzed 28 samples obtained from ovarian cancer patients with respect to the expression of SP as a marker for the presence of cancer stem cells [29]. Side population was encountered more often in recurrent and metastatic patients and SP+ patients had significantly reduced survival. Further, although advanced ovarian cancer mostly initially responds to platinum-based combination treatment, it is usually followed by the chemotherapy-resistant phenotype. One possible explanation for this phenomenon is the CSC-induced drug-resistance: standard therapies fail to target tumor-initiating cells [32]. Recently, cisplatin chemotherapy has been shown to generate residual cells with mesenchymal stem cell-like characteristics *in vitro* [36]. Therefore, these cells need to be targeted with different approaches by identification of specific antigens. However, very few tumor antigens have been described to target the CSC subpopulation.

One currently debated hypothesis is the theory that disseminated and circulating tumor cells, the surrogate marker for minimal residual disease and possibly precursor of systemic metastasis, are cancer stem cells. In breast cancer, Balic et al. reported that early DTC express stem cell phenotype [37] and circulating tumor cells often exhibit epithelialmesenchymal transition markers [31]. In addition, Abraham et al. has shown that a high percentage of CD44+/CD24- cells in the primary tumor correlate with a higher prevalence of distant metastasis [38]. Since the majority of CTC in breast cancer are triple-negative, irrespective of primary tumor's phenotype [39], we may assume that some of these cells reflect stem cell-like subpopulation. This issue, however, has not been studied in ovarian cancer. Whether isolated tumor cells in extraperitoneal sites, such as blood and bone marrow, are in fact ovarian cancer stem cells, remains yet to be cleared.

#### **3. Novel biomarkers in ovarian cancer**

To date, the only tumor marker that has proven to detect ovarian cancer prior to the onset of clinical symptoms and is commonly used in clinical practice is CA125 [40]. However, numerous other biomarkers have been developed over the years and are currently being tested for their usefulness as screening, prognostic or therapy monitoring tools.

#### **3.1 Osteopontin**

Osteopontin (OPN) is a cell surface and secretory glycoprotein containing an arginine– glycine–aspartate motif and is one of the candidate markers identified by high-throughput cDNA microarray techniques. Osteopontin plays a critical role in cellular proliferation, metastasis and apoptosis. Preoperative plasma levels are significantly higher in ovarian cancer patients than in women with benign tumors or in healthy women [41]. OPN levels also seem to correlate with stage of disease. It has been speculated that OPN may complement CA125 expression in a marker panel for recurrence monitoring [42, 43]. When combined with CA125, OPN reaches high sensitivity of 94%. Further, like other cell-surface proteins, OPN may serve as a potential target for the antibody-based therapies. *In vitro*, humanized anti-osteopontin antibody, hu1A12, was effective in inhibiting the cell adhesion, migration, invasion and colony formation and may be a promising therapeutic agent in breast cancer, and possibly other tumor entities, including ovarian cancer, as well [44].

#### **3.2 Haptoglobin**

144 Ovarian Cancer – Basic Science Perspective

character of stem cells [29]. Such cells has been detected in various solid tumors, such as colon [30], breast [31] and ovarian cancer [32-34]. Based on animal models and natural course of the disease (e.g. high recurrence rates, multidrug resistance), it has been postulated that cancer stem cells play a crucial role in ovarian cancer [32, 35]. Szotek et al. detected side population (SP) not only in human ovarian cancer cell lines, but also in primary ascites cancer cells [32]. Hosonuma et al. analyzed 28 samples obtained from ovarian cancer patients with respect to the expression of SP as a marker for the presence of cancer stem cells [29]. Side population was encountered more often in recurrent and metastatic patients and SP+ patients had significantly reduced survival. Further, although advanced ovarian cancer mostly initially responds to platinum-based combination treatment, it is usually followed by the chemotherapy-resistant phenotype. One possible explanation for this phenomenon is the CSC-induced drug-resistance: standard therapies fail to target tumor-initiating cells [32]. Recently, cisplatin chemotherapy has been shown to generate residual cells with mesenchymal stem cell-like characteristics *in vitro* [36]. Therefore, these cells need to be targeted with different approaches by identification of specific antigens. However, very few tumor antigens have been described to target the CSC

One currently debated hypothesis is the theory that disseminated and circulating tumor cells, the surrogate marker for minimal residual disease and possibly precursor of systemic metastasis, are cancer stem cells. In breast cancer, Balic et al. reported that early DTC express stem cell phenotype [37] and circulating tumor cells often exhibit epithelialmesenchymal transition markers [31]. In addition, Abraham et al. has shown that a high percentage of CD44+/CD24- cells in the primary tumor correlate with a higher prevalence of distant metastasis [38]. Since the majority of CTC in breast cancer are triple-negative, irrespective of primary tumor's phenotype [39], we may assume that some of these cells reflect stem cell-like subpopulation. This issue, however, has not been studied in ovarian cancer. Whether isolated tumor cells in extraperitoneal sites, such as blood and bone

To date, the only tumor marker that has proven to detect ovarian cancer prior to the onset of clinical symptoms and is commonly used in clinical practice is CA125 [40]. However, numerous other biomarkers have been developed over the years and are currently being

Osteopontin (OPN) is a cell surface and secretory glycoprotein containing an arginine– glycine–aspartate motif and is one of the candidate markers identified by high-throughput cDNA microarray techniques. Osteopontin plays a critical role in cellular proliferation, metastasis and apoptosis. Preoperative plasma levels are significantly higher in ovarian cancer patients than in women with benign tumors or in healthy women [41]. OPN levels also seem to correlate with stage of disease. It has been speculated that OPN may complement CA125 expression in a marker panel for recurrence monitoring [42, 43]. When combined with CA125, OPN reaches high sensitivity of 94%. Further, like other cell-surface

marrow, are in fact ovarian cancer stem cells, remains yet to be cleared.

tested for their usefulness as screening, prognostic or therapy monitoring tools.

**3. Novel biomarkers in ovarian cancer** 

subpopulation.

**3.1 Osteopontin** 

In a manner similar to other acute phase proteins, increased levels of haptoglobin are observed in inflammatory processes, infections and various cancers, including breast, lung and bladder cancers [45]. Elevation of haptoglobin in blood of ovarian cancer patients has been reported in several studies [46-48]. Zhao et al. has shown that elevated haptoglobin at the time of diagnosis is associated with reduced overall survival in a multivariate analysis [45].

#### **3.3 Human Epididymis Protein 4 (HE4)**

Human Epididymis Protein 4 is a stable disulfide core protein associated with the *WFDC2*  gene that is overexpressed in ovarian cancer, particularly serous and endometrioid histologies. Serum HE4 levels were found to be elevated in ovarian cancer patients in numerous studies [49]. Conclusive data on the feasibility as a screening assay is pending. Holcomb et al. reported a superior specificity of HE4 compared to CA125 for the identification of malignant adnexal masses [50]. An evaluation of a marker panel ROMA (Risk of Ovarian Malignancy Algorithm) utilizing CA125 and HE4 initially yielded promising results [51]. However, subsequent validation in clinical setting did not confirm any benefit compared to use of CA125 alone [52, 53]. As to prognostic relevance, Peak et al. have shown that an elevated serum HE4 level was associated with reduced progression-free survival [54].

#### **3.4 Mesothelin**

Mesothelin is a cell surface glycoprotein that is present on normal mesothelial cells and overexpressed in mesothelioma, ovarian cancer and other malignant tumors [55-57]. As a screening tool, mesothelin was shown to perform comparably to CA125 and might improve cancer detection as a combined marker panel [58].

#### **3.5 B7-H4**

B7-H4 is one of the B7 family members that serve as negative regulators of T cell function. Its overexpression promotes cellular transformation and has been shown in a variety of cancers. Elevated levels of B7-H4 are detected in early-stage ovarian cancer [59].

#### **3.6 Additional markers**

Numerous other biomarkers, such as prostasin, VEGF, macrophage colony stimulating factor, kallikrein 6 and 10, mucin 1, interleukins 6 and 8, apolipoprotein A1, OVX1 and many others, have been identified and yielded promising results [60, 61]. In 2009, the U.S. Food and Drug Administration approved the blood test panel OVA1 for the preoperative assessment of pelvic masses [62]. OVA1 incorporates five markers: CA125-II, transferrin, transthyretin, apolipoprotein AI, and beta 2 microglobulin. To date, none of the currently discussed novel markers has a real potential to replace CA125 in clinical routine.

New Tumor Biomarkers in Ovarian Cancer and Its Prognostic and Clinical Relevance 147

these patients benefit from a more aggressive or prolonged treatment remains to be

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evaluated.

**5. References** 


Table 3. Novel biomarkers in ovarian cancer and their potential impact on diagnostics and therapy.

### **4. Conclusions**

Despite advances in diagnostics and therapy, 60% of women diagnosed with ovarian cancer will eventually suffer from a relapse, resulting in a poor overall survival. Currently, efficacy of therapy is evaluated by physical examinations, radiographic imaging, and evaluation of CA125 levels. There continues to be a need to identify new biomarkers for better prediction and prognostication.

Early hematogenous tumor cell dissemination is a common phenomenon in solid epithelial cancers. There is growing evidence that detection of single tumor cells in blood or bone marrow of ovarian cancer patients is associated with reduced clinical outcome. Whether these patients benefit from a more aggressive or prolonged treatment remains to be evaluated.

#### **5. References**

146 Ovarian Cancer – Basic Science Perspective

**Useful as a screening tool** 

Low specificity; possibly useful in combination with CA125 [41, 43]

Low specificity [46, 48]

Unclear due to partly contradictory results [50, 52, 65]

Not superior to CA125, however use in combination with CA125 possible [58]

Possibly yes in combination with CA125 [59]

Table 3. Novel biomarkers in ovarian cancer and their potential impact on diagnostics and

Despite advances in diagnostics and therapy, 60% of women diagnosed with ovarian cancer will eventually suffer from a relapse, resulting in a poor overall survival. Currently, efficacy of therapy is evaluated by physical examinations, radiographic imaging, and evaluation of CA125 levels. There continues to be a need to identify new biomarkers for better prediction

Early hematogenous tumor cell dissemination is a common phenomenon in solid epithelial cancers. There is growing evidence that detection of single tumor cells in blood or bone marrow of ovarian cancer patients is associated with reduced clinical outcome. Whether

**Useful for therapy monitoring** 

Possibly yes; in combination with CA125 [42]

Unclear; mostly decrease during chemotherapy [47]

No conclusive data

No conclusive data

No conclusive data

**Clinical relevance** 

Limited data on prognostic relevance [63]; possible use in targeted therapy [44, 64]

Prognostic relevance - yes (data from small studies) [45]

Prognostic relevance - yes (data from small studies) [54]

Possible use in targeted therapy [66, 67]

No prognostic value [59, 68]

**Biomarker Function** 

Haptoglobin Acute phase protein

Mesothelin Surface antigen of

B7-H4 Negative regulator

Cell surface protein; plays a role in cellular proliferation, metastasis and apoptosis

> Secreted glycoprotein

mesothelial cells

of T cell function

Osteopontin

Human epididymis protein 4

therapy.

**4. Conclusions** 

and prognostication.


New Tumor Biomarkers in Ovarian Cancer and Its Prognostic and Clinical Relevance 149

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[43] Rosen DG, Wang L, Atkinson JN, Yu Y, Lu KH, Diamandis EP, Hellstrom I, Mok SC,

[44] Dai J, Li B, Shi J, Peng L, Zhang D, Qian W, Hou S, Zhao L, Gao J, Cao Z *et al*: A

[45] Zhao C, Annamalai L, Guo C, Kothandaraman N, Koh SC, Zhang H, Biswas A,

sera of patients with epithelial ovarian cancer. *Neoplasia* 2007, 9(1):1-7. [46] Ye B, Cramer DW, Skates SJ, Gygi SP, Pratomo V, Fu L, Horick NK, Licklider LJ,

profiling and mass spectrometry. *Clin Cancer Res* 2003, 9(8):2904-2911. [47] Ahmed N, Barker G, Oliva KT, Hoffmann P, Riley C, Reeve S, Smith AI, Kemp BE,

Preoperative plasma osteopontin level as a biomarker complementary to carbohydrate antigen 125 in predicting ovarian cancer. *J Obstet Gynaecol Res* 2006,

Berkowitz RS, Mok SC: Osteopontin as an adjunct to CA125 in detecting recurrent

Liu J, Bast RC, Jr.: Potential markers that complement expression of CA125 in

humanized anti-osteopontin antibody inhibits breast cancer growth and metastasis

Choolani M: Circulating haptoglobin is an independent prognostic factor in the

Schorge JO, Berkowitz RS *et al*: Haptoglobin-alpha subunit as potential serum biomarker in ovarian cancer: identification and characterization using proteomic

Quinn MA, Rice GE: Proteomic-based identification of haptoglobin-1 precursor as a novel circulating biomarker of ovarian cancer. *Br J Cancer* 2004, 91(1):129-140. [48] Bertenshaw GP, Yip P, Seshaiah P, Zhao J, Chen TH, Wiggins WS, Mapes JP, Mansfield

BC: Multianalyte profiling of serum antigens and autoimmune and infectious disease molecules to identify biomarkers dysregulated in epithelial ovarian cancer.

G, DiSilvestro P, Granai CO *et al*: The use of multiple novel tumor biomarkers for the detection of ovarian carcinoma in patients with a pelvic mass. *Gynecol Oncol* 

superior specificity in the differentiation of benign and malignant adnexal masses

RJ, Bast RC, Skates SJ: Comparison of a novel multiple marker assay vs the Risk of Malignancy Index for the prediction of epithelial ovarian cancer in patients with a

Moor B, Vergote I: HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm. *Br J Cancer* 

[49] Moore RG, Brown AK, Miller MC, Skates S, Allard WJ, Verch T, Steinhoff M, Messerlian

[50] Holcomb K, Vucetic Z, Miller MC, Knapp RC: Human epididymis protein 4 offers

[51] Moore RG, Jabre-Raughley M, Brown AK, Robison KM, Miller MC, Allard WJ, Kurman

[52] Van Gorp T, Cadron I, Despierre E, Daemen A, Leunen K, Amant F, Timmerman D, De

cancer. *Curr Opin Oncol* 2010, 22(5):492-497.

ovarian cancer. *Clin Cancer Res* 2004, 10(10):3474-3478.

epithelial ovarian cancer. *Gynecol Oncol* 2005, 99(2):267-277.

in vivo. *Cancer Immunol Immunother* 2010, 59(3):355-366.

*Cancer Epidemiol Biomarkers Prev* 2008, 17(10):2872-2881.

in premenopausal women. *Am J Obstet Gynecol* 2011.

pelvic mass. *Am J Obstet Gynecol* 2010, 203(3):228 e221-226.

32(3):309-314.

2008, 108(2):402-408.

2011, 104(5):863-870.


**9** 

*USA* 

**Sensitive Detection of Epithelial Ovarian Cancer** 

Epithelial ovarian cancer (EOC) is the 5th leading cause of death from cancer in women and the main cause of death from gynecological cancer (Barber, 1986). Women have a lifetime risk of ovarian cancer of around 1.5%, which makes it the second most common gynecologic malignancy after breast cancer. Ovarian cancer is often referred to as 'the silent killer' because it frequently causes non-specific symptoms, which contribute to diagnostic delay, diagnosis in a late stage and a poor prognosis. This is one of the reasons for the relatively low, approximately 40%, 5-year survival rate for women diagnosed with advanced EOC. However, when EOC is diagnosed at an early stage this rate increases up to 95% (McGuire *et al.*, 2000). This enhancement demonstrates that early detection of EOC is crucial and it is vital to develop novel diagnostic methods for higher throughput screening of human

One of the important and promising strategies for early cancer diagnosis relies on the development of approaches that can provide accurate detection and identification of specific protein-biomarkers in the serum. These protein-biomarkers would be measured and monitored to yield specific signatures that can be used for the early detection of the disease. Recently, numerous reports demonstrated that a *single* biomarker (example: CA 125, biomarker of ovarian cancer) approach is highly unlikely to yield results that can accurately distinguish cancer samples from healthy ones. This led researchers to explore the idea of using a *basket* of biomarkers (Petricoin *et al.*, 2002; Mor *et al.,* 2005) with the expectation that this approach may yield increased specificity and sensitivity for cancer detection. Using this approach, G. Mor *et al.* reported 95% efficiency discrimination between disease-free and EOC patients, including patients diagnosed with stage I and II disease (Mor *et al.*, 2005). These authors used a blood test, based on the simultaneous identification of four biomarkers: leptin, prolactin, osteopontin, and insulin-like growth factor-II. Petricoin *et al.* reported the use of mass spectroscopy to develop a classifier that could identify serum from patients with ovarian cancer with 100% sensitivity and 95% specificity (Petricoin *et al.*, 2002). In a follow up study, Zhu *et al.* reported similar results (Zhu, 2003). However, questions were raised about tests reproducibility and reliability (Wagner, 2003; Garber,

**1. Introduction** 

 \*

Corresponding Author

samples and new biomarkers discovery.

**Biomarkers Using Tag-Laser Induced** 

**Breakdown Spectroscopy** 

*Optical Science Center for Applied Research,* 

*Delaware State University* 

Yuri Markushin and Noureddine Melikechi\*


## **Sensitive Detection of Epithelial Ovarian Cancer Biomarkers Using Tag-Laser Induced Breakdown Spectroscopy**

Yuri Markushin and Noureddine Melikechi\* *Optical Science Center for Applied Research, Delaware State University USA* 

#### **1. Introduction**

152 Ovarian Cancer – Basic Science Perspective

[67] Ho M, Feng M, Fisher RJ, Rader C, Pastan I: A novel high-affinity human monoclonal

[68] Kryczek I, Wei S, Zhu G, Myers L, Mottram P, Cheng P, Chen L, Coukos G, Zou W:

Relationship between B7-H4, regulatory T cells, and patient outcome in human

antibody to mesothelin. *Int J Cancer* 2011, 128(9):2020-2030.

ovarian carcinoma. *Cancer Res* 2007, 67(18):8900-8905.

Epithelial ovarian cancer (EOC) is the 5th leading cause of death from cancer in women and the main cause of death from gynecological cancer (Barber, 1986). Women have a lifetime risk of ovarian cancer of around 1.5%, which makes it the second most common gynecologic malignancy after breast cancer. Ovarian cancer is often referred to as 'the silent killer' because it frequently causes non-specific symptoms, which contribute to diagnostic delay, diagnosis in a late stage and a poor prognosis. This is one of the reasons for the relatively low, approximately 40%, 5-year survival rate for women diagnosed with advanced EOC. However, when EOC is diagnosed at an early stage this rate increases up to 95% (McGuire *et al.*, 2000). This enhancement demonstrates that early detection of EOC is crucial and it is vital to develop novel diagnostic methods for higher throughput screening of human samples and new biomarkers discovery.

One of the important and promising strategies for early cancer diagnosis relies on the development of approaches that can provide accurate detection and identification of specific protein-biomarkers in the serum. These protein-biomarkers would be measured and monitored to yield specific signatures that can be used for the early detection of the disease. Recently, numerous reports demonstrated that a *single* biomarker (example: CA 125, biomarker of ovarian cancer) approach is highly unlikely to yield results that can accurately distinguish cancer samples from healthy ones. This led researchers to explore the idea of using a *basket* of biomarkers (Petricoin *et al.*, 2002; Mor *et al.,* 2005) with the expectation that this approach may yield increased specificity and sensitivity for cancer detection. Using this approach, G. Mor *et al.* reported 95% efficiency discrimination between disease-free and EOC patients, including patients diagnosed with stage I and II disease (Mor *et al.*, 2005). These authors used a blood test, based on the simultaneous identification of four biomarkers: leptin, prolactin, osteopontin, and insulin-like growth factor-II. Petricoin *et al.* reported the use of mass spectroscopy to develop a classifier that could identify serum from patients with ovarian cancer with 100% sensitivity and 95% specificity (Petricoin *et al.*, 2002). In a follow up study, Zhu *et al.* reported similar results (Zhu, 2003). However, questions were raised about tests reproducibility and reliability (Wagner, 2003; Garber,

<sup>\*</sup> Corresponding Author

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

equipped with 5 µm pore size filters or magnetizing.

B with various concentrations of CA 125.

Using Tag-Laser Induced Breakdown Spectroscopy 155

To illustrate this approach, we show in Figure 1 a schematic of the multi-element coded nano- and micro-particle based assay composed of *2 elements: Si and Fe*. To perform immunoassay we used ovarian cancer biomarkers *Leptin and CA 125* with pairs of relevant monoclonal antibodies. Monoclonal antibodies were biotinylated prior to performing the assay. To mimic blood conditions, all buffers contained about 5% of bovine serum albumin (BSA) (Majoor, 1946). For separation of single and aggregated particles we used test tubes

Fig. 1. Tag-LIBS experimental schematics. **Step a** – biotinylation of the group A antibodies (M86429M); **step b** – attaching biotinylated group A antibodies (M86429M) to silicon oxide particles modified with streptavidin (suspension B); **step c** – attaching of the group B antibodies (M86306M) to iron oxide particles modified with protein G (suspension A); **step d** - incubation (following by magnetizing and washing) of the mixture of suspensions A and

Many optical detection techniques developed to perform assays are based on the use of fluorescent tags (Zal and Gascoignea, 2004). Although these techniques have many advantages, they often suffer from photo bleaching and it is therefore advantageous and

2004). Despite this roller coaster, the need for the development of high-throughput methods and multiplexing assays for the simultaneous detection of multiple analytes is real (Rubenstein, 2010).

Several approaches addressing the development of multiplexing assays have been reported in recent years. C.A. Mirkin *et al.* proposed using oligonucleotides as barcodes (DNA barcode) and nano-particles aggregates that can alter their physical properties (e.g. optical, electrical, mechanical) upon aggregation (Mirkin *et al.*, 2005). The simultaneous measurement of serum proteins and sequence-specific oligonucleotide probes by employing 16 sets of fluorescent microspheres and a flow cytometer has been demonstrated (Fulton *et al.*, 1997). Recently, flow injection inductively coupled plasma mass-spectrometry and macrocyclic metal chelate complexes loaded with different lanthanides have also been used to detect several model peptides and protein (Ahrends *et al.*, 2007). Further, application of multicolor quantum dots for molecular diagnostics of cancer was reported (Smith *et al.*, 2006) and barcodes striped metal nanoparticles were used to provide multiplexed data in various bioassays (Freeman *et al.,* 2005).

Many of the multiplex assays developed recently are based on antigen induced particle aggregation (Gosling *et al.*, 1990). Therefore, it is important to strive to better understand the aggregation mechanism. Various systems have been employed to study the aggregation process. Examples include lipid vesicles (Farbman-Yogev *et al.*, 1998) and metal/metalloid particles (Freeman *et al.*, 2005; Smith *et al.*, 2006). A simplified vesicle aggregation theory was developed (Farbman-Yogev *et al.*, 1998). This theory deals primarily with vesicles made of biotinylated lipid molecules. The presence of streptavidin molecules induces aggregation (flocculation). The vesicles are made of amphiphilic molecules combined to form shell-like bilayer structures. Therefore, the bonds between lipids and vesicles can be weaker than bonds between biotins on lipids and streptavidin molecules in a solution. This theory predicts the dissociation of initial cross-linked molecules from the vesicles and their reaggregation into preferably smaller aggregates (Farbman-Yogev *et al.*, 1998).

Particle based immuno-assays have been reviewed in a number of publications (Gosling *et al.*, 1990; Smith *et al.*, 2006). Metal and metalloid particles are typically modified with the IgG molecules, which are strongly (irreversibly) attached to the surface. The presence of antigen molecules induces aggregation. In contrast to lipid vesicles, solid-state particles are unlikely to lose the cross-linked molecules and the particle aggregates are more stable (Farbman-Yogev *et al.*, 1998). From this perspective, the metal and metalloid nano- and micro-particles seem more appropriate for developing effective immuno-assays.

#### **2. Tagging specific proteins: Multi-element coded nano- and micro-particle assay**

As discussed above, multiplexing combined with particle-based assay provides an effective and promising approach for developing diagnostics. After thorough consideration of advantages and disadvantages of the existing technologies briefly described above, we present a novel type of assay on a base of multi-element coded nano- and micro-particle tags and Laser-Induced Breakdown Spectroscopy (LIBS) as a detection method (Meelikechi and Markushin, patent pending). This approach, developed in our laboratory, relies on the use of nano- and micro-particles composed of different chemical elements to yield single and multi-element code for labeling of the molecules of interest.

2004). Despite this roller coaster, the need for the development of high-throughput methods and multiplexing assays for the simultaneous detection of multiple analytes is

Several approaches addressing the development of multiplexing assays have been reported in recent years. C.A. Mirkin *et al.* proposed using oligonucleotides as barcodes (DNA barcode) and nano-particles aggregates that can alter their physical properties (e.g. optical, electrical, mechanical) upon aggregation (Mirkin *et al.*, 2005). The simultaneous measurement of serum proteins and sequence-specific oligonucleotide probes by employing 16 sets of fluorescent microspheres and a flow cytometer has been demonstrated (Fulton *et al.*, 1997). Recently, flow injection inductively coupled plasma mass-spectrometry and macrocyclic metal chelate complexes loaded with different lanthanides have also been used to detect several model peptides and protein (Ahrends *et al.*, 2007). Further, application of multicolor quantum dots for molecular diagnostics of cancer was reported (Smith *et al.*, 2006) and barcodes striped metal nanoparticles were used to provide multiplexed data in

Many of the multiplex assays developed recently are based on antigen induced particle aggregation (Gosling *et al.*, 1990). Therefore, it is important to strive to better understand the aggregation mechanism. Various systems have been employed to study the aggregation process. Examples include lipid vesicles (Farbman-Yogev *et al.*, 1998) and metal/metalloid particles (Freeman *et al.*, 2005; Smith *et al.*, 2006). A simplified vesicle aggregation theory was developed (Farbman-Yogev *et al.*, 1998). This theory deals primarily with vesicles made of biotinylated lipid molecules. The presence of streptavidin molecules induces aggregation (flocculation). The vesicles are made of amphiphilic molecules combined to form shell-like bilayer structures. Therefore, the bonds between lipids and vesicles can be weaker than bonds between biotins on lipids and streptavidin molecules in a solution. This theory predicts the dissociation of initial cross-linked molecules from the vesicles and their re-

Particle based immuno-assays have been reviewed in a number of publications (Gosling *et al.*, 1990; Smith *et al.*, 2006). Metal and metalloid particles are typically modified with the IgG molecules, which are strongly (irreversibly) attached to the surface. The presence of antigen molecules induces aggregation. In contrast to lipid vesicles, solid-state particles are unlikely to lose the cross-linked molecules and the particle aggregates are more stable (Farbman-Yogev *et al.*, 1998). From this perspective, the metal and metalloid nano- and micro-particles

**2. Tagging specific proteins: Multi-element coded nano- and micro-particle** 

As discussed above, multiplexing combined with particle-based assay provides an effective and promising approach for developing diagnostics. After thorough consideration of advantages and disadvantages of the existing technologies briefly described above, we present a novel type of assay on a base of multi-element coded nano- and micro-particle tags and Laser-Induced Breakdown Spectroscopy (LIBS) as a detection method (Meelikechi and Markushin, patent pending). This approach, developed in our laboratory, relies on the use of nano- and micro-particles composed of different chemical elements to yield single and

aggregation into preferably smaller aggregates (Farbman-Yogev *et al.*, 1998).

seem more appropriate for developing effective immuno-assays.

multi-element code for labeling of the molecules of interest.

real (Rubenstein, 2010).

**assay** 

various bioassays (Freeman *et al.,* 2005).

To illustrate this approach, we show in Figure 1 a schematic of the multi-element coded nano- and micro-particle based assay composed of *2 elements: Si and Fe*. To perform immunoassay we used ovarian cancer biomarkers *Leptin and CA 125* with pairs of relevant monoclonal antibodies. Monoclonal antibodies were biotinylated prior to performing the assay. To mimic blood conditions, all buffers contained about 5% of bovine serum albumin (BSA) (Majoor, 1946). For separation of single and aggregated particles we used test tubes equipped with 5 µm pore size filters or magnetizing.

Fig. 1. Tag-LIBS experimental schematics. **Step a** – biotinylation of the group A antibodies (M86429M); **step b** – attaching biotinylated group A antibodies (M86429M) to silicon oxide particles modified with streptavidin (suspension B); **step c** – attaching of the group B antibodies (M86306M) to iron oxide particles modified with protein G (suspension A); **step d** - incubation (following by magnetizing and washing) of the mixture of suspensions A and B with various concentrations of CA 125.

Many optical detection techniques developed to perform assays are based on the use of fluorescent tags (Zal and Gascoignea, 2004). Although these techniques have many advantages, they often suffer from photo bleaching and it is therefore advantageous and

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

Using Tag-Laser Induced Breakdown Spectroscopy 157

Fig. 2. LIBS experimental set-up consisting of a Nd-YAG Laser pulse laser, prism, focusing lens, bundle of light collecting optical fibers, thermoelectric cooler (TEC), a Helium line and

To implement the multi-element coded approach and demonstrate its potential, we prepared and analyzed two-element composite particles (Markushin *et al.*, 2009). We used 1.5 µm iron oxide biotinylated particles and 3 µm silicon particles with attached avidin. First, two types of control experiments (experiments #1 and # 3) were performed. In control experiments we checked for the possibility of nonspecific binding of iron oxide biotinylated particles to the plastic lab ware components (LIBS spectrum of experiment # 1 on Fig. 3a, 3b). Nonspecific binding was found to be insignificant. In another control experiment, # 3, (Fig. 3a, 3b) iron oxide biotinylated particles were pre-incubated with an excess of avidin molecules that allowed neutralizing the biotin groups of iron oxide particles with avidin. The following incubation of the neutralized iron oxide particles with Si-avidin particles and LIBS analysis demonstrates that nonspecific interactions between both types of microparticles are limited. Some portion of silicon particles with probably bigger than the 3 µm particle size can be trapped by 5 µm pore size filter (short-dashed line on Fig. 3a, 3b). This was confirmed by additional control experiment with filtering of the 3µm size silicon

the Ocean Optics 7-channel spectrometer (OOI spectrometer).

particles over 5 µm pore size filters (data not shown).

useful to explore the development of approaches that do not suffer from such a limitation. To do this, it is useful to note that there is a broad class of materials (metals and non-metals), which are neither fluorescent nor effectively chemically active. These materials can be combined to yield composites that can also be used as tags. This provides a large number of possibilities: combining only 11 of such materials (i.e. iron, gold, silver, platinum, aluminum, copper, titanium, nickel, zinc, tin and copper) into micro- and nano-particles can yield more than 1000 types of different composite particles, some of which are well known alloys such as brass (copper and zinc), bronze (copper and tin), and duralumin (aluminum and copper). Each of these alloys and composites is unique by chemical content and potentially can be used as micro-tags for labeling and detection of ovarian cancer biomarkers.

Below we provide examples of the use of the multi-element coded particle assay for the detection of analytes.

#### **3. Reading the tags: Laser induced-breakdown spectroscopy for early cancer diagnosis**

Following the sample preparation described in the previous section, we focus our discussion on the detection of the micro- and/or nano-particles attached to the specific biomarkers of interest. To perform this step, we use an all-optical technique widely used in many applications ranging from space exploration to quality control but only rarely for medical applications. This technique, known as Laser Induced Breakdown Spectroscopy (LIBS) is well described in the literature and only a brief description is provided in this chapter. For more information, we refer the reader to the excellent body of literature available (Cremers and Radziemski, 2006, Miziolek *et al.* 2006, and Markushin *et al.*, 2009).

LIBS is an analytical technique based on the use of laser pulses intense enough to breakdown the chemical bonds of the constituents of a sample to be interrogated. The experimental arrangement used in our laboratory is shown in Figure 2. By focusing nanosecond long laser pulses onto a sample, a short-lived plasma of the sample is generated. These laser pulses ablate a small quantity of the sample - a few hundreds of nanograms - located on an automated 3-D translational stage. Light emitted by the plasma during cooling is collected by a bundle of optical fibers, which transmits it to a 7-channel Ocean Optics LIBS HR2000+ spectrometer (190 - 970 nm) for analysis. The 10 nsec Qswitched Nd-YAG laser (BM Industries Serie 5000) operating at 1064 nm was used for the sample ablation. About 100 laser shots on 100 different spots on a filter were used to collect the LIBS spectra. For the purpose of this investigation, we used average laser pulse energies of about 70 mJ/pulse, a double convex BK7 focusing lens with a focal length of 70 mm and antireflection coating for 1064 nm was positioned at about 61.7 mm distance from the surface of the filter. LIBS spectra were obtained at ambient atmospheric conditions. To identify the measured atomic and ionic lines, we use the LIBS spectral database developed by our group (OSCAR website, n.d., Rock *et al.*, 2008). Potentially, the multi-element coded assay is able to detect and identify numerous analytes in parallel with minimum interference. This method has the added advantage of requiring as little as a few micro liters of serum specimen. More details of the experiment can be found elsewhere (Markushin *et al.*, 2009, Rock *et al.*, 2008).

useful to explore the development of approaches that do not suffer from such a limitation. To do this, it is useful to note that there is a broad class of materials (metals and non-metals), which are neither fluorescent nor effectively chemically active. These materials can be combined to yield composites that can also be used as tags. This provides a large number of possibilities: combining only 11 of such materials (i.e. iron, gold, silver, platinum, aluminum, copper, titanium, nickel, zinc, tin and copper) into micro- and nano-particles can yield more than 1000 types of different composite particles, some of which are well known alloys such as brass (copper and zinc), bronze (copper and tin), and duralumin (aluminum and copper). Each of these alloys and composites is unique by chemical content and potentially can be used as micro-tags for labeling and detection of ovarian cancer

Below we provide examples of the use of the multi-element coded particle assay for the

**3. Reading the tags: Laser induced-breakdown spectroscopy for early cancer** 

Following the sample preparation described in the previous section, we focus our discussion on the detection of the micro- and/or nano-particles attached to the specific biomarkers of interest. To perform this step, we use an all-optical technique widely used in many applications ranging from space exploration to quality control but only rarely for medical applications. This technique, known as Laser Induced Breakdown Spectroscopy (LIBS) is well described in the literature and only a brief description is provided in this chapter. For more information, we refer the reader to the excellent body of literature available (Cremers

LIBS is an analytical technique based on the use of laser pulses intense enough to breakdown the chemical bonds of the constituents of a sample to be interrogated. The experimental arrangement used in our laboratory is shown in Figure 2. By focusing nanosecond long laser pulses onto a sample, a short-lived plasma of the sample is generated. These laser pulses ablate a small quantity of the sample - a few hundreds of nanograms - located on an automated 3-D translational stage. Light emitted by the plasma during cooling is collected by a bundle of optical fibers, which transmits it to a 7-channel Ocean Optics LIBS HR2000+ spectrometer (190 - 970 nm) for analysis. The 10 nsec Qswitched Nd-YAG laser (BM Industries Serie 5000) operating at 1064 nm was used for the sample ablation. About 100 laser shots on 100 different spots on a filter were used to collect the LIBS spectra. For the purpose of this investigation, we used average laser pulse energies of about 70 mJ/pulse, a double convex BK7 focusing lens with a focal length of 70 mm and antireflection coating for 1064 nm was positioned at about 61.7 mm distance from the surface of the filter. LIBS spectra were obtained at ambient atmospheric conditions. To identify the measured atomic and ionic lines, we use the LIBS spectral database developed by our group (OSCAR website, n.d., Rock *et al.*, 2008). Potentially, the multi-element coded assay is able to detect and identify numerous analytes in parallel with minimum interference. This method has the added advantage of requiring as little as a few micro liters of serum specimen. More details of the experiment can be found elsewhere (Markushin *et* 

and Radziemski, 2006, Miziolek *et al.* 2006, and Markushin *et al.*, 2009).

biomarkers.

**diagnosis** 

detection of analytes.

*al.*, 2009, Rock *et al.*, 2008).

Fig. 2. LIBS experimental set-up consisting of a Nd-YAG Laser pulse laser, prism, focusing lens, bundle of light collecting optical fibers, thermoelectric cooler (TEC), a Helium line and the Ocean Optics 7-channel spectrometer (OOI spectrometer).

To implement the multi-element coded approach and demonstrate its potential, we prepared and analyzed two-element composite particles (Markushin *et al.*, 2009). We used 1.5 µm iron oxide biotinylated particles and 3 µm silicon particles with attached avidin. First, two types of control experiments (experiments #1 and # 3) were performed. In control experiments we checked for the possibility of nonspecific binding of iron oxide biotinylated particles to the plastic lab ware components (LIBS spectrum of experiment # 1 on Fig. 3a, 3b). Nonspecific binding was found to be insignificant. In another control experiment, # 3, (Fig. 3a, 3b) iron oxide biotinylated particles were pre-incubated with an excess of avidin molecules that allowed neutralizing the biotin groups of iron oxide particles with avidin. The following incubation of the neutralized iron oxide particles with Si-avidin particles and LIBS analysis demonstrates that nonspecific interactions between both types of microparticles are limited. Some portion of silicon particles with probably bigger than the 3 µm particle size can be trapped by 5 µm pore size filter (short-dashed line on Fig. 3a, 3b). This was confirmed by additional control experiment with filtering of the 3µm size silicon particles over 5 µm pore size filters (data not shown).

Sensitive Detection of Epithelial Ovarian Cancer Biomarkers

method for the future element coded assay development.

155 ppb

310 ppb

Using Tag-Laser Induced Breakdown Spectroscopy 159

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

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

775 ppb 1.55 ppm

Avidin, ppb

0 500 1000 1500 2000 2500 3000

3.1 ppm

(Markushin *et al.*, 2009))

0

5

10

LIBS intensity, a.u.

62 ppb

0 ppb

15

20

25

Fig. 3. LIBS based identification of two-element Fe-Si composite micro-particles. The fragment of the LIBS spectrum around 288.1 nm Si line (a). The fragment of the LIBS spectrum around 259.9 mn Fe line (b). Solid line – empty filter, dot-dashed line - experiment # 1, dashed line - experiment # 2, short-dashed lines - experiment # 3 (adapted from (Markushin *et al.*, 2009))

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

Fe

a

b

Fig. 3. LIBS based identification of two-element Fe-Si composite micro-particles. The fragment of the LIBS spectrum around 288.1 nm Si line (a). The fragment of the LIBS
