Expression analysis of the 3p25.3-ptelomere in epithelial ovarian cancer

By Vanessa Delphine Rossiny

Department of Human Genetics McGill University, Montreal January 2008

A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Master of Science

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While these forms may be included Bien que ces formulaires in the document page count, aient inclus dans la pagination, their removal does not represent il n'y aura aucun contenu manquant. any loss of content from the thesis. ••• Canada Abstract Microarray expression analysis was carried out to identify genes with a role in epithelial ovarian cancer (EOC). The U133A Affymetrix GeneChip® was used to determine the expression patterns of the 3p25.3-ptel genes represented on the microarray in 14 primary cultures of normal ovarian surface epithelial (NOSE) samples, 25 frozen malignant ovarian tumor samples and four EOC celllines. Seven genes with differentiai expression patterns in the tumor samples compared to the NOSE samples were identified as candidates for further analysis, starting withARPC4, SRGAP3 andATP2B2. Although none ofthe candidates had been previously studied in ovarian cancer, severa! had either family or pathway members that had. Expression patterns seemed unaffected by either tumor histopathological subtype or the allelic imbalances observed with loss of heterozygosity (LOH) analysis. The absence of association with genomic context suggested that differentiai expression was the result oftranscriptional regulation rather than direct targeting.

11 Résumé La technologie de biopuce à ADN a été utilisée dans le but d'identifier des gènes ayant un rôle dans le cancer de l'ovaire d'origine épithéliale (COE). La Ul33A GeneChip® de la compagnie Affymetrix a permis de déterminer les profils d'expression des gènes situés sur la région 3p25.3-ptel et représentés sur la biopuce dans 14 cultures de cellules normales dérivées de l'épithélium de la surface de l'ovaire, 25 tumeurs malignes de 1' ovaire congelées et quatre lignées cellulaires de COE. Sept gènes ayant une expression différentielle dans les tumeurs comparées avec les cellules normales ont été identifiés comme candidats méritant d'être analysés plus en détail, en commençant par ARPC4, SRGAP3 et ATP2B2. Bien qu'aucun des candidats n'ait été identifié auparavant en relation avec le cancer des ovaires, plusieurs ont des membres de leur famille ou de leur processus cellulaire qui 1' ont été. Les profils d'expression ne semblaient pas être affectés par 1'histopathologie des tumeurs ou la présence de pertes alléliques détectées par une analyse de perte d'hétérozygotie. L'absence d'association avec le contexte génomique suggère que 1' expression différentielle des gènes candidats était due à une régulation au niveau du transcriptome plutôt qu'à un ciblage direct des gènes.

111 Acknowledgments I would like to start by thanking Dr. Patricia Tonin for her continued support and understanding throughout the project, and for the intellectually stimulating and collaborative environment she created. This research was supported by funding from CIHR, Genome Quebec/Canada, and the Banque de tissus et de données of the Réseau de recherche sur le cancer of the FRSQ. I thank the Research Insitute of the McGill University Health Centre for providing me with a scholarship. I am truly grateful for the opportunity I had to work with remarkable lab members, past and present: Suzanna Arcand, Marie-Hélène Benoit, Anna Breznan, Ashley Birch, Luca Cavalloni, Neal Cody, Karen Gambaro, Kathleen Klein, Nadège Presneau, Michael Quinn, Paulina Wojnarowicz and Zhen Shen, and would like to thank them for their help and thought-provoking discussions. I thank Nancy Hamel and Tayma Kahlil of Dr. William Foulkes' laboratory for their support. I thank my Supervisory Committee Members Dr. Mark Trifiro and Dr. Jacques Galipeau for their academie guidance. I acknowledge the work carried out by Dr. Anne-Marie Mes-Masson and her laboratory members in the collection of clinical material, DNA and RNA extraction, and maintenance of tissue cultures, as well as the microarray hybridization. I synthesized the eDNA by reverse transcriptase polymerase chain reaction (RT-PCR) in collaboration with Ashley Birch. I carried out the LOH experiments for the 3p26.3 region and analyzed the microarray and RT-PCR expression data and LOH results. Above ali, I would like to express to my family and friends how grateful I am for their support and encouragement, especially Paulina Wojnarowicz and Marie- Hélène Benoit for their insightful help and Ashley Birch for her editorial help during the writing of this the sis.

lV Table of Contents ~ 11 Abstract

111 Résumé

lV Acknowledgements v Table of Contents vm List of Tables ix List of Figures x Abbreviations

~ Sec. Title 1 1 Introduction 1 1.1 Ovarian cancer biology and Qathology 1 1.1.1 Ovarian cancer overview 2 1.1.2 Potential EOC origins 3 1.1.3 EOC classification 4 1.2 Study models chosen to analyze EOC gene exQression Qattems 6 1.3 Cytogenetic and molecular genetic alterations in EOC 6 1.3.1 Cytogenetic analyses provide information on ovarian . . carcmogenes1s 7 1.3.2 Molecular analyses of specifie chromosomal regions and genes 7 1.3.2.1 Cancer genes overview and germline mutations lü 1.3.2.2 Genes, regions and pathways implicated in EOC 12 1.3.2.3 Evidence of a role for 3p in EOC 14 1.4 Project hyQothesis and study aims

15 2 Materials and Methods 15 2.1 Clinical samQles 15 2.1.1 Ovarian cancer samples 15 2.1.2 Primary cultures ofNOSE samples 17 2.1.3 EOC celllines -~ 17 2.2 Nucleic Acid Extraction

v 19 2.3 LOH analysis 20 2.4 Microarray expression data 20 2.4.1 Obtainment of the microarray data 21 2.4.2 Microarray analysis 23 2.5 Reverse-transcriptase PCR (RT-PCR) analysis

25 3 Results 25 3.1 LOH analysis 25 3.2 Expression analysis 25 3.2.1 Microarray gene representation 27 3.2.2 Analysis of the expression data for the primary cultures ofNOSE samples 30 3.2.3 Analysis ofthe EOC cellline gene expression data 34 3.2.4 Analysis of the gene expression data for the TOV samples 34 3.2.4.1 Expression profiles of the TOV expression data 36 3.2.4.2 Two-way comparative analysis between the TOV and NOSE samples 41 3.2.5 RT-PCR analysis 43 3.3 Integration of expression and LOH results

45 4 Discussion 45 4.1 Information on the 3p25.3-ptel chromosomal region 45 4.1.1 Information obtained from the physical and genetic maps 45 4.1.1.1 Physical map information 46 4.1.1.2 Genetic map information 47 4.1.2 End of chromosome information 47 4.1.2.1 Telomeres 50 4.1.2.2 Subtelomeres 50 4.2 LOH analysis supported a role for 3p25.3-ptel genes in EOC 50 4.2.1 AI patterns in the 3p25.3-ptel region may be due to loss or gain ~· 53 4.2.2 Significance ofthe AI patterns observed

Vl 53 4.2.3 The TSG candidate genes and their potential role in EOC /""", 54 4.2.3.1 Close homolog ofL1 (CHLJ) 56 4.2.3.2 Contactin 4 and 6 (CNTN4 and CNTN6) 57 4.2.3.3 The LOH candidate genes may play a role in tumor migration 57 4.3 Discussion of the gene exgression data 57 4.3.1 Expression profiles ofthe NOSE, TOV and EOC samples 58 4.3.2 Two-way comparative analyses identify differentiai expression patterns between NOSE and TOV samples which transcend histopathological subtype and genomic content 62 4.3.3 The microarray candidate genes and their potential role in EOC 62 4.3.3.1 The main microarray candidates (ARPC4, SRGAP3, ATP2B2) 64 4.3.3.2 EDEMJ, ITPRJ and OXTR 66 4.3.3.3 Conclusion 66 4.4 Exgerimental and biologicallimitations of this groject 66 4.4.1 Limitations due to the methods used 67 4.4.2 Limitations due to the samples used 68 4.4.3 Limitations due to the analysis carried out 69 4.5 Future directions

71 References

99 Appendices 99 Appendix I - N ormalized microarray gene expression data for the 14 primary cultures ofNOSE samples and the 25 TOV samples 103 Appendix II- Correlation analyses among the NOSE samples (1 ), between the NOSE and TOV samples (2), and among the TOV samples (3) 106 Appendix III - Ethics committee approval 108 Appendix IV - Qualification for radioisotope use

vii List of Tables

~· Table Title 16 Table 2.1 Primary culture ofNOSE and TOV sample information 18 Table 2.2 Information regarding the spontaneously transformed EOC celllines 24 Table 2.3 RT-PCRprimers 26 Table 3.1 LOH analysis ofmalignant tumor samples (TOV samples; n=21) 28 Table 3.2 RefSeq Genes and Data- October 2007 UCSC (Mar. '06 version; Karolchik et al. 2003) 29 Table 3.3 Analysis of the expression data from the 14 primary cultures ofNOSE samples 32 Table 3.4 Analysis of EOC cellline expression data 33 Table 3.5 Comparison ofEOC and NOSE expression data 35 Table 3.6 Analysis of the expression data from the 25 TOV samples 37-38 Table 3.7 Comparison of TOV and NOSE expression data

V111 List of Figures E.g. Figure Title 31 Figure 3.1 Examples of expression data for the NOSE samples 40 Figure 3.2 Hierarchical cluster analysis of the 25 TOV samples and 14 primary cultures ofNOSE samples 42 Figure 3.3 RT-PCRresults 44 Figure 3.4 Examples of the expression data for the 14 primary cultures ofNOSE samples, 25 TOV samples (grouped according to presence or absence of AI) and the four EOC celllines 48 Figure 4.1 deCODE recombination rates ( cM/Mb; Kong et al. 2002) for the 3p25.3-ptel region of interest (Karolchik et al. 2003)

lX ,_,, Abbreviations % Percentage oc Degrees Celsius 3p Short arm of 8q Long arm of chromosome 8 AI Allelic Imbalance B/F/B Break:age/fusion/bridge bp Ca2+ Calcium ions ces Canadian Cancer Society eDNA Complementary deoxyribonucleic acid CGH Comparative genomic hybridization cM centiMorgan CpG Cytosine- phosphate- guanidine (CG dinucleotide) eRNA Complementary ribonucleic acid DNA Deoxyribonucleic acid dNTP Deoxyribonucleic triphosphate EDTA Ethylenediaminetetraacetic acid EOC Epithelial ovarian cancer EST Expressed sequence tag FBS Fetal bovine serum FNIII Fibronectin 3 GPI Glycosylphosphatidylinositol h Hour HNPCC Hereditary non-polyposis colon cancer lgSF Immunoglobulin superfamily kb Kilo base LMP Low malignant potential LOH Loss of heterozygosity MAS Microarray Suite ~~ Mb Megabase

x r'. min Minute ml Milliliter mm Millimeter mM Millimolar MMCT Microcell-mediated chromosome transfer NCBI National Center for Biotechnology Information ng Nanogram nmol Nanomole NOSE Normal ovarian surface epithelium NOV Normal ovarian surface epithelium OSE Ovarian surface epithelium ov Ovarian ascites PCR Polymerase chain reaction pc en Centromere of the short arm of a chromosome pmol Picomole ptel Telomere ofthe short arm of a chromosome RAMP RNA amplification procedure RNA Ribonucleic acid RS Reliability score RT-PCR Reverse transcriptase polymerase chain reaction sec Second siRNA Small interfering ribobnucleic acid SNP Single nucleotide polymorphism TOV Malignant ovarian tumor TSG Tumor suppressor gene ,..,ci Micro-Curie ,..,g Micro gram ,.._L Micro liter u Unit ucsc University of California Santa Cruz /-~ w Watts

Xl 1. Introduction 1.1 Ovarian cancer biology and pathology 1.1.1 Ovarian cancer overview In the year 2000, an estimated 61,000 new ovarian cancer cases and over 39,000 ovarian cancer related deaths were observed world-wide (reviewed in Parkin, et al. 2001). For 2007, the Canadian Cancer Society (CCS) estimates that while ovarian cancer represented only 2,400 (3 .1%) of the 77,200 new cancer cases diagnosed in women, it was the fifth leading cause of cancer-related deaths after lung, breast, colorectal and pancreatic cancers (CCS 2007b ). With a five year survival rate less than 30%, ovarian cancer is the deadliest gynecological malignancy (Auersperg, et al. 2001; Berrino 1999; Bray, et al. 2002). The lethality of this disease is due to several factors, including either asymptomatic presentation or vague symptoms (Benedet, et al. 2000; Webb, et al. 2004), and the absence ofreliable detection methods (Bast, et al. 1998; Benedet, et al. 2000; DePriest and DeSimone 2003). These factors result in the diagnosis of two-thirds of ovarian cancer cases at a late stage (Benedet, et al. 2000) when long­ term survival rates are only 10-30%, as opposed to the 80-95% rates observed for patients diagnosed at an earl y stage of the disease (McGuire, et al. 1996; Young, et al. 1990). Several ovarian cancer risk factors have been identified, the most important being age (Benedet, et al. 2000; CCS 2007a), with the majority of cases (85-90%) occurring in postmenopausal women (Schulz, et al. 2004). Other important ovarian cancer risk factors include the presence of a family history of breast or ovarian cancer, which is observed in 5-l 0% of ovarian cancer cases (reviewed in CCS 2007a; Prat, et al. 2005), and a woman's reproductive history and corresponding variations in hormone levels (reviewed in Auersperg, et al. 2001; CCS 2007a; Wenham, et al. 2002). An example ofthe latter is that pregnancy, breast-feeding, and the use of oral contraceptives have been shown to have a protective effect against ovarian cancer (Riman, et al. 2004; Risch 2000; Runnebaum and Stickeler 2001; Schildkraut, et al. 2002). Ovarian cancers are classified as either epithelial, stromal or germ cell (. based on morphology. Epithelial ovarian cancers (EOCs) represent approximately

1 90% of all ovarian cancers, with the remaining 10% of ovarian tumors arising from either the structural framework of the ovary (stroma) or the germ cells within the ovary (Auersperg, et al. 2001; CCS 2007b; Liu and Ganesan 2002).

1.1.2 Potential EOC origins EOCs are thought to arise from either the ovarian surface epithelium (OSE) or inclusion cysts and crypts (reviewed in Feeley and Wells 2001). The latter sites undergo metaplastic changes more frequently than OSE cells, possibly due to the existence of a microenvironment which exposes cells to higher concentrations ofboth stromal and autocrine factors (reviewed in Auersperg, et al. 2001). However, not only can rat and murine OSE cells spontaneously acquire a tumorigenic phenotype following repeated subcultures (Godwin, et al. 1992; Testa, et al. 1994) but it is possible to induce both rodent and human OSE transformation into EOC with various genetic modifications (reviewed in Auersperg 2003; Auersperg, et al. 2002). The OSE is a mono layer of inconspicuous epithelial cells representing less than 1% of the ovarian mass which is thought to play a role during oocyte release and subsequent repair of the ovarian surface and cortex via proliferation and migration (reviewed in Auersperg, et al. 2001; Murdoch and McDonnel 2002). The OSE is capable of differentiating along different pathways in response to a variety of environmental factors, implying a relatively uncommitted, pleuripotential initial state which may facilitate neoplastic conversion (reviewed in Auersperg, et al. 2001). The most widespread hypothesis explaining the high frequency of EOCs among ovarian cancers is the "incessant ovulation" hypothesis formulated in 1971 by Fathalla (Fathalla 1971 ). It states that frequent ovulation increases EOC risk because it calls for repair via cell proliferation which is not only permissive for mutation accumulation (Fathalla 1971), but may also promote mutagenesis by releasing reactive oxidants (Murdoch, et al. 2005). This hypothesis originated in part from the observation that factors which protect against ovarian cancer, such ~·· as pregnancy and the use of oral contraceptives, avert ovulation (Riman, et al.

2 2004; Risch 2000; Runnebaum and Stickeler 2001; Schildkraut, et al. 2002). In addition, chickens (the only animais aside from humans to spontaneously develop EOC) rendered anovulatory via food limitation have a lower tumor incidence than ovulatory hens (Rodriguez, et al. 2001), and the previously mentioned subculturing that induces the transformation of rat OSE into EOC mimi cs ovulatory damage (Godwin, et al. 1992; Testa, et al. 1994). However, the observation that decreasing the number of ovulatory cycles decreases ovarian cancer risk by only 10-20% compared to the 50% decrease observed from five years of oral contraceptive use (Wenham, et al. 2002) suggests that other factors also have to be considered. One such complementary hypothesis is that the OSE apoptosis rate, which can be influenced by hormones such as progestins, may also affect EOC frequency (reviewed in Ghahremani, et al. 1999; Ho 2003). Indeed, studies in primates have shown that progestins induce apoptosis in DNA-damaged OSE cells (Rodriguez, et al. 2002; Rodriguez, et al. 1998), and the previously mentioned anovulatory hens experienced a further reduction in tumor incidence following progestin treatment (Rodriguez, et al. 2001). The effect of gonadotropins have also been examined, the theory being that they may overstimulate the OSE and facilitate malignant transformation (Cramer and Welch 1983), however recent studies have failed to support this hypothesis (Akhmedkhanov, et al. 2001; Arslan, et al. 2003). Finally, the protective effects exerted by tuballigation and hysterectomy despite uninterrupted ovulation suggest a role for inflammatory mediators which no longer have access to the ovaries following these procedures (reviewed in Ness, et al. 2000).

1.1.3 EOC classification EOC tumors are classified according to several characteristics, including tumor histopathology and grade (degree of differentiation), and disease stage ( extent of metastasis ), as well as whether they are benign, borderline or low­ malignant potential (LMP), or invasive ovarian tumors (TOVs) (Benedet, et al. 2000; Lounis, et al. 1994). Benign tumors are monolayer proliferations of

3 relatively normal cells while LMP tumors and TOVs exhibit multilayer proliferation and cytologie atypias (Ouellet, et al. 2006). EOC histological subtypes can have different etiological, morphological and genetic factors (Chatzistamou, et al. 2002; Parazzini, et al. 2004; Tsukagoshi, et al. 2003) (reviewed in Bell2005; Feeley and Wells 2001), with the acquisition of Mullerian duct-derived epithelial characteristics being used to classify the three main EOC subtypes. Serous tumors have fallopian-tube characteristics (Auersperg, et al. 2001) and are the most frequent subtype, representing 40-42% ofEOCs (Liu and Ganesan 2002; Schulz, et al. 2004). The second most common EOCs, with a frequency of 10-25% (Liu and Ganesan 2002; Schulz, et al. 2004), are endometrioid carcinomas which are endometrium-like (Auersperg, et al. 2001). Finally, mucinous tumors, which represent 10-12% ofEOCs (Liu and Ganesan 2002; Schulz, et al. 2004), are endocervical-like (Auersperg, et al. 2001). Other tumor types include the mesonephros-like clear cell tumors (5-1 0% of EOCs (Liu and Ganesan 2002; Schulz, et al. 2004)), which are the most chemoresistant and thus lethal (Cannistra 2004; Murdoch, et al. 2005), as well as Brenner, mixed and undifferentiated tumors (Benedet, et al. 2000). Tumors are graded as: GO- LMP, G 1 - well differentiated, G2 - moderately differentiated, and G3- poorly differentiated (Benedet, et al. 2000; Ouellet, et al. 2006). The four disease stages are: SI - tumor is confined to the avaries, SII - tumor involves one or both avaries with pelvic extension, SIII - tumor involves one or both avaries with microscopically confirmed peritoneal metastasis outside the pelvis and/or regionallymph nodes metastasis, and SIV­ distant metastasis beyond the peritoneal cavity (Benedet, et al. 2000). The International Federation of Gynecology and Obstetrics estimates that 75% of patients with TOV s are diagnosed at SIII/IV (Benedet, et al. 2000).

1.2 Study models chosen to analyze EOC gene expression patterns Gene expression patterns have been found to be affected by biological factors such as sample source and study madel (Cvetkovic 2003; Le Page, et al. 2006; Le Page, et al. 2004; Novak, et al. 2002; Zorn, et al. 2003), therefore the

4 three models chosen for the current analysis ofEOC gene expression patterns will be briefly examined in this section. In order to identify EOC-specific gene expression patterns, one must first establish the transcriptome of normal ovarian samples. Severa! types of normal ovarian samples are available which can exhibit slightly different gene expression patterns, including who le ovary, normal tissue from the surrounding or opposing ovary of the cancer patient, and specifie sections of the ovary such as the OSE (Zorn, et al. 2003). In this study, samples were obtained from individuals undergoing surgery for nonmalignant reasons because while the tissue samples from cancer patients may appear normal, genetic changes may already have occurred or contaminating tumor cells may be present (Auersperg, et al. 2002; Zorn, et al. 2003 ). The re sul ting interpatient variability in gene expression was compensated for by using a large panel of normal samples. Furthermore, while whole ovary samples have a higher yield than normal OSE (NOSE) samples, the latter were chosen because the majority oftumors are epithelial and a normal ovary is composed mainly of stroma! tissue (Zorn, et al. 2003). To increase the DNA, RNA and yield of the NOSE samples, and thus enable a more extensive characterization, the NOSE samples were established as primary cultures (Le Page, et al. 2006; Le Page, et al. 2004; Lounis, et al. 1994). A second model was used for the TOV samples, flash-frozen tissue samples, because they are the best representations of the disease and a viable option for ovarian tumors which are densely populated with malignant cells (Le Page, et al. 2004). One of the main disadvantages ofusing fresh or flash-frozen tumor samples is the existence oftumor heterogeneity, which is the presence of tumor subpopulations with different genetic characteristics as well as varying amounts of contaminating normal cells, such as stroma!, vascular and inflammatory cells (Cvetkovic 2003; Le Page, et al. 2004; Novak, et al. 2002). To try to resolve this contamination issue, laser capture microdissection can be used as it allows the separation of different cell populations (Cvetkovic, et al. 2003). However this laber-intensive approach was not used due toits low RNA yield

5 which requires amplification before microarray analysis, which can skew expression profiles (Le Page, et al. 2004). The third model used was EOC celllines which result from the immortalization of primary cultures and allow long-term experimentation and genetic manipulation (Garson, et al. 2005; Le Page, et al. 2004; Provencher, et al. 2000). Although immortalization often results from the introduction of transforming genes (reviewed in Auersperg, et al. 2002; Auersperg, et al. 2001), the four EOC celllines used in this study (TOV81D, TOV21G, TOV112D and OV90) were spontaneously immortalized and have been extensively characterized, analyses which have determined that they are reflective of the initial tumor samples from which they were derived (Le Page, et al. 2004; Lounis, et al. 1994; Provencher, et al. 2000).

1.3 Cytogenetic and molecular genetic alterations in EOC A multitude of cytogenetic and molecular modifications have been observed in EOC, a fraction of which will be presented in this section in order to highlight the complexity of this disease and understand why the chromosome 3p arm, and more specifically the 3p25.3-ptel chromosomal region, is a valid region of interest.

1.3.1 Cytogenetic analyses provide information on ovarian carcinogenesis The karyotypes of solid tumors, such as ovarian tumors, are typically very complex (Mitelman, et al. 1998), with a number of chromosomal imbalances occurring simultaneously. In the case of ovarian cancer, imbalances detected by either karyotype analysis or comparative genomic hybridization (CGH) techniques have been documented for all of the , however sorne are more frequently observed than others (Bello and Rey 1990; Deger, et al. 1997; Gallion, et al. 1990; Hoglund, et al. 2001; Jenkins, et al. 1993; Kiechle, et al. 2001; Suzuki, et al. 2000; Taetle, et al. 1999b). For example, Taetle et al., using a statistical analysis to detect nonrandom breakpoints of chromosomal regions in 244 ovarian cancer samples, identified 13 commonly involved regions, including

6 several chromosome 1 regions and 3pl (Taetle, et al. 1999b). Further analysis revealed that breakpoints in the 3pl and lpl chromosomal regions were associated with a poor prognosis (Simon, et al. 2000; Taetle, et al. 1999a), thus attributing a biological significance to these abnormalities. A number of other cytogenetic analyses have detected recurrent numerical and structural chromosome 3 anomalies, including nonrandom loss of regions or all of the 3p arm, in ovarian cancer (Mertens, et al. 1997; Pejovic, et al. 1992; Petursdottir, et al. 2004; Whang-Peng, et al. 1984; Yonescu, et al. 1996). Upon analysis ofthe genomic imbalances observed in 3,016 malignant solid tumors (including ovarian carcinomas), Hoglund et al. suggested the existence of a temporal order of chromosomal imbalances (Hoglund, et al. 2001). Further analysis of the thirty-one recurrent imbalances identified in the 334 ovarian carcinomas brought forward two hypotheses regarding ovarian carcinogenesis (Hoglund, et al. 2003; Hoglund, et al. 2001). The first hypothesis was the existence oftwo cytogenetic pathways for karyotypic evolution, the +7/+8q/+12 pathway characterized by gain which is observed in low-stage and low-grade tumors, and the -6q/-lq pathway which is a key element in the progression of ovarian carcinomas (Hoglund, et al. 2003; Hoglund, et al. 2001). The second hypothesis was the existence of phases of ovarian carcinoma karyotypic evolution, where an initial phase of increased instability and complexity of chromosome anomalies is followed by their stabilization (Hoglund, et al. 2003). Hoglund et al. also identified whole or partial chromosome 3 loss as an earl y tumorigenic event in a subset of ovarian cancers (Hoglund, et al. 2003; Hoglund, et al. 2001 ).

1.3.2 Molecular analyses ofspecifie chromosomal regions and genes 1.3.2.1 Cancer genes overview and germline mutations Tumors are thought to arise from the clonai expansion of a single cell (D'Amato and Patarca 1998; Hanahan and Weinberg 2000) which, via the accumulation of mutations in genes controlling cellular functions such as proliferation, migration, DNA repair and apoptosis, can grow uncontrollably with

7 genomic instability (reviewed in Haber and Harlow 1997; Liu and Ganesan 2002; Macleod 2000; Sherr 2004). A frequently mutated gene considered as important for tumorigenesis falls into one oftwo simplified categories: oncogenic (dominant) or tumor suppressive (recessive) (reviewed in Liu and Ganesan 2002). Oncogenes are frequently overexpressed and activated by point mutations, amplification, overexpression and translocation (reviewed in Liu and Ganesan 2002). Tumor suppressor genes (TSGs) on the other hand are often underexpressed and contribute to tumorigenesis when both copies of the gene are inactivated by, for example, mutations, deletions, insertions or promo ter methylation, as proposed in Knudson's two-hit hypothesis (Knudson 1993; Knudson 1971) (reviewed in Liu and Ganesan 2002; Sherr 2004). Traditionally, TSGs were defined by three properties (biallelic inactivation in tumors, inheritance of a single mutant allele increasing tumor susceptibility, and germline mutations underlying familial cancer syndromes) and gene transfection leading to suppression of tumorigenicity was considered a requirement in the confirmation of a gene as a TSG. However additional studies designed to identify genes that play a role in carcinogenesis have made it increasingly difficult to define a TSG (reviewed in Sherr 2004). Indeed, the candidate genes' wide range offunctions, sorne ofwhich can result in irreversible phenotypes when lost, as well as their alteration by both the traditional genetic mutations and the more recently discovered epigenetic changes (including DNA methylation and transcriptional modifications) have complicated the confirmation of a candidate gene as a TSG (reviewed in Le Page, et al. 2004; Liu and Ganesan 2002; Presneau, et al. 2005; Sherr 2004). Another complicating concept is that of haploinsufficiency, whereby inactivation of one allele, frequently by deletion, significantly increases tumorigenic susceptibility because the remaining allele does not produce enough protein for wildtype function (reviewed in Sherr 2004). Regardless ofthe definition, loss ofheterozygosity (LOH) analysis, the comparison oftumor and patient-matched normal DNA in search of allelic imbalances in the former, remains a very common tool in the identification of candidate TSGs (reviewed in Macleod 2000; Sherr 2004; Wang 2002).

8 Hereditary cancer cases occur in individuals carrying a germline mutation which increases their cancer susceptibility because all their cells have a first "hit" (Knudson 1993; Knudson 1971). Hereditary EOC represents about 5-10% of cases (reviewed in CCS 2007a; Prat, et al. 2005) and is associated with three syndromes: site-specifie ovarian cancer, hereditary breast and ovarian cancer, and hereditary non-polyposis colorectal cancer (HNPCC) (reviewed in Prat, et al. 2005). The first two syndromes are considered to be part of the same disease for no gene has been identified which confers increased risk for the former, and BRCAI (17q21.31) and BRCA2 (13q13.1) are mutated in approximately 90% of hereditary EOC cases (Karolchik, et al. 2003) (reviewed in Auersperg, et al. 2001; Prat, et al. 2005). BRCA1 and BRCA2 function in sensing and repairing DNA damage, such as double-stranded breaks, and in cell cycle control via p53; germline mutations are associated with a lifetime ovarian cancer risk of 10-30% for BRCA2 and 16-50% for BRCAI mutation carriers, the incomplete penetrance suggesting that other factors influence cancer deve1opment (reviewed in Auersperg, et al. 2001; Cannistra 2004; Prat, et al. 2005; Samoue1ian, et al. 2004). The remaining 10% of hereditary cases are mainly attributable to HNPCC, with individuals having an 80% lifetime risk of predominant!y right-sided colon cancer, as well as an increased risk for other cancers, including a 12% lifetime risk of ovarian cancer (Aamio, et al. 1999) (reviewed in Sherr 2004). In the case of HNPCC, the germline mutations occur in DNA mismatch repair genes, primarily MSH2 (2p21) and MLHI (3p22.2) (Karolchik, et al. 2003) (reviewed in Chung and Rustgi 2003; Prat, et al. 2005; Sherr 2004). DNA mismatch mutations result from mispairing of either single base pairs or microsatellites, short repeated motifs (1-5 nucleotides long), which, if not repaired, lead to microsatellite instability (change in allele length) (reviewed in Manderson, et al. 2000; Prat, et al. 2005; Sherr 2004). It is important to keep in mind that genetic differences are observed between the hereditary and sporadic forms of the disease, which is one of the reasons why it has been complicated to define TSGs. For examp1e, cases of ovarian cancer with BRCAI or BRCA2 germline mutations appear to have a more

9 indolent disease course than somatic cases (Frank, et al. 2002; Scully, et al. 1996) (reviewed in Prat, et al. 2005). Furthermore, somatic mutations arising in BRCAJ and BRCA2 and microsatellite instability, as confirmed by Dr. Tonin's group, are rare in sporadic ovarian cancer (Lancaster, et al. 1996; Lounis, et al. 1998; Manderson, et al. 2000; Manderson, et al. 2002b; Takahashi, et al. 1995).

1.3.2.2 Genes, regions and pathways implicated in EOC It is far beyond the scope of this introduction to review all of the genes which have been implicated in EOC, however a few examples are presented here to demonstrate how LOH analysis can aid in the identification of TSGs, as well as to point out the importance of investigating pathways. Chromosome 17 frequently exhibits allelic imbalances in malignant ovarian tumors (Eccles, et al. 1990; Foulkes, et al. 1993; Papp, et al. 1996; Phillips, et al. 1993; Pieretti, et al. 1995; Russell, et al. 1990; Tavassoli, et al. 1993). The patterns of loss observed often reflect imbalance of the entire chromosome 17, probably as a result of reduction to hemizygosity (Dion, et al. 2000; Foulkes, et al. 1993; Papp, et al. 1996; Pieretti, et al. 1995; Tavassoli, et al. 1993). Nonetheless, minimal regions of overlapping deletions in ovarian tumors have been mapped to 17p 13 (Phillips, et al. 1993; Tsao, et al. 1991; Wiper, et al. 1998), 17q12 (Tangir, et al. 1996), 17q21-23 (Godwin, et al. 1994; Jacobs, et al. 1993; Yang-Feng, et al. 1993), 17q23-25 (Zweemer, et al. 2001a; Zweemer, et al. 2001 b) and 17 q25 (Dion, et al. 2000; Kalikin, et al. 1997; Presneau, et al. 2005). Analy sis of the se chromosomal regions has lead to the identification of se veral genes with important roles in EOC, including TP 53 ( 17p 13.1) and HER2/neu (17q12) (Karolchik, et al. 2003). p53 is a tumor antigen which protects cells from malignant transformation by arresting the cell cycle when damage is detected to allow either repair or apoptosis of damaged cells (Kastan, et al. 1991; Lowe, et al. 1994; Prives 1998; Samuel, et al. 2002; Symonds, et al. 1994) (reviewed in Macleod 2000; Sherr 2004). TP53 LOH is a common occurrence (69-78%) in ovarian cancer (Foulkes, et al. 1993; Launonen, et al. 2000). The frequency of TP53 LOH has been found

10 to increase with tumor grade and to be statistically significantly associated with mutation of TP53 (Manderson, et al. 2002b). TPSJ is the most frequently mutated gene in ovarian cancer (Berchuck, et al. 1994b; Casey, et al. 1996; Havrilesky, et al. 2003; Ozols, et al. 2004), with the highest mutation rates (up to 50%) being observed in late stage EOC tumors (Arcand, et al. 2005; Berchuck, et al. 1994a; Dion, et al. 2000; Kupryjanczyk, et al. 1993; Okamoto, et al. 1991; Wiper, et al. 1998). A relationship between poor survival and mutated p53 protein overexpression in human ovarian cancer has been described (Geisler, et al. 1997; Henriksen, et al. 1994; Klemi, et al. 1995; Levesque, et al. 1995; Rohlke, et al. 1997; Viale, et al. 1997). HER2/neu is a transmembrane glycoprotein with tyrosine-specifie kinase activity which is similar to the epidermal growth factor receptor (also known as HER1) in both structure and sequence (Akiyama, et al. 1986; Yamamoto, et al. 1986) (reviewed in Aguilar, et al. 1999). HER2/neu is involved in the regulation of cell proliferation, differentiation, motility and survival (Wiechen, et al. 1999; Y arden and Sliwkowski 2001; Young, et al. 1996). HER2/neu is overexpressed in 9-30% of ovarian tumors (Felip, et al. 1995; Fukushi, et al. 2001; Rubin, et al. 1993; Slamon, et al. 1989). Overexpression of HER2/neu has been shown to correlate with poor patient survival (Berchuck, et al. 1990; Camilleri-Broet, et al. 2004; Katsaros, et al. 1995; Lassus, et al. 2004) and a more aggressive phenotype (Leary, et al. 1995; Mandai, et al. 1995; Schneider, et al. 1996). Alterations such as gene amplification (Tsuda, et al. 2001) and point mutations (Bargmann, et al. 1986) can activate the receptor, leading to enhanced activation of intracellular signaling pathways such as MAPK and PB KI Akt, which can result in uncontrolled cell proliferation (Yarden and Sliwkowski 2001 ). The PBK/Akt intracellular signaling pathway, along with its role in cellular proliferation, is involved in cell growth, prevention of apoptosis, invasiveness, and neovascularization (reviewed in Gray, et al. 2003; Mills, et al. 2001; Sherr 2004). A number ofthe genes in this pathway have been implicated in EOC, including AKTJ (14q32), AKT2 (19q13), PTEN (10q23), and PIKJCA (3q26) (Karolchik, et al. 2003). AKTJ or AKT2 is activated in a high proportion of

11 ovarian cancer cells due to amplification of AKT (Yuan, et al. 2000), mutation of PTEN (Obata, et al. 1998; Yuan, et al. 2000), and loss of PTEN function, potentially due to amplification of PIK3CA (Kurose, et al. 2001), as well as other as yet unidentified mechanisms. PTEN is a negative regulator of the PI3K/Akt pathway (reviewed in Macleod 2000; Sherr 2004) with a role in apoptosis (Di Cristofano, et al. 1999; Stambolic, et al. 1998) (reviewed in Macleod 2000). PTEN is located close to a found to have a 45% LOH frequency in ovarian cancer (Kurose, et al. 2001) and is mutated in 20% of endometrioid, but not serous or mucinous, EOC tumors (Catasus, et al. 2004; Li, et al. 1997; Obata, et al. 1998; Sato, et al. 2000). Finally, PIK3CA, which encodes a subunit of phosphatidylinositol 3-kinase (PBK) (Gray, et al. 2003), and the AKT2 serine/threonine kinase are amplified and overexpressed in ovarian cancers (Cheng, et al. 1992; Shayesteh, et al. 1999), and activating mutations for the former have been found in ovarian cancer (Levine, et al. 2005; Samuels and Velculescu 2004; Samuels, et al. 2004; Wang, et al. 2005).

1.3.2.3 Evidence ofa rolefor chromosome 3p in EOC Cytogenetic analyses have identified several chromosome 3 re arrangements in ovarian cancer. As previously mentioned, a large number of the chromosome 3 rearrangements observed by karyotype analysis and CGH techniques in ovarian tumors are observed for the 3p arm (Bernardini, et al. 2004; Hoglund, et al. 2001; Mertens, et al. 1997; Pejovic, et al. 1992; Taetle, et al. 1999b; Whang-Peng, et al. 1984; Yonescu, et al. 1996). Furthermore, LOH analyses ofvarious 3p loci have identified LOH frequencies, ranging from 13- 52%, which clustered in three main regions: 3p14-pcen, 3p21-p24 and 3p25-ptel (Dodson, et al. 1993; Ehlen and Dubeau 1990; Fullwood, et al. 1999; Lounis, et al. 1998; Zheng, et al. 1991). Functional evidence supporting a role for 3p genes in ovarian carcinogenesis first came from the microcell-mediated chromosome transfer (MMCT) of who le chromosome 3 into the HEY ovarian cancer cellline for not only did transfection result in suppression of growth and tumorigenicity, but

12 reversion to the transformed phenotype in sorne of the cell fusion hybrids was associated with loss of 3p regions which overlapped the previously mentioned regions of Lü H (Rimessi, et al. 1994 ). A number of 3p genes which may play a role in ovarian tumorigenesis have been identified, including DRRJ ITU3A (3pl4.2) (Karolchik, et al. 2003; Wang, et al. 2000), RASSFJA (3p21.31), RARfl (at 3p24.2), MLHJ (at 3p21.3), and DLECJ (at 3p22.3) (Agathanggelou, et al. 2001; Imura, et al. 2006; Karolchik, et al. 2003; Kwong, et al. 2006), as well as SEMA3F and SEMA3B (3p21.31) (Karolchik, et al. 2003; Tse, et al. 2002; Xiang, et al. 2002). In the case of SEMA3F and SEMA3B, transfection of either gene into the HEY cellline results in suppression oftumorigenicity (Tse, et al. 2002; Xiang, et al. 2002). The mechanisms of inactivation ofthese candidate genes and their role in ovarian cancer have not yet been determined. In the largest and most comprehensive LOH analysis performed of the chromosome 3p arm in EOCs, three minimal regions of LOH, located at 3p25- p26, 3p24 and proximal to a 3p14 marker, were identified by our group in a large panel of ovarian tumors (Lounis, et al. 1998), with the 3p25-p26 chromosomal region having the highest frequency of LOH (Manderson, et al. 2002b ). Although the latter observation was not statistically significant (Manderson, et al. 2002b), comparable results were observed in an analysis of another marker in the 3p25 region by our group (Arcand, et al. 2005) and in the combined analysis of the karyotypes of 304 EOC specimens by Mertens et al. (Mertens, et al. 1997). Also noteworthy is the fact that a few of the previously described revertant cell-fusion hybrids had interstitial deletions which overlapped this region of LOH (Rimessi, et al. 1994). Preliminary analysis ofthe 3p25.3-ptel region in a panel of 124 EOC samples, solid tumors and ascites, by LOH analysis oftwelve polymorphie microsatellite markers, found an LOH frequency of27% among those 120 tumor samples which had at least one informative marker (Tonin, unpublished data). This preliminary ana1ysis also identified a 330 kb minimal region of deletion bound by D3SJ297 and D3S3525 which contained only one candidate gene, CNTN4 (Tonin, unpublished data). f'

13 1.4 Project hypothesis and study aims The hypothesis of my M.Sc. project was that there is at least one TSG in the 3p25.3-ptel region, as defined by D3S3589- ptel, which is important in EOC tumorigenesis. The formulation of this hypothesis was based on the aforementioned observations, notably the frequent LOH observed for this 3p region in EOC (Arcand, et al. 2005; Lounis, et al. 1998; Manderson, et al. 2002b) and functional studies demonstrating that loss of this region can result in the reversion of cell-fusion hybrid clones to a transformed phenotype (Rimessi, et al. 1994). The goals ofthe project were: 1) To determine the transcriptome ofthe 3p25.3-ptel chromosomal region in normal, tumor, and EOC cellline samples using the Affymetrix Ul33A GeneChip®; 2) To carry out a two-way comparative analysis ofthe microarray data between the panel of normal samples and the individual tumor samples and EOC celllines in search of differentially expressed genes; 3) To validate the expression patterns observed via microarray analysis with reverse-transcriptase polymerase chain reactions (RT-PCR); and 4) To correlate expression values ofthe tumor samples with LOH.

14 2. Materials and Methods 2.1 Clinical samples Samples were collected during laparotomies at the Centre Hospitalier de l'Université de Montréal (CHUM)- Hôpital Notre Dame, and assigned a letter code (NOV, normal ovarian surface epithelium; TOV, malignant ovarian tumor; OV, ovarian ascites) and unique sample number, as previously described (Kruk, et al. 1990; Lounis, et al. 1994). The samples were obtained between 1993 and 2003 with informed consent from all participants and ethics committee approval (Ouellet, et al. 2006). The ovarian tumor bank was supported by the Banque de tissus et de données of the Réseau de recherche sur le cancer of the Fonds de la Recherche en Santé du Québec (FRSQ). Clinical data was extracted from the Système d'Archivage des Données en Oncologie (SARDO) which includes entries on initial diagnosis, treatment, and clinical outcomes (Ouellet, et al. 2006).

2.1.1 Ovarian cancer samples Solid malignant ovarian tumors and patient matched peripheral blood lymphocytes were obtained at the time of surgery from a total of 25 patients (Dion, et al. 2000; Lounis, et al. 1994). Ofthese 25 patients, 24 had not undergone pre-operative chemotherapy, however, despite our best efforts to obtain accurate patient information, it was subsequently determined that sample TOV391 was obtained from a patient who had previously received platinum-based chemotherapy (Presneau, et al. 2005). Histopathological subtype, disease stage and tumor grade (Table 2.1) were assigned for each patient sample by a gynecological pathologist according to the criteria established by the International Federation of Gynecology and Obstetrics. A portion of each tumor sample was snap-frozen and stored in liquid nitrogen at the time of surgical resection (Lounis, et al. 1994).

2.1.2 Primary cultures ofNOSE samples NOSE samp1es were obtained by scraping the surface cells of 14 independently ascertained ovaries from women with no persona! history of

15 Table 2.1 - Primary culture of NOSE and TOV sample information Sam pies Histopathology Age* Stage Grade NOV31 P14 Normal 53 NOV61 P7 Normal 26 NOV116 P13 Normal 51 -"'='" NOV207 P8 Normal 42 ii Ill r:: ~ NOV319 P9 Normal 44 -::::~ Ill::!::: NOV436 P12 Normal 32 Cl) ::::1 o.o NOV504 P8 Normal 37 E ~ NOV653 P8 Normal 38 ca ca Ill E NOV848 P4 Normal 50 w ·- oc..en ... NOV910 P5 Normal 49 z NOV1181 P6 Normal 48 NOV1275 P4 Normal 43 NOV1697 P3 Normal 55 NOV1698 P4 Normal 71 TOV800 Serous adenocarcinoma 76 III 3 TOV881 Papillary serous carcinoma 52 IIIC 3-4 TOV908 Serous adenocarcinoma 53 III 3 TOV974 Serous adenocarcinoma 57 IIIC 2 TOV1007 Serous adenocarcinoma 65 III 3 TOV1054 Serous adenocarcinoma 76 II 3 TOV1095 Serous adenocarcinoma 54 III 3 TOV1108 Serous adenocarcinoma 57 III 3 TOV1118 Serous adenocarcinoma 72 III 3 ii) <;ji !!! TOV1127 Serous adenocarcinoma 49 III 2 -er:: 0 TOV1142 Serous adenocarcinoma 48 III 3 Ill ::::1 TOV1148 Serous adenocarcinoma 43 III 2 .!!~ Q.r:: TOV1150 Serous adenocarcinoma 50 ne 3 E CP ca N TOV1159 Serous adenocarcinoma 65 III 2-3 >Ill ...0 TOV1247 Serous adenocarcinoma 73 II 2-3 ou.. TOV1492 Serous adenocarcinoma 73 III 3 ~ TOV1666 Serous adenocarcinoma 52 lA 2 TOV37 Endometrioid adenocarcinoma 72 III 3 TOV837 Endometrioid adenocarcinoma 82 III 3 TOV869 Endometrioid adenocarcinoma 60 III 3-4 TOV921 Endometrioid adenocarcinoma 55 IIIC 3-4 TOV1147 Endometrioid adenocarcinoma 47 1 1 TOV156 Clear cell adenocarcinoma 46 III 2 TOV391 Clear cell adenocarcinoma 53 IIIC 2 TOV760 Clear cell adenocarcinoma 73 IIIC 3-4 *Age: of collection for the NOSE samples, and of d1agnos1s for the TOV samples Note: stage and grade are not available for NOSE samples

16 ,....--.. ovarian cancer who were undergoing prophylactic oophorectomy, and established t as primary cultures as previously described (Kruk, et al. 1990; Lounis, et al. 1994) (Table 2.1 ). Morphological and histochemical analyses confirmed the epithelial nature ofthese primary cultures (Lounis, et al. 1994). The NOSE samples were maintained in Medium OSE (Wisent, St-Bruno, Canada) supplemented with 15% fetal bovine serum (FBS) containing 2.5 j.lg/ml amphotericin B and 50 j.lg/ml gentamycin (Lounis, et al. 1998; Lounis, et al. 1994; Provencher, et al. 2000).

2.1. 3 EOC cell !ines Four EOC celllines were established from three malignant ovarian tumors (TOV81D, TOV112D and TOV21G) and from the cellular fraction of a malignant ovarian ascites (OV90) from chemotherapy naïve patients as previously described (Provencher, et al. 2000) (Table 2.2). The EOC celllines were maintained in Medium OSE (Wisent, St-Bruno, Canada) supplemented with 10% FBS containing 2.5 j.lg/ml amphotericin B and 50 j.lg/ml gentamycin (Lounis, et al. 1998; Lounis, et al. 1994; Provencher, et al. 2000).

2.2 Nucleic Acid Extraction DNA was extracted from 21 frozen tumors and 19 matched peripheral lymphocytes according to the method previously described (Lounis, et al. 1994). Total RNA was extracted from 25 frozen tumor samples, and from 14 primary cultures ofNOSE samples and four EOC celllines grown to 80% confluency in 100 mm petri dishes, with TRizol™ reagent (Gibco/BRL, Life Technologies Inc., Grand Island, NY, USA) as described previously (Tonin, et al. 2001). The number following the letter code and unique sample number of the cultured cells (primary cultures ofNOSE samples and EOC celllines) indicates the passage number at the time of RNA extraction. RNA quality was assessed by gel electrophoresis and the 2100 Bioanalyzer, using the RNA 6000 Nano LabChip kit (Agilent Technologies, Waldbronn, Germany). RNA obtained from the tumor samples was linearly amplified, producing (+)sense RAMP RNA, as described previously (Ouellet, et al. 2005).

17 cl )

Table 2.2 - Information regarding the spontaneously transformed EOC celllines Nude mouse tumor assayd EOCcell Source Histopathological subtype Age8 Stage Grade Survival N b Time of appearance lines (n=4) um er (lwee k s;) 1 TOV81D Tu mor Papillary serous adenocarcinoma 66 IIIC 1-2 > 10 yrb 0/4e - TOV112D Tu mor Endometrioid carcinoma 42 IIIC 3 3mo 4/4 2-5 TOV21G Tumor Clear cell carcinoma 62 III 3 NOe 4/4 3-12 OV90 Ascites Adenocarcinoma 64 IIIC 3 18 mo 2/4 3-12 a Age of diagnosis a Tumor formation in young female athymie nude mice b Patient was alive, with recurrent disease at 5, 7 and 9 years e No tumors seen up to 24 weeks 1 c ND = no data (patient died of post-operative complications) Values represent the number of weeks for solid tu mors to reach 0.5 cm References: Provencher et al. 2000, Tanin et al. 2001

...... 00 2.3 LOH analysis LOH analysis is a PCR-based assay designed to compare the DNA from an individual' s normal and tumor cells to determine wh ether or not there is a genetic material imbalance in the latter. Of the 25 samples for which microarray expression data was available, LOH analysis oftwo polymorphie microsatellite markers, D3S1307 and D3S1297, was performed on the 21 samples that had available tumor-derived DNA. Nineteen of the se samples also had available patient-matched constitutional DNA. The two markers, chosen because oftheir proximity to the previously identified 330 kb minimal region of deletion defined by D3Sl297- D3S3525 (Manderson 2004), are less than 1 Mb apart in the 3p26.3 chromosomal region based on the March 2006, hg18 assembly of the University of California Santa Cruz (UCSC) Browser (Karolchik, et al. 2003). D3S13 07 is located in the seventh intron of contactin 6 ( CNTN6) and D3S1297 is located between CNTN6 and CNTN4 (Karolchik, et al. 2003). A third polymorphie microsatellite marker, D3S1270 (Karolchik, et al. 2003), was used to improve informativity of LOH results. The genetic markers are described in the Genome DataBase (Letovsky, et al. 1998). PCR amplification ofthese microsatellite markers was performed in a 12.5 ~L volume containing 200 ng of genomic DNA; 1.25 ~Ci of e5S]dATP (Perkin­ Elmer, Woodbridge, Canada); lX PCR buffer (lnvitrogen, Burlington, Canada); 2.5 nmol each of dCTP, dGTP and dTTP; 0.3 nmol dATP; 1.5 mM MgCh; 15 pmol of each primer; and 1 U ofTaq polymerase (Invitrogen, Burlington, Canada). The PCR conditions were 3 min at 95"C followed by 30 cycles of 95°C for 30 sec, 59°C for 30 sec and 72°C for 30 sec. The reaction products were diluted 2:3 with stop buffer (90% formamide, 10 mM EDTA, 10% bromophenol blue and 10% xylene cyanol) and heated at 95"C for 10 min before loading on a 5% polyacrylamide (Bio-Rad Laboratories, Mississauga, Canada) denaturing gel. The products were electrophoresed at 70 W at room temperature for 1.5 to 2.5 h. Gels were dried at 80°C and autoradiographed at room temperature for 2-3 days on Kodak Biomax MR film (Perkin-Elmer, Woodbridge, Canada). LOH was scored based on the absence or a difference in the relative band intensity of alleles

19 in tumor DNA compared to patient matched normal DNA (Table 3.1 Panel A). AU samples positive for LOH at individualloci were analyzed at least twice in independent assays.

2.4 Microarray expression data 2. 4.1 Obtainment ofthe microarray data Gene expression microarray data for the 25 TOV samples, 14 primary cultures ofNOSE samples and four EOC celllines was obtained using the Affymetrix GeneChip® Human Genome U133 series A oligonucleotide microarray (Affymetrix, Santa Clara, CA). The Ul33A microarray contains 22,216 probe sets representing an estimated 18,400 transcripts, including 14,500 well-characterized genes (www.affymetrix.com). Biotinylated hybridization targets were prepared from total RNA as previously described (protocol available at www.genomequebec.mcgill.ca/ovarian). Briefly, the hybridization target was prepared from 20 J.lg of total RNA, extracted as described above, which was reverse-transcribed to double-stranded eDNA with oligo-dT primer containing a T7 RNA polymerase binding site. This eDNA was then in vitro transcribed to eRNA with biotinylated dUTP and dCTP. The resulting eRNA "target" represents a labeled 50- to 100-fold linear amplification of the eDNA sample. To reduce secondary structure, the target was fragmented in 40 mM Tris acetate, 100 mM potassium acetate and 30 mM MgCl (pH 8.1) at 95°C. Hybridizations were performed with 15 J.lg oftarget eRNA. Following washing and staining, microarrays were scanned using a Hewlett Packard GeneArray scanner (Palo Alto, CA). Hybridizations and scanning were performed once per sample at the McGill University and Genome Québec Innovation Centre (www.genomequebec.mcgill.ca). Gene expression values were calculated from the scanned images with the Affymetrix GeneChip® MAS5 software (Affymetrix® Microarray Suite, Santa Clara, CA) which generates a single average difference ratio (raw expression value) from the 11 probe pairs corresponding to one probe set. The software also provides a reliability score (RS) for each probe set

20 (Ambiguous (A), Marginal (M), or Present (P) call) based on the variability of hybridization within each probe set when comparing the 11 perfect-match probes to their corresponding 11 mismatched probes (www.affymetrix.com). A high reliability score (P call) indicates that hybridization is similar across the matched probes and minimal for the mismatched probes, whereas probe sets with a borderline (M call) or low (A call) reliability score exhibit less obvious differences which can be attributed to either low expression or poorly designed probe sets (www.affymetrix.com) (Arcand, et al. 2004). Probe sets representing chromosome 3 genes or ESTs were retrieved from the entire data set, using the associated accession numbers, with Extractor©2003 Lypny and Tonin (Presneau, et al. 2003b), a data filtering software application, coded with the xTalk scripting language MetaCard (www.MetaCard.com), from the UniGene Homo sapiens October 2005 database, based on the National Center for Biotechnology Information (NCBI) UniGene Build 184 (www.ncbi.nlm.nih.gov/UniGene). The Affymetrix NetAffx™ Batch Query tool (Liu, et al. 2003) was also used to identify and obtain information, including target sequence, regarding the probe sets representing chromosome 3p25.3-ptel genes and ESTs. Additional mapping information was acquired from the UCSC Human Genome Browser database (Karolchik, et al. 2003), which includes the BLAT se arch feature capable of determining the location of a given sequence (Kent 2002). Based on these databases, 1,147 probe sets representing 735 chromosome 3 genes and ESTs (Birch, et al. 2007) were retrieved and ofthese, 52 probe sets representing 31 genes mapped to the 3p25.3-ptel interval under investigation. Candidate genes were further investigated using the NCBI (www.ncbi.nlm.nih.gov), GeneCard (www.genecards.org), and Human Genome Database (Letovsky, et al. 1998) databases.

2. 4. 2 Microarray analysis T o eliminate systematic biases and allow comparison of independently generated data sets, the raw expression data set was re-scaled by a normalization process whereby the raw value for each probe set was multiplied by 100 and

21 divided by the mean of the raw expression values of the given sample data set, as previously described (Arcand, et al. 2004; Novak, et al. 2002; Presneau, et al. 2003a). This normalization process was performed using the entire data set prior to extraction of chromosome 3p25.3-ptel expression values. Ofthe initial 52 3p25.3-ptel probe sets under investigation, four were removed because their target sequence was not in the same orientation as the target gene, and one was removed be cause it mapped to the intron of the target gene (Karolchik, et al. 2003). Although this reduced the number of probe sets to 47, the number of genes represented by the U133A microarray was unaffected. For the two-way comparative analysis described below, an additional analysis cri teri on was the presence of at least one high reliability score in the expression data from the primary cultures ofNOSE samples, TOV samples or EOC celllines, as data derived from probe sets containing only low reliability scores is unreliable. Using these criteria, the number of probe sets under investigation was reduced to 36 representing 28 genes. A two-way comparative analysis of the primary cultures ofNOSE samples versus the EOC celllines or TOV samples was carried out with the expression values ofthe 36 probe sets. Probe sets were considered to be differentially expressed when the individual TOV sample or EOC cellline expression value exhibited a three-fold difference from the mean of the expression values of the 14 primary cultures ofNOSE samples, and fell outside of the maximum or minimum expression range exhibited by the 14 primary cultures ofNOSE samples. A two-tailed Student's t-test was carried out with Microsoft's Windows XP Excel to determine whether the mean of the 14 primary cultures of NOSE samples were statistically different from the mean of the 25 TOV samples for a given probe set, taking into consideration sample size, with P values less than 0.05 being considered statistically significant. The correlation coefficient was also calculated with the same program to determine the similarity in overall expression profiles in all possible two-way comparative analyses of each NOV and TOV sample.

22 An unsupervised hierarchical clustering analysis (Pearson's correlation) of the normalized expression values for the 36 probe sets was performed for the NOV and TOV samples using GeneSpring™ software (Silicon Genetics).

2.5 Reverse-transcriptase PCR (RT-PCR) analysis RT-PCR analysis was carried out in order to validate the expression patterns observed with microarray analysis. Due to restrictions in RNA availability, RNA from only eight primary cultures ofNOSE samples, 21 TOV samples, and four EOC celllines was available for the analysis. Based on LOH and microarray analyses, the following genes were analyzed by RT-PCR: CHLJ, CNTN6, CNTN4, ARPC4, EDEMJ, SRGAP3 and ATP2B2. In order to make eDNA, approximately 1!J.g oftotal RNA was treated with DNAse I, Amplification Grade (Invitrogen, Burlington, Canada) according to the manufacturer's instructions, and then reverse-transcribed with Superscript™ III First-Strand Synthesis System for RT-PCR (Invitrogen, Burlington, Canada) using random hexamers, also according to the manufacturer's instructions. Gene-specifie PCR assays were performed in a 25 !J.L volume containing 2 !J.L of a 1: 10 dilution of reverse transcribed eDNA, 1x PCR buffer (Invitrogen, Burlington, Canada), 200 nM each of deoxynucleoside triphosphates ( dNTPs ), 1.5 mM MgCh, 15 pmol of each primer (Alpha-DNA, Montreal, Canada), and 0.5 U of Hot-Star Taq polymerase (Qiagen, Mississauga, Canada). The PCR conditions were 15 min at 95°C followed by 30-35 cycles of 95°C for 30 sec, 58°C for 30 sec, and 72°C for 30 sec. Primers were designed, where possible, to amplify a region which overlapped the target sequence of a probe set mapped to the gene of interest (www.affymterix.com) (Karolchik, et al. 2003) (Table 2.3), using the Primer3 design software (Rozen and Skaletsky 2000). Products were electrophoresed on 1% agarose gels and visualized by ethidium bromide staining. For comparative analysis, RT-PCR was performed with eDNA prepared from purchased placenta and testis RNA (Stratagene, Cedar Creek, TX, USA). PCR products were compared with the intensity of 18S RNA as previously described

23 ~,')

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...., ,.-._ cc !} ~· -n () N F' w n Primers (5' - 3') ...... Gene 1 1" ~~~~th'l Affv~~t;ix -Pl N 0 0 -...J CHL1 IGATGACATCTCCACTCAAGG IACTGAATTTCTGGGTCTGG 1 157 1 204591_at RM'~Offoii!'S ~ '-" ...."'d §" F,.....,...,... . n.... 'AATTTCCTCACTGCTGCTT 1 188 1 207195_at 1 ·- -·- ntil ..0 ~ TCGCTGAATGGCTTAATTTC 1 158 1 na ::sn ()n til 0' 'ACATCTGCTCTGTGTGGAA 1 213 1211672 s at f!;@·~~:;;IJ .... n Pl () :::r ...... n til 'ACCAGATTCACGTCCAGAT 1 178 1 203279_at ---s•; - ...... ---" ""'------· -··-- • (JO n ::sn Pl r"Qri.;J.,I!I"'G, ::s GGCAATAGGTTTGATTCTGA ICGTTTGGTCGTAAGGTAGAG 1 189 1 209794_at • --.~ .. pa 0...... ~

rq~o e; ATP2B2ITCTCACAGTTTTCCATCTCC ITCCTTCATTCCTGACAAGAG 1 206 1 204685_s_at Il • l'tef$@

3.2 Expression analysis 3. 2.1 Microarray gene representation Chromosome 3 is estimated to be 199Mb with 1,585 annotated gene loci, where, with the exclusion of the 122 pseudogenes, the average gene density is 8.8 genes per Mb (Muzny, et al. 2006). Although overall chromosome 3 is gene-poor relative to other chromosomes, it contains two gene-dense clusters, one of which overlaps a portion of the 3p25.3 chromosomal region under investigation (Muzny, et al. 2006). The 3p25.3 gene-dense cluster contains at least 28 known genes in a 1.3 Mb interval that overlaps the region of interest (Table 3.1, panel B). The entire 3p25.3-ptel region under investigation is 10.8Mb and contains 49 known genes.

25 'c,\ ') ; ,_/

Histopathological Panel A PanelS Sample Il Summary of Al subtype 1 3p26.3_{0.7 Mb) 1 3p25.3 (1.3 Mb) in 3p25.3-ptel

TOV800 TOV881 TOV908 TOV974 TOV1007 + TOV1095 -- .. m.~..... TOV11 08 Jlllillîllliît - 1 + Serous l TOV1127 TOV1142 1 1 + TOV1150 TOV1159 TOV1247 + TOV1492 1 + TOV1666 TOV37 + TOV837 + Endometrioid 1 TOV869 + TOV921 + TOV156 Clear cell 1 TOV391 1 1 + T

N 0\ The 47 U133A probe sets that were mapped to this 3p25.3-ptel chromosomal region represented 31 of the 49 genes in the region, thus 63.3% of the genes under investigation were represented by the U133A microarray (Table 3.2). From this initial data set, the expression values ofthe probe sets which did not have at least one high reliability score in any ofthe 14 primary cultures of NOSE samples, 25 TOV samples or four EOC celllines were excluded from the two-way comparative analyses between the NOSE and TOV or EOC samples. These expression values were not analyzed further because the data is not as reliable as that from probe sets with at least one high reliability score, meaning that it is not possible to distinguish values attributed to low gene expression or to inadequate probe set design resulting in unspecific (background) hybridization (Arcand, et al. 2004). The data analyses, including the two-way comparisons, were therefore carried out with the expression values of the 36 probe sets representing 28 genes (57.1% of the genes in the region) which had at least one high reliability score in any of the 14 primary cultures ofNOSE samples, 25 TOV samples or four EOC celllines.

3.2.2 Analysis ofthe expression data for the primary cultures ofNOSE samples Gene expression data from the 14 primary cultures ofNOSE samples was analyzed to identify the NOSE transcriptome of the 3p25.3-ptel chromosomal region under investigation. This analysis was carried out using fold difference comparisons between the maximum and minimum expression values ofthe 36 probe sets containing at least one high reliability score (Table 3.3). Ofthese 36 probe sets, six had expression values with low reliability scores in all14 primary cultures ofNOSE samples. Therefore, the five genes represented by these six probe sets (CNTN6, GRM7, SRGAP3, TTLL3 and ATP2B2) had either low expression values in primary cultures ofNOSE samples or were not properly represented on the microarray (Arcand, et al. 2004). Although these six probe sets ali had gene expression values below 50, low expression values did not imply the absence of high reliability scores within a

~·· probe set. For example, the two probe sets representing OGG 1, which had

27 '

Reviewed 2 Predicted Validated Reviewed 2

.~·

28 37.4 0.9 8.8 3.9 14 9.1 1 4.4 4.1 1.1 13 14.6 8 11.1 11.2 1.0 4 ' 1 72.5 24.7 55.4 58.3 0.9 5 10 4 46.7 0.6 20.2 21.0 1.0 6 8 6 31.9 2 14.4 14.6 1.0 7 0 14 96.7 15.8 36.4 34.6 1.1 8 4 10 112.4 5.1 55.5 52.0 1.1 9 0 14 521.5 128.5 253.4 219.7 1.2 10 0 14 488.5 96.2 329.3 338.6 1.0 11 0 14 168 56.3 104.6 111.0 0.9 12 14 0 22.1 6.7 14.8 14.5 1.0 13 0 14 298 22.4 105.3 81.2 1.3 14 0 14 128.1 27.1 56.5 44.9 1.3 15 0 14 634.6 18.1 204.8 152.7 1.3 16 14 0 36.7 12.1 23.0 23.4 1.0 17 0 14 211.4 78.3 165.7 167.9 1.0 18 0 14 106.2 54.4 73.8 75.4 1.0 19 13 1 36.8 15 27.7 27.4 1.0 20 9 5 14.6 1.8 1 7.0 6.5 1.1 21 3 11 52.2 14.1 31.8 33.9 0.9 22 2 12 54.7 33.3 45.7 46.9 1.0 23 2 12 197.7 60.3 137.7 135.9 1.0 24 0 14 312.7 60.3 170.8 176.7 1.0 25 4 10 117.8 11.3 66.5 69.2 1.0 26 0 14 272.4 187.5 187.5 1.0 27 14 0 18.3 8.7 7.7 1.1 ':';'.r. )1 28 0 14 73 18 1!1. iÏ .. 1. 40.1 38.5 1.0 29 0 14 112.3 38.5 2.9 ' 74.3 72.4 1.0 30 0 14 71.7 21.4 lllll~l-~ll~~ 43.2 38.3 1.1 TMEM111 31 0 14 420 264.6 253.6 1.0 184 ~ no EST 32 13 1 8.3 0.7 5.0 5.8 0.9 TATDN2 33 0 14 129.5 88.9 1.5 ' 112.7 115.0 1.0 34 0 14 674.9 251 2.7 421.4 425.4 1.0 35 14 0 18.4 3.6 12.1 11.1 1.1 36 14 0 5.3 0.7 1.7 1.0 Numbers: bold- probe sets with at least one high b RS =reliability score c max/min: lighter grey highlight when fold change is between three and ten; darker grey highlight when fold change is greater than ten d mean/median: highlight when fold change is greater !han 1.5

maximum expression values of only 14.6 and 52.2 within the primary cultures of NOSE samples, each had at least one high reliability score. These two probe sets were among 30, representing 23 genes, which had at least one high reliability score in the primary cultures ofNOSE samples. These genes had varying expression values, with sorne genes displaying maximum expression values in the lower range of gene expression, such as IL5RA (14.6) and BRPFJ (36.8), and ,..--. r others with maximum expression values in the higher range, such as ARL8B

29 (488.5) and SEC13 (674.9). Ofthese 30 probe sets with at least one high reliability score, 17 probe sets, representing 16 genes, had high reliability scores for all 14 primary cultures ofNOSE samples. While most oftheses genes had at least one value equal to or greater than 100, including EDEMI, LMCDJ and MTMR14, this was not always the case. For example, the CJDEC and CRELDJ genes had maximum expression values of 18 and 21.4, respectively. The NOSE transcriptome of the 3p25.3-ptel region was also examined using fold difference comparisons between the maximum and minimum expression value of a given probe set. A three-fold cutoff was used to describe expression, as this cutoffwas used in the two-way comparative analyses with the EOC and TOV samples. As can be seen in Table 3.3, a number of genes exhibited a three-fold difference between the maximum and minimum expression values, with several genes exhibiting up to a ten-fold difference, suggesting highly variable gene expression within this chromosomal region in NOSE cells. However, upon doser examination, it is apparent that this may be an overrepresentation. For example, while both 211323_s_at (JTPRJ) and 206825_at (OXI'R) had at least one high reliability score and a greater than ten-fold difference between the maximum and minimum expression values, the numerical range of expression varied substantially (Figure 3.1 ). A correlation analysis of all possible two-way comparisons among the 14 primary cultures ofNOSE samples was carried out to determine the similarity of the NOSE samples. The average correlation coefficient was 84%, with values ranging from 58% to 98%, suggesting that sorne of the genes in the region were indeed highly differentially expressed in the NOSE samples (Supplementary Correlation Table).

3.2.3 Analysis ofthe EOC cellline gene expression data Gene expression data was also analyzed for four previously described EOC celllines. These EOC celllines have been well characterized (Table 2.2); three (OV90, TOV21G and TOV112D) are tumorigenic in nude mice while one (TOV81D) is non-tumorigenic (Provencher, et al. 2000). The present analysis consisted of a two-way comparison between the individual expression values of

30 the EOC celllines and the maximum, minimum and mean values of the 14 primary cultures ofNOSE samples for the 36 probe sets under investigation. Differentiai expression was considered to be present when the individual EOC cellline expression value exhibited a three-fold difference from the mean of the expression values of the 14 primary cultures ofNOSE samples, and feil outside of their maximum or minimum expression range.

Figure 3.1 - Examples of expression data for the NOSE samples

211323_s_at (ITPR1) 78 fold difference (min: 0.6, max: 46.7) 700 r-· 600 500 400 300 200 100 0 - IIBI IIBI lilil l'- c.o ('1) ()) 00 - -00 10 00 -N ('1) "<"'" - - "<"'" "<"'" a.. a.. a.. a.. a.. a.. a.. a.. a.. a.. a.. ""' ""' a.. a.. ""' ""' "<"'" "<"'" l'- ()) 00 ('1) a.. 0 l'- 10 00 00 ()) "<"'" 10 "<"'" 0 "<"'" 0 c.o c.o l'- ()) ~ "<"'" c.o ('1) 00 c.o ('1) 10""' ()) ('1) "<"'" N c.o 0 "<"'" "<"'" > >""' > > > ~ "<"'" "<"'" "<"'" > 0 0 0 0 0 0 > > z 0 6 0 0 6z z z z z z z z z ~z 6 z z z

206825_at (OXTR) 35 fold difference (min: 18.1, max: 634.6'-)------, 700

600 +--~~~~~~- 500 400 +------

300+-~~~~~~~~~~~~~--~~

200 +--~-

100 +--~~~~~----,

0 --f-l!ll--,..-liill!IL-,-JIIIIIIIL.,.- l'- c.o ()) ('1) N 10 ('1) 00 00 00 a.. a.. a.. a.. "<"'" a.. a.. "<"'" a.. a.. a.. a.. "<"'" a.. a.. ""' a.. ""' ""' ""'a.. "<"'" "<"'" ()) l'- 10 0 ('1) 00 00 l'- 00 "<"'" ()) c.o l'- "<"'" c.o 10 0 ()) "<"'" 0 ('1) c.o ('1) N ()) 10""' 00 c.o ('1) "<"'" > "<"'" "<"'" > "<"'" ~ > >""' "<"'" > ~ ~ 0 > ~ > 0 > 0 0 0 > 0 0 z 0 0 0 0 0 6z z z z z z z z z z z z z

31 In regards to the individual samples, Table 3.4, which highlights the maximum and minimum EOC cellline expression values for a given probe set, revealed that OV90 had the lowest expression value in 53% of the probe sets. OV90 also stood out in the two-way comparative analysis (Table 3.5), as it had the greatest number of differentially expressed genes, the majority of which were underexpressed, relative to the mean of the NOSE samples. TOV81D exhibited the fewest number ofthree-fold differences in the two-way comparative analysis, with only IL5RA exhibiting expression values greater than three-fold different from the NOSE mean (Table 3.5).

1 5 0.8 2 8.7 3.2 3 17 1.4 4 70.3 17.8 p 5 41.9 6.2 A 6 43.7 3.4 A 7 70.5 13.5 A 8 99.1 28.7 p ' p 9 478.3 96.3 ' 430.5 10 491.9 240.1 11 153.2 52.7 .;rr·r r~ 7008_s_at GRM7 12 21 12.8 14.9 A 8574_s_at LMCD1 13 57 18.3 p p LOH3CR2A 14 30.2 27.1 p p OXTR 15 108 16.5 33.4 p p SRGAP3 11 1 A 16 20.5 13.5 :llllll'Ill1iiii1 '•J:'ï ! lili'IA ' SETD5 17 168.8 49 : 138.8 p 102.4 p 222143_s_at MTMR14 18 95.3 58.1 •••p ~~~~~00W'"'~IJ~"~Ii ffil :e "'~Œ~~ ~ P 204481 at BRPF1 19 48.1 16.9 ' 21 A 21.8 A 20530(s_at IOGG1 20 21 7.9 A ' 8.5 A 205760_s_at OGG1 21 51.3 20.2 p ' 31.1 p 204392_at CAMK1 22 74 34.9 45.4 p A 215273_s_at TADA3L 23 128.2 40.2 77.7 A p 211672_s_at ARPC4 24 181.1 59.3 79.7 p p 217818_s_at ARPC4 25 51.9 2.6 35.3 A p p 217817 _at ARPC4 26 254.4 67 p ' 154.9 210129_s_at TTLL3 27 14.3 3.8 : 9.5 A 219398_at CIDEC 28 29.3 20.8 25.1 p -p 64440_at IL17RC 29 98.2 43.4 -·p 71.2 p 203368_at CRELD1 30 44.5 13.5 : 32.4 p p 217882_at TMEM111 31 236.6 153.8 : 185.8 p p 203844_at 32 7.4 3.1 : 3.2 A A 203648_at 33 148.2 84.8 -p p 213 : 252.3 p 8.7 ...A 0.9 bold- probe b RS =reliability score • Highlights: a cell has a light grey highlight when it is the minimum expression value for thal probe set, and a r, dark grey highlight when it is the maximum expression value for thal probe set.

32 (\

204591_at CHL1 8.8 207195_at CNTN6 4.4 211516_at IL5RA 11.1 218142_s_at CRBN 55.4 211323_s_at ITPR1 20.2 216944_s_at ITPR1 14.4 203710_at ITPR1 36.4

201169_s_at BHLHB2 55.5 1 201170_s_at BHLHB2 253.4 1 217852_s_at ARL8B 329.3 i 203279_at EDEM1 104.6 1 217008_s_at GRM7 14.8 21857 4_s_at LMCD1 220244_at LOH3CR2A 206825_at OXTR 209794_at SRGAP3 221806_s_at SETD5 222143_s_at MTMR14 204481 at BRPF1 205301=s_at IOGG1 205760_s_at OGG1 204392_at CAMK1 215273_s_at TADA3L 211672_s_at ARPC4 217818_s_at ARPC4 217817 _at ARPC4 210129_s_at TTLL3 219398_at CIDEC 64440_at IL 17RC 203368_at CRELD1 217882_at TMEM111 203844_at no EST 203648_at TATDN2 207707 _s_at SEC13 204685_s_at P282 216120 *Fold change over: cells with a diagonal line through them > dark grey highlight (light grey font) when fold change is more than threefold over and EOC expression value is above the NOSE maximum expression value; > light grey highlight when fold change is less than threefold over and EOC expression is within the range of NOSE values *Fold change under: > dark grey highlight (light grey font) when fold change is more than three­ fold under and EOC expression value is below the NOSE minimum expression value; > medium grey highlight when fold change is more than threefold under but EOC expression value is within the range of NOSE values; > light grey highlight when fold change is less than threefold under and EOC expression value is within the range of NOSE values

33 IL5RA was also the only gene in the two-way comparative analysis for which there was a clear difference between the non-tumorigenic and tumorigenic samples, with the expression value of the former being both three-fold below the NOSE mean and less than the minimum NOSE expression value, and expression values in the latter being greater than the NOSE mean. Another gene that stood out in the two-way comparative analysis was fTP RI for it was the only gene that was both three-fold overexpressed (OV90) and underexpressed (TOV21G) in the EOC celllines. CHLJ, LMCDJ and OXTR are noteworthy because all four EOC celllines showed decreased expression relative to the mean of the NOSEs, with two or three EOC celllines reaching the three-fold cutoff.

3.2.4 Analysis ofthe gene expression data for the TOV samples 3.2.4.1 Expression profiles ofthe TGV expression data The expression data of the 25 TOV samples was examined in the same manner as the expression profiles of the NOSE samples. In this analysis, therefore, the fold differences between the maximum and minimum expression values of gene expression were compared to data associated with the 36 probe sets (Table 3.6). Pive ofthese 36 probe sets, representing four genes (IL5RA, ITPRJ, ARPC4 and TTLL3), had low reliability scores in all of the TOV samples. These five probe sets had low maximum expression values, as did sorne ofthe 31 probe sets representing 26 genes which had at least one high reliability score in the TOV samples, including GRM7 (44.1) and CAMKJ (51). The expression values ofthe probe sets with at least one high reliability score varied, with GRM7 and CAMKJ having the lowest maximum expression values for the TOV samples, and SETD5 (445.9) and SEC13 (484.9) having the highest maximum expression values. Of the 31 probe sets with at least one high reliability score, five probe sets representing five genes (CRBN, BHLHB2, ARL8B, SETD5 and TMEMJJJ) had high reliability scores in all 25 TOV samples, and all five had at least one value greater than 100.

34 r, Probe set

5.6 3.0 13.3 11.3 82.9 81.2 5 25 13.7 4.6 4.0 1.2 6 25 23.2 7.2 7.2 1.0 7 10 15 95.8 5 21.8 15.1 1.4 8 18 7 214.7 2.2 34.6 20.5 9 0 25 573.2 92 266.2 267.6 1.0 10 0 25 427.3 119.6 254.9 242.1 1.1 11 14 11 67.9 11.4 37.0 34.5 1.1 12 19 6 44.1 8.4 22.4 21.6 1.0 13 3 22 175.8 23.1 76.3 63.4 1.2 14 8 17 90.7 20.6 45.5 42.6 1.1 15 2 23 350.9 18.5 89.5 56.3 16 18 7 213.2 19.9 98.3 93.0 1.1 17 0 25 445.9 94.4 227.5 215.1 1.1 18 2 23 122.1 44.3 72.9 68.9 1.1 19 20 5 78.1 14.3 44.8 40.1 1.1 20 16 9 19.3 1.2 9.5 10.5 0.9 21 8 17 93.6 19.5 44.7 39.9 1.1 22 21 4 51 3.9 28.3 28.5 1.0 23 12 13 177.7 27.7 118.7 118.6 1.0 24 14 11 166.3 10.8 59.2 41.5 1.4 25 25 0 55.4 1.6 7.5 2.9 26 2 23 325 45.5 211.3 211.3 1.0 27 25 0 52.7 3.9 15.8 13.5 1.2 28 12 13 201.2 13.8 49.9 38.9 1.3 29 9 16 121.1 42.2 78.1 73.6 1.1 30 6 19 78.3 16.2 34.1 32.9 1.0 31 0 25 243.3 66.1 157.6 154.8 1.0 19 6 17.7 1.7 8.1 6.9 1.2 3 22 192.9 61.7 123.9 131.4 0.9 1 24 484.9 235.1 216.2 1.1 14 11 171 39.0 25.7 ' 24 1 42 8.4 5.3

b RS = reliability score c max/min: lighter grey highlight when fold change is between three and ten; darker grey highlight when fold change is greater !han ten d mean/median: highlight when fold change is greater !han 1.5

When examining the variability of TOV sample expression within a given probe set by comparing its maximum and minimum expression values, all but two probe sets had a fold difference greater than or equal to the three-fold cutoff, and many had a fold difference greater than ten. However, as mentioned in the NOSE analysis, the differences may be overestimated due to high variability of low expression values (Figure 3.1).

35 The similarity of the expression profiles within the group of TOV samples was assessed with a correlation analysis of all possible two-way comparisons among the 25 TOV samples for the 36 probe sets under investigation. The resulting average correlation coefficient of 78%, with values ranging from 40% to 96%, supported the notion that the TOV samples exhibited highly variable expression for sorne ofthe genes in the region (Supplementary Correlation Table).

3.2.4.2 Two-way comparative analysis between the TOV and NOSE samples To identify genes that were differentially expressed between the NOSE and TOV samples, a two-way comparative analysis was carried out between the individual expression data of the 25 TOV samples, and the maximum, minimum and mean values of the 14 primary cultures ofNOSE for the 36 probe sets under investigation (Table 3.7). A difference in expression was considered significant when there was at least a three-fold difference between the individual TOV expression value and the mean of the NOSE samples. While the focus of the analysis was on those genes that exhibited a significant fold difference and fell outside of the maximum or minimum expression range exhibited by the NOSE samples, genes that only fulfilled the first criterion were also examined. Although the TOV samples belonged to three different histopathological subtypes of ovarian cancer, previous LOH analyses have described the presence of AI ofthis region in all subtypes (Lounis, et al. 1998; Manderson, et al. 2002b; Provencher, et al. 2000). Therefore, an analysis of the two-way comparisons regardless of subtype was performed. This analysis revealed that gene expression differences of at least three-fold between the TOV and NOSE samples were more frequently the result of underexpression than overexpression, and that few genes consistently exhibited expression values that were differentially expressed according to both criteria (three-fold difference and outside the range ofNOSE gene expression).

36 ') ''\

Probe set

9.1 14.6 72.5 46.7 31.9 96.7 15.8 112.4 5.1 521.5 128.5 488.5 96.2 168 56.3 22.1 6.7 298 22.4 128.1 27.1 634.6 18.1 36.7 12.1 211.4 78.3 106.2 54.4 36.8 15 14.6 1.8 52.2 14.1 54.7 33.3 197.7 60.3 312.7 60.3 117.8 11.3 272.4 108.2 --z:sï 325 18.3 1.3 1111 52.7 73 18 ~ 201.2 112.3 38.5 2.9 121.1 71.7 21.4 - 78.3 420 184 ~ 243.3 8.3 0.7 - 17.7 129.5 88.9 1.5 i 192.9 674.9 251 =~zm~ 484.9 18.4 3.6 .. 171 ·-·-·--- -· '"" --- Il 5.3 0.7 ~J,;i·jl 42.6 ··- .maximin: light grey highlight when fold change is between three and ten; dark grey highlight when fold change is greater than ten • t-test: highlight when the P value is below 0.05 •Fold change over (cens have a diagonalline through them): dari<. grey highlight (and light grey font) when fold change is more than threefold over and EOC expression value is above the NOSE maximum expression value; medium grey highlight when fold change is more !han threefold over but EOC expression is within the range of NOSE expression values; light grey highlight when fold change is less than threefold over and EOC expression is within the range of NOSE expression values. •Fold change under: dari<. grey highlight (and light grey font) when fold change is more than three-fold under and EOC expression value is below the NOSE minimum expression value; medium grey highlight when fold change is more !han threefold under but EOC expression value is within the range of NOSE expression values; light grey highlight when fold change is w less !han threefold under and EOC expression value is within the range of NOSE expression values...... :] ) ·~ ! }

Probe set Genename 'TOV800 TOV881 TOV908 TOV974 TOV1007 TOV1054 TOV1095 TOV1108 TOV1118 TOV1127 TOV1142 TOV1148 TOV1150 TOV1159 TOV1247 TOV1492 TOV1666

6

6

53

12

47 88

6

12

65 94 6

12

6 12

6 41 59 grey highlight (and light grey font) when fold change 1s more man mreetold over value; medium grey highlight when fold change is more !han threefold over but EOC expression is within the range of NOSE expression values; light grey highlight when fold change is less !han threefold over and EOC expression is within the range of NOSE expression values. *Fold change un der: dark grey highlight (and light grey font) when fold change is more than three-fold un der and EOC expression value is below the NOSE minimum expression value; medium grey highlight when fold change is more th an threefold under but EOC expression value is within the range of NOSE expression values; light grey highlight when fold change is less th an threefold under and EOC expression value is within the range

\.;.) of NOSE expression values. 00 * % >3F under = percent of TOV sam pies with a fold-difference below the NOSE sample mean greater th an three; bold and red - greater !han 50% * % >3F over = percent of TOV sam pies with a fold-difference above the NOSE sample mean greater than three; bold and red - greater !han 50% As it is known that different histopathological subtypes may exhibit different molecular genetic aberrations (Chatzistamou, et al. 2002; Parazzini, et al. 2004; Tsukagoshi, et al. 2003) (reviewed in Bell2005; Feeley and Wells 2001), the two-way comparative analysis focused on the serous subtype which had the most samples available for analysis (n=17). While no gene had expression values that fulfilled both analysis criteria in all serous samples, three fulfilled both criteria in at least 50% of the TOV samples: ARPC4 (underexpressed), SR GAP3 ( overexpressed) and ATP2B2 ( overexpressed). The samples that did not fulfill the analysis criteria for these genes nonetheless exhibited fold changes in the same direction. When only the fold change criterion was taken into consideration, three additional genes (ITPRJ, BHLHB2, and EDEMJ) showed differentiai underexpression in at least 50% of the serous samples, with most of the other samples having fold changes in the same direction. The notable exception was TOV1159, which fulfilled both criteria for BHLHB2 in the opposite direction. Two genes that were one sample short of fulfilling the fold change cri teri on in 50% of samples and had the remaining samples with fold changes in the same direction were CHLJ (overexpressed) and OXTR (underexpressed). These eight genes, three which fulfilled both criteria and five which fulfilled the fold change criterion, had expression patterns in the five endometrioid and three clear cell subtype samples which were similar to those observed in the 17 serous samples. In addition to the two-way comparative analysis, two statistical tests were carried out. The first was a two-tailed heteroscedastic t-test to determine whether the TOV and NOSE means for a given probe set were statistically significantly different (P=0.05) (Table 3.7). The t-test confirmed that the difference between the TOV and NOSE samples was significant for seven of the eight genes (with the exception being BHLHB2). The two genes with the most significant differences were SRGAP3 (2.2E-7) and EDEMJ (7.7E-7). The second statistical test was a correlation analysis of all possible two-way comparisons between the 25 TOV samples and the 14 primary cultures ofNOSE samples for the 36 probe sets under investigation (Supplementary Correlation Table). This analysis yielded an average

,.-~ .. / correlation coefficient of 75%, with values ranging between 24% and 96%,

39 confirming that sorne of the genes in the 3p25.3-ptel region under investigation had expression values which differed significantly between the TOV and NOSE samples. Another analysis that was carried out with the expression data of the 36 probe sets representing the 3p25.3-ptel genes under investigation was an unsupervised hierarchical clustering. As can be seen in Figure 3.2, the 14 primary cultures ofNOSE samples and 24 of the 25 TOV samples clustered separately, further supporting the concept that the 3p25.3-ptel region undergoes gene expression changes in TOV samples. Although sample TOV 1054 clustered with the NOSE samples, it did not stand out in the correlation analyses.

Figure 3.2- Hierarchical cluster analysis of the 25 TOV samples and 14 primary cultures ofNOSE samples

1

1 1

<'"")-.:t' C"' <'"") -.:t' (.0 ..-a_ool.C)-.::t"OO.....-a_coa_-.::t" ena_ a..l.C)a..a..~a..a..~a..coa..~a...... ~ <"~(.000<"~ ~ w~~~ •(CcnC'"">cnco cncol.C)~~ cncnw~•.....-.....-cn•.....-co<"~•~l.C)••.....-l.C)(C~ .....-C"'~.....- ...... ~C'"">(Cl.C)(,C•.....-.....-..-~~~~(C•w.....-.....-cnco~.....-.....-~.....-.....-C'"">.....-<"~~<"~.....-l.C)W ..-.....-<"~cn~l.C)•.....-(0.....-co(C~.....- ...... co~co.....-.....-.....-.....-C'"">co.....-.....-.....-cn.....-.....-co.....-.....-cncn..-.....-~ >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ooooooooooooooooooooooooooooooooooooooo zzzzzzzzzzzzzz~~~~~~~~~~~~~~~~~~~~~~~~~

The two-way comparative analysis and statistical tests not only confirmed that sorne of the 3p25.3-ptel genes had different expression patterns in the NOSE and TOV samples and identified those genes which were most likely contributing to these differences, but also identified those genes which were not differentially expressed in any samples and thus were low priority candidates as genes that play a role in EOC tumorigenesis, including CRBN, IL17RC and CRELD 1. Finally, it

40 was mentioned in the Materials and Methods section that despite our best efforts to obtain accurate patient information and pre-treatment tumors, it was determined at a later point intime that sample TOY391 was obtained from a patient who had previously undergone platinum-based chemotherapy (Presneau, et al. 2005), however this sample did not stand out in any of the gene expression analyses.

3.2.5 RT-PCR analysis Microarray expression values are influenced by a number of variables (reviewed in Novak, et al. 2002), therefore RT-PCR analysis was carried out to validate the microarray expression patterns of seven candidate genes (ARPC4, EDEMI, SRGAP3, ATP2B2, CHLI, CNTN6 and CNTN4) (Figure 3.3). To make sure that the microarray and RT-PCR analyses were measuring the same genomic region, the RT-PCR primers were designed, where possible, to overlap the U133A microarray target sequence for the gene under investigation (Figure 2.3) (www.affymterix.com) (Karolchik, et al. 2003). ARPC4, EDEMI, SRGAP3, andATP2B2 were identified as differentially expressed in the TOY samples relative to the primary cultures ofNOSE samples using two way-comparative analyses. RT-PCR analysis confirmed the differentiai expression patterns observed, with most of the TOY samples exhibiting fainter expression bands than the NOSE samples for ARPC4 and EDEMI, and expression bands for SR GAP 3 and ATP2B2 being visible for TOY but not NOSE samples. Microarray expression data was available for two of the three genes, CHLI and CNTN6, previously identified as candidate TSGs by LOH analysis (Manderson 2004). In the case of CNTN6, expression could not be detected by RT-PCR analysis. While this may be due to low expression values, thus validating the low microarray expression values observed (maximum of25.6), it was not possible to exclude the possibility that this was the result of an error in primer design. CHLI had low microarray expression values in the NOSE samples and slightly higher expression values in the TOY samples, especially in sample TOY1142, pattern which was confirmed by RT-PCR analysis, thus further validating CHLI as a candidate gene. The third LOH candidate, CNTN4, was not represented by the U133A Affymetrix GeneChip®, therefore the only expression

41 data available for this gene carne from the RT-PCR analysis. While faint bands of expression were observed in the majority of sarnples, no difference was observed between the TOV and NOSE sarnples. The overall EOC expression patterns for the six genes, determined with the expression values (Table 3.4) and two-way comparative analyses (Table 3.5), were qualitatively confirmed by the semi-quantitative RT-PCR analysis, although quantitative differences were sometimes observed, as previously described (Birch, et al. 2007; Presneau, et al. 2005; Presneau, et al. 2003b).

42 3.3 Integration of expression and LOH results The LOH analysis was carried out to provide a context for gene expression, and thus to determine whether an imbalance in the genetic material of the tumor samples was responsible for the differentiai expression patterns observed for certain genes in the 3p25.3-ptel region. Overall, gene expression values appeared unaffected by the presence or absence of AI, and, as exemplified in Figure 3 .4, this was true whether the genes were identified by LOH analysis or were differentially expressed in the TOV samples compared to the NOSE samples.

43 ''\ ) ) .;

, cë" Expression values r:::: Expression va~ue~...... Expression values .... _.. _.. N 1\J ....Jo. ....Jo. N N W 1\J .,.. 0) CD C> 1\J .,.. c.n 0 c.n C> c.n c.n C> c.n C> c.n C> Ill 11) 0 C> C> C> C> C> C> 0 C> C>OC>C> C> C> C> C> C> C> C> C> Dl w 3::.:.. NOV31 P14 1 NOV31 P14 NOV31 P14 i"'C • NOV61 P7 NOV61 P7 NOV61 P7 iftr NOV116P13 NOV116 P13 NOV116 P13 -~~~ NOV207 P8 NOV207 P8 NOV207 P8 (Q 3 NOV319 P9 z NOV319 P9 NOV319 P9 J o"5!. - 0 r:::: 11) - (/) NOV436 P12 m NOV436 P12 NOV436 P12 "CIII NOV504 P8 NOV504 P8 NOV504 PB c.-11) 0 NOV653 P8 !!il "' NOV653 P8 "' Dl0 -::::T NOV848 P4 ;r NOV848 P4 "0 11) ~(1) :,' 0 NOV910 P5 NOV910 P5 ffi 0 11) NOV1181 P6 "' NOV1181 P6 a>< -· "C NOV1275 P4 NOV1275 P4 1 ::::1 .... NOV1697 P3 NOV1697 P3 lee m )> NOV1698 P4 0-~~~ -· ~--- u 1cn "C 0 'Ltpcfl~itll::0 ., ::0 0 Ci! ::::1 TOV391 G) TOV760 ,::I: Ill Q. TOV760 r 0 )> TOV800 11) Dl -'=" ., ::::1 !il" TOV800 TOV837 0- ~ w lE; r-,) 11) 0 TOV881 ...... TOV881 0 .... TOV908 ...... ~ TOV908 .... !7 TOV974 --1 co 0 TOV974 J2 Dl 11) ...... CD :lE 0 c- ...... TOV1007 ...... TOV1007 CD .. < S:=;: ol'< en ~.,.. TOV1054 CD TOV1054 0"' ...... 0"'""' "' 1= ::::I"C TOV1108 5.3 TOV1108 s."'l n :::!. 11) TOV1118 ;::~111.1 1: TOV1118 3 TOV1127 1 ~ ~ TOV1127 ~il1;0 Dl TOV1147 "' TOV1147 ;- TOV112D P87 :::1(") (") Ill N TOV21G P56 Ul TOV21G P56 m~ m ~ OV90 P54 OV90 P54 -1 • 0 +:­ +:- - < 4. Discussion Combined, the microarray, RT-PCR, statistical and clustering analyses confirmed that the 3p25.3-ptel chromosomal region under investigation contains genes which were differentially expressed in the TOV samples compared to the primary cultures ofNOSE samples, suggesting that they may play a role in EOC. Interestingly, the expression analyses also indicated that differentiai expression seemed independent of both tumor histopathology and the presence or absence of AI (for which both interstitial and telomeric patterns were identified). The expression analyses identified seven candidate genes, ARPC4, SRGAPJ, ATP2B2, EDEM1, ITPR1, CHL1 and OXTR for further investigation, while the LOH analysis confirmed the previously identified CHL1, CNTN4 and CNTN6 (Manderson 2004) as candidate genes. Before discussing the significance ofthese findings and the potential role of the candidate genes in EOC, it is important to examine the location and genomic content of the region of interest for its genetic attributes may influence recombination in a cancer context.

4.1 Information on the 3p25.3-ptel chromosomal region 4.1.1 Information obtainedfrom the physical and genetic maps 4.1.1.1 Physical map information By comparing the genomes of several mammalian species, Murphy et al. found that chromosomal rearrangements were mainly due to a limited number of chromosome breakpoint regions which were reused in both evolutionary and cancer contexts (Murphy, et al. 2005). Further examination ofthese breakpoint regions revealed that their gene densities were greater than the genome-wide average, they frequently had telomeres and centromeres nearby, and segmentai duplications were present in the majority ofthe primate-specifie breakpoints (Murphy, et al. 2005). Segmentai duplications are DNA blocks 10-400 kb in size and of2:95-97% similarity which have been frequently hypothesized to play a role in the chromosomal rearrangements associated with human diseases such as cancer (reviewed in Shaw and Lupski 2004; Stankiewicz and Lupski 2002). Indeed, segmentai duplications are susceptible to homologous recombination, a

45 process which, by creating AI, may play a role in carcinogenesis (reviewed in Bishop and Schiestl 2003; Stankiewicz and Lupski 2002). Human chromosome 3 was analyzed by Muzny et al. who found a large pericentric inversion which occurred after the split of Hominidae from Ponginae (Muzny, et al. 2006). Both inversion breakpoints, one ofwhich mapped slightly downstream to the region under investigation in 3p25.1 (15,239 -15,163 kb; NCBI build 35), were characterized by segmentai duplications (Muzny, et al. 2006). In addition, comparison of the human genome with the mo use genome (Eppig, et al. 2005) revealed that the gene order of a human chromosomal region extending slightly beyond the 3p25.3-ptel region under investigation was similar to the gene order of the mouse 6qE1-3 chromosomal region. Also noteworthy is the fact that the region un der investigation is characterized by sorne of the features associated with breakpoint regions (Murphy, et al. 2005), including overlap with a chromosome 3 gene dense cluster (Muzny, et al. 2006) and presence of a telomere and several segmentai duplications (Karolchik, et al. 2003). Combined, this data suggests that the 3p25.3-ptel region ofinterest may be close to an evolutionary chromosomal breakpoint and may thus, given the previously mentioned association between evolutionary and cancer breakpoints (Murphy, et al. 2005), be more susceptible to breakage in a cancer context than other genomic regions.

4.1.1.2 Genetic map information Recombination rates are obtained by dividing genetic distance, which is a measure ofhomologous recombination, by physical distance. Recombination rates vary substantially between individuals and genomic regions, and although differences in DNA sequence account for up to 32% of this variation, they do not explain the gender differences observed (Kong, et al. 2002; McVean, et al. 2004; Nachman 2002; Yu, et al. 2001). Indeed, male and female crossover rates have a genomic correlation of only 57% (Kong, et al. 2002), in large part due to the discrepancies observed at the telomeres and centromeres, whereby greater rates are observed at the telomeres in males and at the centromeres in females (Kong, et al. 2002; Nachman 2002; Yu, et al. 2001). Furthermore, although variations in

46 recombination rate at the mega-base scale reflect differences in recombination hotspot distribution and intensity at the kilo-base scale (McVean, et al. 2004; Myers, et al. 2005), these features have been found to be similar in men and women (Myers, et al. 2005). Therefore, recombination rate differences between males and females are likely due to other factors, possibly epigenetic factors which play a role in recombination regulation. The 3p25.3-ptel region under investigation extends to the telomeres where a significant difference between the male and female recombination rates can indeed be observed (Figure 4.1 ). Although the sex-averaged recombination rate for this region indicates the presence of a recombination 'jungle' (a region with an atypically high recombination rate of at least 3.0cM!Mb (Yu and Feingold 2001)), the female-specific recombination rate, the only one relevant when analyzing ovarian cancer, is in fact quite low, especially in the most telomeric region. This suggests that recombination in this region is highly controlled in women.

4.1.2 End ofchromosome information The 3p25.3-ptel region under investigation extends to the 3p telomere and subtelomere, therefore it is important to discuss their role in genomic stability.

4.1.2.1 Telomeres The telomere, a nucleoprotein complex located at the end of linear chromosomes, is essential for the maintenance of genomic integrity and cellular proliferation because it protects chromosomes from recombination, exonuclease degradation and end-to-end fusion, and prevents the activation of double-stranded break repair pathways which can then lead to permanent growth arrest and cellular senescence (reviewed in Neidle and Parkinson 2003; Satyanarayana, et al. 2004; Sedivy 2007). In humans, telomeres consist oftwo to ten kb of (TTAGGG)n tandem repeats, all ofwhich are double-stranded except for the terminal3' 100- 200 bp which is single-stranded due to the 'end-replication problem': the inability ofDNA polymerases to elongate without a template (reviewed in Neidle and Parkinson 2003; Satyanarayana, et al. 2004). Telomeric DNA is stabilized via a

47 -· -·-· ~~ ~~9€S€a -Cl) ~09€S€a + ë> M' c: 0 ~ :::J 0 Cb N E ;;-> ~ ni c:: • Cb Q) (.)

- ... L69~S€0 .c~ .. () "E Ill ... ~69€S€a ~ • ... 9v9t>S€a -Ill • L€9~S€0 -~ Q) ~ ~69€S€a .5 • -.... 0 c 0 ·œ e a; Il t>O€~S€0 c.. ·• 1 ,- -M Cl) ..,; (ij N g~g~s€a E c.. •• M .cQ) .... +Cl) .. (ij .e E N' .S:! 0 0 N "C ni Cl) Cl Q) E Cl) -C) > c 'ca 0 ~ Cl) *"' .c ... 90ŒS€0 :!!: • • ~ t -:!!: ~ ... 02:9~S€a Ill • • Q) ... • • 090€S€a -Ill.. c 0 ;:; ... 0€9€S€a Ill c • • :a ... 9l9€S€0 E • • ()0 ..Q) w c 9lvlS€G 0 OLl~S€0 (,) 1 \ \ LO€~S€0 Q) '0 1 6€9~S€0 ..... ~ 1 LB€lS€0 ...; • Q) .. ~ ::s Cb C) E ü: ~ ~

CD 1() .... "' N ~ 0

48 capping mechanism which involves the G-quadruplex and t-loop/D-loop ~··. r secondary DNA structures as weil as a multitude ofproteins, including DNA repair (reviewed in Neidle and Parkinson 2003; Satyanarayana, et al. 2004). Despite this stabilization, progressive telomere shortening (50-100 bp per ceil division) occurs in most somatic ceils until a crisis point is reached and cellular senescence is induced (reviewed in Neidle and Parkinson 2003; Satyanarayana, et al. 2004). Before birth, telomeres are lengthened by telomerase, a reverse transcriptase enzyme with its own RNA template which can elongate the overhang in the 5' -3' direction, however telomerase is inactivated in normal adult cells (reviewed in Satyanarayana, et al. 2004). Telomeres are much more susceptible to damage than the rest of the genomic DNA (Oikawa and Kawanishi 1999; Oikawa, et al. 2001; Petersen, et al. 1998) and are frequently lost in cancer cells because of an increased frequency in stalled replication forks and double-stranded breaks as a result of the 'end­ replication problem' (reviewed in Mumane 2006). DNA damage in the telomeric or subtelomeric regions often occurs in cells that are deficient in DNA repair (reviewed in Mumane 2006). Telomere loss initially results in telomeric instability which may favor tumor formation (Brunori, et al. 2005) by increasing mutation rate and genomic instability (Hackett, et al. 2001). Telomere loss in tumors with deficient cell cycle checkpoints is frequently followed by sister chromatid fusions and multiple breakage/fusion/bridge (B/F/B) cycles, resulting in both extensive DNA amplification and progressive terminal deletions (reviewed in Mumane 2006). However, immortalization and long-term proliferation require functional telomeres, therefore the B/FlB cycles are foilowed by the addition of a new telomere to the end of the broken chromosome (reviewed in Mumane 2006). This addition is achieved by either telomerase, which is reactivated in up to 90% oftumors, nomeciprocal translocations with the end of other chromosomes, or translocations involving duplications, ail ofwhich can potentiaily result in AI (reviewed in Mumane 2006; Satyanarayana, et al. 2004). Telomere instability is nearly universal in epithelial tumors (reviewed in Mumane 2006), including ovarian tumors (Gisselsson, et al. 2005; Hoglund, et al. 2003).

49 4.1.2.2 Subtelomeres Subtelomeres are the extremely dynamic and variable 10-500 kb DNA transition sequences between the telomeres and single copy chromosome-specifie sequences (reviewed in Linardopoulou, et al. 2005; Mefford and Trask 2002; Riethman, et al. 2005). These regions are made up of segmentai duplications, internai human telomeric-like sequences and/or gene transcripts (many ofwhich are pseudogenes) which exhibit extensive variations in length, copy number and chromosomallocation between individuals due to frequent sequence shuffling in both germline and somatic cells (reviewed in Linardopoulou, et al. 2005; Mefford and Trask 2002; Riethman, et al. 2005). This extreme variability has limited subtelomere characterization, making their exact function unclear, but it has been suggested that they may play a role in phenotypic diversity, rapid adaptation to new environments through the diversification of gene families, and buffering telomeric transcriptional silencing (reviewed in Linardopoulou, et al. 2005; Mefford and Trask 2002; Riethman, et al. 2005). Additionally, they may function in telomere maintenance by allowing the binding of telomerase components and the alternative lengthening oftelomeres via recombination (reviewed in Mefford and Trask 2002; Riethman, et al. 2005). The propensity of subtelomeric sequences, especially segmentai duplications, to undergo rearrangements with homologous sequences dispersed throughout the genome can be deleterious by either creating an imbalance of genetic material, which can result in human diseases and syndromes such as mental retardation, or affecting the regulation and expression of dosage-sensitive genes by positioning them under the effect of telomeric silencing (reviewed in Bishop and Schiestl2003; Linardopoulou, et al. 2005; Mefford and Trask 2002; Stankiewicz and Lupski 2002; Yu, et al. 2005).

4.2 LOH analysis supported a role for 3p25.3-ptel genes in EOC 4. 2.1 AI patterns in the 3p25. 3-ptel region may be due to loss or gain Preliminary analysis of a panel of 124 tumors identified a 330 kb minimal region of deletion defined by D3S1297- D3S3525 (Manderson 2004). Although CNTN4 was the only known gene in this region, the minimal region of deletion

50 was not identified because of unique interstitial del etions but as a result of overlapping, independent LOH data, therefore the genes in the surrounding region could not be excluded as TSG candidates (Manderson 2004). To provide the microarray expression values with a genomic context, LOH analysis was performed for the 3p26.3 region defined by D3SJ307- D3S1297 which is adjacent to the minimal region of deletion. The first marker is located in CNTN6 and the second is 0.1 Mb upstream of CNTN4 (Karolchik, et al. 2003), so the analysis provided information regarding the LOH status ofthe three genes (CHLJ, CNTN6 and CNTN4) previously identified as TSG candidates (Manderson 2004) (Table 3.1, Panel A). Different LOH patterns were identified, including heterozygosity of3p26.3 (TOV800), telomeric imbalances ofthe CHLJ and CNTN6 alleles (TOV1492), and interstitial imbalances of the CNTN4 alleles (TOV1108), confirming these three genes as TSG candidates. Additional information for these genes was available from the radiation hybrids created via transfer of chromosome 3 DNA fragments into the OV90 EOC cellline (Cody, et al. 2007) which is monoallelic for the 3p arm (Lounis, et al. 1998). This trans fer resulted in the creation of five radiation hybrids with human chromosome 3 fragments, two which were tumorigenic, and three which were non-tumorigenic (Cody, et al. 2007). In the two tumorigenic and two of the three non-tumorigenic hybrids, a transferred 3p26 fragment containing CHLJ and CNTN6 could be detected, and the two tumorigenic hybrids also had 3p25.3 fragments (Cody, et al. 2007). Although this data would suggest that the 3p25.3- ptel region under investigation does not contain TSGs important in EOC, it is important to keep in mind that the OV90 alleles for these genes may be active in this cellline and that LOH of this region has only been observed in a quarter (27%) of the samples examined (Tonin, unpublished data), implying that the genes in this region are probably not important in all TOV samples. Furthermore, in the case of CHLJ and CNTN6, closer examination determined that these genes were not expressed in the hybrids which had maintained the 3p26.3 fragment (Cody, et al. 2007). Therefore, despite the absence of functional evidence of TSG

51 ability, the 3p25.3-ptel genes, including CHLJ, CNTN6 and CNTN4, remain valid candidates for further investigation. The analysis of the 3p25.3-ptel region was extended with the previously obtained 3p25.3 LOH data for D3Sl597 and D3S3594 (Arcand, et al. 2005) (Table 3.1 Panel B). The D3Sl597- D3S3594 region spans 1.3Mb and contains the 29 most proximal known genes under investigation, including ARPC4 and ATP2B2 (Karolchik, et al. 2003). Combined, these LOH analyses revealed that AI of one or more 3p25.3-ptel polymorphie markers was a frequent occurrence in TOV samples of ali histopathological subtypes, with LOH patterns ranging from heterozygosity in the region, to telomeric or interstitial AI of portions of the region, to AI of the entire region. Furthermore, LOH analysis of markers located close to the 3p centromere (D3Sl274 and D3S2318 at 3p12.3 and 3p11.2 respectively (Karolchik, et al. 2003)) revealed that sorne samples may have AI of the entire 3p arm (Tonin, unpublished data) (Table 3.1, Panel C). LOH analysis examines relative allele copy number oftumor versus patient-matched normal DNA. While the name of the analysis implies that an allele is lost in the tumor sample relative to the constitutional DNA, in reality the analysis only identifies AI, which can result from either loss or gain of an allele. Indeed, even complete absence of one of the two DNA bands in tumor samples does not necessarily imply complete loss of an allele for it has been observed in cases of gross gene amplification (Arcand, et al. 2005). Therefore, it is possible that the observed 3p25.3-ptel AI patterns were due to amplifications rather than deletions. Since the tumor samples were frozen and not subjected to laser capture microdissection in order to preserve RNA integrity for expression analysis, the presence oftwo alleles could also be due to either tumor heterogeneity (the existence of multiple tumor subpopulations exhibiting different structural and numerical abnormalities) or normal cells that infiltrate the tumor, such as stromal, vascular or inflammatory cells (Cvetkovic 2003; Le Page, et al. 2004; Novak, et al. 2002).

52 4. 2. 2 Significance ofthe AIpatterns observed Two possible explanations for the occurrence of AI are either a selective pressure to inactivate or amplify specifie genes, or the existence of an underlying genomic instability which is revealed in a cancer context and results in random allele loss or gain. Supporting the selective advantage hypothesis is the presence of interstitial imbalances that are more targeted than telomeric imbalances, as well as the previously mentioned low recombination rate for this region in females (Figure 4.1 ). Indeed, the latter may indicate that recombination is tightly regulated by epigenetic factors and thus only a strong selective pressure can overcome this regulation and cause AI. However, it is also possible that recombination regulation is modified or absent in a cancer context, in which case the genetic imbalances observed could reflect genomic instability. As previously discussed, the 3p25.3-ptel region contains segmentai duplications, may be located close to a region containing evolutionary breakpoints, and is located next to the 3p telomeric and subtelomeric regions which are vulnerable to AI in a cancer context. All of these factors point to the possible existence of an underlying genomic instability. Although both hypotheses can be supported, the arguments for the former are reinforced by the AI frequency observed, especially if it is due to allele loss which can result in haploinsufficiency, a mechanism whereby inactivation of only one allele can have deleterious effects and may have a role in carcinogenesis (reviewed in Santarosa and Ashworth 2004; Smilenov 2006).

4.2.3 The TSG candidate genes and their potential role in EOC The LOH candidate genes (CHLJ, CNTN6 and CNTN4) are neuronal cell adhesion molecules of the immunoglobulin superfamily (IgSF) (Kamei, et al. 1998; Wei, et al. 1998; Yoshihara, et al. 1995). The genes are structurally similar with six immunoglobulin-like (Ig-like) domains, four (CHLJ) or six (CNTN6, CNTN4) fibronectin type III (FNIII) domains, and either a glycosylphosphatidylinositol (GPI)-anchoring domain (CHLJ) or a transmembrane domain (CNTN6, CNTN4) which anchors them to the cell surface (Brummendorf, et al. 1993; Rutishauser, et al. 1988; Wei, et al. 1998). The Ig-like

53 domains mediate adhesion (Brummendorf and Rathjen 1996) and the FNIII domains are linkers which increase the intermembrane distance in cell adhesive complexes and may have other, as yet undefined, functions (Shapiro, et al. 2007).

4.2.3.1 Close homolog ofLI (CHLJ) Of the three candidate genes, CHLJ is the best characterized. CHLJ is expressed in the developing neurons of the central and peripheral nervous systems, and at low levels in areas of the mature brain displaying high plasticity (Hillenbrand, et al. 1999; Holm, et al. 1996; Liu, et al. 2000). CHLJ can also be upregulated during neuronal regeneration following injury (Chaisuksunt, et al. 2000; Zhang, et al. 2000). CHL1 plays a role in neurite outgrowth, migration and positioning (Buhusi, et al. 2003; Demyanenko, et al. 2004; Hillenbrand, et al. 1999). For example, Buhusi et al. examined the ability ofCHL1 to promote haptotic cell migration in vitro in the human embryonic kidney cellline HEK293 and found that CHL 1 promoted integrin-dependent cell migration toward extracellular matrix proteins through the c-Src, MAPK and PI3K/Akt intracellular signaling pathways (Buhusi, et al. 2003 ). Furthermore, changes in cell adhesion molecule expression patterns can affect cell-cell and cell-matrix interactions, which mediate the invasion and growth of neoplastic tissues (Pignatelli and Vessey 1994), therefore suggesting a potential role for CHLJ in tumor cell migration. In addition to its role in axonal guidance, CHLJ is capable of increasing the survival of cerebellar, dopaminergic and motor neurons (Chen, et al. 1999; Nishimune, et al. 2005) when the MEK and PI3K/Akt signaling pathways are functional, which could indicate an antagonistic effect for CHLJ on the cell death pathway (Nishimune, et al. 2005). ln a cancer context, this ability to rescue cells from cell death could enable cellular proliferation, immortalization and tumor development, suggesting a biological significance to the increased expression of CHLJ observed in TOV samples. Also of interest is the interaction of CHLJ with the PI3K/Akt pathway (Buhusi, et al. 2003; Nishimune, et al. 2005), for, as previously mentioned, a number of the members of this pathway have been

54 associated with ovarian cancer (Kurose, et al. 2001; Obata, et al. 1998; Wang, et al. 2005; Yuan, et al. 2000). CHLJ mouse knockouts exhibit both brain malformations and dysfunction, such as misguided axons and impaired sensorimotor gating, and behavioral abnormalities, such as altered exploratory behavior and decreased anxiety in novel environments (Demyanenko, et al. 2004; Irintchev, et al. 2004; Montag-Sallaz, et al. 2002). While no cancer has been reported in the mouse knockouts, the mice were sacrificed at a young age (4-8 months) to detect neurological abnormalities (Demyanenko, et al. 2004; Irintchev, et al. 2004; Montag-Sallaz, et al. 2002), precluding the observation of cancer susceptibilities that may occur at adult stages of development. In humans, CHLI has been associated with an increased risk for schizophrenia (Sakurai, et al. 2002) and is a candidate for the 3p deletion syndrome which is caused by variable-sized telomeric deletions of the 3p armand is mainly characterized by mental retardation (Angeloni, et al. 1999). A review of the 3p deletion literature (more than 40 papers) did not reveal increased cancer susceptibility in these individuals; however, the oldest individual reported was only 26 years old (Tazelaar, et al. 1991). CHLI has been identified as a candidate gene for hereditary prostate cancer in a genome-wide linkage analysis ofFinnish families, although a screening for exonic mutations did not reveal any disease-segregating sequence variants (Rokman, et al. 2005). CHLI was also identified as a candidate gene in a microarray analysis designed to identify genes differentially expressed between uveal melanoma tumors with and without monosomy of chromosome 3, although again, no structural or epigenetic alterations were detected (Tschentscher, et al. 2003). A potential role for CHLI in EOC is further supported by the observation that the closely related gene LI augments cell migration and tumor growth in ovarian carcinomas (Gast, et al. 2005), is an ovarian cancer-associated antigen (Euer, et al. 2005) and that anti-LI antibodies are capable ofinhibiting intra­ peritoneal tumor growth (Arlt, et al. 2006). The fact that LI plays a role in ovarian cancer when overexpressed suggests that CHLI may play a role in cancer when its expression is increased rather than decreased, potentially explaining why CHLI

55 expression was increased in the TOV samples compared to the NOSE samples (independently of AI). This increase is in accordance with previous studies (Buhusi, et al. 2003; Chen, et al. 1999; Nishimune, et al. 2005), and may also be an alternate explanation as to why knockout mice and individuals with 3p deletion syndrome do not appear to have an increased cancer susceptibility.

4.2.3.2 Contactin 4 and 6 (CNTN4 and CNTN6) CNTN4 expression has been detected in a number ofhuman adult tissues, including the ovary (Hansford, et al. 2003; Zeng, et al. 2002). CNTN4 has been found to promote neurite outgrowth in vitro (Y oshihara, et al. 1995) and may be involved in synaptogenesis (Saito, et al. 1998). The expression profiles of CNTN4 in human tissues suggest a role in both the earl y growth of developing axons and in the maintenance of the adult nervous system (Hansford, et al. 2003). Furthermore, CNTN4 has been identified as a candidate gene responsible for the differentiation potential ofhuman neuroblastoma cells, tumors derived from embryonal neural crest cells (Hansford, et al. 2003). Like CHLJ, CNTN4 has been identified as a 3p deletion syndrome candidate gene (Fernandez, et al. 2004). CNTN6 is thought to be involved in synaptic tuning during maturation and may function in the maintenance or reorganization of the neural network in adulthood (Lee, et al. 2000). Mice homozygous for a targeted CNTN6 disruption display impaired coordination without any obvious morphological or physiological abnormalities in the brain (Takeda, et al. 2003). The absence of cancer susceptibility in the mice may be due to their sacrifice at an early age. CNTN6 was identified by microarray analysis as a gene differentially expressed between two rat strains that show differences in prostate cancer susceptibility (Y amashita, et al. 2005). It was also identified as a candidate gene for human prostate susceptibility in the Finnish genome-wide linkage, although like CHLJ, disease-segregating sequence variants were not identified (Rokman, et al. 2005). CNTN2 is a member of the same IgSF contactin subfamily which is located in the 1q32 chromosomal region (Karolchik, et al. 2003) and has neurite outgrowth functions similar to those described for CNTN4 and CNTN6

56 (Felsenfeld, et al. 1994; Furley, et al. 1990; Kunz, et al. 1998). CNTN2 was found to be amplified and aberrantly expressed in high-grade gliomas, and interference with its availability or clown-regulation of its expression inhibited glioma cell migration in vitro (Rickman, et al. 2001 ). These experiments strongly support potential roles for CNTN4 and CNTN6 in tumor migration.

4.2.3.3 The LOH candidate genes may play a role in tumor migration CHLJ, CNTN6 and CNTN4 play important roles in brain development by promoting axonal migration. Although none of the se genes have been identified in relation to ovarian cancer prior to this study, they have been identified as candidate genes in other cancers. Furthermore, the observations that structurally and functionally similar family members LI and CNTN2 have been shown to increase tumor cell migration support the concept that without being primary targets, CHLJ, CNTN6 and CNTN4 might still play a role in aspects of EOC tumorigenesis such as migration.

4.3 Discussion of the gene expression data 4.3.1 Expression profiles ofthe NOSE, TOVand EOC samples Anal y sis of the gene expression values for the 14 primary cultures of NOSE samples revealed that the majority of the genes represented by the 36 probe sets were expressed in normal cells. An important observation was the existence of substantial variations within the NOSE samples for a given gene when the fold differences of the maximum and minimum expression values of a representative probe set were examined (Table 3.1). Although sorne ofthese differences may have be en overestimated due to the high variability of low expression values (Arcand, et al. 2004; Novak, et al. 2002), the existence of significant NOSE sample variability for the genes in this region was supported by the correlation analysis, which retumed correlation coefficients as low as 58% for sorne of the sample pairs (Supplementary Correlation Table). While the observed differences may be due to technical factors, such as RNA quality and probe set specificity, the use of a three-fold cutoff means the differences more likely reflect normal population variability (Arcand, et al. 2004), which includes gene expression

57 variations between alleles (Lo, et al. 2003; Pastinen, et al. 2004 ). Although the analysis of the two-way comparisons with the TOV or EOC samples was complicated by the highly variable NOSE expression values, overall the NOSE samples were more similar to themselves (84%) than to the TOV samples (75%) (Supplementary Correlation Table). The same analyses demonstrated that TOV samples also had highly variable expression for sorne of the 3p25.3-ptel genes, with an average correlation coefficient of78% (range of 40% to 96%) (Supplementary Correlation Table). This variability may be explained by the previously mentioned technical and population factors, as well as additional factors specifie to tumors and/or the use of frozen tissue samples, rather than the more homogeneous primary cultures. The se include tumor heterogeneity and contamination of normal cells, as well as the presence of AI, although the latter did not appear to affect the expression values of any of the genes in the region ofinterest (as exemplified in Figure 3.4). As for the EOC celllines, the observation that OV90 had the lowest expression value in 53% of the probe sets is consistent with previous analyses which found that this sample is hemizygous for the 3p arm (Lounis, et al. 1998).

4. 3. 2 Two-way comparative analyses identify differentiai expression patterns between NOSE and TOV samples which transcend histopathological subtype and genomic content The two-way comparative analysis, in conjunction with the correlation and unsupervised hierarchical cluster analyses, established that the expression profiles of the TOV samples were significantly different from those of the primary cultures ofNOSE for the 3p25.3-ptel chromosomal region, confirming that one or more genes in this region may play a role in EOC development and/or progression. Indeed, the lowest correlation coefficients were obtained when all possible two-way comparisons between the 25 TOV samples and the 14 primary cultures ofNOSE samples for the 36 probe sets under investigation were analyzed, with a minimum correlation coefficient of 24% (Supplementary Correlation Table). Furthermore, the expression values ofthe 36 probe sets

58 analyzed were sufficient to cluster almost ail the TOV samples separately from the NOSE samp1es in the unsupervised hierarchica1 cluster ana1ysis. Analysis of the two-way comparisons between the individual expression data of the 25 TOV samples, and the maximum, minimum and mean values of the 14 primary cultures ofNOSE samples (Table 3.7) in a histopathological subtype­ independent manner revealed that differences in gene expression of at least three­ fold or greater were more often the result of underexpression than overexpression. This could be interpreted as indicating that the previously described patterns of AI are in fact genomic losses which resulted in reduced expression. However, analysis of the microarray expression data in relation to AI revealed that the latter did not account for the patterns of differentiai over- or under- expression, suggesting that they were due to epigenetic modifications, such as altered transcriptional activity. Although this type of regulation implies that the candidate genes identified are not "classical" TSGs because they are not biallelically inactivated by genetic means such as LOH or somatic mutations (Knudson 1993) (reviewed in Sherr 2004), the definition of a TSG has evolved to include epigenetic modifications (reviewed in Le Page, et al. 2004; Liu and Ganesan 2002; Presneau, et al. 2005; Sherr 2004) and thus the candidate genes may still qualify as TSGs with important roles in EOC. Two-way comparative analysis ofthe 17 serous TOV samples and the panel of 14 primary cultures ofNOSE samples identified seven genes ofinterest for further investigation, which were validated by t-test analysis, as well as a number of low priority genes which had similar expression patterns in the TOV and NOSE samples. The five endometrioid and three clear cell histopathological subtype samples had gene expression patterns similar to those identified in the 17 serous TOV samples, implying that the candidate genes may play a role in aspects of EOC tumorigenesis which are not specifie to a given histopathological subtype. This is noteworthy because it contrasts with the numerous studies which report that the genetic alterations observed in EOC are unique to specifie histopathological subtypes (Chatzistamou, et al. 2002; Parazzini, et al. 2004; Tsukagoshi, et al. 2003) (reviewed in Bell2005; Feeley and Wells 2001).

59 Three of the seven candidates (SRGAP3, ATB2B2 and CHLJ) had differentiai expression patterns consistent with overexpression in the serous TOV samples, and four (ARPC4, EDEMJ, ITPRJ and OXTR) had patterns consistent with underexpression. ARPC4, SRGAP3 andATP2B2, the main candidates, were also identified in a gene expression analysis of the entire chromosome 3 (Birch, et al. 2007), and ITPRJ was identified in a global analysis (Tonin, unpublished data). ARPC4, SRGAP3 andATP2B2 are the main candidates because the expression data of more than 50% of the serous TOV samples was more than three-fold different from the mean of the NOSE samples and fell outside of the maximum or minimum expression range exhibited by the NOSE samples. The other candidates were examined because the majority of serous TOV samples fulfilled the most important criteria (a difference from the mean greater than three-fold), which previous analyses have shown is more likely the result of biological than technical variability (Arcand, et al. 2004). It is indeed possible that the second criterion, expression values outside the maximum or minimum NOSE sample expression range, is too severe when dealing with tumor heterogeneity, or probe sets that may be limited in specificity or sensitivity and thus unable to properly detect expression values below a certain level. Given these limitations, the microarray gene expression patterns were verified by RT-PCR analysis which successfully validated the microarray expression patterns of ARPC4, EDEMJ, SRGAP3, ATP2B2, CHLJ and CNTN6 (Figure 3.3). The main limitation of this analysis was its inability to detect low expression values, such as those observed for CNTN6. This analysis also revealed that CNTN4, the LOH candidate not represented on the Ul33A microarray, appears to have similar expression in the TOV and NOSE samples. RT-PCR analysis was also carried out for the EOC celllines, which at first glanee appeared to have expression patterns more similar to the NOSE cultures than the TOV samples. However, analysis of the two-way comparative data between the EOC and NOSE samples revealed a more complex story for ARPC4, EDEMJ, SRGAP3, ATP2B2 and CHLJ (Table 3.5). In the case of ARPC4 and ATP2B2, the three tumorigenic EOC celllines had fold changes in the same

60 direction as the TOV samples, and each had one sample (OV90 and TOV112D respectively) with expression values greater than three-fold different from the NOSE sample mean. EDEMJ had expression values less than or equal to the NOSE sample mean in the three tumorigenic celllines, similar to what was observed for the TOV samples. On the other hand, fold changes for SRGAP3 and CHLJ in the EOC celllines were in the opposite direction from those observed in the TOV samples. The fact that the microarray gene expression data did not support the initial observation of a similarity in RT-PCR expression between the EOC celllines and the NOSE samples suggests that the latter may be a reflection ofRNA quality. Indeed, the EOC celllines and NOSE samples were cultured, and therefore the RNA available was of a higher quality than that available for the TOV samples. The two other 3p25.3-ptel genes ofinterest identified in the serous TOV samples, fTP RI and OXTR, were also mentioned during the analysis of the two­ way comparisons between the EOC celllines and NOV samples. OXTR expression in the EOC celllines was similar to that observed in the TOV samples, with a fold-change greater than three in the tumorigenic celllines and a change in the same direction for TOV81D. While ITPRJ was three-fold underexpressed in TOV21G, which is consistent with the TOV observations, it was also overexpressed by greater than three-fold in OV90. Two genes that came up during the two-way comparative analysis ofthe EOC celllines, LMCDJ and IL5RA, did not stand out in the analysis ofTOV sample data, although both had one or more TOV samples with a least a three-fold change in the same direction as their EOC cellline counterparts. While many of the genes of interest showed similar gene expression patterns in both EOC celllines and TOV samples, sorne gene expression patterns exhibited discrepancies. Although these discrepancies may be due to the different type of sample materials used ( cultured versus frozen), they may also be due to histopathological differences, as none of the celllines, but most of the TOV samples, are of the serous histopathological subtype. The expression patterns in the EOC celllines were often consistent with tumorigenicity and previous observations. For example, OV90, which is

61 hemizygous for the 3p arm (Lounis, et al. 1998), had the greatest number of genes with expression values exhibiting at least a three-fold difference below the mean of the NOSE samples. TOV81D, on the other hand, is non-tumorigenic and frequently shows morphological characteristics and expression profiles similar to NOSE samples (Manderson, et al. 2002a; Provencher, et al. 2000). Therefore, it was not surprising that only one gene was differentially expressed in the two-way comparative analyses ofTOV81D and the NOSE samples. Furthermore, as briefly pointed out above, the expression profiles of the TOV samples were more frequently similar to the expression profiles of the tumorigenic samples than to those ofTOV81D. In conclusion, the expression analysis successfully identified both high priority and low priority gene candidates for further investigation. It was also determined that there appeared to be no relationship between expression and AL Although the absence of a relationship suggests that epigenetic factors were responsible for the observed patterns of differentiai expression, the sample size was too small to attribute a statistical significance to this observation. Nonetheless, the candidate genes are more likely to be the downstream effectors of transcription factors whose modulation is important during tumorigenesis than primary targets.

4.3.3 The microarray candidate genes and their potential role in EOC 4.3.3.1 The main microarray candidates (ARPC4, SRGAP3, ATP2B2) ARPC4 is the fourth of seven actin related protein 2/3 complex (Arp2/3) subunits, and although ARPC4's precise role is unknown, the Arp2/3 complex plays an important role in the control of actin polymerization, thus affecting a number of cellular processes including cell shape (Frankel and Mooseker 1996; Welch 1999), mobility (Mullins, et al. 1998) and endocytosis (Schaerer-Brodbeck and Riezman 2000). Analysis of the Arp2/3 subunit interactions revealed that ARPC4 is located at the heart of the complex, suggesting that ARPC4 disruption would prevent proper assembly of the complex (Zhao, et al. 2001). ARPC4 has been studied in a cancer context, but its role is controversial since both increased

62 and decreased expression have been reported. For example, Kaneda et al. identified a decrease in ARPC4 expression in human gastric cancers which was expected to result in dysplastic cell morphology (Kaneda, et al. 2002; Kaneda, et al. 2004). Their analysis of the flanking CpG islands revealed that promoter hypermethylation was not responsible for the decreased expression patterns observed, suggesting that decreased expression might be the result of transcriptional regulation by tumor-related genes (Kaneda, et al. 2002; Kaneda, et al. 2004), which is similar to the conclusion drawn in this study. On the other hand, Otsubo et al. found increased Arp2/3 complex levels at the invasive front of colorectal cancer cells as well as in the adjacent stromal cells, which suggested a role in the preparation of the stromal environment to facilitate cancer cell invasion (Otsubo, et al. 2004). Despite the discrepancies observed between the two studies, which may be due to the different types of cancers examined or the different approaches used, they suggest potential roles for ARPC4 in EOC cell morphology, mobility and invasion. SRGAP3 is a Cdc42/RAC1 GTPase activating protein which has been found to negatively regulate cell migration by perturbing the actin and microtubule cytoskeleton (Yang, et al. 2006). Disruption of this gene has been found to have a critical role in the cognitive impairment of 3p deletion patients, potentially by impairing the ability of the cytoskeleton to support the neurite outgrowth and structural changes associated with neuronal and synaptic plasticity (Endris, et al. 2002). SRGAP3 binds to ROBO and plays a critical role in the SLIT-ROBO signal transduction pathway which regulates neuronal migration and axonal branching, potentially by decreasing the level of active Arp2/3 complexes (Wong, ·et al. 2001 ). While this model of indirect repression of ARPC4 by SRGAP3 is consistent with their opposite fold changes in TOV compared to NOSE samples, overexpression of SRGAP3 in cancer cells is counterintuitive for it would limit proliferation and motility. Nonetheless, ROBO, an IgSF member (like CHLJ, CNTN6 and CNTN4), is methylated in human cancers (Dallol, et al. 2002) and predisposes mice to lung adenocarcinomas and lymphomas when disrupted (Xian, et al. 2004). Furthermore, the SLIT -ROBO pathway has been

63 implicated in both developmental and tumorigenic angiogenesis (Klagsbrun and Eichmann 2005), thus supporting further analysis of SRGAP3 in EOC. 2 A TP2B2 is one of four plasma membrane Ca +-A TPases responsible for 2 the transport of Ca + from the cytoplasm to the extracellular space and th us it plays a critical role in intracellular calcium homeostasis (reviewed in Silverstein and Tempel 2006). ATP2B2 is primarily expressed in the central nervous system (Stahl, et al. 1992), but has also been found to be increased by more than a hundred fold during lactation in the rat mammary glands (Reinhardt, et al. 2000). 2 ATP2B2 knockout mice show reduced milk Ca + levels (Reinhardt, et al. 2004), as well as deafness and ataxia (Lane 1987), suggesting that ATP2B2 has a role in maintaining calcium homeostasis during haïr cell transduction, however the mice did not show increased cancer susceptibilities. ATP2B2 has not been previously studied in a cancer context, but calcium homeostasis is crucial for cell survival.

4.3.3.2 EDEMI, ITPRI and OXTR EDEM1 is part of the endoplasmic reticulum-associated degradation (ERAD) pathway which targets misfolded proteins for degradation by the ubiquitin-dependent proteasome system in order to maintain protein folding efficiency (Cabral, et al. 2001; Liu, et al. 1999; Parodi 2000a; Parodi 2000b), and secretory capacity (Eriksson, et al. 2004). EDEM1 is a stress-inducible ER membrane protein which is responsible for extracting folding-incompetent glycoproteins from the calnexin cycle and targeting them for degradation (Hosokawa, et al. 2001; Jakob, et al. 2001; Nakatsukasa, et al. 2001). Molinari et al. found that overexpression of EDEMl protein levels resulted in faster release of folding-incompetent proteins from the calnexin cycle and earlier onset of degradation, whereas EDEMI clown-regulation prolonged folding attempts and delayed ERAD (Molinari, et al. 2003). While the underexpression observed in the TOV samples may not seem advantageous to cancer cells, it is possible that tumorigenesis is dependent on abnormally folded proteins. EDEMI has not been previously studied in a cancer context.

64 ITPRI (inositol 1,4,5-triphosphate receptor type 1) is an intracellular second messenger which releases calcium from the endoplasmic reticulum 2 (Yamada, et al. 1994). ITPRI-mediated Ca + signaling is involved in modulating cell growth and death pathways (Jayaraman and Marks 1997; Marks 1997) and the release and proliferation ofvascular smooth cells (Wang, et al. 2001). The 2 importance of ITPRI-mediated Ca + release for cellular proliferation is controversial for while growth is reduced and cell cycle progression is delayed in ITPRI-deficient T cells (Jayaraman and Marks 1997), it is unaffected in T cells from an fTPRI-deficient mo use (Hirota, et al. 1998), discrepancies which may be due to compensation by other isoforms (Jayaraman and Marks 1997). In addition, a role for ITPRI in calcium-mediated release of cytochrome c and subsequent amplification of the apoptotic signal has been described (Boehning, et al. 2003). fTPRI has been studied in severa} cancer context including breast (Soghoian, et al. 2005), bladder (Tsunoda, et al. 2005) and liver cancer (Paterlini-Brechot, et al. 2003). Paterlini-Brechot et al. identified nine Hepatitis B Virus DNA insertion sites located in key regulatory genes, and found that both the fTPRI and the telomerase gene were targeted in more than one liver tumor (Paterlini-Brechot, et al. 2003). While underexpression of fTPRI could be disadvantageous to cancer cell growth, it could improve survival by reducing the apoptotic signal. OXTR is a receptor for oxytocin which has been shown to modulate behavior and cognition in severa} species (reviewed in Engelmann, et al. 1996). OXTR regulates parturition and myometrial contractility (Fuchs, et al. 1982), breast milk let-clown (Soloff and Femstrom 1987), and reproductive behaviors in the mammalian central nervous system (Insel and Shapiro 1992). OXTR is expressed in the brain areas that are associated with social behavior, including reproductive and parenting behaviors, affiliation and attachment, social memory and reactivity to social stress in non-human mammals (Carter 1998; Young and Wang 2004). Accordingly, OXTR has been associated with autism spectrum disorders in two-genome wide scans (Lauritsen, et al. 2006; McCauley, et al. 2005) and severa! genotyping studies (Jacob, et al. 2007; Lerer, et al. 2007; Wu, et al. 2005). OXTR was found to be overexpressed in doxorubicin resistant breast

65 carcinoma cells in comparison to their drug sensitive progenitors (Turton, et al. 2001), as well as in endometrial adenocarcinomas (Cassoni, et al. 2000), which is contrary to what was observed in this study.

4. 3. 3. 3 Conclusion In conclusion, although none of the genes have been previously studied in ovarian cancer, a number were either directly implicated in other cancers or had family or pathway members which were. Two cellular functions that emerge frequently are calcium and actin regulation which are important for cell survival and mobility. Similar expression results were often observed in the literature and in this study, which, in spite of sorne of the discrepancies o bserved, confirms that these genes are valid candidates.

4.4 Experimental and biologicallimitations ofthis project 4. 4.1 Limitations due to the methods used LOH analysis was useful in establishing a genomic context for the expression data obtained, however the analysis was limited by the inability to distinguish allele gain from allele loss in the tumor samples, as well as by the sensitivity of this method to the presence oftumor heterogeneity and contaminating normal cells. The microarray analysis enabled the simultaneous examination of multiple 3p25.3-ptel genes, however the incomplete representation of this region on the U133A microarray, with 42.9% of the genes being either not represented or represented by probe sets which did not have high reliability scores and thus could not be reliably analyzed, means that a potential candidate may have been overlooked. A drawback that was common to both the microarray and RT-PCR methods was the unreliable detection of low expression levels. In the case of microarray expression data, this is often a reflection of probe set variability in sensitivity and specificity, which may be responsible for the observation of different expression values for probe sets representing the same gene. This probe set variability may lead to the detection of different isoforms or to an overall poor

66 gene representation. Another probe set specifie limitation, but this time regarding the interpretation of microarray data, is the fact that the sequence of the mismatched Affymetrix probe set is proprietary. Microarray analysis has the ad vantage of being able to detect expression differences regardless of how they came about, which was particularly useful for this project because the analysis suggested that the differentiai expression observed was due to epigenetic modifications. However, although these changes can be detected, it is not possible to determine whether the genes identified are differentially expressed as a cause or a consequence oftumor development, orto identify the gene(s) responsible for transcriptional modification.

4. 4. 2 Limitations due to the samples used Another important factor which influenced the interpretation of the microarray data was sample size. A strength of the current study was the use of a panel of 14 NOSE samples, rather than only one sample, as a control, and the use ofNOSE itselfrather than whole ovary as a comparative tissue, thus allowing the establishment of the transcriptome of OSE rather than stroma! cells (Zorn, et al. 2003). Nonetheless, the NOSE transcriptome established suggested substantial variation within the normal population, thus more NOSE samples are required to rule out the existence of sampling bias and facilitate the differentiation of normal and pathological variation. A number of TOV samples which successfully represented different AI patterns, from absence to presence in different regions, were also examined; however the sample size of the individual AI groups was insufficient to attribute a statistical significance to the observed absence of a relationship between AI and differentiai expression. Furthermore, increasing the sample size of the endometrioid and clear cell subtypes would increase the reliability of a conclusion regarding the relationship, or absence of one as found in this study, between differentiai expression and tumor histopathology. This is however complicated by the fact that tumors of this subtype are not nearly as common as tumors of the serous histopathological subtype (Liu and Ganesan 2002; Schulz, et al. 2004).

67 The use of different study models, such as frozen tumor samples and primary cultures ofNOSE samples, may also have affected gene expression, although a colleague examined the effect of growth conditions and found that few chromosome 3 genes were affected (Cody, et al. in preparation). Furthermore, the EOC celllines were useful in providing tumor samples which were devoid of tumor heterozygosity and contaminating normal tissue, and even exhibited AI of the region ofinterest in sorne ofthe samples (OV90 and possibly TOV21G). The celllines were also spontaneously transformed (Lounis, et al. 1994) and thus were not as genetically modified as celllines which are immortalized by the introduction oftransforming genes (reviewed in Auersperg, et al. 2002; Auersperg, et al. 2001 ).

4. 4. 3 Limitations due to the analy sis carried out It is also important to critically examine the methods chosen to treat the data and the analysis criteria chosen for they can have a significant effect on the conclusions drawn from microarray analysis. For example the overestimation of the variability of low expression values would have been limited if the expression values had been rescaled to 100 (Arcand, et al. 2004; Novak, et al. 2002). This threshold reassignment would considerably alter the conclusions drawn in this study and may explain why no gene has been confirmed as a TSG in EOC, although the limited number of probe sets examined did allow for the manual assessment of the potential impact oflow expression values for a given probe set. Furthermore, the data was normalized (to eliminate systematic biases when comparing different samples) which can alter relative expression values, however the method used has been shown to have little sensitivity to extreme values (Novak, et al. 2002). Finally, a three-fold cutoffwas used because previous analyses with cell line replicates have shown that fold changes associated with technical variability occur below this cutoff (Arcand, et al. 2004). Although expression values may be influenced by the fact that the microarray data for these replicates was obtained

~. from an older Affymetrix microarray (Hu6800) and algorithm (Mas4 ), there is '

68 surprising consistency across different GeneChips® and algorithms and the data is readily validated by RT-PCR, especially when the strict three-fold cutoffis used. In fact, the three-fold cutoffused may be too severe when examining tightly regulated genes and, as previously mentioned, expression values may fail to fall outside the maximum or minimum NOSE sample expression range when dealing with tumor heterogeneity or probe set variability.

4.5 Future directions The interpretation of the LOH data regarding the genomic content of the tumor samples could be improved if combined with fluorescent in situ hybridization (FISH) of 3p25.3-ptel markers and analysis of the region with the recently available SNP arrays. These analyses could help resolve whether or not the LOH results obtained were due to loss of an allele in the tumor samples. The technology available to study gene expression is still evolving and the use of more recent microarrays may help examine tho se genes which were not properly represented on the U133A microarray. Furthermore, these more recent microarrays may be better able to accurately detect low expression values. A better assessment of the expression values of the genes identified in this study, especially those with low expression values such as CNTN6, is also possible with real-time quantitative RT-PCR. As previously mentioned, the conclusions drawn in this study regarding the absence of a relationship between differentiai expression and both AI and tumor histopathology could be statistically validated if a larger number of samples were used, especially of the endometrioid and clear cell subtypes. Using a larger panel of tumors would also enable the examinati on of the differentiai expression of the candidate genes in relationship to clinical characteristics su ch as disease stage, tumor grade, age of diagnosis, response to therapy and patient outcome, and may thus identify valuable disease indicators. The potential differences introduced by the use of primary cultures of NOSE samples as a control reference may be avoided with the use of OSE brushings which are collected be fore remo val of the ovary to prevent disruption

69 and are not cultured (Zorn, et al. 2003). However, there is sorne debate regarding the validity of the se control samples because, unlike tumor cells, they are quiescent and thus observed differences could be due to differentiai growth properties (reviewed in Auersperg, et al. 2002; Auersperg, et al. 2001). Primary cultures ofNOSE samples on the other hand can successfully be kept as either quiescent or actively diving cell populations (reviewed in Auersperg, et al. 2002; Auersperg, et al. 2001 ). These future directions do not bring into question the potential EOC role of the seven candidate genes identified in this study, genes which can be further analyzed using se veral approaches. The use of tissue arrays and immunohistochemical analyses would help examine whether the differentiai expression patterns observed at the mRNA level had biological consequences by determining whether the differences were reflected at the protein level. Furthermore, molecular genetic analyses, which are already underway (Tonin, unpublished data), may identify mutations in tumor DNA which would support a causative role in EOC. The current analyses suggest that epigenetic modifications are the potential cause of the differentiai expression patterns, thus methylation analysis of CpG islands of the candidate genes, which all but one candidate gene (CNTN6) have (Karolchik, et al. 2003), is also warranted. Functional studies, using for example the EOC celllines, can also help identify a role for the candidate genes in EOC, especially for those genes which had similar patterns of expression in the TOV samples and at least one of the EOC celllines. For those genes that were found to be overexpressed in TOV samples compared to the panel of primary cultures ofNOSE samples, small interfering RNA (siRNA) technology can be applied to inhibit gene candidates in vitro in cell lines (Lu, et al. 2005; Tebes and Kruk 2005). For those genes exhibiting underexpression in the TOV samples, transfection into the celllines could lead to suppression of tumorigenicity and thus confirm these genes as TSGs, as has been observed with transfection of SEMA3F and SEMA3B into the HEY cellline (Tse, et al. 2002; Xiang, et al. 2002).

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98 Appendix I- Normalized microarray gene expression data for the 14 primary cultures ofNOSE samples and the 25 TOV samples

99 ') ')

NOV31 NOV61 NOV116 NOV207 NOV319 NOV436 NOV504 NOV653 NOVB4B NOV910 NOV11B1 NOV1275 NOV1697 NOV169B P ro be set 5 Gene name Num ber P14 P7 P13 PB P9 P12 PB PB P4 P5 P6 P4 P3 P4 20459t:=:at? GHL1 1.3 A '3.8 A 1.4 A 3.B·A z:6 A 3:9A 37.4 P · 1.BA M·A 13:9·p 0:9 A :;,32 P ''6:"4 A 6.9 A 207195_at CNTN6 1.3 A 4.6 A B.9 A 2.1 A 4.4 A 1.6 A 1 A 3.B A 6.4 A 2.2 A 2.9 A 6.3 A 6.9 A 9.1 A 211516_at IL5RA 11.6A B.1A 12.5A 12.4A BA 10.7A 9.9A 10.BA 11.9P 11.6A 10.7A 13.9A 14.6A 9.1A 21B142_s_at CRBN 46.1 P 61.5 P 5B.4 P 72.5 P 5B.2 P 67.B P 62 P 57.1 P 65.3 P 72 P 24.7 A 4B.4 P 41.6 P 39.6 P if1'323:s..:.~t FreRt"" 20 M 0.6 A 30.1 P ,23.B A 25.3 Mg. 21.9 M 19 ~· ''17.3 A 23.5 P 4.6 A 31 f 46.7 P 216944_s_at ITPR1 17.3 A 12.6 A 31.B P 13.B A 2 A 21.3 P 15.7 P 2.1 A 10.7 A 15.3 P 2.6 A 21.1 P 3.2 A 31.9 P 203710_at ITPR1 33.1 P 15.B P 36 P 37.B P 1B.B P 42.B P 40.3 P 22.4 P 40.7 P 31.9 P 17.6 P 50.B P 25.2 P 96.7 P 201169 s at BHLHB2 15.9 A 5.1 A 27.1 A 7B.7 P 7B.3 P 96.3 P 47 P 112.4 P 37.3 P 22.2 A 43.9 P 6B.2 P BB.2 P 56.9 P 2of17(slat stïu-182 ;206."1 P 21B.9 P 168:7P 264:7 F! tss,9''pi" 3sa:7, E 1n.6 P 521.s:Pf28Et3 P 12B;5:Ft .·2tBTP 22~;lJP 211 R 217B52_s_at ARLBB 329.7 P 4BB.5 P 356.4 P 339 P 33B.4 P 390 P 316.B P 346.B P 250.3 P 464.6 P 96.2 P 255.7 P 29B.4 P 33B.B P 203279_at EDEM1 12B.5 P 110.3 P 114.7 P 111.7 P 16B P 120.B P 102.B P 124.1 P 56.6 P B7.4 P 56.3 P 76.B P 124.B P B0.9 P 21700BsatGRM7 11.5A 14.BA 17.4A 19.1A 13.4A 9.2A 6.7A 22.1A 12.7A 11.9A 19.4A 14.2A 14.9A 19.6A 21B57~s~~t L:.MG0:1 22;4 P 46.3 P 72;a·p 298 P 94:,t:p; 121Ù P , • 46:1 P 17··F~ P 62~'4/P 90.1~ 46:3 P 64.4 p 124i9 P 220244_at LOH3CR2A 34.2 P 34.3 P 36 P 12B.1 P 66.5 P 96.5 P 39.4 P 76.B P 61.3 P 41.2 P 27.1 P 40.4 P 4B.5 P 60 P 206B25_at OXTR 562.9 P 1B.1 P 156.3 P 634.6 P 64.2 P 101.1 P 199.1 P 173.B P 2B4 P 149 P 22 P 101.3 P 77.7 P 322.6 P 209794at SRGAP3 15.2A 13.1A 21.BA 25.5A 21.1A 25.BA 32.8A 36.7A 12.1A 25.7A 17.BA 24.6A 22.2A 27A 22f§Q(s_aï:9l:Tè5',. 14t3 P :'1:S.M;;P 1tmssp '~f9~\.P 148.5 ~ 168:2 P .Hl1.4 P 222143_s_at MTMR14 55.3 P 93.7 P 74.7 P 59.3 P 76.2 P 77.6 P 54.4 P 64.4 P 76.9 P 94.3 P 76 P 106.2 P 66.5 P 2044B1 at BRPF1 15 A 22.6 A 24.7 A 2B.7 A 26.B A 2B.4 A 27.1 A 35.7 P 26.2 A 35.2 A 27.6 A 36.B A 21.2 A 2,0~301:s7~t~OGG1 .. ·1 14.6 P 1.BA . UA .. ~.5 P 7.2 M B.1 A 5.5 A 3.BA, 13.2 P 4.2 A 6.5 P 6.3 P 205[6Q;Is_at ~Gti•" •··· ?8J!;.M J 31f.8 P 52.2sP; 33:6 P H:1tM' ~5,8 P 204392_at CAMK1 54.7 P 33.3 P 47.7 P 46 M 49 M 40.3 P 49.3 P 54.5 P 53.5 P 42.9 P 36.3 P 215273_s_at TADA3L 103.4 A 122.9 P 153.3 P 145.5 P 60.3 A 192.1 P B5.7 P 191.5 P 174.8 P 126.2 P 197.7 P 211672 s at ARPC4 136 P 165.1 P 1B6.4 P 175.3 P 1B4.1 P 60.3 P 1B1.5 P 312.7 P 16B.5 P 272.6 P 17B.1 P 21781(~)~ ~gPG4' '6à.2 P 70.2 96.B P '11.3 A 49.B:P 9'1.5 P 17.B·P 80.9 P 217B17_at ARPC4 272.4 P 196.7 P 171.2 P 191.3 P 177.2 P 10B.2 P 234.3 P 202.9 P 190.3 P 1B4.6 P 139.9 P 239.5 P 210129_s_at TTLL3 5.1 A 13.8 A 1.3 A 2.4 A 18.1 A 8.8 A 5.9 A 16.8 A 6.7 A 3.8 A 1.8 A B.6 A 18.3 A 21939B at CIDEC 34.4 P 1B P 33.2 P 33.2 P 40.B P 39.7 P 73 P P 37.7 P 47.6 P 4B.6 P 39.3 P 31.7 P 6.f44Qjt ll.FRe · 'css.2'P' 3B.5 A"' H2.3P · 76.3f' 81.9 P 82.1 62.8P ' 98:9'P ~70.3~r' "1'68:s·.R.. 85.9 P 20336B_at CRELD1 21.4 P 32.3 P 64.3 P 5B.2 P 35.1 P 35.4 P 57 P 33.1 P 36.B P 71.7 P 52.3 P 39.7 P 22.1 P 217BB2_at TMEM111 257.7 P 245.2 P 245 P 353 P 234.9 P 260.9 P 2B4.6 P 1B4 P 1B9 P 302 P 420 P 275.2 P 249.5 P 202.7 P ng 7A 5.BP 7A 5.BA 4.9A 5.BA 0.8A 6.7A 0.7A 1.2A 3.3A BA B.3A SA 1-16 114 P {Oi.5 f? 105t2;f:l:, '9f6 P '122.2.P 10l;7 ,,8~~9 R 1'-iB.. fle; 123:1-P 0 1?4~6.P" 12S.5P' 121.4 P 674.9 p 363.7 p 349.6 p 477.7 p 463.6 p 419.3 p 358.5 p 343.6 p 441.7 p 251 p 561.7 p 431.5 p 313.9 p 44B.2 p ,...... l"'v.,vo"-"-'"'l"'r"-0>"- 1 35 1 B.6A 7.7A 17.2A 10.2A 14.7A 15.5A 14A 16.7A 10.9A 11.2A 10.9A 3.6A 9.2A 1B.4A :§; 216120satATP2B2 36 0.9A 0.9A 1A 2.2A 0.9A 0.9A 3A 5.3A 0.7A 1A 1.7A 2.1A 2.1A 0.9A ) .)

Probe set Gene name TOV881 TOV908 TOV974 20459;tJf" 9.2'A' 72I4'P 3Q:'3P 1].3;A 6;2A 13Z1 P 5t.s·p· 6KfP> ~·:2521? 207195_at 2.6 A 12.3 A 7.8 A 1.5 A 1.4 A 1.6 A 1.4 A 3.2 A 1.2 A 14.2 A 1.8 A 9.1 A 18 A 36.1 A 12.8 A 17.9 A 4.9 A 8.5 A 15 A 13.9 A 10 A 17.1 A 11.3 A 10.1 A 67.8 p 82.3 p 65.5 p 64 p 76.7 p 81.2 p 137.9 p 96.8 p 89.3 p 62.6 p 44.8 p 88.8 p .• ftA t.'3 A 2TA . 4': 1 A 1 J:,; '1;3.7 A i'1é3·A. 5.8 A 4.3 A 7.2 A 3.2 A 3.1 A 2.9 A 7.2 A 10.7 A 0.5 A 9.6 A 3.7 A 2.8 A 8.7 A 17.4 P 14.7 P 35.6 P 16.4 A 13.3 A 20.2 P 23.8 P 25.4 P 11.7 A 14.4 M 15.1 A 12.2 P 33.4 P 2.2 A 15.3 A 19.6 A 16.4 A 2.3 A 51 P 20.5 A 35 P 18 A 53.6 P 30 A 18.9 A 32.4 A 1Ei~&'.P' M7:F> "22~~1'P '52ô!:7.P 4il9.SP 2€16;4 P . 312.5 267.&,"p; 217852_s_at ARL88 10 318.4 p 286.1 p 352.7 p 248.2 p 242.1 p 277.4 p 157.3 p 395.7 p 203.9 p 221.1 p 172.6 p 119.6 p 256.7 p 203279_at EDEM1 11 24.7 A 31.9 A 46 P 31 A 35.2 M 22.5 A 45.9 P 41.1 P 38 M 49.4 P 45.3 P 11.4 A 51.9 P 217008_s_at GRM7 18 A 11 A 42.8 A 19.8 A 11.6 A 21.9 A 23.6 P 12.9 A 14.6 M 14.5 A 40.8 P 29.5 P 23.7 P 2H~S7~,,~~~r'\'J-MçQ1 99.4 P s3.4P' sr:s~~ ~1Qs:.3 ..1l J;18,~P J;O?;~ &8,~,R 50.6P 175.8;.1? P., 7s:·P 220244_at LOH3CR2A 47.7 P 42.6 P 33.3 A 55.6 P 62.8 P 47.4 P 28.4 A 47.5 P 41.4 P 41.9 P 61.3 P 28.6 A 63.2 P 206825_at OXTR 40.1 P 34 P 244 P 350.9 P 103 P 56.3 P 18.5 A 112.4 P 38.4 P 274.4 P 135.7 P 35 P 62 P 209794 at SRGAP3 74.7 P 110.8 A 82.1 A 184.1 P 199.6 P 48.2 A 109.1 A 82.6 A 183.1 P 126.8 P 52.1 A 138.9 P 103 P 221~ost$_!cii~sSîfds F 2o'3;.p ~6!~ ·~ 29454 P ~'32:8P 31.'p·:e. '471:6 P 222143_s_at MTMR14 47.6 p 47.9 p 102.2 p 65.6 p 93.7 p 51.2 p 60.1 p 57 p 70.3 p 68.9 p 68.5 p 81.6 p 58.8 p 204481 at BRPF1 19 45.1 A 78.1 P 39.1 A 48.8 A 39.3 A 33.2 A 49 A 49.5 P 38.8 A 34.7 A 38.2 A 71.3 A 66 P 14.1 P 19.3 P 9.1 A 11.1 P 8.8 A 12.7 P 15.3 P 10.5 P 8.6 A 1.2 A 3.1 A 12.4 P 10.7 M ~~;î~ttiHg~~~. L ~~ "'3s.9 R · ~&r3 f! 44:7 P 40, 53 p. 36.2>f;~ 204392_at CAMK1 22 35.6 P 18.1 A 25.9 A 34.5 A 34.8 A 38.9 A 23.2 A 24.7 A 215273_s_at TADA3L 23 119.5 P 118.6 P 136.3 M 137.3 P 149.7 P 107 P 122.6 A 131.9 P 211672_s_at ARPC4 24 A 23.9 A 15.3 A 96.1 P 49.5 P 126.9 P A 75 P 4~l&ta_s~?t~ Ai;{fÇ-4 1.~ A 12.6 A 1 4o?:.>!iA 217817_at ARPC4 26 211.3 P 325 P 280.1 P 281.3 P 300.3 P 114.4 P 162.1 P 275.8 P 252.8 P 123.5 P 45.5 A 148.3 P 261.7 P 210129_s_at TTLL3 27 5.2 A 13.6 A 25.4 A 18.6 A 10.7 A 3.9 A 12 A 25.5 A 12.1 A 12.7 A 19.9 A 18 A 11.3 A 219398 at CIDEC 28 201.2 P 51.9 P 54.7 A 168.9 P 33.3 M 41.5 P 37.1 A 34.7 P 45.5 P 27.2 P 50.7 P 38.9 A 38.8 P ~~4li_~t ~ •.. •.Jfj":?~C·. 42-,Z:f! 1:. .1 84'.2 p 91 203368_at CRELD1 30 27.2 P 30.9 P 46 A 21.3 P 20.7 A 16.2 P 27.7 P 40.4 P 34.6 P 20.8 M 21.2 P TMEM111 31 179.9 p 233.4 p 198.7 p 154.8 p 116 p 114.3 p 106.1 p 181.9 p 117.8 p 215.4 p 133.3 p na 32 4.6A 17.1P 17.7A 7.3P 4.9A 1.7A 5.8A 9.9P 2.5A 8P 11P

"33' '••••••'c<.... vP2 • 1159;3 P 72,1 66.1,~ 34 220.6 p 284.7 p 187 p 219.8 p 147.5 p 303.9 p 195.9 p 273.3 p 196.7 p 181.2 p 135 p 216.2 p 371.4 p _. 35 55.8 P 30.7 A 12.4 A 8.7 A 23.9 A 96.6 P 43.9 P 171 P 23.4 A 13.1 A 25.7 M 8.8 A 55.2 P 36 7.7 A 7.6 A 3.6 A 2.6 A 1.8 A 17.3 A 10.3 A 42.6 P 3.8 A 2.1 A 4.2 A 4.6 A 4.7 A 0_. ) )

Sercus (continued) 1 Endometrioid (n=5 Probe set NumberiTOV1159 TOV1247 TOV1492 TOV16661 TOV37 TOV837 TOV869 TOV921 TOV1147 20459~_at 219.8 P ,33:5 P'" 752:2"A 10:.9A ;?.SA 14AM 48.fP 16.P 207195_at 10.5A 1.2A 6.6A 0.7A 2.4A 7.3P 9.8A 1.4A 25.6A 3A BA 211516_at IL5RA 14 A 7.4 A 9.4 A 6.9 A 6.3 A 11.3 A 7 A 8.2 A 14.9 A 22.1 A 25.7 M 218142_s_at CRBN 162.5 p 94.1 p 117.7 p 85.6 p 73.2 p 67.9 p 93 p 83.3 p 52 p 87.7 p 52.8 p 21 f3z3:.:_s~at I'TPR.1 . f2:5A 3.4 A .5.3A. ..· ·~.7A 4.8 A 2o7;· 2:a;A "10T3 A 4.2 A. 216944_s_at ITPR1 11.8 A 5 A 21.6 A 2.6 A 8.4 A 4.6 A 8.4 A 8.9 A 23.2 A 8.3 A 3 A 20371 O_at ITPR1 14.5 P 10.9 P 95.8 P 9.4 A 25.3 P 11.6 M 5 A 13.9 A 38.4 P 36.9 P 16 P 201169_s_at BHLHB2 214.7 P 3.3 A 14.9 A 21.1 A 4.8 A 42 P 19.2 A 57.6 P 95 P 46.3 A 26.9 A 20'Il]b}.{Jlt E3HU:tl32. 5êfE8"P7 toa.s:P 395 178P" 1227:4"?" .294.5 P ?367:'1:) 57~;2o;P' "33:E9P 322.6P 217852_s_at ARL8B 427.3 p 261.4 p 234 p 232.7 p 283.3 p 217.7 p 211.8 p 245.1 p 232.9 p 360 p 214.3 p 200:.6 p 203279_at EDEM1 62.5 P 34.5 A 32.6 P 28 M 28.5 A 39.3 M 39.6 A 29.8 P 30.3 P 67.9 P 34.3 A 22.5 A 217008 s at GRM7 21 A 21.8 P 10.5 A 23.5 A 8.4 A 13.2 A 21.6 M 29.6 A 18.8 A 44.1 M 27.6 A 35.7 P ~1857 4=s1at4filMbb1 pc;;'"· 23:1 P 127;.3~Pc 52,9 poi-êi'1o3.. s P 54;6rP 50,•7 P ·108~9P10 220244_at LOH3CR2A 31.4 A 29.1 A 90.7 P 69 M 20.6 A 28.9 P 34.1 A 54.4 P 49.6 P 58.8 P 206825_at OXTR 76.2 P 23.7 P 67 P 107.5 P 33.7 P 42.1 P 151.4 P 24.6 A 52.6 P 85.3 P 209794_at SRGAP3 81.8A 71.6A 93.2A 115.4A 21.8A 29.7A 95.7A 51.3A 19.9A 75A 22 WO.P~s-~ $;El;P5 3®:·H~ P· 445.9." P 1sta e • 25&;6 P 127:3 p 222143_s_at MTMR14 88.3 P 78.7 P 71.3 P 118.2 P 44.3 P 90.5 P 52.3 A 204481 at BRPF1 59.3 A 47.4 A 32.5 A 49.7 A 61.7 P 36.8 A 14.3 A 7.9 A 10.5 A 15.8 A 13.2 M 9.3 A 1.9 A 10.8 A ig~~&tiiT~dtJggg~f .~'" 1 42:"8 30.6M 204392_at CAMK1 22 21.1 A 30.3 A 35.5 P 28.5 A 21.3 A 34.6 A 215273_s_at TADA3L 23 175.2 P 115.6 A 173.5 P 100 P 83.2 A 140:.8 P 211672 s at ARPC4 24 10.8 18.5 74.5 P 108.4 P 32.4 A 66.9 A ?2f7B18lsiaf ;ttiRPG4 ;~s ·· j6~5P.Y · ~t~:sAî' s:s.A 3.5 A 217817_at ARPC4 26 208 P 229.3 P 208.7 P 166.2 P 140.2 P 141.6 P 271.4 M 306.7 P 210129_s_at TTLL3 27 13.5 A 18.3 A 14.1 A 13 A 7.9 A 8.5 A 22.4 A 8.2 A 219398 at CIDEC 28 35.2 P 38 M 25.8 A 31.3 A 41.9 P 33 A 42.2 A 55.2 M 6~(}[~t 1[1 7Ré 29 93:6 P sai3~P 68.6 p 85.5 P; • a4':<4 Pè 92:2 A "67:.9 A ·~ 66."5 lW. 203368_at CRELD1 30 32.2 P 43.5 P 41.5 P 39.6 P 32.9 A 47.4 P 29 P 33.3 A 39.8 P TMEM111 31 169.3 p 176.2 p 142.2 p 211.6 p 66.1 p 164.2 p 243.3 p 135.1 p 135 p ng 32 6.6 P 12.2 A 6.9 A 6.3 A 9.7 A 4.9 A 6.8 A 11 A 12.4 A 33'· P 124A li ;+f:~92.9;p• .13f_J 61.7. P; 34 365.6 P 192.1 P 186.3 P 180.2 P 249.3 P 157.5 P 274.3 P 77.7 A 330.7 P 484.9 P 177.4 P 267.3 P ,__... 35 57.8 P 31.7 P 56.4 P 15.9 A 17.4 A 2.7 A 58.2 P 4.7 A 16.6 A 80.8 P 18.8 A 44 P 0 36 12.6A 2.1 A 4.1 A 5.3A 5A 6.9A 10.8A 10.9A 7.5A 21.1 A 3.3A 7.1 A N Appendix II -Correlation analyses among the NOSE samples (1 ), between the NOSE and TOV samples (2), and among the TOV samples (3)

103 Correlation Tables - Dark grey highlight when correlation value is greater than 85%; Light grey highlight when correlation value is less than 50%

Correlation analysis (1)

NOV31 P14 NOV61 P7 NOV116P13 NOV207 P8 NOV319 P9 NOV436 P12 NOV504 P8 NOV653 P8 NOV848 P4 NOV910 P5 NOV1181 P6 NOV1275 P4 NOV1697 P3 NOV1698 P4

co 0'> 0 co r- 0'> 0'> Correlation N co co analysis (2)

TOV37 0.69 0.81 TOV156 0.64 0.75 0.77 0.80 0.72 TOV391 0.55 0.77 0.81 0.73 0.81 0.81 0.70 0.80 0.70 0.71 TOV760 0.62 0.79 0.82 0.77 0.78-0.70 0.84 0.73 0.77 TOV800 0.59 0.77 0.80 0.77 0.69 0.75 0.82 0.76 0.70 0.72 TOV837 0.60 0.78 0.77 0.81 0.63 0.80 0.82 0.58 0.78 0.72 0.75 TOV869 0.73 0.79 0.82 0.83 '± A 0.81- 0.77 0.69-0.79 0.84 TOV881 0.62- 0.77 0.79 0.76 0.80 0.59 0.79 0.80 0.65 0.79 0.71 0.75 TOV908 0.71 0.82 0.78 0.72 0.76 0.81 0.74 0.84 0.82 0.74 0.68 0.80 TOV921 0.67 0.54 0.51 0.53 0.54 0.50 0.64 TOV974 0.72 0.64 0.67 0.78 0.63 0.67 0.73 0.71 0.83 0.67 ' 0.68 0.58 0.77

TOV1007 0.53 0.75 0.64 0.58 0.65 0.71 0.67 0.65 0.73 0.70 1 0.67 0.60 0.68 TOV1054 0.56 0.72 0.69 0.57 0.78 0.82 0.68- 0.77 0.62 0.53 0.79 0.69 0.67 TOV1095 0.55 0.76 0.69 0.50 0.77 0.77 0.72 0.74 0.80 0.64 0.59 0.79 0.68 0.68 TOV1108 0.64- 0.80 0.63 0.81 0.83 0.81 0.77 0.81 0.83 0.56 0.80 0.75 0.78 TOV1118 0.50 0.73 0.66 0.70 0.69 0.71 0.61 0.73 0.67 0.51 0.72 0.65 0.66 TOV1127 0.76 0.68 0.78 0.82 0.73 0.78 0.83 0.84 0.84 0.75 0.58 0.78 0.74 0.81 TOV1142 0.61 0.53 0.59 0.60 0.56 0.53 0.59 ~li;,:,:;,qi,!l~~~~~~\~!l'l' 0.56 0.55 0.57 TOV1147 0.61 0.82- 0.85 0.72 0.73 - 0.83 0.76 TOV1148 0.52 0.67 0.66 0.68 0.57 0.72 0.59 0.54 0.72 0.61 0.64 TOV1150 0.65 0.77 0.77 0.71- 0.79 0.82 TOV1159 0.55 0.81 0.83 0.70 0.58 0.82 0.78 0.70 TOV1247 0.55 0.80 0.78 0.66 0.80 0.81 0.76 TOV1492 0.51 0.68 0.73 0.65 0.71 0.61 0.62 0.56 0.60 0.60 0.69 0.63 O.

104 )

~ ~ LO 00 00 ~ N ~ 00 0 m ~ N <0 <0 o 10 m o ~ N ~ ~ ~ 10 10 ~ m <0 U") 0 0 0 ~ ~ ~ ~ ~ ~ ~ ~ N ~ ID Correlation ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ analysis (3) > > > > > > > > > > > > >

TOV869 1 TOV908 TOV921 TOV974 TOV1007 TOV1054 TOV1095 TOV1108 TOV1118 TOV1127 TOV1142 TOV1147 TOV1148 TOV1150 TOV1159 247 TOV1492 TOV1666 ...... 0 Vl Appendix III - Ethics committee approval

106 Appendix IV - Qualification for radioisotope use

108