Chapter 1 Jteuiem of £it&uUwie Chapter 1

Chapter 1: Review of Literature

1.1. Introduction to Ovarian Cancer 1.2. Classification of Ovarian Cancer 1.3. Presentation and Patliology 1.4. Molecular Features Redefine Origins of Ovarian Cancer 1.5. Precursors of HGSC: Ovarian Surface Epithelia or Epithelia 1.6. Modalities of Spread of Cancer 1.7. Cun^ent Approaches for Treatment 1.8. Molecular Classification in Various Cancers 1.9. Molecular Classification in High-Grade Serous Ovarian Adenocarcinoma 1.10. Need for Further Studies

1.1. Introduction to Ovarian Cancer Ovarian cancer is a cellular anomaly leading to atypical, asymptomatic growth in the , which rapidly spreads to distant organs and results in diagnosis at a late stage of the disease. According to the National Cancer Institute - Surveillance, Epidemiology, and End Results (SEER) Program fact sheet, the number of new cases in the United States of America (USA) identified during 2008-2012 was 12.1 per 100,000 women per year, while the number of deaths during the same period reported were 7.7 per 100,000 women per year ("Cancer of the Ovary - SEER Stat Fact Sheets" 2016). Overall 28.3% patients have a 5-year survival as documented in SEER 2005-2011. In all, ovarian cancer accounts for maximum deaths as compared to any other gynecological associated tumors within the USA. In India, ovarian cancer ranks within the top five most common cancers as per the Consolidated Report of Hospital Based Cancer Registries: 2007-2011 (Indian Council of Medical Research, ICMR) and the number of projected cases (Takiar, Nadayil, and Nandakumar 2010). Further, 5-9% of female cancer burden in cities is of ovarian cancer as is included in registries (Figure 1.1). The occurrence of ovarian adenocarcinoma and papillary adenocarcinoma is 24.6% (Dirbugarh) to 63.6% (Guwahati)(NCRP2013).

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Other Breast 128,468 (23.9%) 144,937 (27X)

Leukamia _, 12.913 (2.4%) Oesophagus 14,622 (2.7%) Lung 16.547 (MX) ,/ I Uterr Up, oral cavity 122.844 (22.9%) 23,161 (4.J%)

Stomach 19,711 (1.7%)

Ovary Colorectum 26.834 {i.0%] 27,415 (5.1%)

Other Breast 128.468 (23.9%) 70,218 (21.5%)

Leul

Lung 15,062 (4.6%) Lip, oral cavity Cervix Uteri 67,477 (20.7%) 15.631 (4.8%)

Stomach 18,320 (5.6%)

Ovary Colorectum 19,549 (6.0%) 20,789 (6.4%)

Figure 1.1. Estimated number of ovarian cancer a. incidence and b. mortaiity in india in 2012 (source: International Agency for Research on Cancer, lACR). 1.2. Classification of Ovarian Cancer Ovarian cancer is morphologically and biologically a highly heterogeneous disease. The World Health Organization (WHO) classification of ovarian tumors based on histology has categorized ovarian tumors as

i) Surface epithelial-stromal tumors;

11) Sex cord-stromal tumors;

iii) Genm cell tumors;

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iv) Germ cell sex cord-stromal tumors; v) Tumors of the rete ovarii; and vl) Miscellaneous tumors (Tavassoli and Devilee 2003).

Tumors developing from surface epithelia, sex cord and germ cells together have high incidences over others (Figure 1.2).

The surface epithelial-stromal tumor is the most common type and refers to ovarian malignancies developing from the mesothelial lining of the ovary. Serous tumors within epithelial-stromal tumors are further sub-classified intofc>enign, borderiine and malignant (Chen et al. 2003). The benign tumors usually form cysts and do not lead to poor differentiation (anaplasia) features; borderiine tumors may contain anaplastic features; they do not invade the but may metastasize to the peritoneum, while malignant tumors are usually large in size, show anaplastic features and frequently invade the stroma. The malignant type includes the low and high grade serous adenocarcinoma.

Recent approaches of molecular and genetic mutations coupled with histology divides surface epithelial-stromal tumors into high-grade serous adenocarcinoma (HGSC, 70%), endometrioid carcinoma (10%), clear cell carcinoma (10%), mucinous carcinoma (3%), and low-grade serous carcinoma (LGSC, <5%) (Prat 2012). Each of these subtypes have unique epidemiologic and genetic risk factors as well as biologic behavior such as tumor spread and chemotherapeutic response (Vaughan ef a/. 2011; Tan ef al. 2013). Serous tumors are occasionally described as serous papillary tumors owing to their architectural pattern. Of all the malignancies within ovarian cancer, HGSC is the most lethal form among all gynaecological cancers. HGSCs are aggressive, usually identified in advanced stage and hart}or p53 mutations. The origin of cells leading development of HGSC is not cleariy evident (Kurman 2013). Endometrioid adenocarcinomas consist of glandular structures with stratified, on-mucinous epithelium and may occasionally be pooriy differentiated. Clear cell adenocarcinoma is characterized by clear cells having distinct cytoplasmic membranes in a papillary or tubular pattern. Mucinous tumors have cells with intracytoplasmic mucin and are usually curable (D'Angelo, Prat, and D'Angelo 2010). The LGSC are indolent and develop from well-established 'atypical proliferative (borderline) tumors', and harbor mutations in Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), B-Raf proto-oncogene, serine/threonine kinase (BRAF) and erb-b2 receptor tyrosine kinase 2 (ERBB2) genes (Kumian 2013).

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Sex cord-stromal tumors are uncommon tumors that involve supporting cells of the gonads and originate from sex cords and stroma of ovary. These are usually diagnosed at early stage and are usually surgically eliminated (Ray-Coquard et al. 2014). These include granulosa- tumors like granulosa cell tumors, thecoma, cellular fibroma, fibroma, fibrosarcoma, stromal tumor with minor sex cord elements, sclerosing stromal tumor, signet-ring stromal tumor; Sertoli-stromal cell tumors like Sertoli-Leydig cell tumors, Sertoli cell tumor, Stromal-Leydig cell tumor; Sex cord-stromal tumors or mixed cell types; and Steroid cell tumors. Genu cell tumors usually arise in patients of a younger age group and mostly are attributed to . These tumors being chemo-sensitive have better disease outcomes (Brown et al. 2014). Germ cell sex cord-stromal tumors are also infrequent and most are of Mixed Germ cell sex cord- stromal tumors type (Roth and Cheng 2015), while tumors of the rete ovarii are very rare (Ram et al. 2009).

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Fallopian tube

Uterus Ovary —

Released egg

Ovary with Developing stages

Surface epithelium Sex cord-stromal tumors Germ cell tumors stromal tumors

Granulosa cell Primitive germ cell Serous tumors tumors cn tumors Endometrioid Sertoli cell tumors Biphasicor tumors I riphasic teratoma Mixed or I o T Monodermal aMucinou s tumors CIC unciassifieci J \ teratoma Steroidcell ClearCell tumors tumors

Transitional Cell EI tumors Undifferentiated Q carcinoma

Figure 1.2. WHO histological classification of tumors of ovary: Ovarian cancer is dominated by occurrence of three main ovarian cancer types viz. surfece epithelium- stromai tumors, sex cord-stromal tumors and germ ceii tumors. Germ cell sex cord- stromal tumors, tumors of the rete ovarii and miscellaneous tumors are very rare and have not been represented in above cartoon. Surface epithelium-stromal tumors also include rare tumors described as squamous ceil tumors and mixed epithelial tumors.

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1.3. Presentation and Pathology Most patients with ovarian cancer are diagnosed at a late stage and hence the cancer is considered as "silent killer". Generally, ovarian carcinoma patients have regionally advanced disease and metastases within the peritoneal cavity. The , fallopian tube and ovaries along with sigmoid colon may be largely involved (Lengyel 2010). The primary site of metastases is usually the omentum which is neariy transformed by tumor in most cases. Patients are reported to experience following symptoms (Bankhead et al. 2008; Lengyel 2010; Ebell, Gulp, and Radke 2015; Rutten et al. 2015) -

i) Abdominal or bloating, dyspepsia, frequent urination; ii) Abdominal pain, discomfort, abdominal distension due to the development of tumor in omentum affecting small and large bowel; iii) Fatigue, weight loss, anorexia and depression; iv) Some patients may experience abnormal uterine bleeding; and v) Ascites development.

Adenocarcinomas appear from macroscopically unnoticeable protrusions to above 20cm in diameter. They can be either unilateral or bilateral as in two-thirds of diagnosed cases. Further it is observed that adenocarcinomas are fomied only in one-third of all cases when detected at stage I. Eariy stage tumors are well differentiated, solid, exhibit cysts having soft papillae inside. The papillae are relatively softer than the tumor surface. High grade tumors have poor cellular differentiation, are mostly solid and may have multinodular masses with peritoneal ascites accumulation in late stage disease (Shen- gunther and Mannel 2002). Necrosis and hemorrhage also accompanies large-sized tumors (Tavassoli and Devilee 2003).

Histopathology of serous adenocarcinoma varies from glandular to solid to papillary and is unlike other ovarian carcinomas (Soslow 2008). Glands if present, are usually in-egular, however, solid tumors are usually pooriy differentiated and occur in sheets that may have slight papillary clusters parted by stroma (Tavassoli and Devilee 2003). Most cases exhibit papillary and micro-papillary organization (Soslow 2008). The papillae are mostly irregular with high amount of branching and cellularity. Psammomma bodies (unusual calcified bodies) may occur in variable quantities. The stroma can be present in variable amounts (Tavassoli and Devilee 2003). Nuclear atypia is frequently seen and mitotic activity is also observed to be ample (Cho and Shih 2009).

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V^A^^) ^>«l

Figure 1.3. Representative histologicai images of a) low grade serous, b) low grade endometrioid, c) clear ceil, high-grade serous, d) high grade endometrioid, e) mucinous invasive carcinoma at 64x. Case slides provided by Armed Forces IMedicai College (AFMC), Pune. Scale bar is lOOpm.

1.4. Molecular Features Redefine Origins of Ovarian Cancer Apart from histology, heterogeneity within ovarian cancer is also determined based on molecular characteristics of the tumor that together can provide better prognosis (Cho and Shih 2009). Epidemiological studies on mutations in AT-rich interactive domain 1A [SWI-like] (ARID1A) gene link endometriosis and clear cell and endometrioid carcinomas (Jones et al. 2010; Wiegand et al. 2010). Recent pathological, genomic and molecular findings suggest extra-ovarian origin of tumor (Lee et al. 2007). Invasive mucinous ovarian cancer is predicted to have metastasized from intestinal tumors to ovary (Zaino et al. 2011; Kelemen and Kobel 2011). Moreover, previously identified high-grade endometrioid cancers upon genomic studies have been re-classified as serous cancers (Madore et al. 2010). These developments have promoted sub-classification of above histotypes based on cellular signaling, mutations or gene expression (Ho et al. 2004; Tothill et al. 2008). The revised origin based classification is indicated in Figure 1.4.

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a. Non-Reproductive tissue origin b. Reproductive tissue origin

Stomach Large Intestine Fallopian tube

HJeh Grade Serous

> I Endometrioid

Mucinous

Figure 1.4. Ovarian Cancer development: Recent findings on origin of major ovarian cancer types involving a. Non-reproductive tissue and b. Reproductive tissue as origin.

Thus, histopathoiogicai, molecular and genetic researches have led to the development of an improved model for ovarian carcinogenesis. These observations are supplemented by mutational profiling studies perfomried over the years that advocate the presence of at least two pathways that culminate in ovarian cancer (Gross et al. 2010). Based on these two pathways, ovarian carcinogenesis is categorized as -

i) Type I - those with clearly defined precursor lesions, and develop into low-grade serous, endometrioid, clear cell and mucinous forms. They are characterized by presence of high rate of mutations in BRAF, KRAS and ERBB2, high gene expression of cytochrome c oxidase subunit I (C0X1), cytochrome c oxidase subunit 2 (C0X2), solute earner family 2 member 1 (SLC2A1) and nitric oxide synthase 2 (iNOS), low proliferation rate, upto 55% 5-year survival and median survival of 141 months (Shih and Kunrian 2004; Ali-Fehmi etal. 2011).

ii) Type II - those for lesions that are not clearly described, though the tumor may originate de novo from ovarian surface epithelium or fallopian tube (Kurman and Shih 2008; Landen, Bin-er, and Sood 2008). They develop into HGSC, carcinosarcomas and undifferentiated carcinomas. Type II tumors have mutations in TP53, relatively low expression of C0X1, C0X2, Glut1 and iNOS, high proliferation rate, 5-year survival of nearly 28-30% and median survival of 60 months (Shih and Kurman 2004; Ali-Fehmi et al. 2011). These are aggressive in nature and metastasize to the pelvis.

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Figure 1.5 illustrates the possible pathways leading to Type I and Type II ovarian cancers.

b) Typ«l Type I Type I Low High Clear Cell Mucinous Grade Grade carcinoma carcinoma pSJ i\ /P"^ i Endometrioid carcinoma PIKJCA ^ Invasive carcinoma ARAOIA ' c J a Oncogen FBXW74 ' PT£N / K-RAS I Intraepithelium carcinoma ARADIA ^ PIK3CA / Endometriosis Endometriosii t Mucinous borderline tumour t OncogOncogenie c • /— ' HNF-1 beta-positive I K-RAS HNF-1 beta negative Inclusion cyst cells IncUision cyst cellt I Mucinous cystaderrama J

Tissues present In pelvic region

d) Benign serous cystadenoma I Ovarian surface Inclusion cysts Fallopian tube K-RAS Peritoneum BRAF ERBB2 Apoptosis inhibitors Grovrth factors (AKT2) (EFGR, HER2) Serous borderline tumor 1 Unknown etiology I

Proliferation index A 1 HER2/neu \ grade serous I HLA-G ^ Genetic instability arcinoma I b apoE 'v UA Activation pl6 Inactivation Type I

High grade serous | adenocarcinoma J

Type II

Figure 1.5. Schematic depicting molecular events leading to development of (a-d) Type I and e) Type 11 ovarian cancer.

In an interesting study, a panel of immunohistochemical markers was developed to improve the diagnostic accuracy as well as inter-observer concurrence (Kobel et al. 2014). The histological observations supplemented by six markers viz. Wilms tumor 1

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(WT1), progesterone receptor (PR), Hepatocyte nuclear factor 1 homeobox B (HNF-1B), p53, p16 and ARID1A specified that diagnosis of type of ovarian cancer can be performed with high reliability. Figure 1.6 shows the proposed scheme for this assessment of ovarian cancers.

Primary ovarian cancer

p53 wild type & PR-ve patchy pl6

LGSC , Mucinous cancer

WTl+ve & p53 over PR-ve/ HNFlB-ve/ expression or ARDIA -ve p53-ve with diffuse WTl-ve& PR+veor HNFlB-ve pl6 dear cell cancer HGSC Endometrioid cancer

Low Grade High Grade

Figure 1.6. Diagnosis of ovarian carcinoma is enhanced through usage of IHC marlcers.

1.5. Precursors of HGSC: Ovarian Surface Epithelia or Fallopian Tube Epithelia Classically, fjerceptions about the origin of HGSC have been limited to the ovary. Nevertheless, the role of fallopian tube in development of endometrioid cancer and clear cell cancer (Kurman and Shih 2011), and spread of carcinomas between ovary and endometrium (Snyder, Bentley, and Robboy 2006; Bagby et al. 2013) makes it crucial to examine its role in development of HGSC.

Conventional Theories on HGSC Development

Ovarian carcinoma is supposedly caused by 'incessant ovulation' (Fathalla 1971). Thus, the frequency of ovarian neoplasms developing in pre-puberty is far less as compared to unmanned and infertile women, and observations on egg production by domestic fowl that had no seasonal breaks had high incidence of development of adenocarcinoma. In

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essence, this theory anticipated the continuous exposure of the Ovarian Surface Epithelial (OSE) cells towards ovulation that leads to increased chances of development of adenocarcinoma. This has found evidence in epidemiological studies where nulliparous women were considered to be at relatively higher risks (Pennuth-Wey and Sellers 2009).

The histological and molecular lessons associating OSE to HGSC have not classically involved studies on fallopian tube involvement in the disease (Yang et al. 2002; Bell 2005). A recent analysis by Konishi and colleagues performed on Horiuchi dataset showed that 8 of 9 patients presented with stage III did not have lesions in adjacent regions (Horiuchi et al. 2003; Koshiyama, Matsumura, and Konishi 2014). This raises the possibility of HGSC development from ovaries. However, possibility of non-detection of small lesions in fallopian tube does exist, and in absence of any conclusive report on OSE being the sole origin of HGSC, the possibilities of 'extra'-ovarian origin cannot be ruled out.

Current outlook: Fallopian Tube as HGSC Origin Site

Piek and colleagues reported transformation in the fallopian tubes of patients having familial BRCA mutations and history of ovarian cancer, and who undenft/ent risk-reducing bilateral salpingo-oophorectomy (BSO) (Piek et al. 2001). In this comparative study between two groups of which the test group included twelve women, six had dysplasia, five had hyperplasia and one had no histological aberrations in contrast to control group of thirteen women who did not present with any aberrations. The dysplastic and hyperplastic lesions did not involve stromal invasion. Later Piek e^ al. proposed hereditary serous cancer could possibly originate from fallopian tube epithelia (FTE) and further spreads to ovarian surface or peritoneum (Piek et al. 2003). Dysplastic lesions in FTE are called "serous tubal intraepithelial carcinomas (STIC)". The high-grade STICs are carcinomas having originated from the fallopian tube, are present towards the fimbriated end of the tube i.e. next to the ovarian surface and are characterized by presence of high frequency of mitotic figures, hyperchromasia and atypia (Kindelberger et al. 2007; Jarboe et al. 2008; Salvador et al. 2008; Carison et al. 2008) The frequency of STIC was observed to be 59% of all HGSC cases while none were identified in endometrioid and mucinous cancers (Przybycin et al. 2010). STICs are reported to exhibit gamma- H2A histone family member X (Y-H2AX) overexpression and shorter

Page 12 Chapter 1 telomeres which occurs in pre-invasive epithelial lesion (Gorgoulis et al. 2005; Kuhn et al. 2010). Hence, it is hypothesized that STICs are not secondary metastatic sites from ovarian surface carcinoma but instead precursors present in fallopian tube that metastasize to ovary and peritoneum (Chene et al. 2013). The presence of STIC can be confirmed by staining with p53 and Ki67 (clone MIB1) (Vang, Shih, and Kumian 2013). Cases with aben-ant p53 staining and at least 10% of nuclear Ki67 are considered to be STIC. Thus, STICs could be possible site of origin for HGSC which spreads by exfoliation or other means to ovarian surface.

Figure 1.7. Originating sites of ovarian cancer: a) Transformation of ovarian surface epithelia leading to development of carcinoma, b) Dissemination of epitheiiai ceiis from serous tubai intraepithelial carcinoma (STIC) developed on fallopian tube to the denuded ovarian surface post ovulation. The implanted ceils later develop into inclusion cyst that leads into carcinoma expansion.

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1.6. Modalities of Spread of Cancer Currently the modes of cellular migration in cancer have been documented as being either - an active Epithelial-to-Mesenchymal Transition (EMT) or a passive dissemination. EMT is a physiological mechanism in which cells with epithelial features transition into mesenchymal features in order to facilitate their migration to a secondary site. EMT is essential during embryonic development and tissue repair as well as is activated in fibrosis and cancer metastases (Thiery et al. 2009). The initiation of EMT in cells takes place by numerous signals like growth factors, hormones, activation of transcription factors (Thiery et al. 2009). Within a tumor, some of the carcinoma cells undergo forfeiture of epithelial characteristics to acquire mesenchymal properties to invade extracellular matrix and cause distant metastasis. At the molecular level this phenomenon takes place by upregulation of transcription factors like Snail, Slug, Zeb1, Zeb2, Twist, FoxC2 and their effector proteins like vimentin, N-cadherin and microRNAs like miRlOb and miR21 (Thiery et al. 2009; Kalluri and Weinberg 2009; Zeisberg and Neilson 2009). Further, EMT also involves simuftaneous downregulation of E-cadherin, claudins, occludins, microRNA 200 family among others (Thiery et al. 2009). In HGSC, the progression from epithelial-to-mesenchymal phenotype is considered complex due to inherent expression of mesenchymal markers like vimentin and N-cadherin, though upregulation of Snail and Slug and downregulation of E-cadherin has been documented (Kun-ey etal. 2009; Elloul etal. 2010; Davidson, Trope, and Reich 2012).

On the other hand, passive dissemination involves direct spread of the primary tumor to the secondary sites, first usually to omental tissue. Omentum is fatty tissue contributing to immune response by possessing macrophage rich zones (milky spots) and prevents infection by its physical limitation. The exfoliated cancer cells are considered to be the major source of gross peritoneal disease. It is presumed that the dislodged cells are carried by the peritoneal fluid upon biological movement to the nearby sites like peritoneum and omentum. Nevertheless, the spread of cancer to retroperitoneum or distant sites has not been understood enough for being attributed to this phenomenon only (Castadot et al. 2005; Akin et al. 2008; Lim et al. 2009). Interestingly, the answer to question of single or multiple cells dislodging simultaneously remains elusive, though experiments with cell clumps in form of spheroids have shown high abilities of affinity to secondary sites via binding of integrins to fibronectin, among others (Burleson et al. 2004).

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Another mode of migration that has been reported in tumor spread in the body is by Circulating Tumor Cells (CTCs). The occurrence of CTCs in blood of ovarian cancer patients has been recently reported for diagnosis and predicts Progression Free Survival (PFS) and Overall Survival (OS) (Poveda et al. 2011; Obemayr et al. 2013; Liu et al. 2013). However, their exact mode of mechanism as an altemate route to metastases has not been clearly elucidated. Moreover, the predominance of the disease within the abdomen cannot be overlooked (Tarin et al. 1984). The primary distribution of metastases has been mostly the fallopian tube, contralateral ovary, omentum and peritoneum (Sehouli et al. 2009).

a.

Transformation RWMNH

BB3BMP"iBS

MET

Normal Organ

Spread in secondary site

Figure 1.8. Schematic representing the modes of cellular migration a. Epithelial-to- Mesenchymal Transition (EMT): Some tumor cells undergo phenotypic shift from epithelial to mesenchymal thereby allowing them to transverse Into the blood vessel. This Is transpired by expression of EMT-transcription factors Mice Slug and downreguiation of

Page 15 Chapter 1 cell-cell adhesion molecules like E-cadherin. The circulating tumor cells (CTCs) upon extravasation undergo phenotypic reversal I.e. Mesenchymal-to-Eplthellal Transition (MET) to develop In secondary tumor, b. Cooperative Cell Migration (CCM) allows group of cells tightly bound by cellular Junctions, expressing high amount of E-cadherIn and protected by Extra-Celluiar Matrix (ECM) to survive sloughing from primary tumor site to implant and develop at secondary site.

1.7. Current Approaches for Treatment Current accepted standard-of-care treatment(s) for advanced ovarian carcinoma are based on large-population studies, and consist primarily of aggressive surgery ("cytoreduction" or "tumor debulking" or "primary debulking surgery, PDS") and chemotherapy (PDS-CT). The chemotherapy regimen most commonly uses combination of platinum-based (like cisplatin and carboplatin) and taxane based molecule (like paclitaxel). Griffiths first demonstrated that patient's survival inversely con-elated to the residual disease mass post-PDS (Griffiths 1975). Cytoreduction accomplishes clearing of the carcinoma from the pelvis, through an en bloc resection of the ovarian tumors, reproductive organs, and if required - the sigmoid colon, with a primary bowel re- anastomosis. This is due to known observations that ovarian tumors are mostly restricted within the peritoneal cavity, and aggressive cells permeate the mesothelium lined surface to grow above the peritoneal reflection in the pelvis. Even substantial omental tumors only perfuse the superficial bowel serosa and not the deeper layers, hence resection of the transverse colon is seldom required (Bristow et al. 2008). The goal of surgical treatment is to remove as much tumor as possible; several studies have convincingly shown that cytoreduction results in improved patient survival (Bristow et al. 2002; Winter et al. 2008). Further, the volume of residual disease is more important than the stage for prognosis. Optimal surgery refers to maximum residual tumor of <1 cm in diameter that has decreased over a period of time from <2 cm and hence shows improved survival compared to suboptimal or non-optimal debulking (Kumian and Shih 2008). Reduction in residual tumor has led to efficient chemotherapy and higher survival (Bristow et al. 2002). Further, in the same study, it was observed that patients with complete resection have 1.6 times longer survival. Importantly, the load of pre-operative tumor and residual post-operative disease fomn crucial prognostic indicators of survival (Bristow et al. 1999; du Bois et al. 2009; Vergote et al. 2010; Horowitz et al. 2015). However, it remains a dilemma of which patients are expected to benefit from debulking (Naik et al. 2008) and the best time to perform the surgery.

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Optimal debulking is possible in only 30-60% of HGSC cases (Covens 2000; Dauplat et al. 2000). Hence, physicians in such cases avoid PDS and go for alternate treatment strategy. This is usually required in patients with stage IIIC/IV where the widespread dissemination of tumor makes cytoreduction difficult and patient is inoperable. In such cases, patients have to undergo neo-adjuvant chemotherapy (NACT) and Interval Debulking Surgery (IDS). The patient is usually given three rounds of chemotherapy before tumor resection. This is followed by post-surgical chemotherapy. Comparisons between the results of two treatment strategies i.e. NACT and IDS have been extensively studied. Kuhn et al. studied patients having >500ml ascites. They found that relative to PDS-CT group, higher percentage of optimal surgeries and improved median survival time (MST) were reported in the NACT group (Kuhn et al. 2001). Hegazy and colleagues selected the treatment of PDS-CT or NACT on stage 11 I/I V patients based on tumor respectability as determined through laparoscopy or laparotomy (Hegazy et al. 2005). The patients receiving NACT (average age of 58.7 years) were older as compared to PDS-CT (average age of 53.6 years). Interestingly, the authors did not report any difference in proportion of overall survival. In another study, Lee et al. selected stage IIIC/IV patients for NACT based on imaging studies like computed tomography (CT)-scan or magnetic resonance imaging (MRI) (Lee et al. 2006). Patients unwilling to undergo NACT received PDS-CT. They reported better optimal surgery in NACT group as compared to PDS-CT group. However, significant change in survival was not detected. Remarkably, both Hegazy et al. and Lee et al. found less blood loss in NACT patients as compared to PDS-CT group. Giannopoulos and colleagues reported similar observations (Giannopoulos et al. 2006). In their study, they found median hospital stay (7 versus 8 days), intraoperative blood loss (500 vs. 1000 ml), as well as chances of requirement for Intensive Care Unit (ICU) (5.7 vs. 48.3%) were considerably less in the NACT group vis-a-vis PDS-CT group. In all, NACT appears to reduce surgical complications without compromising on the patient's survival. The significance of neo­ adjuvant chemotherapy (NACT) as well as interval debulking surgery (IDS) has been recognized in comparison to the primary debulking surgery (Vergote et al. 2010). This study compared NACT (platinum and taxane based combination) and PDS patient groups. The outcome of 670 out of 718 patients randomized into either of treatment showed that the biggest residual tumor was £l cm in diameter in approximately 41% post-PDS patients while it was present in nearly 80% patients after IDS. In all, NACT appears better than the PDS strategy for patient outcomes.

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Further, a solitary report established patients with high Tumor Infiltrating Lymphocytes (TIL) had high possibility of complete cytoreduction, possibly due to an enhanced tumor control by body's immunity (Zhang et al. 2003). This report detected CD3+ tumor infiltrating T cells within the tumor in 54.8% of total cases wherein the 5-year OS was 38% as compared to 4.5% in patients lacking TILs. Further, the presence of TILs also improved clinical response post debulking and Platinum-based chemotherapy, delayed recun-ence and death. Additionally, tumors with TILs were associated with high expression of interleukin-2 (IL-2), interferon-gamma (IFNy) and lymphocyte-attracting chemokines.

1.7.1. Drug Response Perceptive HGSC patients are responsive DNA-damage afflicting agents like platinum-based dnjgs in chemotherapy. However, resistance develops in 80-90% of cases wherein the patients exhibit extensive spread of disease. This is essentially due to presence of high spatial, temporal, cellular and genomic heterogeneity (Cooke et al. 2010; Cowin et al. 2012; Bashashati etal. 2013; Schwarz etal. 2015). Drug resistance mechanisms include diverse modalities such as AKT activation, loss of BRCA1 methylation either through mutation or of methylation, over expression of ATP Binding Cassette Subfamily B Member 1 {ABCB1) among others (Sakai et al. 2008; Stronach et al. 2011; Nonquist et al. 2011; Patch et al. 2015). Interestingly, AKT targeting has revealed promising clinical activity when used together with carboplatin and paclitaxel in early clinical studies (Phase lb/ II) involving platinum-resistant ovarian carcinoma cases (Blagden et al. 2014). However, as the number of paired cases (before and after recun-ence) that have been analyzed is relatively less, detailed knowledge of pathways leading to drug resistance remains elusive as yet. Newer inhibitors with molecular targets like Poly (ADP Ribose) Polymerase (PARP) inhibitors, histotype-specific treatment among others are under various phases of clinical trials. Open-ended, unanswered questions include - relapse due to genetic change in expanding self-renewing population, emergence and expansion of drug-resistant clones, change in small molecule constitution in tumor microenvironment, all of which can vary from patient to patient.

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1.7.2. Effects of Mutation on Treatment Certain genes are commonly associated with ovarian cancer, especially hereditary ovarian cancer and affect the outcome of therapy. Cass et al. studied the survival of Ashkenazi mutation earners in BRCA1 and BRCA2 with advanced ovarian cancer (Cass et al. 2003), and identified a median survival of 91 months in caniers in contrast with 54 months in non-carriers with advanced sporadic ovarian cancer. Importantly, BRCA1 and BRCA2 mutation harboring patients responded to primary anti-neoplastic chemotherapy better (21/29 cases) than patients with sporadic cancer (9/25 cases).

Another similar study was performed by Tan and colleagues who compared ovarian cancer patients carrying hereditary BRCA1/2 germline mutations with non-hereditary ovarian cancer patients matched for age, stage, year of diagnosis (Tan et al. 2008). This study identified improved response to platinum based chemotherapy in SRC/A-mutation carriers than sporadic cancer patients with drug response to be better in first, second and third line of chemo-therapy. Specifically, 95.5% patients with BRCA1/2 mutations responded to first line treatment, 91.7% to second and 100% to third line treatment as compared to 59.1%, 40.9% and 14.3% responses respectively in these the control patient group. Additionally, complete response was observed in 81.8% patients harijoring BRCA1/2 mutation as compared to 43.2% in control group towards first line platinum chemo-therapy; similariy relapse time after first line chemotherapy was 5 years and 1.6 years in BRCA1/2 mutation caniers and non-caniers respectively.

Crijnen and colleagues probed whether ovarian cancer patients from families diagnosed with Lynch syndrome have better survival as has been shown for colorectal cancer in Lynch syndrome by Watson and group (Watson et al. 1998; Crijnen et al. 2005). This analysis demonstrated that the mean diagnosis age of Lynch syndrome-associated ovarian cancer (49.5 years) was considerably lesser as compared to sporadic ovarian cancer (60.9 years). Further, the cumulative 5-year survival rate for ovarian cancer was not radically dissimilar between patients with ovarian cancer in Lynch syndrome (64.2%) and control group patients (58.1%). In summation, the authors concluded that both the conditions should be treated similariy i.e. like sporadic ovarian cancer. In both of the above situations, the merit of study relied on the sample size and the control group selected and hence, its interpretation needs exercise of caution.

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1.8. Molecular Classification in Various Cancers The main limitation in successful cancer treatment is an inability to target therapies towards molecularly distinct tumor types that may presently be identified as a single tumor type based on histopathological presentation and tumor morphology. This is best exemplified in acute leul

However, in solid cancers, the sole use of tumor morphology is realized to have major limitations, since similar appearing tumors respond differently to the same drug regime. Initially classification based on specific biological representation than unbiased observations was considered as being insufflcient basis (Golub et al. 1999). However, the differential expression of proteins as detected through immunohistochemistry (IHC) as a determinant of molecular classes has been taken up over the last few decades and the approach is used extensively to personalize treatment in breast cancer based on the expression of homional receptors. IHC, developed more than 30 years ago, is the basis of breast cancer classification into ER-positive and ER-negative categories (Pusztai et al. 2006). Subsequent addition of markers - progesterone receptor (PR) and Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) or Her2/neu have led to revolutionizing the therapeutic arena. However, besides breast cancer, a continuous process of identification of single gene molecular markers for other cancers was not very successful.

1.8.1. Role of High Throughput Platforms in Development of Classification Approaches The last couple of decades have seen enormous contributions by studies based on microan'ay-based gene expression in cancer research. Technological breakthroughs have occurred in molecular approaches involving transcriptomics, genomics, driven by high-throughput DNA sequencing as well as proteomics that can be performed at cell and tissue level. Various approaches are used to build these platforms, bringing more options for sample processing and analysis. For example, genome-wide expression can be studied using different DNA microarray platforms with those using oligonucleotide

Page 20 Chapter 1 being most favored (Klein et al. 2002; Hardiman 2006). Similariy, various platfoms exist for other 'omics' approaches. The significance of these platforms belies the fact that these are the snapshots of events happening at the presently pooriy understood cellular level. The fields of genomics, transcriptomics and proteomics gained significant contributions by bioinformatics to give a comprehensive data and understanding. Interestingly, many of these technologies echo the complexity of data generated without doubting the method or platfonn used (Sotiriou and Piccart 2007). One of the significant wori< was the use of hierarchical clustering for identification of specific gene expression patterns of four breast cancer subtypes (Perou et al. 2000). Subsequently numerous ways of classification have been developed and the WHO recognized 18 distinct sub­ types in invasive breast cancer (Weigelt and Reis-Filho 2009). Continued development of incorporating larger samples led to discovery of additional sub-types (Farmer et al. 2005; Hu et al. 2006; Doane et al. 2006; Herschkowitz et al. 2007; Pari

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Table 1. Molecular approaches for Classification of Various Cancers

S.No Cancer Basis of Classification Reference 1. Ovarian Cancer miRNA; Exome Sequencing, Copy Tothill ef al. number analysis, mRNA & miRNA 2008; TCGA expression, DNA methylation Network 2011; Creighton et al. 2012; Vertiaak efa/. 2013 2. Glioblastoma DNA copy number, gene expression and McLendon ef al. Multiforme DNA methylation aberrations; WGS, DNA 2008; Brennan copy number, mRNA sequencing, mRNA efa/. 2013 expression profiling, CpG DNA methylation, miRNA expression & Protein expression profiling 3. Juvenile Gene Expression Bresolin ef al. Myelomonocytic 2010 Leukaemia 4. Lung Squamous DNA copy number, somatic exonic TCGA Network Cell Carcinoma mutations, messenger RNA sequencing, efa/. 2012 mRNA expression and promoter methylation, whole genome sequencing (WGS), microRNA sequencing 5. Colon cancer Gene Expression Marisa ef al. 2013 6. Endometrial Whole genomic sequencing, exome Kandoth ef al. Carcinoma sequencing, mRNA & miRNA expression, 2013 RPPA and DNA methylation 7. Head and Neck Gene expression and copy number Walter ef al. Squamous Cell alterations; HPV signatures using miRNA, 2013; Lawrence Carcinoma DNA methylation, gene expression and efa/. 2015 somatic nucleotide substitutions. 8. Leiomyosarcoma Array comparative genomic hybridization Italiano, (CGH) and gene expression an-ay Lagarde, and Brulard2013 9. Malignant Pleural Transcriptomic expression of primary de Reynies ef al. Mesothelioma MPM cultures 2014 10. Prostate Cancer Expression of CRISP3, ERG and PTEN Al Bashir ef al. 2014 11. Lung Cancer Molecular profiling using mRNA, Collisson ef al. microRNA, DNA sequencing with Copy 2014 number, methylation and proteomic analyses 12. Papillary Thyroid DNA copy number, somatic exonic Agrawal ef al. Carcinoma mutations, mRNA expression, miRNA 2014 expression. Protein expression and DNA methylation 13. Urothelial DNA copy number, somatic mutation, Weinstein ef al. Bladder mRNA and microRNA (miRNA) 2014 Carcinoma expression, protein and phosphorylated protein expression, DNA methylation, transcript splice variation, gene fusion, viral integration, pathway perturbation, clinical correlates and histopathology 14. Acute Myeloid Whole-genome Sequencing KIco efa/. 2014

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Leukemia 15. Breast Ductal Genomic and transcriptomic data Aliefa/. 2014 Carcinoma 16. Gastric SNP array somatic copy-number analysis, Bass ef a/. 2014 Adenocarcinoma whole-genome sequencing, mRNA sequencing, mlRNA sequencing, an'ay- based DNA methylation profiling, reverse- phase protein anray 17. Chromophobe RNA sequencing, DNA methylation Davis ef a/. 2014 renal cell an-ays, mlRNA sequencing, SNP arrays, carcinoma WEG, WGS, mtDNA sequencing 18. Lower Grade Exome sequencing, DNA copy number, Brat ef a/. 2015 Glioma DNA methylation, mRNA expression, microRNA expression and targeted expression 19. Metastatic Gene expression profiling Cirenajwis ef al. Melanoma 2015 20. Breast Lobular Whole-exome DNA sequencing, RNA Ciriello ef al. Carcinoma sequencing, mlRNA sequencing, SNP 2015 arrays and DNA methylation 21. Cutaneous Whole-genome and exome-sequencing, Akbani ef al. Melanoma RNA sequencing 2015 22. Prostate Cancer Exome & whole-genome DNA At>eshouse ef al. sequencing, RNA & miRNA sequencing, 2015 SNP arrays, DNA methylation, RPPAs 23. Papillary Renal- Whole-exome sequencing, copy-number Linehan ef al. Cell carcinoma analysis, mRNA & miRNA sequencing, 2015 DNA-methylation, proteomic

1.9. Molecular Classification In High-Grade Serous Ovarian Adenocarcinoma Prognostic concerns are among the primary matters that involve ovarian cancer patients as well as oncologists. A strong approach for HGSC classification is needed to aid both clinicians and patients at large. Attempts to identify presence of subtypes within high grade serous ovarian adenocarcinoma (HGSC and other ovarian carcinomas have been made by various groups. Expression profiling of serous carcinomas of various stages and grades gave distinct profile for HGSC as against those of low-grade or low malignant potential (Bonome ef al. 2005; Meinhold-Heeriein ef al. 2005). Gene expression epitomizes principal genomic information with the potential to detennine ovarian cancer sub-types. Several studies have used genomic expression to define sub­ types as well as biomaricers (Wong, Cheng, and Mok 2001; Spentzos ef al. 2004; Berchuck et al. 2005; Hartmann ef al. 2005; Jazaeri ef al. 2005; Spentzos ef al. 2005; De Smet ef al. 2006). A significant report on HGSC classification was by Tothill ef al. which used microarray gene expression on 285 endometrioid and serous tumors of ovary,

Page 23 Chapter 1 fallopian tubes as well as peritoneum (Tothill et al. 2008). This report was the first describing class discovery in correlation with clinical information, and identified six molecular subtypes of which four represented high-grade ovarian and 2 represented endometrioid cancers. Each class was associated with specific functionalities based on the dominant expressed gene clusters as follows -

• Cluster 1 (C1) - high stromal response subtype; displayed high extent of tissue desmoplasia; • Cluster 2 (C2) - high immune signature subtype; expressed high adaptive immune response genes; • Cluster 4 (C4) - low stromal response subtype; one of the largest groups and displayed low stromal response; • Cluster 5 (C5) - mesenchymal, low immune signature subtype, high levels of developmental transcription factors like high-mobility group members (HMGA2, TCF7L1, and TOX) and homeobox genes {H0XA7, H0XA9, HOXA10, HOXD10, S0X11), extracellular matrix proteins {CLDN6, C0L9A1, C0L4AS); and low expression levels of immune cell markers {PTPRC, CD45), mucins and kallikreins. Additionally, the low serum CA125 was identified.

Another significant classification approach was earned out by The Cancer Genome Atlas (TCGA) Network that identified the deregulated pathways involved in HGSC (TCGA Network 2011). Herein, the tumors were classified using various parameters including mutational status, methylation of genes, expression of tissue markers, copy number variation among others. Four molecular groups were identified and on the basis of genes present in the respective clusters, these were defined as -

• immunoreactive, characterized by high gene expression of CXCL11, CXCL10, CXCR3 • proliferative, characterized by high gene expression of HMGA2, and S0X11, MCM2 and PCNA and low expression of MUC1 and MUC16 • differentiated, characterized by high gene expression of MUC1, MUC16, SLPI or • mesenchymal, identified by high gene expression of HOX genes, FAP, ANGPTL1 and ANGPTL2.

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Verhaak et al. studied previous work carried out by Tothill et al. and TCGA Network and identified presence of multiple sub-types in HGSC (Verhaak et al. 2013). The authors used TCGA database and integrated the subtypes and prognostic classifiers to develop prognostic framework called "Classification of Ovarian Cancer" (CLOVAR) subtype and CLOVAR survival signatures. This CLOVAR signature comprises of 198 genes encompassing significant genes from the TCGA sub-types, and has an advantage over the previous classification by allowing usage of medium-throughput platforms for gene expression profiling. Further application of these studies has shown good response in patients belonging to mesenchymal groups when treated with bevacizumab (Winterhoff et al. 2014; Gourley et al. 2014). Another group showed sensitivity of C5-like cell lines towards vinca alkaloids (Tan e^ al. 2013). Another approach following Tothill et al. for developing a classification based on limited number of genes was proposed by Leong and colleagues (Leong et al. 2015). This work evaluated four different platforms viz. TaqMan-PCR based low-density arrays (LDAs), Nanostring that uses barcoded oligoes and does not require enzymatic procedure, Fluidigm microfluidics-based platforms and lllumina targeted next-generation RNA sequencing. These fomiats and platforms were used to classify previous samples that were earlier assayed by Affymetrix microarrays. The study identified a set of 48-genes that accurately classified 100% of the samples into respective groups using a Nano-string based assay. This method decreases the dependency on micro-array based analyses.

Yang et al. used an integrated genomic analyses approach to reveal miRNA-based classification scheme for serous ovarian cancer (Yang et al. 2013). The study used TCGA database for selecting serous ovarian cancer cases and independent cohorts to classify ovarian cancer into -

• integrated Mesenchymal (iM), having a master microRNA regulatory network, high Slug,fibronectin, and associated with poor overall survival (OS); and • integrated Epithelial (IE), associated with increased E-cadherin, decreased Slug, fibronectin, vimentin; and longer OS.

Further, previous work in our laboratory was carried out considering the spread of cancer either by active invasion and passive dissemination (Gardi et al. 2014). Two different gene expression clustering approaches viz. M-classification (supervised, metastases-gene signature based) and W-Classification (unsupervised, based on

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Weighted Gene Correlation Networl< Analysis, WCGNA) were used. M-classification relied upon metastases - associated gene signature which formed the pre-requisite for analyses. The metastases gene signature was derived using EMT-Transcription Factor {TWIST1, SNAI1 & SNAI2) global targets and Lysophosphatidic acid (LPA) and Sphingosine-1-Phosphate (S1P) associated genes. Eventually, the metastases- associated gene signature comprising of 39 genes was generated. Further, both approaches of M-classification and W-classification methods revealed presence of three classes. The approach used for derivation of the classes is given schematically in Figure 1.9. The samples stratified in classes by M-Classification and W-Classification upon overlap show distribution of core tumor samples into predicted three classes with sample distribution as Classl (n=77), Class2 (n=99) and Class3 (n=51). In addition, the class- specific functional gene associations were derived. Genes associated with Classl and Class2 validated in independent datasets. These classes were thus continued for further analyses while Class3 was discontinued.

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Gene expression based identification of Molecular classes

Identification of global targets LPA-SIP mediated signaling for Snail, Slug and Twistl associated genes

common targets 99 genes common targets 61 genes ', MADifUlysis I

39 metastases -associated gene signature |

M-Classification W-classificatlon Classification of 359 tumor samples based on 359 tumor samples classified based core 39 metastases associated gene signature on functional modulegenes

1. —• Silhouette clustering (k=3) Removal of outliers I 2. -• Consensus Clustering (samples=328) k-means clustering of 359 samples i —^.Hierarchical clustering (samples=286) i i 3-classes as - Classl (n=127), 3-tumorclass - Classl(n=97), Class2(n=137), Class3(n=96) Class2 (n=119), Class3(n=70) L Overlap among classes ~D Y Three Robust classes identified as Classl (n=77), Class2(n=991, Class3(n=511

Figure 1.9. Flowchart summarizing approach used for HGSC molecular classification carried out previously in our lab.

The biological features of Classl and Class2 classes are -

• Classl, predicted to metastasize in EMT-independent manner, have high genetic instability and immune evasion. • Class2, predicted to metastasize in EMT-dependent way, express large amount of extracellular matrix (ECM) and interferon signaling proteins.

Appropriate ceil lines representing each class were also predicted based on computational analyses of their gene expression datasets.

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1.10. Need for further studies The previous work performed in our lab predicted presence of distinct molecular classes within HGSC. The corroboration of molecular classes defined on the mode of metastases could shed more knowledge on inter-tumor heterogeneity. This could be the plausible cause of failure of unifonn chemotherapeutic response and high incidence of recurrence. However, if these different sub-types of HGSC are identified to be responsive to specific class to drugs or inhibitors, they could be more effectively controlled. Thus, a study that validates the presence of molecular classes based on mode of migration will help in better management of HGSC and develop strategies for cost-effective therapeutic intervention.

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