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DNA methylation as a biomarker in human reproductive health and disease
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DNA methylation as a biomarker in human reproductive health and disease
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DNA METHYLATION AS A BIOMARKER IN HUMAN REPRODUCTIVE HEALTH AND DISEASE by Sahar Houshdaran A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOCHEMISTRY & MOLECULAR BIOLOGY) August 2009 Copyright 2009 Sahar Houshdaran ii Dedication This dissertation is dedicated with all my heart to my parents, my sister, and my brother. Their unparalleled intellect, sophisticated wisdom, and unconditional love spread my wings and introduced me to the limitless sky. iii Acknowledgments I could not have been any luckier. I was thirteen when I decided on my PhD major. Every step of the way from that day until now has been towards this goal. There are not very many people in the world, who know exactly what they want in life. Not very many who have discovered their true passions and talents. Even fewer are those, whose passions and goals match. And finally, only some end up lucky enough to fulfill their dreams. I could not have felt any luckier. In this path, yes, I admit, I have worked relentlessly, through despair and hardships. But I would not have reached here alone. I would not have been here if my parents had not cherished every single question I had when I was a curious little girl. Oh, there were too many and they never told me I was too young to understand. Their knowledge and sophistication always helped me comprehend more and ask even more. Along the way, they helped me find my passion. They exposed me to everything: music, sports, science, literature, arts, everything possible. And once I knew, they never stopped supporting me in its pursuit. But, that’s only part of the story. I had to travel far and long for what I wanted, and they had already built the strength and independence in me, before I knew it. They gave me the wings and thought me how to fly, high, forever. But no one, no matter how strong could ever survive without love, unconditional love. And I was, once again lucky enough. They built us a home, founded on love. Their love and my siblings’ love has been the reason to look forward to tomorrow, no matter how hard it had been at times. And, my sister and my brother… the two, who have supported me with their wisdom, love, and kindness. The two, who bared my absence so that I could fulfill my iv goals. The two, who never stopped supporting me and loving me. The two younger souls, whose advise I have always needed, as they always knew better, in everything. They are and have been the light and the hope, in the toughest of times, and the pillars of logic and knowledge. I just could not have been any luckier. And my friends, who have supported me here, so far from home. One day, few years ago, when I thought my world was collapsing, I looked around and I saw them all gathered. They stayed till the sky was blue again. They never left my side, in the best moments and in the most difficult, in the fun and in grief, success or failure. And today, when I couldn’t have been any luckier, I send my deepest love and gratitude to Mom, Dad, Sepideh, and Soheil and hope that the dedication of this small dissertation could show a small fraction of the depth of my owe, love, and appreciation, as I will never be able to repay them for their gift of happiness. v Table of Contents Dedication ii Acknowledgments iii List of Tables vii List of Figures viii Abstract x Chapter 1: Introduction 1 DNA methylation in normal development and disease 1 Part I: Male infertility 3 Background 3 DNA methylation and male infertility 4 Part II: Ovarian cancer 6 Background 6 DNA methylation in ovarian cancer 7 A: Epigenetic profiling of ovarian cancer 11 B: Biomarker development for early detection of ovarian cancer 12 Early detection in ovarian cancer 12 DNA methylation in serum/plasma 13 DNA methylation as blood-based or proximal sample-based Biomarker for early detection of epithelial ovarian cancer 14 Chapter 2: Widespread epigenetic abnormalities in poor quality Human sperm 15 Chapter 2 Abstract 15 Chapter 2 Introduction 16 Results 17 Discussion 28 Materials and Methods 30 Chapter 3: Epigenetic profiling of ovarian cancer 36 Chapter 3 Abstract 36 Chapter 3 Introduction 37 Results 40 Discussion 48 Materials and Methods 52 vi Chapter 4. DNA methylation biomarkers for early detection of ovarian cancer in blood-based or proximal fluid based assays 57 Chapter 4 Abstract 57 Chapter 4 Introduction 58 Results 59 Discussion 80 Materials and Methods 84 Chapter 5. Discussion 91 Bibliography 100 Appendices 119 Appendix A 119 Appendix B 121 vii List of Tables Table 1.1 Alphabetical list of hypermethylated genes in ovarian carcinoma 9 Table 2.1 Trend p-values for associations between MethyLight results and semen parameters 18 Table 2.2 Number of sperm samples within each category of semen parameters that was analyzed on the Illumina platform 25 Table 3.1 Clinical and histological information of the tumors 43 Table 4.1 Specification of each methodology using three various DNA isolation Kits 70 Table 4.2 Sequences of the original and the’ relaxed’ primer and probes 78 Table 4.3 Tumors subtypes used in the comparison of the original and the ‘relaxed’MethyLight reactions 78 Table 4.4 Performance of the ‘relaxed’ MethyLight reactions in comparison to the original reactions 79 viii List of Figures Figure 2.1 Box plots illustrating associations between semen parameters and level of methylation (PMR) in DNA isolated from 65 study sperm samples 21 Figure 2.2 Cluster analysis of 36 MethyLight targets in 65 study sperm DNA samples 23 Figure 2.3 Results of Illumina analysis of 1,421 autosomal sequences in DNA isolated from sperm and buffy coat 26 Figure 2.4 Illumina analysis of 1421 autosomal loci in sperm and PBL DNA 27 Figure 3.1 Two-dimensional hierarchical cluster analysis of 91 MethyLight markers on 15 ovarian cancer cell line 41 Figure 3.2 Two-dimensional hierarchical cluster analysis of 808 Illumina GoldenGate loci of 27 ovarian primary tumors, 15 ovarian cell lines and two control buffy coat samples 45 Figure 3.3 Heat-map of subtype-specific DNA methylation markers in ovarian tumors 46 Figure 3.4 Two-dimensional unsupervised hierarchical cluster analysis of 27,578 CpG dinucleotides tested on 145 gynecological malignant, benign, and normal samples. 49 Figure 3.5 4931 most variable Infinium probes on normal, benign and malignant gynecological samples 50 Figure 4.1 Schematic summery of step-wise marker development and screening 60 Figure 4.2 Heat-map summary of steps of marker selection 63 Figure 4.3 Heat map of the final 14 reactions on ovarian tumors 64 Figure 4.5 Multiplexed digital MethyLight analysis of 300 µl of poled serum samples from cases, controls, and their dilutions 65 Figure 4.6 Selection of the most possible promising pre-op serum sample 67 ix Figure 4.7 Performance of the four multiplexed markers on patient serum by digital MethyLight 68 Figure 4.8 Comparison of DNA concentration recovered using various methods. A, in plasma, and B, in serum 72 Figure 4.9 Multiplex analysis of MethyLight reactions 75 x Abstract Epigenetics refers to the changes in gene expression that are not accounted for by the changes in DNA sequence. DNA methylation is one of the main epigenetic mechanisms in mammals. It contributes to various biological processes such as cellular differentiation, gametogenesis, and cancer. We investigated DNA methylation abnormalities in the male and female reproductive tract. During gametogenesis germ cells undergo epigenetic reprogramming, defects of which may lead to compromised spermatogenesis. Using MethyLight and Illumina GoldenGate assays we found a broad abnormal epigenetic defect associated with abnormal semen parameters. We propose that the underlying mechanism may be improper erasure of DNA methylation during germline reprogramming. Ovarian cancer has the highest mortality rate of all gynecologic malignancies. Survival is strongly stage-dependent but there are no effective screening methods available. Ovarian cancer is also very heterogeneous. While each subtype presents with different clinical, pathological, and therapeutic characteristics, all ovarian cancer patients are treated uniformly. We used DNA methylation assays to investigate epigenetic differences between the subtypes and to find potential blood-based early detection biomarkers. We observed subtype-specific DNA methylation profiles suggesting that each subtype might be a distinct disease. Stepwise marker screening resulted in a panel of four markers that could distinguish pooled sera of patients from controls, but should be further investigated on multiple independent serum samples. 1 Chapter 1: Introduction DNA methylation in normal development and disease There are about 200 different cell types in a mammalian body and almost all, with the exception of the lymphocytes, share an identical genotype. During development, most of these cells differentiate without detectable changes in DNA sequences, while differentiation of any cell type is associated with spatial and temporal gene expression patterns which are controlled by genetic and epigenetic mechanisms. DNA methylation is one of the main epigenetic modifications of the mammalian DNA. It is a covalent modification of the 5’ carbon of cytosine, which occurs predominantly in the context of CpG dinucleotides. DNA methylation is known to contribute to various biological processes such as X- inactivation [104], genomic imprinting [104], silencing of parasitic sequences [187], cancer progression [87] and aging [80]. It is also believed to be involved in the regulation of tissue-specific transcription and development [24, 105]. Indeed, DNA methylation is essential for development since null mutations of DNA methyltransferases (Dnmts) are embryonic lethal [105]. It has been shown that formation of cell type- specific DNA methylation pattern accompanies the production of various cell types [46, 151, 174]. The preference of Dnmt1 for hemi-methylated DNA suggests a mechanism for maintenance of specific patterns of methylation in the genome, which are perhaps imposed at defined developmental time points in precursor cells and are inherited by their descendants. These specific DNA methylation patterns could accompany cell or tissue differentiation and specific patterns of gene expression. Earlier studies estimated that about 60% of protein coding genes is linked to CpG islands, long stretches of dense CpG dinucleotides. Database analysis for tissue- 2 specific promoters estimates that about half of tissue-specific genes are linked to CpG islands [151]. CpG islands are generally believed to be methylation-free in normal tissues, however some are found to be methylated. Studies on genomic imprinting and X- chromosome inactivation have shown that during development a small yet significant proportion of CpG islands become methylated and the associated promoter remains stably silent. Extensive studies in the mouse have shown that development is accomplished by bimodal DNA methylation reprogramming. The first evidence of lineage-specific de novo methylation is observed in the blastocyst. The inner cell mass (ICM), which gives rise to the entire embryo becomes hypermethylated, whereas the trophectoderm (TE) forming most of the placenta is undermethylated. The next evidence of remodeling is found during germ cell development. Primordial germ cells (PGC) are highly methylated as descendents of ICM. As PGCs enter the developing germinal ridge they undergo rapid genome-wide demethylation essential to remove parental imprinting marks. De novo methylation follows to create gamete-specific methylation and imprinting marks. Therefore, abnormal characteristics in the germline or the gametes, such as abnormal sperm morphology could be associated with abnormal DNA methylation reprogramming. In the past decade, a substantial body of evidence accumulated that shows extensive DNA methylation abnormalities in cancer cells [13, 88]. In general, cancer cells undergo global hypomethylation, while most aberrant hypermethylation events occur at CpG islands [88]. Global hypomethylation is perhaps mostly due to hypomethylation of repetitive elements, that are methylated and silenced in normal cells [187]. Global hypomethylation may lead to activation of silenced transposons, genomic 3 instability and hypomethylation of protooncogenes and therefore contribute to oncogenesis [5, 39, 49, 61]. Aberrant CpG Island hypermethylation of promoters has been shown to be associated with accompanying gene silencing [72, 86]. Inactivation of some tumor suppressor genes [50, 72, 87, 88] by this mechanism was proposed as one of the two hits in Knudson’s two hit hypothesis in neoplasia [88]. The occurrence of DNA hypermethylation in all types of cancer makes it well-suited as a biomarker that can be amplified with PCR-based approaches. Part I: Male infertility Background About 15% of all couples attempting pregnancy are infertile. Male factor infertility accounts for about 40 to 50% of these cases [119], which could be due to acquired conditions such as endocrinologic alterations, varicocele, cryptorchidism, exposure to toxicants or chromosomal abnormalities ranging from numerical abnormalities, long arm Y chromosome microdeletions, or point mutations. However, a large portion of infertile men (about 30%) remains with idiopathic etiology [96]. DNA methylation has been shown to play an important role in cellular differentiation, gametogenesis, and in establishing genomic imprints in mammals [6, 104, 178]. Therefore, abnormal epigenetic programming of the germline could be a possible mechanism compromising fertility of some men currently diagnosed with idiopathic infertility. 4 DNA methylation and male infertility Well-defined causes of male-factor infertility include congenital and acquired dysfunction of the hypothalamic-pituitary-testicular endocrine axis, anatomic defects, chromosomal abnormalities, and point mutations [114, 119, 149]. However, these diagnoses account for only a small proportion of cases, and the etiology remains unknown for most male-factor infertility patients [153, 175]. Abnormal epigenetic programming of the germline is proposed as a possible mechanism compromising fertility of some men currently diagnosed with idiopathic infertility. The mammalian germline undergoes extensive epigenetic reprogramming during development and gametogenesis. In males, significant chromatin remodeling occurs during spermatogenesis [51, 138], and widespread erasure of DNA methylation followed by de novo DNA methylation occurs developmentally in two broad waves [23, 104, 138, 141, 144]. The first occurs before emergence of the germline, establishing a pattern of somatic-like DNA hypermethylation in cells of the pre-implantation embryo that are destined to give rise to all cells of the body, including germ cells. The second widespread occurrence of erasure takes place uniquely in primordial germ cells. Subsequent de novo methylation occurs during germ cell maturation and spermatogenesis, establishing a male germline pattern of DNA methylation that remains hypomethylated compared with somatic cell DNA [6, 19, 104, 127, 145, 178]. Disruption of one or more of these epigenetic processes may lead to abnormal spermatogenesis and compromised sperm function. A small number of studies have addressed the epigenetic state of the human male germline. Substantial variation in DNA methylation profiles is reported in 5 ejaculated sperm of young, apparently healthy men. Notable distinctions were observed both between samples from separate men and among individually assayed sperm from the same man [55]. Although this variation suggests that DNA methylation may be used as a biomarker of sperm quality, semen quality and fertility were not assessed in this study [55]. Several previous studies did assess sperm DNA methylation together with either sperm quality or fertility outcomes. However, measures of DNA methylation were limited, consisting of either immunostaining - a single and somewhat nonspecific measure [16]- or sequence specific measures made at only one or two imprinted genes [68, 116, 118]. To assess sperm DNA methylation at a more representative set of targets, we selected a much larger set of sequence-specific assays for use in the present study. We measured DNA methylation in ejaculated spermatozoa, interrogating sequences in repetitive elements, promoter CpG islands, and differentially methylated regions (DMRs) of imprinted genes. We validated our findings in a larger independent sample size and observed similar results. To address the possible role of epigenetic programming in abnormal human spermatogenesis, we related sequence-specific levels of DNA methylation to standard measures of sperm quality. This is the first study to describe the epigenetic state of abnormal human sperm using an extensive panel of DNA methylation assays. 6 Part II: Ovarian cancer Background Ovarian cancer is the leading cause of death among all gynecological cancers in the United States [83]. It is the 6 th leading cause of all cancer death among women despite its low incidence. The relative 5-year survival rate for advanced-stage disease remains less than 30%, compared to more than 90% for patients diagnosed with localized disease [20, 30]. Unfortunately, the majority of cases present with advanced-stage disease resulting in high mortality rates associated with ovarian carcinomas. This is in part due to the hidden anatomical location of the ovaries, lack of effective screening methods and non-specific symptoms until it is spread outside the ovaries. Tumors of this organ can arise from different cell types but the majority are epithelial in origin [30]. Epithelial ovarian tumors are very heterogeneous with several histological subtypes. The four most frequent subtypes are serous, endometrioid, mucinous, and clear cell. These share the common characteristic of resembling tumors of other organs of the reproductive tract [30]. Occasionally several subtypes are present within the same tumor, leading some to argue that these tumors may share a common cell of origin. However, these subtypes differ histopathologically and with regards to their associated risk factors [89, 130, 133] suggesting that the cell of origin may be different. Despite the clinical and histopathologicall heterogeneity, most ovarian cancer patients are treated uniformly, by surgery followed by chemotherapy. In some patients the disease would become chemo-resistant, with its mechanism currently unknown [20, 131, 181]. Furthermore, the origin of epithelial ovarian tumors as well as hormonal effects on the development and progression of these tumors are currently highly debated 7 [112, 188]. While it is obvious that these heterogenic behaviors of ovarian tumors stems from molecular differences, these differences are ill defined. DNA methylation in ovarian cancer Recently, identification of specific epigenetic alterations for various cancers, including ovarian cancer, has been an area of intense research. Most studies have utilized candidate gene approaches to identify hypermethylated CpG islands, generally of tumor suppressor genes. Some studies have investigated hypomethylation of specific regions. New studies interrogating larger number of loci are emerging with the advancements in the development of more high-throughput techniques. In the next few paragraphs I have provided some examples for these epigenetic alterations in ovarian carcinoma. The first comparison of DNA methylation changes –both globally and at a specific locus- among various ovarian neoplasias [34] was published in our institute and showed that global methylation levels in tumors of Low Malignant Potential (LMP) and carcinomas were lower than in cystadenomas. The specific locus, MyoD1, was unmethylated in cystadenomas but was found to be methylated in 50% of the cases in both LMP and carcinomas. It should be noted that the phenotypic features of LMP tumors are intermediate between those of benign and malignant tumors, but the steps of carcinogenesis are undefined for ovarian cancer. BRCA1 (breast cancer susceptibility gene 1) is the most extensively studied gene due to its role in breast and ovarian cancers. In ovarian cancer, promoter hypermethylation of this gene has been shown to be associated with loss of gene expression [9, 31, 194], and is mostly reported in tumors harboring LOH at the BRCA1 gene [31, 52]. No correlation 8 of promoter hypermethylation and histological stage was found [194]. However, BRCA1 silencing was shown to be associated with high-grade tumors [195, 201]. Other examples of hypermethylated genes in ovarian cancer include RASSF1A [3, 77, 135, 171, 202], OPCML [147, 171, 207], ARHI [203, 204], and TCEAL7 [37]. It should be noted that the DNA methylation frequency of these genes varies between independent reports. Table 1.1 summarizes a list of hypermethylated genes in ovarian carcinomas and the frequency of their methylation as reported in the literature. In ovarian neoplasms global DNA hypomethylation was shown to increase with malignancy [34]. For example, heterochromatin regions of chromosome 1 centromere were shown to be frequently hypomethylated in ovarian cancers [124]. The main sequence in this heterochromatin region is satellite 2 (Sat2). The degree of malignancy was shown to significantly correlate with the extent of satellite 2 hypomethylation. Another example is the hypomethylation status of NBL2, which is a tandem DNA repeat in acrocenric chromosomes. Hypomethylation of NBL2 was shown to be related to the state of malignancy and was only observed in the carcinomas [125]. It is hypothesized that hypomethylation may lead to chromosomal instability [95, 165], which is a feature in most cancers. Aberrant epigenetic changes in ovarian cancer have been investigated for their prognostic potentials, associations with therapeutic responsiveness, and survival. Hypermethylation of IGFBP-3 was shown to be associated with higher risk of disease progression and death, specifically in early stage disease [196]. Lack of hypomethylation of Chr1 Sat2 was significantly associated with a longer relapse-free survival [193]. In late stage ovarian tumors, DNA methylation of at least one of three genes, MGMT, BRACA1 9 and GSTP1 was associated with improved response to chemotherapy [171]. Molecular analysis of various ovarian neoplasia has aimed to address steps of carcinogenesis as well as finding early detection markers, mechanisms of chemo-resistance, risk assessment and prognosis. Table 1.1. Alphabetical list of hypermethylated genes in ovarian carcinoma. Alphabetical list of some genes reported to be hypermethylated in ovarian carcinoma. Fraction methylated refers to the number of tumors showing DNA methylation for the corresponding locus. Percent methylated is derived from this. S, serous; E, Endometrioid, CC, Clear Cell; M, Mucinous; U, Undifferentiated; A, Adenocarcinoma; N/A, means the subtypes were not specified. This table is adapted with some changes from a table of hypermethylated gene list in ovarian carcinoma as published by Barton et.al. [10]. All mentioned references in this table, while also reported by Barton et.al., were independently verified for the data in the above table by this author. Gene Name Fraction Methylated Percent Methylated Subtype Analyzed Ref BRCA1 44/215; 8/50; 12/98; 4/31; 12/50; 2/20; 18/221; 2/43; 20/64; 13/106; 5/49; 11/88; 8/49 20%; 16%; 12%; 13%; 24%;10%; 8%; 5%; 31%; 12%; 10%; 13%; 16% S, E, CC, M, U [9, 21, 27, 29, 52, 62, 77, 135, 157, 171, 194, 195, 201] CCND2 16/71 23% S, E, CC, M [142] CDH1 34/80; 10/46 43%; 22% S, E, CC, M, U, A [115, 205] CDH13 9/49; 6/46; 10/51; 4/6 18%; 13%; 20%; 67% S, E, CC, M, U, A [91, 115, 135, 198] CDKN2A 3/16; 12/49; 4/46 19%; 24%; 9% S, E, CC, M [69, 77, 201] CDKN2B 0/88; 17/89 0%; 19% S, E, CC, M [157, 169] DAPK 0/106, 3/16; 0/80; 20/30 0%; 19%;0%, 67% S, E, CC, M, U [38, 77, 171, 172] DR4 10/36 28% N/A [74] ESR1 117/215 56% S, E, CC, M, U [195] FANCF 0/106;4/19; 5/18 0%; 21%; 28% S, non-S [170, 171, 189] GATA4 9/15 60% E, CC [186] GATA5 5/15 33% S, E, CC [186] 10 (Table 1.1, Continued) GPR150 4/15 27% S, E, CC, M [28] HIC1 13/75; 17/49; 14/88; 46/89 17%; 35%; 16%; 52% S, E, CC, M [135, 157, 169, 171] HOXA9 26/51 51% S, E, CC, M [198] HOXB5 6/52 12% S, E, CC, M [198] HOXD11 1/15 7% S, E, CC, M [28] Hsulf-1 12/16 75% N/A [155] IGFBP3 104/235 44% S, E, CC, M, U [196] ING1 21/88 24% S, E, CC, M, U [150] ITGA8 2/15 13% S, E, CC, M [28] MGMT 1/26; 2/46 4%; 9% S, E, CC, M, U [115, 207] MINT25 9/75; 13/88 12%; 16% S, E, CC, M [157, 207] MLH1 6/106; 19/215; 6/88; 3/24; 1/51; 7/75; 5/68 6%; 9%; 7%; 13%; 2%; 9%; 7% S, E, CC, M, U [36, 71, 157, 158, 195, 198, 207] MYO18B 2/15 13% S, E, CC, M [200] OPCML 57/69; 25/75; 20/43 82%; 33%; 46% S, non-S [43, 147, 171] PTEN 5/58; 4/49; 15/89 9%; 8%; 17% S, E, CC, M [169, 171, 201] PYCARD 15/80; 8/20 19%; 40% S, E, CC, M, U [4, 172] RARB 1/49; 5/46; 15/89 2%; 11%; 17% S, E, CC, M, U, A [84, 115, 169] RASSF1A 7/46; 28/106; 23/47 15%; 26%; 50% S, E, CC, M, U, A [115, 171, 198] RIZI 20/89 23% S, E, CC, M [169] SCGB3A1 5/52 10% S, E, CC, M [198] SFRP1 4/76; 2/17 5%; 12% S, E, M [167, 171] SOCS1 10/43 23% N/A [163] SOCS2 6/42 14% N/A [163] TCF2 26/98 26% S, E, CC, M, U [173] TES 7/10 70% N/A [177] THRA 20/88 23% S, E, CC, M [157] TP73 0/106; 7/88 0%; 8% S, E, CC, M [157, 171] 11 A: Epigenetic profiling of ovarian cancer It has long been appreciated that ovarian tumors are very heterogeneous, histologically and clinically. However, current treatment protocols as well as most biomarker discovery studies are not subtype-specific. The differences among various subtypes stems from molecular differences, that are not fully characterized. Furthermore, the origin of epithelial ovarian tumors as well as hormonal effects on the development and progression of these tumors are currently highly debated [112, 188]. Therefore, the development of molecular profiling markers is quite crucial since each sub-type presents with a different prognostic and therapeutic response. These studies could provide insight to better understanding the natural history of the disease, cell of origin, and histogenesis. A persisting caveat in the majority of ovarian cancer studies aiming for profiling or early detection marker development is choosing the appropriate source as a normal control in ovarian cancer, similar to majority of epithelial malignancies. In the case of ovarian cancer, not only is the current proposed cell-of-origin as Ovarian Surface Epithelium (OSE) under constant debate, but also obtaining adequate quantities for molecular analysis from this single cell layer has been extremely challenging. Although some groups argue that inclusion of the stromal cells may help discover aberrant changes in the tumor microenvironment, others have argued that stromal contamination may obscure the critical changes that occur in the epithelium. Several groups have used short-term cultures of OSE, referred to as NOSE or HOSE, or NOSE cells immortalized with SV40 large T-antigen, IOSE. Reports from these studies collectively suggest that even short- term manipulation of these cells would require careful interpretation of the results. It is noteworthy, however, that for studies aiming to discover markers based on tumor profiles 12 in remote media, the lack of such normal controls could be tolerated, and these markers could be screened against the remote media of choice. B: Biomarker development for early detection of ovarian cancer Early detection in ovarian cancer Many efforts in the search for breakthrough cancer treatments have been unfruitful, since to date surgical intervention remains the main course in cancer treatment. However, what has become evident is that in order to reduce the mortality associated with cancer, main attention should be given to the early detection of cancer. The best example with regards to gynecological malignancies comes from cytological screening for cervical cancer. It has been shown that PAP smear screening reduced cervical cancer incidence from 25 to 8 cases per 100,000. This has resulted in a significant drop in the death rate from 13 to 3 per 100,000. The importance and need for effective early detection screening tools for ovarian cancer becomes more apparent when comparing the stage-specific relative 5-year survival rates of ovarian cancer patients, which is below 30% for late-stage disease, to above 92% in stage I disease. So, what are the challenges in the development of early detection screening assays for ovarian cancer? First, in comparison to the cervix the ovaries are only accessible by invasive procedures. Second, there is no consensus about the cell of origin or the steps of carcinogenesis for epithelial ovarian cancers, while a well-established precursor lesion exists for cervical cancer. The current available screening techniques for ovarian cancer, such as ultrasound examination and ovarian palpation through pelvic exam are insensitive 13 and non-specific. The most widely measured molecular marker for ovarian cancer is CA- 125, with a sensitivity below 64%, and which is generally only used to screen ovarian cancer patients for recurrence. The objective in developing biomarkers for early detection of ovarian cancer is to find markers to detect ovarian cancer at an early enough stage for successful treatment. Also, lessons from the success of the cervical cancer screens suggest that the screening tools must be noninvasive and inexpensive in order to be widely accepted in the population. Blood-based biomarkers provide such promise. DNA methylation in serum/plasma As mentioned above, it is not always possible to obtain primary tissues for early detection screening in a non-invasive or minimally invasive procedure, as is possible for breast cancer, prostate cancer, or cervical cancer. Cancer patients have been reported to have higher circulating DNA in their blood [148]. We and others have shown that epigenetic abnormalities can be detected in this DNA or the DNA found in other fluids [56, 57, 143, 191]. Even though blood-based biomarkers seem an attractive tool for early detection of cancer, there are several challenges to this approach. It is difficult to find enough tumor-derived DNA in the blood at a stage early enough for curative resection. Also, the free-floating DNA in the blood could possibly arise from various cells/organs either as normal processes or as a consequent of other non-cancerous health problems. 14 DNA methylation as a blood-based or proximal sample-based biomarker for early detection of epithelial ovarian cancer The occurrence of aberrant hypermethylation events in many types of cancer, makes DNA methylation an ideal marker that can be used for early detection analysis. The use of DNA methylation as a marker in early detection studies is based on the notion that tumor cells while undergoing necrosis or apoptosis, release their DNA into the bodily fluids, such as blood, and that the distinctive abnormal methylation can be detected in the DNA from these tumors. There are many studies aimed at identifying protein or RNA biomarkers for the early detection of cancer. There are two advantages to the use of DNA methylation: first, DNA can be readily amplified, and second, in comparison to RNA, is more stable. In addition to DNA methylation, other changes, mainly genetic changes such as mutations or deletions, could also be interrogated using tumor-derived DNA. DNA methylation has the advantage that can be designed to focus on a specific region of the genes, such the CpG island, rather than scanning the entire gene for mutation. Also, several markers can be combined to improve the specificity of cancer detection. However, there are several downsides to DNA methylation markers: the extent of detected methylation may vary based on assay platform, the locations and number of the interrogated CpGs within the gene. Sample processing methods such as bisulfite conversion and DNA integrity can cause variability in the results. In the case of using DNA methylation as an early detection biomarker, especially in the bodily fluids, sensitivity of detection is highly affected by the background DNA, unlike protein markers. 15 Chapter 2: Widespread epigenetic abnormalities in poor quality human sperm Chapter 2 Abstract Male-factor infertility is a common condition, and the eiology is unknown for a high proportion of cases. Abnormal epigenetic programming of the germline is proposed as a possible mechanism compromising spermatogenesis of some men currently diagnosed with idiopathic infertility. During germ cell maturation and gametogenesis, cells of the germline undergo extensive epigenetic reprogramming. This process involves widespread erasure of somatic-like patterns of DNA methylation followed by establishment of sex-specific patterns by de novo DNA methylation. Incomplete reprogramming of the male germline could, in theory, result in both altered sperm DNA methylation and compromised spermatogenesis. We determined concentration, motility and morphology of sperm in semen samples collected by male members of couples attending an infertility clinic. Using MethyLight and Illumina assays we measured methylation of DNA isolated from purified sperm from the same samples. Methylation at numerous sequences was elevated in DNA from poor quality sperm. We further validated this observation on a larger independent sample size using the Illumina GoldenGate platform. This is the first report of a broad epigenetic defect associated with abnormal semen parameters. Our results suggest that the underlying mechanism for these epigenetic changes may be improper erasure of DNA methylation during epigenetic reprogramming of the male germline. 16 Chapter 2 Introduction Approximately five million women in the United States reported difficulty in achieving a pregnancy in comprehensive surveys conducted by the CDC and National Survey of Family Growth from 1982- 1995 [1, 32, 184] indicating that ten to twenty percent of couples attempting pregnancy are infertile. Preliminary follow-up data for 1990-2002 confirm this percentage [197]. Infertility is defined as the inability to achieve conception or to sustain pregnancy. Therefore, male factor infertility could include defects in the production of conceiving sperm or in the male genome that can affect normal embryonic growth and development. DNA methylation plays a significant role both in gametogenesis and in normal development. Epigenetic reprogramming of the germline occurs both during development and gemetogenesis. Spermatogonia and spermatocytes are hypomethylated and, as spermatozoa pass through the epididimis, some testis-specific genes become hypermethylated and remain methylated in the vas deferens [6]. This change in DNA methylation is associated with sperm differentiation and may be critical in sperm maturation with any defects leading to infertility. In rats the number of spermatids and spermatozoa was reduced after exposure to 5-azacytidine, a DNA methylation inhibitor [47]. Alteration of germline DNA methylation in mice exposed to reproductive toxicants was correlated with abnormal sprematogenic capacity [152]. It was reported that a global decrease of DNA methylation in sperm could affect the pregnancy rate without affecting the fertilization rate [16]. DNA methylation is also central to establishing imprinted genes that play crucial role in placental growth and their disruption can affect fertility [182]. 17 Results Sample analysis Samples were collected from 69 men during clinical evaluation of couples with infertility. Standard semen analysis of various parameters such as volume, sperm concentration, sperm morphology, sperm motility, semen viscosity, and presence of contaminating white blood cells or epithelial cells was conducted on all of the 69 samples. Among these samples semen volume ranged from 0.5 to 7.8 ml; total count 0 to 864 million sperm; total motile count 0 to 396.3 million sperm; and percentage normal sperm forms 0 to 26%. Sperm isolation from semen samples, sperm DNA isolation and quantitation, and preliminary DNA methylation analysis was performed while we were blinded to semen parameters. MethyLight analysis We evaluated 294 MethyLight reactions (Supplementary Table 1) for the presence of methylation in sperm DNA from an anonymous semen sample obtained from a sperm bank. The 35 selected reactions (Table 2.1) were used to assay sperm DNA from 69 study samples. DNA concentration measurements and initial MethyLight analysis of four samples did not yield detectable results suggestive of either no or a miniscule number of sperm present in these semen samples. This was consistent with the fertility clinic report. Three out of four samples were azospermic semen samples and the fourth was a severe oligospermic semen sample. These four samples were excluded from subsequent statistical and cluster analyses. At many of the 35 sequences, methylation levels were elevated in DNA from poor quality sperm. Three parameters, sperm 18 concentration, motility and morphology were associated with abnormal DNA methylation. However, no association was observed for semen volume. Associations with each of sperm concentration, motility and morphology were observed for five sequences: MT1A, PLAGL1, NTF3, SAT2CHRM1, and HRAS (Table 2.1). Table 2.1. Associations between MethyLight results and semen parameters. * Belongs to cluster 2 (see Figure 2.2). ‡ Assay interrogates a non-differentially methylated sequence. Association of DNA methylation was assessed over the following categories of semen parameters: Concentration (<5, 5-20, >20 x10 6 sperm per ml), Morphology (<5%, 5-14%, >14% normal sperm forms), Motility (<10, 10-50, >50 total motile sperm count (x10 6 )). Significance threshold was obtained by controlling the false-discovery rate at 5% using the Benjamini and Hochberg approach. Parameter of Standard Semen Analysis MethyLight Reaction Concentration Motility Morphology *HRAS.HB.144 0.00006 0.00001 0.06265 *NTF3.HB.251 0.00029 0.00026 0.00464 MT1A.HB.205 0.00048 0.00026 0.00119 *PAX.8.HB.212 0.00086 0.00405 0.05143 *DIRAS3.HB.043 0.00109 0.00159 0.06016 *PLAGL1.HB.199 0.00213 0.00255 0.01951 *SFN.HB.174 0.00307 0.00804 0.79899 *SAT2CHRM1.HB.289 0.00448 0.00109 0.06793 *MEST.HB.493 0.00711 0.00373 0.00359 RNR1.HB.071 0.02 0.04 0.89 CYP27B1 0.02 0.05 0.10 MADH3.HB.053 0.09 0.15 0.35 BDNF.HB.257 0.11 0.05 0.26 PSEN1.HB.263 0.16 0.27 0.81 CGA.HB.237 0.23 0.34 0.93 SERPINB5.HB.208 0.23 0.64 0.80 ICAM1.HB.076 0.24 0.29 0.05 MINT1.HB.161 0.24 0.60 0.34 19 (Table 2.1, Continued) PTPN6.HB.273 0.24 0.09 0.08 ALU.HB.296 0.25 0.29 0.87 CYP1B1.HB.239 0.28 0.42 0.61 SP23.HB.301 0.28 0.48 0.48 IFNG.HB.311 0.33 0.22 0.93 C9.HB.403 0.37 0.35 0.89 GP2.HB.400 0.41 0.39 0.94 GATA4.HB.325 0.45 0.20 0.12 UIR.HB.189 0.48 0.47 0.70 TFF1.HB.244 0.48 0.96 0.93 LDLR.HB.219 0.51 0.39 0.11 SASH1.HB.085 0.51 0.15 0.15 ABCB1.HB.051 0.54 0.27 0.16 HOXA10.HB.270 0.63 0.84 0.13 MTHFR.HB.058 0.70 0.38 0.43 LINE1.HB.330 0.87 0.47 0.14 LZTS1.HB.200 0.90 0.95 0.73 SMUG1.HB.086 0.90 0.36 0.76 ‡ IGF2.HB.345 0.91 0.71 0.11 Testing the association of abnormal imprinting with poor quality sperm PLAGL1 is maternally imprinted. The possible involvement of imprinted genes in normal spermatogenesis was first suggested by Marques e.t al. [118]. They observed an association between low concentration of mature sperm in the semen of some oligospermic patients and a modest imprinted defect in the paternally imprinted H-19 locus. However no abnormal changes were observed at another imprinted locus, MEST, which is maternally imprinted and therefore not methylated in the sperm. Our MethyLight assay for PLAGL1 interrogates a differentially methylated CpG island [183]. To determine whether other maternally imprinted genes are methylated in abnormal sperm, we used MethyLight to interrogate the differentially methylated sequence of DIRAS3. At this sequence we also observed greater DNA methylation in samples with 20 poorer semen parameters (Figure 2.1). These results appeared to conflict with those of Marques et. al. [118] who reported no association between low sperm count and methylation of a DMR in a third maternally imprinted gene, MEST. We therefore used MethyLight to assess the methylation status of a differentially methylated MEST sequence investigated by these authors [118], and found elevated DNA methylation to be significantly associated with poor semen parameters (Figure 2.1), in agreement with our PLAGL1 and DIRAS3 results. After correction for multiple comparisons, estimated associations between results of each of the 37 MethyLight assays and sperm concentration were significant for HRAS, NTF3, MT1A, PAX8, DIRAS3, PLAGL1, SFN, SAT2CHRM1 and MEST (Table 2.1, Figure 2.1). We then subjected MethyLight data from 36 of the assays to unsupervised cluster analysis. (Data for SASH1 were not included, because methylation at this sequence was detected in only one sample.) This analysis identified three distinct clusters of sequences based on DNA methylation profiles in the 65 samples (Figure 2.2). Notably, the middle cluster shown in Figure 2.2 includes eight of the nine sequences (all except MT1A) individually associated with semen parameters. This middle cluster includes not only three sequences that are differentially methylated on imprinted loci, but also three single copy sequences specific to non-imprinted genes, and a repetitive element, Satellite 2 [190] (reaction named SAT2CHRM1). This result indicates that sperm abnormalities may be associated with a broad epigenetic defect of elevated DNA methylation at numerous sequences of diverse types, rather than a defect of imprinting alone as previously suggested [118]. 21 Figure 2.1. Box plots illustrating associations between semen parameters and level of methylation (PMR) in DNA isolated from 65 study sperm samples. DNA methylation was measured by MethyLight. Methylation targets were sequences specific to the genes HRAS, NTF3, MT1A, PAX8, PLAGL1, DIRAS3, MEST and SFN and the repetitive element Satellite 2 (SAT2CHRM1). P-value for trend over category of semen parameter is given for each plot. Rows: DNA methylation targets; columns: semen parameters. 22 To learn more about the possible extent of this apparent defect, we used the Illumina GoldenGate DNA methylation platform to conduct DNA methylation analysis of 1,421 sequences in autosomal loci. We included in this analysis DNA from the anonymous sperm sample used in the MethyLight screen (Figure 2.3, columns S), two purchased samples of buffy coat DNA allowing us to observe methylation patterns in somatic cells (Figure 2.3, columns 1-2), and seven study sperm DNA samples remaining after MethyLight analysis (Figures 2.2-2.3, columns A-G). Results of Illumina analyses appear in Figure 2.3. A large number of genes were similarly methylated in both sperm DNA and buffy coat DNA (blue regions on the left bar, I; red regions on the right bar, III), while others tended to be more methylated in DNA isolated from only one of these cell types. Boxes enclose sequences for which we observed particularly strong patterns of cell type-specific methylation. Box 1 identifies 19 sequences with sperm-specific DNA methylation. At these sequences, methylation profiles of all DNA from samples of study sperm (A-G) closely resemble those from the anonymous sperm sample and differ greatly from those of buffy coat DNA. Box 2 identifies 102 sequences with buffy coat-specific DNA methylation. This set is larger in number than the sperm-specific set, as expected, given that sperm DNA is reportedly hypomethylated compared with somatic cell DNA [127]. The buffy coat-specific set comprises 7.2% of the 1,421 sequences including the majority of DMRs associated with imprinted genes that are on the Illumina panel. At many buffy coat-specific sequences, DNA methylation was elevated in study sperm DNA, most notably in sample A that had been isolated from sperm with the lowest concentration among samples A-G. 23 Figure 2.2. Cluster analysis of 36 MethyLight targets in 65 study sperm DNA samples. Left: dendrogram defining clusters; rows: 35 methylation targets; columns: 65 study samples ordered left to right on sperm concentration (samples A-G were also included in Illumina analyses (see Figure 3) with poor to good concentration (blue), motility (purple), and morphology (green) represented by darkest to lightest hue; body of figure: standardized PMR values represented lowest to highest as yellow to red. X=missing 24 Methylation of sample A DNA is elevated (β>0.1) at 76 of the 102 sequences in box 2, including all 10 that are known DMRs associated with imprinted genes. Several factors assure us that our observations did not arise from somatic cell contamination of separated sperm samples [117]. Somatic cells are far larger than sperm and are readily identified by microscopic evaluation of semen samples. Even if somatic cells are present in the neat ejaculate, the Isolate® sperm separation technique is specifically designed to separate spermatozoa from somatic cells and miscellaneous debris [44]. Moreover, although microscopic evaluation of semen samples conducted before sperm separation identified white blood cells in five of the 65 neat semen samples, we found that excluding results on these five samples from statistical analyses had minimal effect on associations between DNA methylation and semen parameters (results not shown), and DNA from these samples were excluded from Illumina assays. Validation of the genome-wide epigenetic abnormality with a large sample size The preliminary data from the Illumina GoldenGate platform on a limited sample size was suggestive of a broad epigenetic defect in abnormal human sperm. We sought to confirm this finding on a large sample size. These samples included abnormal sperm with one or a combination of abnormalities in sperm concentration, sperm morphology, and sperm motility. These samples were obtained from 75 patients attending the fertility clinic for clinical evaluation. We also included 18 normal sperm samples that were obtained from a sperm bank. During the preparation of samples for DNA methylation analysis, nine samples failed the quality control tests and were excluded from further analysis. Table 2.2 shows the division of the 66 abnormal sperm samples 25 based on two semen parameters (this table does not include the 18 normal sperm samples obtained from the Sperm Bank). Sperm Concentration (million/ml) 0 < 5 5 to 20 > or = 20 TOTAL Morphology % normal 0 3 1 4 < or = 5 1 6 5 12 5 to 14 14 19 33 > or =14 3 14 17 TOTAL 3 2 23 39 66 Table 2.2. Number of sperm samples within each category of semen parameters that was analyzed on the Illumina platform. The larger sample size of both poor quality and normal sperm, confirmed our initial findings of widespread epigenetic abnormalities in poor quality sperm. The result of this analysis are shown in Figure 2.4. Similar to the previous tertile figure, the 1421 autosomal sequences were first ranked by increasing β-values in the PBL DNA and then were divided into the tertiles I, II, and III. Within each tertile, the sequences were ordered by median β-value of samples with normal sperm concentration from the Sperm Bank (Group B). Samples are ordered from left to right from poor to normal sperm concentration. Number of samples in each group of sperm concentration is provided in Table 2.2. The box on tertile III, depicts the sequences that show hypermethylation in the abnormal sperm samples which resembles normal PBL methylation patterns. 26 Figure 2.3. Results of Illumina analysis of 1,421 autosomal sequences in DNA isolated from sperm and buffy coat. Seven study sperm samples (A-G; with values of sperm concentration (million sperm/ml) A:20, B:56, C:62, D:67, E:75, F:94, G:95), screening sperm (S), two buffy coat (1-2). Level of DNA methylation scored as β-value. Color: β-value for column sample at row sequence (green: β<0.1; yellow: 0.1≤ β≤0.25; orange 0.25<β≤0.5; red: β>0.5). MI and PI: maternally and paternally imprinted genes (black bar). Sequences assigned to tertile of median β-value among buffy coat DNA samples (I, II, III) and sorted within tertile on median β-value among sperm DNA samples. Box 1: sequences with sperm-specific DNA methylation; Box 2: sequences with buffy coat-specific DNA methylation 27 Figure 2.4. Illumina analysis of 1421 autosomal loci in sperm and PBL DNA. Sixty six study samples from patients (Group A), 18 normal samples from the sperm bank (Group B), one PBL sample (C), one whole genome amplified DNA as negative control (D), and one M. SssI treated DNA as positive control (E) were analysed on Illumina GoldenGate Cancer Panel I. The 1421 autosomal loci were ranked based on the β-value of the PBL sample and then divided into tertiles. Within each tertile loci are ranked top to bottom on median β-value among sperm samples in Group B. Samples within each tertile are sorted left to right from low to high sperm concentration. 28 Discussion Our observations are consistent with a broad epigenetic abnormality in poor quality human sperm, in which levels of DNA methylation are elevated at numerous sequences in several genomic contexts. Previous studies of DNA methylation in poor quality human sperm interrogated only imprinted loci, measuring methylation of sequences in only one or two genes [68, 116, 118]. In the only study addressing the relationship between DNA methylation and fertility outcomes, immunostaining was used to measure genome-wide levels of DNA methylation in samples of ejaculated sperm collected for conventional in vitro fertilization (IVF). No association was observed between sperm DNA methylation and either fertilization rate or embryo quality in 63 IVF cycles [16]. There was, however, a possible association with pregnancy rate after transfer of good quality embryos. Interpretation of these results is limited by both small sample size and the use of a single summary measure of genome-wide DNA methylation. Because of the large number of methylation targets in the human genome, only sequence-specific measures of DNA methylation are expected to reveal variation at individual sites. These include millions of repetitive DNA elements for which DNA methylation is postulated to silence parasitic and transposable activity. There are also large numbers of target sequences corresponding to single copy genes. Examples include thousands of promoter CpG islands for which methylation appears to mediate expression of genes in a tissue- and lineage-specific fashion, and DMRs associated with dozens of imprinted genes for which parent-of-origin DNA methylation marks are believed to mediate monoallelic expression in somatic cells. 29 Sequence-specific measures were used in three previous studies investigating the relationship between methylation of human sperm DNA and spermatogenesis [68, 116, 118]. One study assessed DNA from spermatogonia and spermatocytes microdissected from seminiferous tubules of biopsied testicular tissue with spermatogenic arrest. DNA profiles consistent with correctly established paternal imprints were reported in all samples [68]. In the remaining two studies, DNA profiles were measured at specific DMRs associated with each of two genes, one paternally and one maternally imprinted. The resulting profiles were related to concentration of ejaculated sperm, an indicator of sperm quality. One of these studies reported correctly erased maternal imprints and correctly established paternal imprints in DNA from sperm of low concentration [116]. By contrast, the second reported that although maternal imprinting of MEST was correctly erased in DNA from sperm of low concentration, methylation at an H19 sequence typically de novo methylated in spermatogenesis was incomplete in these samples [118]. No compelling explanation was offered for the apparently differing results of these studies. It is noteworthy, however, that each addressed sequences of only one or two imprinted genes, an extremely small and specialized subset of DNA methylation targets in the human genome. Data from these published studies could not, therefore, have revealed a disruption involving large numbers of genes, or shown that genes that are not imprinted are also affected. Our high- throughput analysis addressing hundreds of DNA methylation targets was far more likely to reveal such a defect. Elevated DNA methylation could, in theory, arise from either de novo methylation or improper erasure of pre-existing methylation. Although we cannot rule 30 out the possibility that processes responsible for de novo methylation are inappropriately activated in abnormal spermatogenesis, disruption of erasure seems a simpler mechanism. Further evidence from the literature that support our hypothesis is discussed in Chapter 5. If, as we now postulate, improper erasure of DNA methylation in primordial germ cells results in an epigenetic defect of sperm, some categories of male factor infertility may be added to the growing list of diseases of adulthood that have fetal origins, and etiologic studies addressing events at this early stage of development will be needed. Materials and Methods Semen samples Study semen samples were collected by 69 consecutive men ages 22-49 years who were partners of women undergoing evaluation for infertility at the Endocrine/Infertility Clinic of the Los Angeles County/University of Southern California Keck School of Medicine Medical Center. One additional semen sample was obtained from a sperm bank. The study was approved by the Institutional Review Board of the University of Southern California. Informed consent was not required because this research involved stored materials that had previously been collected solely for non- research purposes and were anonymous to the researchers/ authors. Semen analysis Standard semen analysis was performed using WHO criteria and Strict Morphology as previously described [2]. Semen volume, sperm concentration and 31 motility, and leukocyte count were measured using the MicroCell chamber (Conception Technologies, San Diego, CA). Sperm morphology was assessed with the use of prestained slides (TestSimplets, Spectrum Technologies, Healdsburgh, CA), and percentage of morphologically normal sperm was documented. The samples were categorized according to concentration (<5, 5-20, >20 million sperm/ml), motility (<10, 10-50, >50 total motile sperm count (x10 6 )), and morphology (<5%, 5-14%, >14% normal) of sperm [2, 65]. Presence of any white blood cells, round cells, or epithelial cells was recorded. Following semen analysis, samples were stored at -30°C until processing for molecular analysis. Sperm separation from seminal plasma Semen samples were allowed to thaw at 37 o C. Sperm were separated from seminal plasma using Isolate ® Sperm Separation Medium (Irvine Scientific, Santa Ana, CA), a density gradient centrifugation column designed to separate cellular contaminants (including leukocytes, round cells, and miscellaneous debris) from spermatozoa [44]. Separation was performed according to the manufacturer’s protocol [79], and the purity of separated sperm from contaminating cells was documented by light microscopy. DNA isolation DNA was isolated from purified sperm as previously described [97], with 0.1X SSC added to the Lysis buffer, and samples incubated at 55°C over night or longer to complete the lysis procedure. 32 Laboratory analysis of DNA methylation Sodium bisulfite conversion was performed as previously described [190]. The amount of DNA in each aliquot was normalized, and a bisulfite-dependent, DNA methylation-independent control reaction was performed to confirm relative amounts of DNA in each sample. MethyLight analyses were performed as previously described [190]. Reaction IDs and sequences of the primers and probes used in the 294 MethyLight reactions are provided in Table S1 (sections A-B). Thirty-five MethyLight reactions were selected for analysis of study sperm DNA samples based on cycle threshold (C(t)) values from analysis of the anonymous sample of sperm DNA. In brief, C(t) value is the PCR cycle number at which the emitted fluorescence is detectable above background levels. The C(t) value is inversely proportional to the amount of each methylated locus in the PCR reaction well, such that a low C(t) value suggests that the interrogated sequence is highly methylated. We interpreted C(t) values of 35 or less as an indication that a given sequence was methylated in the anonymous sample and selected 33 reactions on this basis. We included three additional reactions for which C(t) values slightly exceeded 35. Two (CYP27B1 and HOXA10) were selected based on gene function potentially related to fertility, and one (a non-CpG island reaction for IFNG) based on prior observation of hypomethylation in tumor versus normal tissue (data not shown). When multiple reactions for a single locus resulted in C(t) values of less than 35, we selected only the reaction with the lowest C(t) value. Results of MethyLight analysis were scored as PMR values as previously defined [190]. 33 Following MethyLight analyses, DNA remained from a subset of abnormal samples with greater sperm concentration. Illumina analysis was performed on sodium bisulfite converted sperm DNA of selected remaining samples, the anonymous semen sample, and purchased buffy coat DNA (HemaCare ® Corporation, Van Nuys, CA) at the USC Genomics Core. Sodium bisulfite conversion for Illumina assay was performed using the EZ-96 DNA Methylation Kit (ZYMO Research) according to manufacturer’s protocol. Illumina Methods and reagents are as previously described [22]. The primer names are listed in Table S2, identifying the 1,421 autosomal sequences on the GoldenGate Methylation Cancer Panel I, more fully described elsewhere [78]. Results of Illumina assays were scored as β-values [22]. Statistical association analyses of MethyLight data Associations between the ranked MethyLight data and categorized semen values (Table 1.1) were tested using simple linear regression, with the semen characteristic categories scored as 0: low, 1: mid, 2: high. For selected sequences, boxplots of the methylation values (on the log (PMR+1) scale) are shown in Figure 2.1. The top and bottom of the box denote the 75 th and 25 th percentiles, and the white bar the median. Error bars are drawn to the observation farthest from the box that lies within 1.5 times the distance from the top to the bottom of the box, with values falling outside the error bars denoted as lines. Results of this analysis were included in Figure 2.1 for sequences associated with sperm concentration using the Benjamini and Hochberg procedure [17] to control the false discovery rate at 5%. 34 Cluster analysis of MethyLight data Hierarchical cluster analysis of 36 loci was performed, using correlation to measure the distance between any two loci and Ward’s method of linkage [90]. SASH1 was omitted from the cluster analysis because only a single sample showed positive methylation. The 65 study samples were ordered from left to right by increasing semen concentration. Display of Illumina data llumina data were displayed graphically in Figure 2.3 and 2.4 with results for study samples ordered left to right in columns by sperm concentration. Rows corresponding to each of the 1,421 sequences were divided into three tertiles of median β-value among buffy coat DNA samples (I, II, III), then sorted within tertile by median β- value among all sperm DNA samples in Figure 2.3 and by median β-value among all normal sperm samples from the sperm bank in Figure 2.4. In Figure 2.3, box 1 contains all sequences in tertile I with median β-value among sperm DNA samples >0.5; box 2 contains all sequences within tertile III with median β-value among sperm DNA samples <0.1. Maternal or paternal imprinting status of each locus was scored according to the categorization of R. Jirtle [63]. All sequences specific to genes imprinted in humans were individually reviewed to determine whether they have been reported as belonging to a DMR for which parent of origin marks are maintained by DNA methylation [8, 11, 12, 15, 41, 45, 60, 67, 73, 85, 94, 107, 108, 122, 123, 128, 139, 140, 160, 162, 164, 185, 35 206]. Sequences meeting these criteria were scored as maternally imprinted (MI) or paternally imprinted (PI) with an indicator set for each on Figure 2.3. 36 Chapter 3. Epigenetic profiling of ovarian cancer Chapter 3 Abstract Epithelial ovarian cancer comprises several histological subtypes, each with an unknown natural history and uncertain cell of origin. The four major subtypes are serous, endometrioid, clear cell and mucinous. This study has two goals: First, to identify DNA methylation signatures associated with subtypes of ovarian cancer which could contribute to diagnostic, etiologic, and mechanistic studies. Second, to identify cancer-specific DNA methylation biomarkers that could be used in further early detection analysis. We employed MethyLight, Illumina GoldenGate and Infinium DNA methylation analysis platforms to screen both ovarian cancer cell lines and primary epithelial ovarian tumors of various subtypes. We found that ovarian cancer cell lines have more frequent and more diverse CpG island hypermethylation than primary tumors. We identified multiple subtype- and cancer-associated markers, of which a small minority corresponded to functional silencing of the associated gene, while most appeared to be passenger events without functional implications. Identification of DNA methylation signatures associated with subtypes of ovarian cancer could suggest that these subtypes may be distinct diseases. These findings may contribute to diagnostic, etiologic, and mechanistic studies. Further statistical analyses are warranted to understand associations of subtype-specific markers with different patient outcomes. 37 Chapter 3 Introduction Ovarian tumors are derived from different cell types. Histogenesis-based classification- formulated in 1999 by World Health Organization (WHO) and adopted by the International Federation of Gynecology and Obstetrics (FIGO) - divides them into the following: surface epithelial tumors, stromal tumors, sex-cord tumors, germ cell tumors, and secondary tumors. However, the majority of ovarian tumors are epithelial tumors, which have very little in common with the other tumor subtypes. We have only addressed epithelial tumors in this study. Classification of epithelial ovarian tumors `Epithelial ovarian tumors are very heterogeneous with several histological subtypes. The four most frequent subtypes are serous, endometrioid, mucinous, and clear cell in the order of frequency reported in clinic. These tumors share the common characteristic of resembeling tumors of other parts of the reproductive tract. Serous epithelial carcinoma comprises about 50% of ovarian carcinomas. These tumors, at their well-differentiated lesions usually present with ciliated columnar cells, forming finger-like projections around papillae. The inner lining of the cyst is usually filled with serous fluid [53]. Endometrioid ovarian carcinomas comprise about 25% of ovarian carcinomas. These tumors have glandular structures that are lined with columnar cells and are sometimes filled with bloody materials at their well-differentiated lesions. 38 Mucinous ovarian carcinomas, with a frequency of 10 to 15%, generally form mucin-filled cysts. The cells are filled with mucin that pushes the nucleus to the basal pole. Clear cell ovarian carcinomas, are the least frequent sub-type with a 5% frequency. These tumors at their well-differentiated lesions present with cells of clear cytoplasm that are low columnar to cuboidal [53]. These features mentioned above are usually present as well-differentiated lesions, but with tumors presenting higher grades, histological diagnosis is often difficult. The cell of origin of these epithelial tumors is under persistent debate and the natural history is very poorly understood. Any attempt for developing early detection strategies, as well as understanding the associated risk factors, prognosis, and response to chemotherapy cannot fully succeed without understanding the normal counterpart and the natural history of these tumors. The most favored hypothesis until recent years is that epithelial ovarian tumors arise from ovarian surface epithelium (OSE), which is a single mesothelial cell layer lining the ovarian surface. The other hypothesis suggests that ovarian tumors arise not from the ovaries but from different parts of the reproductive tract, such as the fallopian tubes, endometrium, and endocervix. There are several arguments that support this hypothesis but challenge the former: First, ovaries have a distinct embryonic origin from other parts of reproductive tract, namely the fallopian tubes, endometrium, and endocervix, which are derived from mullerian ducts. However, serous epithelial tumors have similar characteristics as the fallopian tubes, endometrioid epithelial tumors resemble the endometrium, and mucinous epithelial tumors resemble the endocervix. It is very difficult to propose a mechanism to explain how such tumors 39 could arise from the epithelial surface of the ovaries, which is not derived from the mullerian ducts, but share common features with the organs that derived from the mullerian ducts. These common features are not only histological but also molecular analysis has recently confirmed such similarities. For example, one group [35] examined gene expression patterns of several members of the HOX gene family in both normal segment of the reproductive tract and various subtypes of ovarian tumors. They showed that serous, endometrioid, and mucinous ovarian subtypes express the same members of the HOX gene family as found in normal fallopian tubes, normal endometrium, and normal endocervix [35]. Besides the lack of consensus on the cell of origin of ovarian epithelial tumors, the natural history of tumor progression is also poorly understood. Ovarian epithelial tumors are divided based on their malignant potential into three groups of cystadenoma, tumors of low malignant potential (LMP) and adenocarcinoma. But is not clear whether these tumors are distinct diseases or represent different stages of a single disease continuum. The answer to this question and a better understanding of the cell of origin in the ovarian tumors is crucial for further advancements of clinical management and to this end extensive molecular analysis of these tumors should be undertaken. A parallel advantage of high-throughput molecular analysis of the ovarian tumors is to discover markers that could be potentially used as early detection markers. To this end, we investigated DNA methylation profiles of various subtypes of ovarian cell lines and tumors, to discover subtype-specific markers. We also included various 40 normal tissues such as fallopian tubes, normal endometrium and white blood cells as normal controls. Results DNA methylation analysis of ovarian cancer cell lines Cell lines are widely used in cancer biology for various purposes such as mechanistic studies, marker discovery, and drug development. It is known that cancer cell lines undergo various genetic and epigenetic alterations in culture. Therefore, prior to utilizing these cell lines to investigate epigenetic profiles of ovarian tumors, mainly in search for early detection markers, we sought to investigate how closely the ovarian cell line epigenetic profiles resemble that of the primary tumors. We included 15 ovarian cancer cell lines in our collection to investigate the epigenetic profiles of these cell lines using MethyLight and Illumina assays and determine how closely do they resemble the primary tumors. MethyLight analysis of ovarian cancer cell lines We compared DNA methylation profiles of 15 ovarian cancer cell lines using MethyLight reactions. These cell lines were provided to us by the laboratory of Dr. Charles Drescher at the Fred Hutchinson Cancer Research Center in Seattle. We selected a total of 102 MethyLight reactions from a potential of 263 reactions for this analysis. These reactions were selected based on their performance on a prescreening of 60 ovarian tumors of benign, borderline, and adenocarcinoma, and other studies in our laboratory on various cancers such as breast and colorectal cancers. Ten reactions showed an absolute 41 zero PMR in all of the cell lines. One reaction had a PMR of less than 5 in only one cell line and was completely unmethylated in all other cell lines. We chose the remaining 91 reactions for an unsupervised cluster analysis of the cell lines as shown in Figure 3.1 and observed that the examined ovarian cancer cell lines have distinct methylation profiles. We also observed that these cell lines did not cluster based on the ovarian cancer subtype from which they were originated. Figure 3.1. Two-dimensional hierarchical cluster analysis of 91 MethyLight markers on 15 ovarian cancer cell lines. Cell lines and their subtype of origin are listed on the left. Ovarian cancer cell lines have distinct DNA methylation profiles. They also do not cluster based on the subtype of their origin. Darker shades represent more DNA methylation. 42 Epigenetic profiling of ovarian cell lines using Illumina GoldenGate Cancer Panel I In this analysis, we investigated DNA methylation profiles of the same 15 ovarian cancer cell lines that were used in MethyLight analysis (see Figure 3.2). We made the three following observations in the unsupervised hierarchical cluster analysis shown in Figure 3.2: first, all ovarian cell lines form a distinct cluster from the primary tumors; second, they show on average higher levels of DNA methylation compared to primary tumors; and third, they do not sub-cluster based on the histology of the subtype of their origin, which was previously observed in the MethyLight analysis of these cell lines. We concluded that due to these differences the ovarian cell lines might not be useful as a model system for investigating epigenetic profiles of the ovarian tumors as well as for early detection marker discovery. Epigenetic profiling of epithelial ovarian tumors using Illumina GoldenGate Cancer Panel I There are several reports [146, 208] suggesting differences in gene expression profiles among subtypes of epithelial ovarian cancer. We first sought to investigate whether we could detect such differences in the epigenetic profiles of epithelial ovarian tumors. We employed the Illumina GoldenGate Cancer Panel I- a global DNA methylation approach- to assess the epigenetic profile of 27 primary tumors, which consisted of 15 serous, 9 endometrioid, and 3 clear cell. The clinical and histopathological information for the primary tumors is summarized in Table 3.1. The GoldenGate Cancer Panel I bead-array quantitatively measures the DNA methylation 43 status of 1,505 CpG dinucleotides at 807 genes in bisulfite treated genomic DNA. We selected the CpG dinucleotide for each gene closest to the area immediately upstream (– 100 bp) of each transcription start site for our further data analyses. DNA methylation measurement is calculated as the β-value, which represents the ratio of the methylated signal over the total fluorescent signal on a scale between 0 and 1 [22]. Tumor No. Histology Stage Age Menopausal Status Race CA125 T1 Serous IIIC 82 Post White 134 T2 Serous IIIC 74 Post White 1023.4 T3 Serous IIIC 42 Pre NA 1075.3 T4 Endometrioid IA 55 Post White 371.5 T5 Endometrioid IB 46 Pre NA 3897 T6 Endometrioid IB 47 Post White 5201 T7 Endometrioid IC 38 Post White 1720 T8 Serous IIIC 49 Post White 8000 T9 Endometrioid IIIB 51 Post White 2301 T10 Serous IVA 58 Post White 6706.4 T11 Clear cell IA 51 Pre NA 25 T12 Endometrioid IIIC 75 Post NA 112 T13 Serous IIIC 77 Post White 1828 T14 Serous IIIC 74 Post White 994 T16 Serous IIIC 43 Pre NA 480 T18 Endometrioid IIB 43 Pre White 206.9 T19 Serous IIIC 70 Post White 1504 T20 Serous IIIC 56 Post White 396 T21 Serous IIIC 69 Post White 835 T22 Endometrioid IIIC 58 Post White 2662 T23 Serous IIIC 59 Post White 918 T24 Serous IIIC 48 Post White 12 T25 Endometrioid IIA 58 Post White 75 T26 Clear cell IC 58 Post White 24 T27 Serous IIIC 68 Post White 49 T28 Clear cell IC 45 Pre White 705 T29 Serous IIIC 49 Pre White 195 Table 3.1. Clinical and histological information of the tumors. 44 An unsupervised hierarchical cluster analysis of the 807 DNA methylation measurements on the ovarian tumors is shown in Figure 3.2. In general, the primary tumors cluster mainly on the basis of their histology, suggesting these different subtypes have distinct DNA methylation profiles. We observed that the majority of the serous and endometrioid tumors form distinct clusters; however, this was not observed for the three clear cell tumors. To identify subtype-specific DNA methylation markers, we employed a two- sample t-test analysis to compare the methylation levels of each Illumina locus in all tumors of one subtype to all tumors of the other two subtypes as described in Methods. All loci with an adjusted p-value of less than 0.05 in at least one comparison (see Methods) were selected as subtype-specific. We identified 65 subtype-specific markers, which are shown in Figure 3.3. Additional information for these markers is presented in Suplementary Table 1 . It should be noted that while 16 out of 65 markers are common in more than one subtype, their methylation status is different in each subtype, being hypermethylated in one and hypomethylated in the other. Gene Expression Analysis of Subtype-Specific Ovarian Surface Epithelial Markers We measured gene expression of our subtype-specific markers in the primary tumors using HEEBO, a 70mer oligonucleotide microarray to investigate whether subtype-specific changes in DNA methylation are associated with changes in gene activity. For each subtype-specific marker we conducted a two-sample t-test comparing gene expression in the subtype of interest to the other two subtypes to identify subtype- specific markers with statistically significant changes in gene expression. Details of the 45 analysis are provided in Methods. The resulting p-values are shown in Table 2 for the 20 highly significant markers, and for all significant markers in Supplementary Table 1. Figure 3.2. Two-dimensional hierarchical cluster analysis of 808 Illumina GoldenGate loci of 27 ovarian primary tumors, 15 ovarian cell lines and two control buffy coat samples. Cell lines form a distinct cluster independent of the subtype they originated from and show higher levels of DNA methylation in comparison to primary tumors. Serous and endometrioid tumors cluster mainly on the basis of their histology. T, Tumor; CL, Cell line; S, Serous; E, Endometrioid; CC, Clear cell; BC6, Buffy coat sample #6; BC8, Buffy coat sample # 8. Yellow color indicates hypermethylation. Blue color indicates hypomethylation. 46 Figure 3.3. Heat-map of subtype-specific DNA methylation markers in ovarian tumors. Tumors are grouped by histological subtype and ordered from top to bottom by high to low overall mean β- value within subtype. Markers are grouped by histological subtype and sub-divided into two groups: “Lower”, indicates subtype-specific markers that have lower DNA methylation compared to the other two subtypes; “Higher”, indicates subtype-specific markers that have higher DNA methylation compared to the other two subtypes. Markers within each subgroup are ordered from left to right by low to high mean β- value. DNA methylation values range from hypomethylated (blue) to hypermethylated (yellow) as indicated by the legend bar. T, Tumor; DCC*, an endometrioid marker with lower DNA methylation value compared to serous, but higher compared to clear cell; CTLA4** an endometrioid marker with higher DNA methylation value compared to serous, but lower compared to clear cell; NEFL*** a clear cell marker with higher DNA methylation value compared to serous, but lower compared to endometrioid. 47 Of the total 65 subtype-specific markers, six markers showed a significant negative association between their DNA methylation and gene expression. The other 59 markers either showed a positive association (11), no significant association (45), or expression data was lacking (3). Hypermethylation of promoter 5’ CpG islands of some genes is associated with gene silencing [26, 36, 54, 136]. We found that the probes for the six markers with a negative association are all located close to the transcription start sites of their respective genes, but that only one of the six markers is located within a CpG island as defined by Takai and Jones criteria [168]. Epigenetic profiling of subtypes of ovarian tumors, benign gynecological diseases, and several normal controls from gynecologic tract using Illumina Infinium platform The preliminary data from small numbers of ovarian tumors on about 800 genes, suggested that there are statistically significant differences in the epigenetic profiles of subtypes of epithelial ovarian tumors. Therefore, we investigated epigenetic profiles of subtypes of epithelial ovarian cancer on a high throughput platform, Illumina Infinium platform, that interrogates DNA methylation status of more than 14,000 genes in more than 27, 000 CpG sites. In this analysis, we included benign ovarian tumors, LMP tumors, endometrioisis biopsies, and normal controls including fallopian tumors, normal endometrium, and normal buffy coats. We tested 78 malignant ovarian cancer tumor samples, including: 54 serous epithelial ovarian cancer, two serous low malignant potential, three mucinous epithelial ovarian cancer, one mucinous low malignant 48 potential, eight clear cell epithelial ovarian cancer, and 11 endometriod epithelial ovarian cancer. 59 benign and normal samples included: two serous cystadenomas, nine endometriomas, 17 endometriosis biopsies, 28 endometrial biopsies, and three normal fallopian tube biopsies. Cluster analysis of Infinium data A two-dimensional hierarchical cluster analysis of all 145 samples using all 27, 587 CpG sites did not reveal neither distinct subset of genes nor tumors (Figure 3.4). Also, we did not observe much difference between the tumors and the normal samples. However, when 4,931 most variable probes were selected (see Methods) and samples were arranged based on their histology, as seen in Figure 3.5, each subtype appears to have a distinct epigenetic profile. Furthermore, we observe that ovarian carcinomas have epigenetic profiles that are distinct from normal gynecological tissues. Discussion We analyzed the epigenetic profiles of subtypes of ovarian cancer, ovarian cancer cell lines, benign diseases of the gynecologic tract, and the buffy coat using MethyLight, Illumina GolenGate cancer Panel 1, and the infinium platform. Based on MethyLight and GoldenGate data, we found that ovarian cancer cell lines tended to have more diverse and frequent CpG island hypermethylation than the primary tumors. This observation is consistent with reports describing differential gene expression patterns between cell lines and primary tumors [58, 75, 137]. Therefore, while cell lines remain a 49 Figure 3.4. Two-dimensional unsupervised hierarchical cluster analysis of 27,578 CpG dinucleotides tested on 145 gynecological malignant, benign, and normal samples. Blue indicates low DNA methylation. Yellow indicates high DNA methylation. Black represents failed reactions, for which data is not available. 50 Figure 3.5. 4931 most variable infinium probes on normal, benign and malignant gynecological samples. 4931 most variable infinium probes with SD/SD max > 0.25. Subtypes of ovarian malignancies have distinct profiles. Normal and benign diseases of gynecologic tract (endometriosis samples) have similar profiles but distinct from malignant subtypes. viable model system for molecular mechanistic studies, they may not be as useful for marker discovery or profiling analyses. Unsupervised cluster analysis of the primary tumors tested on the GoldenGate platform showed that the small number of clear cell tumors did not form a separate cluster. However, serous and endometrioid tumors each formed two main clusters. This suggests that these tumors have distinct DNA methylation profiles. This finding led us to identify subtype-specific DNA methylation markers. We observed that the majority of 51 serous-specific markers were hypomethylated, while the majority of both endometrioid and clear cell markers were hypermethylated. About a quarter (16 out of 65) of the subtype-specific markers were in common between two or three subtypes, but have a statistically significant differential DNA methylation status for each subtype. Therefore, both the cluster analysis and subtype-specific profiling data are suggestive of different tumorigenic mechanisms and/or cells of origin for each subtype. Further epigenetic profiling studies including fallopian tube, Ovarian Surface Epithelium (OSE), peritoneum or endometrium could shed light on the cell of origin for various types of ovarian cancer. We investigated the relationship between promoter DNA methylation and gene activity of these markers by gene expression analysis. Although the association between promoter CpG island hypermethylation and gene silencing is well established, we found relatively few subtype-specific markers with a statistically significant inverse relationship between promoter DNA methylation and gene expression. This confirms what has recently been observed in diffuse large B-cell lymphoma [132], and nicely underscores that promoter DNA methylation and gene expression should not be viewed as a simplistic inverse relationship. Actively expressed genes may show promoter DNA methylation, if transcription is initiated at an alternative promoter. Genes lacking promoter DNA methylation may not be transcribed for other reasons, such as the lack of appropriate transcription factors, or the presence of other repressive epigenetic marks, such as histone H3K27 trimethylation. In addition, promoter CpG island methylation may reflect the past epigenetic state at that locus, regardless of expression status. For example, we and others have reported that occupancy and modification by Polycomb repressors in stem cells can predispose loci to aberrant CpG island hypermethylation [192]. Alternatively, groups of 52 genes can undergo concordant CpG island hypermethylation as a consequence of an unidentified epigenetic defect, as has been described for the CpG Island Methylator Phenotype (CIMP) observed in colorectal cancer [190]. Many of the epigenetic changes observed in such cases are likely passenger events without functional consequence for gene expression, and which therefore do not provide a selective advantage to the tumor. Materials and Methods Sample collection Ovarian tumors from three main sources: 1) Seattle samples: Tumor samples with the Laird IDs of (11501, 11502, 11503, 11504, 11505, 11506, 11507, 11508, 11509, 11510, 11511, 11512, 11513, 11514, 11515, 11516, 12379, 12380, 12381, 12382, 12383, 12384, 12385, 12386, 12387, 12388, 12389, 12390) were obtained from the laboratory of Dr. Patrick Brown at Stanford University as isolated DNA samples. The tumors tissues for these samples were obtained from patients with informed consent at the Fred Hutchinson Cancer Research Center. The study was approved by the Institutional Review Board of the Swedish Hospital in Seattle. The tissue acquisition, processing and storage were conducted by the POCRC, which is located at the Fred Hutchinson Cancer Research Center. The quality of tumor tissues, the percentage of cancer cells and the histological analysis of the tumors were determined via a centralized pathology review at the POCRC repository. All samples were distributed in a de-identified manner and cannot be traced back to patients. The annotation that accompanies the de-identified samples includes the histological subtype of the tumor. 53 Tumor samples with the Laird IDs of 12499, 12487, 12490, 12493, 12496, were obtained as fresh frozen tumor tissues from the laboratory of Dr. Charles Drescher at the Fred Hutchinson Cancer Research Center. We received the samples in a de-identified manner and cannot be traced back to patients. The annotation that accompanies the de-identified samples includes the histological subtype of the tumor. The DNA isolation was performed in the Laird laboratory as follows: A portion of these fresh-frozen tumor samples was cut and lysed in the lysis buffer as described previously [97]. 2) USC samples: Tumor samples with the Laird IDs of (12766, 12767, 12862, 12865, 12853, 12854, 12854, 12867, 12869, 12863, 12765, 12852, 12866, 12762, 12763, 12764, 12855, 12868) were obtained as tumor DNA from the USC/ Norris Tumor and Tissue Bank supervised by Dr. Michael Press at Norris Cancer Center, USC. These tumors were obtained from patients with informed consent and the study was approved by the Institutional Review Board of the University of Southern California, Keck School of Medicine. The percentage of cancer cells and the histological analysis of the tumors to qualify for our analysis were determined by Dr. Michael Press. We received all samples in a de-identified manner that cannot be traced back to patients. The annotation that accompanies the de-identified samples includes the histological subtype of the tumor. 3) Duke Samples: Tumor samples with Laird IDs of (14401, 14402, 14403, 14404, 14405, 14406, 14407, 14408, 14408, 14409, 14410, 14411, 14412, 14413, 14414, 14415, 14416, 14417, 14418, 14419,14420, 14421, 14422, 14423, 14424, 14425, 14426, 14427, 14428, 14429, 14430) were kindly provided by Dr. Andy Burchuck at Duke university. We were blinded to the histology of these samples prior to data generation and initial data analysis and these samples were used as blinded controls. All samples were analyzed for 54 tumor cell percentage to contain more than 90% tumor cells. We received all samples as de-identified samples and cannot track back to the patients. Cell Lines: A panel of 15 ovarian adenocarcinoma cell lines was sent to our laboratory from the laboratory of Dr. Charles Drescher at the Fred Hutchinson Cancer Research Center in Seattle. They obtained these cell lines as follows: the following cell lines were purchased from American Type Culture Collection (ATCC, Manassas, VA): ES-2, CaOV3, SKOV3, OV-90, TOV-21G, and TOV-112D. Cell lines A2780, A1847, IGROV1, OVCAR3, OVCAR5, OVCAR10, and PEO-1 were provided by the Pacific Cancer Research Consortium (POCRC). The 2008 cell line was donated by Dr. George Coukos at the Abramson Family Cancer Research Institute at the University of Pennsylvania. The HEY cell line was supplied by Dr. Naoto Ueno at UT M.D. Anderson Cancer Center. All cell lines were cultured according to the specifications outlined by ATCC. Cell lines were grown to 80%confluence, then serum-deprived in 0.5% FBS for 24 hours before harvesting. Cells were washed twice with Hank’s Balanced Salt Solution (Invitrogen, Carlsbad, California) and drained. 1mL of SDS extraction buffer (0.1M NaCl, 20mM Trizma base, 25mM EDTA, 0.5% w/v SDS) was added directly to the plate to lyse the cells. Cellular lysate was scraped off the plates, pipetted into a cryovial, and stored at -80ºC until DNA extraction. Lysates were treated with 200ug/ml ProteinaseK at 50 °C overnight. DNA was precipitated with one volume of Isopropanol and dissolved in TE -4 buffer. 55 DNA methylation analysis using Illumina GoldenGate Cancer Panel I Illumina analysis was performed on sodium bisulfite converted DNA of 27 primary ovarian tumors and 15 ovarian cell lines and two buffy coat samples from two healthy older females at the University of Southern California Epigenome Center. Sodium bisulfite conversion and Illumina analysis was performed as described previously [76]. Illumina Methods and reagents were previously described [22] . DNA methylation values from the Illumina assays were scored as β-values [22]. Statistical methods for Illumina GoldenGate Cancer Panel I analysis DNA Methylation Cluster Analysis: To visualize the DNA methylation patterns among ovarian cancer cell lines and tumors, we applied two-way average-linkage unsupervised hierarchical clustering. We viewed the clusters in Treeview. Subtype-Specific Marker Selection: To select ovarian cancer subtype-specific markers, we included only the 310 loci whose β-values were highly variable among tumors. We defined highly variable loci as those that had median absolute deviation (MAD) greater than two times the MAD of the β-values from a single whole genome amplification experiment. We performed two-sample t-tests comparing each subtype to the other two subtypes. P-values from the t-test were adjusted for multiple comparisons by controlling the false discovery rate. All statistical analyses were performed using the R software package. 56 Cluster analysis of Infinium data We used all 145 gynecological samples and four controls (two WGA as negative controls and two SssI treated DNA as positive controls) and all 27, 578 Infinium probe readouts to generate an unsupervised hierarchical cluster analysis. We used Cluster software and uncentered correlation in average linkage to generate the cluster. Selection of 4931 most variable loci We calculated Standard Deviation (SD) and SD maximum for each probe across all samples except controls and calculated SD/SD max. We selected all probes with SD/SD max > 0.25 which resulted in 4931 probes. 57 Chapter 4. DNA methylation biomarkers for early detection of ovarian cancer in blood-based or proximal fluid-based assays Chapter 4 Abstract Epithelial ovarian carcinoma is the leading cause of death from gynecologic cancer in the US. About 70 to 85% of patients present with advanced stage disease, for which the five-year relative survival rate is below 30%. However, this rate is more than 90% for localized disease. This suggests that improved early detection could reduce ovarian cancer death rates. Unfortunately, no reliable methods of early detection are available. We aimed to find tumor-specific DNA methylation markers for early detection of ovarian cancer in the blood. The use of DNA methylation markers for sensitive detection is based on the premise that during carcinogenesis DNA methylation undergoes alterations, and that these aberrant alterations can be detected in tumor-derived DNA that is released into the bodily fluids such as serum. Because DNA quantity in serum is miniscule, we maximized the detection sensitivity of our assay, and optimized DNA recovery from the serum. To maximize assay sensitivity we used Digital Methylight, which detects a single abnormally methylated molecule in a large volume of unmethylated DNA. Sample processing was optimized to maximize tumor-specific DNA yield, while minimizing leukocyte DNA contamination. For biomarker discovery we utilized both candidate gene approaches and genome-wide analysis. We identified 177 markers, which were further analyzed in a stringent step-wise sieving strategy. Markers that were methylated in Buffy coat and plasma from healthy individuals were eliminated. Fourteen markers were retained, of which 4 were multiplexed using the Digital 58 MethyLight platform and could discriminate between pooled sera from healthy individuals and pooled sera from ovarian cancer patients. The utility of these samples should be further investigated on multiple serum samples. The persisting challenges of the limited amounts of tumor DNA in the blood suggests that alternative bodily fluids may hold a better promise in early detection screenings. Chapter 4 Introduction Epithelial ovarian carcinoma is the leading cause of death from gynecologic cancer in the US. About 70 to 85% of patients present with advanced stage disease, for which the five-year relative survival rate is below 30%. However, this rate is more than 90% for localized disease. This suggests that improved early detection could reduce ovarian cancer death rates. Unfortunately, no reliable methods of early detection are available. The goal of this study is to find tumor-specific DNA methylation markers for early detection of ovarian cancer in the blood, or proximal fluids. The use of DNA methylation markers for sensitive detection is based on the premise that during carcinogenesis DNA methylation undergoes alteration, and that these aberrant alterations can be detected in tumor-derived DNA that is released into the bodily fluids such as serum. This study had three parallel specific aims: Specific Aim I: Biomarker development for early detection of epithelial ovarian cancer. Specific aim 1 is biomarker discovery. Both primary tumor samples and cell lines were analyzed using a candidate gene approach by MethyLight technology, and genome-wide analysis by microarray, and Illumina GoldenGate Cancer Panel I. The collection of markers identified from these technologies were further analyzed in a stringent step-wise 59 sieving strategy. Markers that were methylated in buffy coat and plasma from healthy individuals were eliminated. Fourteen markers were retained, of which four were multiplexed using the Digital MethyLight platform and could discriminate between pooled sera from healthy individuals and pooled sera from ovarian cancer patients. Specific Aim II: Optimization of sample processing. DNA quantity in serum and plasma is miniscule. To overcome this problem we maximized the DNA recovery from the serum/plasma. To this end, we compared several available DNA extraction methodologies to ensure the maximum recovery of low amounts of DNA in serum/plasma. Sample processing was optimized to maximize tumor- specific DNA yield, while minimizing leukocyte DNA contamination. Specific Aim III: Technology development To maximize assay sensitivity our lab developed Digital MethyLight, which detects a single abnormally methylated molecule in a large volume of unmethylated DNA. We also investigated multiplexing of the markers to be used as a marker panel in a single reaction. Results Specific Aim I: Biomarker development for early detection of epithelial ovarian cancer. Step 1) Marker development In order to ensure the development of sensitive and specific DNA methylation markers we used a plethora of resources. These ranged from a candidate gene approach to genome-wide platform screenings, and from direct measurements of DNA methylation to gene expression analysis of reactivated genes. Many of these analyses were performed in 60 parallel and in collaboration with several colleagues and the University of Southern California or other institutions. A total of 177 markers were selected from the existing MethyLight reactions in the Laird laboratory, from the literature, by 5-azaCdr treatment of ovarian cell lines to select the reactivated genes that were silenced by DNA methylation, and by Illumina GoldenGate cancer panel I. The selection of these markers from each approach is explained in detail in the Methods section. The current HUGO gene names, Laird lab MethyLight reaction ID numbers, and the source for all of the 177 markers are summarized in Supplementary Table 2. The schematic of marker selection and the screening steps is summarized in Figure 4.1 and explained in detail in the following pages. Figure 4.1. Schematic summery of step-wise marker development and screening. 61 Step 2) Counter-screening of markers in normal buffy coat samples These markers were developed for the purpose to be used as early detection blood-based biomarkers. Optimal markers should not be methylated in the blood of healthy individuals while methylated in patient blood. Since the major source of DNA in the blood is from the peripheral white blood cells, good markers should have no background methylation in the PBL DNA. Therefore, the next step in marker development was to counter-screen the 177 markers in the PBL of healthy individuals and eliminate any markers that were found to be methylated. To this end, we screened our 177 markers on 50 ng DNA from two buffy coat samples obtained from two healthy older females over 60 years of age. We used the cycle threshold (C(t)) values of these reactions and eliminated any reactions that had a C(t) value of less than 37 in either of the buffy coat samples. This reduced the reaction numbers to 102. The C(t) values for the two buffy coat samples are also shown in the Supplementary Table 1. Step 3) Counter-screening of markers in normal plasma We next screened these 102 reaction on 100 µl plasma equivalent DNA from 10 healthy older females >60 years of age. In this screening step we eliminated any reaction with any detectable levels of DNA methylation in any of the 10 plasma samples. This is an extremely stringent criterion and as discussed in the discussion section, should probably be revised or re-visited. However, at the time of this analysis we sought to choose the markers that had no backgrounds in plasma of healthy controls. This further reduced the reaction number to 58 reactions, which were then tested on the primary tumors. 62 Step 4) Screening of markers in ovarian primary tumors The 58 reactions from Step 3 were then analyzed on the ovarian primary tumors. We retained 14 markers that displayed the highest frequency of DNA methylation in the primary tumors. The heat-map of this analysis is shown in Figure 4.3. In order to select the best panel of markers to screen patient sera, we sought to multiplex the best combinations of these markers. This analysis is explained in the Specific Aim III. The multiplexing analyses rendered a panel of four markers to be further used in screening patient sera. Step 5) Screening of markers in patient sera We first investigated the performance of these multiplexed makers in a pooled sera sample from healthy controls and cancer patients. These samples were kindly provided to us by Dr. Nicole Urban at the Fred Hutchinson Cancer center in Seattle. As observed in Figure 4.5, our multiplexed markers can detect pooled patient sera from pooled normal controls. 63 Figure 4.2. Heat-map summary of steps of marker selection. 177 markers were tested on 2 normal buffy coat samples. Red indicates C(t) values <37, indicative of methylation. 102 markers were selected on 10 control plasma samples. Red indicates any C(t) value < 50. Green indicates no methylation. 58 reactions with no detectable methylation were tested 20 tumor DNA from Austria, 21 tumor DNA from USC, and 16 tumor DNA from FHRCC. 64 Figure 4.3. Heat map of the final 14 reactions on ovarian tumors. MethyLight reaction IDs of the 14 reactions are atop each column. C(t) values of SssI treated DNA, Buffy coat sample # 6 (BC6) and Buffy coat sample # 8 (BC8) for each reaction are given. Numbers within the heat map are PMR values. Frequency of methylation for each reaction across the tumors samples is calculated for various cut offs of PMR and are provided below each column. Reactions that performed under multiplexing condition of 4, 10, or 14 reactions are indicated with Y, and those that failed are marked with X (see multiplexing experiment). 65 Figure 4.5. Multiplexed digital MethyLight analysis of 300 µl of poled serum samples from cases, controls, and their dilutions. Dilutions indicated the dilution of pooled sera of the cases with pooled sera of controls. Numbers on the y-axis represent the number of detected molecules using Digital MethyLight. The four final reactions were multiplexed and tested on 300 µl of each serum dilutions. The number of methylated molecules for undiluted pooled sera cases is beyond the background of one molecule detected in undiluted pooled control sera and the 1:16 dilution. The HB number and corresponding genes of the marker panel are as follows: HB-224, LRRC41; HB-415, CLDN5; HB-417, FOXE1; HB-194, SCGB3A1. This result encouraged us to investigate the performance of these markers on a serum sample form a single patient. We are in possession of a collection of samples, including primary tumors and sera that were collected from the patients in a longitudinal order from the pre-op stage prior to the patients’ expiration. We have used the primary 66 tumor of these patients in the above steps of marker selection and have the information of the methylation status of our four selected markers in these tumors. To select the best serum sample to investigate the performance of our markers we selected a serum sample from one of the patients based on the following criteria: 1) we chose a patient who had a relatively short survival based on the date of the first serum sample collection to the last date of the serum collection. The logic is that there is a high possibility that this patient was either diagnosed with a very malignant ovarian carcinoma, or was diagnosed at a higher stage. This would translate into faster cancer cell turnover and consequently possibly higher amounts of circulating tumor DNA in her serum. 2) Among the patients who had a short survival period, we then selected the serum sample from a patient who showed relatively higher levels of DNA methylation of the four markers in her primary tumor, see Figure 4.6. From the longitudinal collection of the sera from this patient, we selected the pre-operation serum samples, which is anticipated to have the highest amount of tumor DNA (unless the tumor had relapsed very rapidly and extensively). Using Digital MethyLight, we tested the performance of the four multiplexed markers on the serum sample from patient No. 20, and compared the results to that of water samples, repeated 10X, and 10 serum samples from healthy individuals. We first tested serum100 µl equivalent in duplicate, followed by 300µl serum equivalent. As observed in Figure 4.7, the number of Digital MethyLight hits remains at the background level. 67 A B Figure 4.6. Selection of the most possible promising pre-op serum sample. A. Number of months spanning the first serum sample and the last serum sample that was collected from each patient. Patients with less than one year between the two dates are marked by an astrics. The pre-op serum sample from patient number 20 was selected based on this data and the DNA methyaltion frequency of the four markers on her tumor DNA was selected for further analysis. B. Heatmap of DNA Methylation of the final four markers in corresponding tumor DNA. The heatmap of DNA methyaltion of the four markers for each of the 21 patients is shown. Tumors are numbered from 1 to 20 and are displayed from top to bottom. The final four markers are shown with their HB numbers and the corresponding genes are as follows: HB-224, LRRC41; HB-415, CLDN5; HB-417, FOXE1; HB-194, SCGB3A1. Tumor number 20 (marked with a black astrics) was selected since three of the four reactions appeared to have high levels of DNA methyaltion. This tumor also belong to the patient with less than one year of “postulated’ survival. 68 Figure 4.7. Performance of the four multiplexed markers on patient serum by digital MethyLight. The first panel shows digital MethyLight analysis of four multiplexed markers on 100ul serum equivalent of patient serum. The second panel shows the same analysis in duplicate. The third panel is the same analysis using 300 ul serum equivalent. Water controls and 10 plasma samples from healthy controls are also tested. As observed, the number of hits in the patient serum does not exceed that of normal plasma from healthy controls. 69 Specific Aim II: Optimization of sample processing. A parallel goal to biomarker discovery was to optimize sample-processing protocols. The DNA amounts in the serum or plasma are extremely miniscule. Furthermore, there is a risk of contamination from the white blood cell DNA. Therefore, we optimized a protocol for isolation of serum and plasma from whole blood to ensure maximum recovery of free-floating DNA in serum/plasma and minimize the contamination from white blood cell DNA. The steps of serum/plasma isolation from blood are explained in the Methods section. We also investigated the recovery of DNA both from serum and plasma using three available techniques/Kits known to us at the time of this study and also compared them the classic DNA isolation technique using phenol/chloroform extraction followed by ethanol precipitation. Results We compared the following three different DNA isolation methods: 1) Qiamp viral RNA kit, Qiagen; 2) Blood DNA kit, Qiagen; and 3) ZR Serum DNA kit, Zymo Research. All steps of purifications were performed based on manufacturer’s recommendations and any changes are summarized in Table 4.1. Comparison of DNA Concentration amounts in Serum and Plasma To estimate the amount of recovered DNA using each of the seven tests, we used Real-Time PCR. Two reaction a C-Less (HD-344) and ALU-based reaction (HD- 16) were developed in our laboratory and were used in this analysis to estimate the 70 Test No. Methodology Volum of serum/plas ma (µl) No. of columns Amount of carrier µg Elution T1 Qiagen vRNA 1200 2 5.6 x 2 combined T2 Qiagen vRNA 600 1 5.6 separate T3 Qiagen vRNA 600 2 5.6 x 2 separate/combined T4 Qiagen blood kit 600 2 5.6 x 2 separate/combined T5 Qiagen blood kit 600 1 5.6 separate T6 Zymo 1200 N/A N/A N/A T7 Zymo 600 N/A N/A N/A Table 4.1. Specification of each methodology using three various DNA isolation Kits. T1 to T7, Test numbers; Carrier, the carrier is provided by the Qiagen viral RNA kit that is lyophilized polyA synthetic RNA. Separate, each column eluted in the required volume; Combined, the second column eluted in the elution volume used for the 1 st column; Separate/combined, each column eluted in the required volume, and the 2 separate columns were combined. Each test was conducted based on the manufacturer’s recommendations and any modification is summarized the in this table. amount of DNA recovered from control serum and control plasma utilizing each method. Results for each test is shown in Figure 4.8 for plasma and serum. We observed that for all seven tests in both reactions there was more DNA recovered from the serum in comparison to plasma. Qiagen Blood kit, when modified to use two separate columns and twice the amount of carrier performed better in maximizing the recovery of free DNA in both plasma and serum samples compared to both ZR Serum DNA kit (Zymo) and Qiamp viral RNA kit. We then compared the DNA 71 recovery from Qiagen Blood kit to the standard DNA isolation protocols using phenol/chlorophorm extraction followed by ethanol precipitation. The recovery of DNA from both methods yielded comparable results (data not shown), and we therefore used Qiagen Blood kit with the modifications described in the T4 experiment for further serum and plasma DNA isolations. Specific Aim III: Technology development Digital MethyLight There are two major challenges involved in sensitive detection of aberrant DNA methylation molecules in the blood: One is the specific detection of tumor DNA in a pool of DNA found in blood, and the other is the technological challenge of detecting such very low-abundance molecules. The former challenge requires a high signal-to-noise- ratio, which is to find markers that biologically present no background in the blood, and technologically are optimized for the mehtylation-specific priming and methylation- specific probing (MethyLight). The second challenge requires technological advancements in sensitive detection of low abundance molecules as the amount of DNA found in the plasma or serum is very miniscule. Digital MethyLight, which was developed in our laboratory, is based on the premise of Digital PCR: the premise of amplification of individual DNA molecules by distribution of the sample over multiple reaction wells. This will reduce the concentration of various competing molecules in the PCR reaction as well as the DNA molecules to less than a one molecule per well, providing a digital readout of the template in the distributed sample. Our laboratory combined the advantage of high specificity of 72 Figure 4.8. Comparison of DNA concentration recovered using various methods in plasma and serum. HB-344 indicates the C-Less reaction. HD-16 indicates the Alu reaction. Tests are ordered from left to right based on the same order (T1 to T7) as indicated in Table 4.1. MethyLight with the sensitivity of detecting up to one template molecule per sample in a technique named Digital MethyLight. We have used this technique to screen our best markers on both individual and pooled patient sera as described in Specific Aim I. 73 Multiplexing. In our effort to identify a universal marker that would detect all cases of ovarian cancer, it has become increasingly clear that no one marker would be able to detect all cases of this cancer. Similar investigation on other cancer types in our laboratory yielded similar results. Therefore, we aimed to use a panel of markers that would together detect as many cases as possible. One obvious challenge in applying this approach to blood- based biomarkers is the limitation in the amounts of DNA in the serum or plasma, which limits the number of assays one could potentially apply to screen the serum/plasma. One way to overcome such problem is to multiplex the best set of markers to be used in a single reaction. Results Determining the minimum required concentration of primers Our step-wise sieving analysis in search of blood-based biomarkers yielded 14 markers (see specific Aim I) which were subject to multiplexing analysis to find the best panel of makers that could be used in combination to increase sensitivity. Our primer and probe concentration for each MethyLight reaction was 0.3 ng/mL and 0.1 ng/mL, respectively. We explored the literature for the least reported concentration of primers that could be used effectively in a PCR reaction. Based on our investigation the least reported amount for a PCR primer is 0.1 ng/mL. Therefore we first compared the 14 reactions using various concentration on bisulfite converted fully methylated SssI treated PBL DNA. We found that at 0.1 ng/mL concentration either the C(t) value was 74 unaffected or improved. Therefore we used the lower concentration of 0.1 ng/mL in the next multiplexing analysis. Determining the best panel of markers for multiplexing We investigated the performance the top 4, 10, and 14 reactions in a multiplex panel separately. For each analysis, we compared one MethyLight reaction using its primers and probes in combination to only the primers of all other MethyLight reactions excluding the probes, and to another assay that included all primers and all probes. For the 4x and 10X experiment the analysis was performed in triplicate and for the 14X analysis it was performed in duplicate. Figure 4.9 shows the results of this analysis for the 4X and 10X experiment. Relaxing MethyLight Reactions Our final panel of markers, even though multiplexed and assayed using Digital MethyLight, did not detect more methylated molecules in the individual patient serum compared to 10 normal control plasma samples, see Figure 4.2. While there are several explanations for this negative finding as discussed in the discussion, one possible explanation was the stringent nature of the MethyLight reactions. Our MethyLight reactions are designed to detect only fully methylated DNA molecules at all of the CpG dinucleotides interrogated in each reaction, which are about nine CpGs on average for each reaction. Abnormal DNA methyltion of a CpG island in a tumor could present heterogeneous or complete methylation. MethyLight does not detect heterogeneous methylation events. In analyzing the methylation profile of a tumor using 75 Figure 4.9. Multiplex analysis of MethyLight reactions. A. Multiplex analysis of the top 4 reactions. B. Multiplex analysis of the top 10 reactions. Average C(t) values of the triplicate experiments are given. Error bars represent standard error. Blue bars represent the C(t) value of a single reaction. Purple bars represent the C(t) values for a reaction with all four primer sets (figure A), or all 10 primer sets (figure B) in addition to the primer and the probes of the indicated reaction. Green bar represent the C(t) value of a reaction for all four (Figure A), or all 10 (figure B) primer and probe sets. 76 (Figure 4.9, Continued) A B 77 abundant amounts of DNA, exclusion of heterogeneous methylation events are not presenting a challenge, and in fact it was the goal of many analyses in our laboratory. However, in the case of sensitive detection of miniscule amounts of DNA molecules in plasma or serum, such stringent detection criteria could result in reducing the sensitivity of our assays. To investigate this possibility, we designed “relaxed” MethyLight reactions for the four reactions that were previously used as a panel. The sequences of the new primer and probe sets as well as the original reactions are presented in Table 4.2. The sources and subtypes of the tumors that were used in this experiment are provided in Table 4.3. In brief, there are seven serous, one mucinous, one clear cell and one endometrioid adenocarcinoma tested in this study. The C(t) values of the comparison between the original and relaxed reactions are shown in Table 4.4. HB-194 interrogating the CpG island of SCGB3A1 gene, detects 4 out of 10 tumors however with relatively high C(t) values. There are two sets of relaxed forward and reveres primers designed for this gene. The various combination of these relaxed primers and probes does not lower the C(t) values in the tumors and only in one test increases the number of the tumors from 4 to 6 with C(t) values of less than 50. While each reaction may detect a different set of tumors the frequency or the C(t) value did not improved and in some cases worsened. A similar observation was made for the other three reactions when compared to their corresponding relaxed primers/probes. 78 Table 4.2. Sequences of the original and the’ relaxed’ primers and probes. Tumor Number Sample Type Subtype 1 primary tumor SEROUS OVA CA 2 primary tumor SEROUS OVA CA 3 primary tumor SEROUS OVA CA 4 primary tumor SEROUS OVA CA 5 primary tumor SEROUS OVA CA 6 primary tumor SEROUS OVA CA 7 primary tumor SEROUS OVA CA 8 primary tumor MUCINOUS OVA CA 9 primary tumor CLEAR CELL OVA CA 10 primary tumor ENDOMETRIOID OVA CA Table 4.3. Tumors subtypes used in the comparison of the original and the ‘relaxed’ MethyLight reactions. 79 Table 4.4. Performance of the ‘relaxed’ MethyLight reactions in comparison to the original reactions. The Original reaction is given on the top followed by one or more of ‘relaxed’ reactions. The C(t) values of 10 tumors and two buffy coat (BC 6 and BC 8) are given. 80 Discussion The lack of a screening strategy to detect early-stage disease likely contributes to the high mortality rate of ovarian cancer. Ovarian cancer presents with very few, if any, specific symptoms. Twenty percent of patients are diagnosed at stage I and II when the disease is still confined to the ovary. In patients diagnosed with advanced disease, the 5- year survival rate ranges from 20% to 25%, depending on the stage and grade of tumor differentiation. Of these patients, 80% to 90% will initially respond to chemotherapy, but less than 10% to 15% will remain in permanent remission. Therefore, an adequate screening test for early detection of ovarian cancer should greatly improve patient survival. Currently, in some institutions, the screening strategy for ovarian cancer is annual pelvic examinations. In developing an early detection marker it is important to consider the following criteria: the marker should have wide applicability and should be relatively non-invasive. This would increase patients’ compliance, as is evident in cervical cancer screening using Pap smear. A blood-based biomarker would meet such criteria. One currently available blood-based marker is CA-125, which has been used for screening of ovarian cancer patients for recurrence of the disease. CA-125 antigen may show elevation in healthy or non-cancerous patients while during the early stages of the cancer its levels remain within the normal range. Some studies suggest that the use of CA-125 in combination with radiological examinations for the high-risk population could be a useful early detection screening tool [113, 120, 156]. However, low sensitivity and specificity of CA-125 diminishes the utility of this marker as an early detection marker [18, 59]. In this analysis, we aimed to look for developing epigenetic blood-based 81 markers for early detection of ovarian cancer. We proposed to look for tumor-specific aberrant DNA methylation markers in the blood. In comparison to a protein marker such as CA-125, tumor-specific DNA can be easily amplified by PCR, and therefore be more sensitive. While blood-based early detection DNA markers are ideal, there are several challenges inherent in their development. There is a pool of free-floating DNA from various cell types including the white blood cells in the serum/plasma. Therefore, the handling and processing of serum/plasma should be optimized to minimize the rupture and contamination with the white blood cell DNA. The amount of tumor-derived DNA is likely very miniscule in the blood, which requires the advancements of very sensitive techniques. The greatest challenge in developing early detection markers is the biology of tumor growth and progression. A microscopic tumor would have much slower rate of necrosis and apoptosis as well as smaller cell number, and therefore the amounts of free-floating tumor DNA in the blood stream at very early stages of the disease would be –if not impossible- extremely difficult to recover. In this extensive analysis in search for a blood-based biomarker, we had three parallel aims, first, to select the most promising tumor markers; second, to increase the sensitivity of marker detection in the blood; and third, to optimize serum/plasma isolation and DNA recovery procedures. To compile a list of the most tumor-specific markers, we analyzed the following: markers that were reported in the literature to be methylated in the ovarian cancer; promising markers from multiple analyses of various cancers in our laboratory; markers that showed reactivation after 5-Aza-Cdr treatment of ovarian cancer cell lines; and, markers with highest DNA methylation from high-throughput screening of various ovarian tumors on Illumina GoldenGate and Infinium platforms. 82 To develop the best panel of markers we used a step-wise stringent screening strategy against normal PBL and plasma samples and selected a potential panel of four markers. The choice of blood DNA as a normal comparison was influenced in part by the goal of developing a blood-based test, and in part affected by the unclear identity of the cell of origin of ovarian cancer [48]. However, the lack of a clearly defined cell of origin or control tissue is not necessarily an impediment for the purpose of selecting DNA methylation markers for blood-based assays, since white blood cells are likely the major source of contaminating non-tumor-derived DNA in the blood. To increase the sensitivity of our assay, our laboratory developed Digital MethyLight that can potentially detect one target molecule. To further increase the sensitivity we multiplexed these reactions to be used in Digital MethyLight. We optimized serum/plasma isolation and DNA recovery from these media. We also compared the amounts of plasma DNA with serum DNA in each of the purification methods and concluded that serum contains higher amounts of DNA. However, this does not necessarily suggest that serum is a better medium for tumor DNA isolation than plasma, even though in many clinical laboratories it is conventional to use serum samples, perhaps since most protein markers are serum-based. In quantitative analysis of circulating DNA the difference between plasma and serum could be significant. Several studies have reported that the higher amount of DNA in the serum compared to plasma is due to the ex vivo lysis of blood cells after coagulation [101, 111]. If target DNA constitutes a small portion of free floating DNA, as is the case for tumor DNA, the interference of the background DNA should be minimized to increase the sensitivity of tumor DNA detection. On the other hand, we observed that lower amounts of DNA 83 during the steps of DNA isolation, and perhaps Sodium bisulfite conversion, adversely affects the DNA recovery, even with the addition of a carrier. Therefore, further investigation is required to better understand whether serum or plasma is the medium of choice. This was not an issue in screening our marker panel since only serum samples from patients were available to us. The first round of screening of the markers was performed on a pooled serum sample of 50 patients and was compared to a pooled serum sample of 50 controls. We were able to detect methylated molecules in the pooled patient sera above the background of the pooled normal sera. This result was relatively encouraging. However, an important caveat in any study on pooled samples is the possible presence of one or more outlier samples that, in this case, could contribute to a false positive result. Despite this possible caveat, we further tested our marker panel on 10 serum samples (including a pre-op sample) that were collected from one patient prior to diagnosis and during her follow-ups until patient’s expiration. The corresponding tumor sample from this patient had shown high DNA methylation in three of the four markers in our panel. DNA methylation for none of the 10 serum samples, including the pre-op, was above the background. Since this experiment was conducted on samples from only one patient, much cannot be concluded until further analysis on multiple independent serum samples. However, several explanations could be offered for this negative result: degradation of DNA in the serum samples due to many years in archive; small volume of tested serum, heterogenic DNA methylation in the tumor, very small amounts of tumor DNA, or the nature of tumor-derived DNA in the blood. Depending on the mechanism of tumor DNA release (discussed further in Chapter 5) DNA integrity could differ. For example, when tumor cells undergo necrosis, their DNA degrades. 84 Depending on the chromatin structure some regions of DNA would be less protected and may degrade more extensively, and would not be detected by an assay that interrogates this region. In moving forward, the choice of other media such as vaginal and uterine samples should be investigated. In comparison to blood, these media are more proximal, less diluted, and may have slower rates of DNA clearance. It is possible that some of the hypermethylated loci in ovarian tumors that failed our stringent screenings against the blood might hold a better chance when screened in these media. Materials and Methods Sample collection and DNA isolation Ovarian Tumors: Tumor samples with the Laird IDs of (11501, 11502, 11503, 11504, 11505, 11506, 11507, 11508, 11509, 11510, 11511, 11512, 11513, 11514, 11515, 11516, 12379, 12380, 12381, 12382, 12383, 12384, 12385, 12386, 12387, 12388, 12389, 12390) were obtained from the laboratory of Dr. Patrick Brown at Stanford University as isolated DNA samples. The tumors tissues for these samples were obtained from patients with informed consent at the Fred Hutchinson Cancer Research Center. The study was approved by the Institutional Review Board of the Swedish Hospital in Seattle. The tissue acquisition, processing and storage were conducted by the POCRC, which is located at the Fred Hutchinson Cancer Research Center. The quality of tumor tissues, the percentage of cancer cells and the histological analysis of the tumors were determined via a centralized pathology review at the POCRC repository. All samples were distributed in 85 a de-identified manner and cannot be traced back to patients. The annotation that accompanies the de-identified samples includes the histological subtype of the tumor. Tumor samples with the Laird IDs of 12499, 12487, 12490, 12493, 12496, were obtained as fresh frozen tumor tissues from the laboratory of Dr. Charles Drescher at the Fred Hutchinson Cancer Research Center. We received the samples in a de-identified manner and cannot be traced back to patients. The annotation that accompanies the de- identified samples includes the histological subtype of the tumor. The DNA isolation was performed in the Laird laboratory as follows: A portion of these fresh-frozen tumor samples was cut and lysed in the lysis buffer as described previously [97]. Tumor samples with the Laird IDs of (12766, 12767, 12862, 12865, 12853, 12854, 12854, 12867, 12869, 12863, 12765, 12852, 12866, 12762, 12763, 12764, 12855, 12868) were obtained as tumor DNA from the USC/ Norris Tumor and Tissue Bank supervised by Dr. Michael Press at Norris Cancer Center, USC. These tumors were obtained from patients with informed consent and the study was approved by the Institutional Review Board of the University of Southern California, Keck School of Medicine. The percentage of cancer cells and the histological analysis of the tumors to qualify for our analysis were determined by Dr. Michael Press. We received all samples in a de-identified manner that cannot be traced back to patients. The annotation that accompanies the de-identified samples includes the histological subtype of the tumor. Cell Lines: A panel of 15 ovarian adenocarcinoma cell lines was sent to our laboratory from the laboratory of Dr. Charles Drescher at the Fred Hutchinson Cancer Research Center in Seattle. They obtained these cell lines as follows: the following cell lines were purchased from American Type Culture Collection (ATCC, Manassas, VA): ES-2, 86 CaOV3, SKOV3, OV-90, TOV-21G, and TOV-112D. Cell lines A2780, A1847, IGROV1, OVCAR3, OVCAR5, OVCAR10, and PEO-1 were provided by the Pacific Cancer Research Consortium (POCRC). The 2008 cell line was donated by Dr. George Coukos at the Abramson Family Cancer Research Institute at the University of Pennsylvania. The HEY cell line was supplied by Dr. Naoto Ueno at UT M.D. Anderson Cancer Center. All cell lines were cultured according to the specifications outlined by ATCC. Cell lines were grown to 80%confluence, then serum-deprived in 0.5% FBS for 24 hours before harvesting. Cells were washed twice with Hank’s Balanced Salt Solution (Invitrogen, Carlsbad, California) and drained. 1mL of SDS extraction buffer (0.1M NaCl, 20mM Trizma base, 25mM EDTA, 0.5% w/v SDS) was added directly to the plate to lyse the cells. Cellular lysate was scraped off the plates, pipetted into a cryovial, and stored at -80ºC until DNA extraction. Lysates were treated with 200ug/ml ProteinaseK at 50 °C overnight. DNA was precipitated with one volume of Isopropanol and dissolved in TE -4 buffer. Gene Expression Analysis This experiment was performed at the laboratory of Dr. Patrick Brown at Stanford University, San Jose, CA. DNA and RNA Extraction from Ovarian Tumors The tumor tissues were disrupted in a Biospec Tissue-Tearor Model 985370-395 in the presence of TRIzol (Invitrogen) with a ratio of 10ml TRIzol per 50mg of tissue. Following an extraction step of 0.2 ml of Chloroform per 1ml of TRIzol, the upper 87 aqueous phase was removed for RNA isolation. The remaining inter-phase and the phenol phase was briefly stored in 4°C for DNA isolation. The upper aqueous phase was processed for total RNA according to Invitrogen protocol and further purified using RNeasy Mini kit (Qiagen). Genomic DNA was isolated by back-extraction from the TRIzol inter-phase and the phenol phase as described in page 4 of the Ambion website [126]. Detailed amplification and labeling protocols are available at the Brown lab website [62]. Microarray Analysis Microarray experiments were performed as described at the Brown lab website [64]. Briefly, 500 µg of total RNA from each of the ovarian tumors were amplified using Amino Allyl Message Amp™ II aRNA Kit (Ambion, Austin, TX, USA). The aRNA were labeled with Cy5 and co-hybridized with Cy3 labeled Stratagene reference aRNA. For some samples, the mRNA was amplified and hybridized in duplicate or triplicate. The samples were then hybridized to oligonucleotide microarrays (HEEBO), which consist of 44,544 seventy-mer probes and printed at the Stanford Functional Genomics facility. Detailed information is available at the Stanford Genomics facility website [7]. The arrays were scanned in a low-ozone environment using a GenePix 4000A microarray scanner and images were analyzed with Genepix 5.0 (Axon instruments, Union City, CA). The raw data were deposited into Stanford Microarray Database and can be viewed at the SMD website [14]. 88 Microarray Data Processing By Spot: A spot quality filter was applied to every spot on each array. Only spots with a ratio of intensity/background > 1.5 in either channel were included. The spots that flagged manually by visual inspection of spot uniformity were not included. The Log (base2) of R/G Normalized Ratio (Mean) was calculated for each spot. Ratios were normalized for each array such that the distribution of log ratios for the array had a mean of 0.00. Empty and control spots were dropped after normalization. Spots with the same Locus Link ID were averaged. By Array: Replicate hybridizations of the same amplified mRNA were averaged by Locus Link ID. Hybridizations of independent amplifications of the same sample mRNA were averaged by Locus Link ID. Selection of existing DNA methylation markers in the Laird lab The Laird laboratory has developed a technique called MethyLight and currently has developed more than 700 MethyLight reactions. These markers were developed for genes that: a) were reported in the literature to be hypermethylated in cancer; b) were known (or suspected) to be involved in tumorigenesis, based on mutation, gene loss, gene function studies; or c) were identified as hypermethylated in our laboratory. At the time that this study was started over 300 MethyLight markers were available in the laird laboratory. These markers were used to screen various types of tumors including ovarian tumors and several normal tissues and cell types. We used the preliminary data from these analyses to select the most informative markers. We excluded all markers that were methylated in normal tissues and the markers that were unmethyalted in various screened 89 tumors. We retained all markers that were methylated in one or more tumor types as well as those markers that their informativness was not readily apparent. Selection of markers from 5aza-Cdr treated cell lines and literature Dr. Chana Palmer selected the top 20 reactions for the three ovarian cell lines, OVCAR3, OVCAR5, and CaOV3 that were subjected to 5-aza-Cdr treatment and a mock treatment (see Methods for 5-aza-Cdr experiment). Reactions were ranked based on the fold induction after 5-aza-Cdr treatment and the top 20 reactions from each cell line were selected. Markers with the potential of interferon-dependent induction were eliminated. Menedez et. al. published a similar study using OVCAR3 ovarian cancer cell line. We included 17 markers that came from Menendez paper. Some genes overlapped with the markers from our cell line analysis reducing the number from 77 to 68 markers from these two studies. We assigned scores based on the rank of these reactions in each analysis and then merged the scores (if a reaction was ranked in more than one analysis the scores were added) and ranked the genes again. The tie between reactions with the same scores was broken when we took in consideration the average fold induction for these genes. We ranked them and gave priority to the genes that have a CpG island. Selection of markers from Illumina GoldenGate cancer panel I. The area under the curve (AUC) of a receiver-operating characteristic (ROC) was calculated to find markers that distinguish between ovarian primary tumor and cell lines and normal controls. We also calculated p-values for the comparison between these samples using a Mann-Whitman test. The reactions were ranked by both analyses. We 90 then selected the top 5 reactions that had the two following criteria: that they interrogated a CpG island and that their DNA methyaltion β-values in the ovarian tumors was higher than 0.5. Plasma Isolation from Whole Blood Blood is collected in anticoagulant containing tubes (purple caps) and kept on Ice. It should be processed at RT within 6h of collection. Blood is spun for 10min at 300g (1,100 RPM in the clinical centrifuge). The tubes remain in the centrifuge and are spun for another 10 min at 1,600g (3,000 RPM in the clinical centrifuge). Plasma is the top layer and is collected all except at least 0.5cm above the Buffy coat without disturbing the buffy coat and the RBC layer. Plasma is collected in one 15 ml conical tube, mixed and transferred into 2 ml round-bottom screw-cap tubes and spun for 10min at 16,000g (max speed) on the tabletop centrifuge at RT. The supernatent was collected, aliquoted into 1.2ml /tubes and stored at -80°C until ready to purify the DNA. Serum Isolation from Whole Blood Blood was collected in non-anticoagulant containing tubes (clot tubes, red caps) kept RT and allowed to coagulate for 15 min. Blood is spun for 10min at 300g (1,100 RPM in the clinical centrifuge). The tubes remain in the centrifuge and are spun for another 10 min at 1,600g (3,000 RPM in the clinical centrifuge). The serum is collected leaving behind at least 0.5cm above the clot, transferred into one 15 ml conical tube, mixed, aliquoted into 1.2ml /tubes and stored at -80°C until ready to purify the DNA. 91 Chapter 5: Discussion The goal of this research has been to discover and evaluate DNA methylation- based biomarkers, both in human reproductive health, in the case of male infertility, and in human disease, in the case of ovarian cancer. In chapter 2 of my thesis, I described the novel finding of a widespread epigenetic abnormality in poor quality human sperm. We first screened 294 MethyLight reactions using a single anonymous semen sample. Thirty-six reactions with detectable DNA methylation were further analyzed on 65 sperm samples. Unsupervised cluster analysis revealed distinct groups of DNA methylation behavior. A group of loci was found to be more frequently methylated in sperm samples of various abnormalities. This group of loci included imprinted and non-imprinted genes as well as a repetitive element, which suggested the possibility of a broader epigenetic defect associated with poor human sperm. The broad epigenetic defect was first observed in a pilot study of 8 of the same 65 samples on the Illumina GoldenGate Cancer Panel I, and was further validated by a larger independent sample size on the same platform. We observed higher levels of DNA methylation in the abnormal sperm samples compared to the control samples, a pattern resembling that of the somatic cells. We postulated two mechanisms that could lead to the observed abnormal hypermethylation: 1) incomplete erasure of DNA methylation in the primordial germ cells that are normally unmethylated in mature sperm, or 2) abnormal de novo methylation during spermatogenesis. Several factors point to disruption of erasure in primordial germ cells as underlying the defect that we postulate. In mice, widespread erasure of DNA methylation has been shown to occur in both the pre-implantation embryo and again, uniquely, in primordial germ cells around the time that they enter the genital ridge. Primordial germ 92 cells arise from cells of the proximal epiblast, which have themselves embarked upon somatic development, as shown by expression of somatic genes [70, 199]. The germ cell lineage must therefore suppress the somatic program, which in mice is accomplished in part by genome-wide erasure of DNA methylation soon after germ cells migrate to the genital ridge [66, 99, 100, 102, 104, 106, 161, 166, 179]. Incomplete erasure of DNA methylation at this stage of germ cell development has been postulated to explain transmission of variable phenotypes in several well-characterized mouse models [92, 121, 134]. This erasure affects DNA methylation on single copy genes, imprinted genes and at least some repetitive elements [66, 161]. Therefore, its disruption could in theory result in the type of pattern we observe in poor quality sperm, with elevated levels of DNA methylation at DNA sequences of each of these types. Further, because this erasure is confined to primordial germ cells, we anticipate that its disruption would be compatible with normal somatic development. In humans, primordial germ cells colonize the genital ridge at about 4.5 weeks of gestation. We are not aware of data describing DNA methylation in the human germline at this date; however, the DMR in MEST at which we found elevated DNA methylation in poor quality sperm is reportedly unmethylated in the male germline by week 24 of gestation [93]. We have not investigated potential causes of disrupted erasure. However, weeks 4.5-24 of gestation represent post-implantation stages of development wherein fetal physiology may be influenced by maternal factors and environmental compounds that cross the placenta. Possible origins of male infertility as early as 4.5 weeks of human gestation have not been studied. However, transient in vivo chemical exposure at 7-15 days post conception, which includes the analogous stage of murine 93 development [66, 100], results in spermatogenic deficits in rats with grossly normal testes [42] and may be associated with elevated methylation of sperm DNA [33]. Taken together, the observations we report here suggest that epigenetic mechanisms may contribute to some cases of male factor infertility and that additional investigation of epigenetic mechanisms is warranted. Research relating sperm DNA methylation profiles to fertility outcomes is underway in our laboratory, and studies addressing pathophysiology associated with aberrant sperm DNA methylation may provide long- awaited mechanistic insights into abnormal sperm function. The other goal of my research was investigating epigenetic profiles of ovarian cancer subtypes and the development of DNA-methylation based biomarkers for early detection of ovarian cancer. Ovarian cancer is a heterogeneous disease. The four major subtype of epithelial ovarian carcinoma are serous, endometrioid, mucinous, and clear cell. It is postulated that these subtypes are distinct diseases with different cell of origin and natural history. At the molecular level, these subtypes differ in their gene expression profiles and association with genetic risk factors. Clinically, oncologists and pathologists noted differences in their response to chemotherapy and frequency, especially based on stage at diagnosis. Majority of advanced-stage carcinomas are of serous subtype, while majority of early-stage patients present with nonserous carcinomas. In chapter 3, we compared epigenetic profiles of different subtypes of ovarian carcinomas using MethyLight, Illumina GoldenGate and Infinium. We observed differences in epigenetic profiles of each subtype. From the Illumina GoldenGate analysis we identified some DNA methylation loci with statistically significant differences in their epigenetic state between 94 subtypes. Unsupervised cluster analysis and visual examination of the Infinium data also showed differences in the epigenetic profiles of these subtypes. Further extensive and in- depth statistical analysis of especially Infinium data is required to elucidate epigenetic profiles of these subtypes. Gene ontology analysis such as gene function, molecular pathway, GC content, and PolyComb occupancy would examine molecular and perhaps pathologic differences among these subtypes. CA-125 measurements, clinical, histological and survival data are available for almost all of the cases studied on the Infinium platform. Further statistical analysis would determine the correlation of these features with subtype-specific epigenetic profiles. Should significant differences between each subtype emerge from these analyses, it would support the hypothesis that these subtypes are distinct diseases. Since pathologists can distinguish between various subtypes (except in cases of highly undifferentiated tumors), developing subtype-specific markers may have very little use, if any, in the clinic. However, it can provide further evidence that these subtypes are different diseases and therefore, should be managed and investigated differently. One example where such insight was not taken into account and perhaps the results could have been different was our own research as presented in chapter 4. In search for ovarian cancer markers we grouped these subtypes together (as has been a common practice in ovarian cancer studies) both in hope of finding one or more universal ovarian caner marker(s) and for the sake of greater sample size. Perhaps, that is the reason why we observed that majority of hypermethylated markers have low frequencies in the ovarian tumors. Instead, it is possible that stratifying the tumor samples and selecting subtype-specific markers could have been more successful. It would not be surprising if the lack of ovarian cancer early detection and/or prognostic markers stems 95 from this common practice in research. This practice makes validation studies- particularly- difficult, even though many candidate markers are currently available. It should be noted, that the proposed strategy of subtype-specific studies comes with a specific challenge: For any clinical marker its positive predictive value (PPV), which is the fraction of positive results that are true positives, should justify early detection benefits against the risks and costs associated with false positives. Because of the low incidence of ovarian cancer, a screening test must be highly specific, especially since a false positive might result in invasive procedures (laparotomy or laparoscopy) and death. For an early detection ovarian cancer marker a PPV of at least 10% was suggested [81]. This means that in the US with the annual incidence rate of 35 per 100,000 in woman over age 50, a test must have 99.7% specificity at 80% sensitivity [129]. Obviously, the specificity to select each subtype, due to lower incidences, would increase. This is a challenge in developing subtype-specific screening markers, and might have been a contributing reason that most researchers grouped all the subtypes together. However, unstratified analyses of ovarian subtypes may undermine important clinical and biological findings. This observation that some subtypes of ovarian cancer have more methylation than others (our data and other studies) suggests that the sensitivity of DNA methylation markers may not be uniform for all these subtypes. Perhaps, for each subtype, a different screening method could be tailored, such as combining different DNA-based biomarkers with serum protein markers and/ or imaging. If the risks and costs associated with a false positive test are reduced, markers with lower PPV might still be useful. Obviously, for rare cases of ovarian carcinoma such studies are more 96 challenging and would require extensive collaboration among multiple cohorts where samples are expertly evaluated, stored and processed. Next, using DNA methylation data, we aimed to identify ovarian cancer- associated markers, which could potentially serve as blood-based or proximal fluid-based biomarkers for sensitive detection of primary or recurrent disease. Two of several remaining challenges of this research are determining the best screening medium and discovering markers that can detect cancer at an early stage. Several studies, including the present work as described in chapter 4, have focused on the diagnostic application of the circulating DNA in the serum/plasma. It has been shown that in cancer patients the concentration of circulating DNA in serum is much higher when compared with healthy controls [103]. Furthermore, this concentration was reported to be significantly elevated in patients with metastases as compared with nonmetastatic disease [103]. However, there are many unanswered questions regarding the biology of circulating DNA including, but not limited to, the production and clearance mechanisms. The mechanism leading to the presence of free-floating tumor DNA in the serum/plasma of cancer patients and, for that matter, other bodily fluids are not fully understood. Proposed hypotheses include active secretion [159] lysis of circulating tumor cells [154], and direct leakage during cellular necrosis or apoptosis [82]. The possibility of active DNA secretion stems from in vitro studies of cultured cell lines that have been shown to release newly synthesized DNA [159]. The next hypothesis is based on the premise that the circulating tumor cells in micrometastasis undergo lysis and their DNA is release into the bloodstream. While possible, it is hard to imagine that such mechanism 97 is the sole source of tumor DNA in the serum/plasma. Tens of thousands of circulating tumor cells in the bloodstream should undergo lysis to account for the average 150-200 ng/mL of free DNA in the serum/plasma of cancer patients. The study by Cristofanilli et. al. [40] that shows minuscule number of circulating tumor cells, further challenges this hypothesis. The involvement of cell death, via apoptosis or necrosis, has been supported by several studies. It was shown that tumor DNA in the plasma of cancer patients exists in fragments that are multiples of 180 bp [82]. This is similar to DNA degradation by caspase-activated DNase in apoptosis. In trauma patients presenting tissue injury [98, 109], plasma DNA amounts was shown to be elevated, and the increase was shown to be correlated to the severity of the injury. In another study on placental tissue, apoptosis induced by oxidative stress lead to free fetal DNA amounts in plasma [176]. While these studies support the release of DNA into the bloodstream following cellular death, it is still unclear whether necrosis or apoptosis is the responsible mechanism in the tumor cell death. Furthermore, the presence and the amount of tumor-DNA in blood could be cancer-dependent. For example, in the case of intestinal or colorectal cancers, located in the active sites of absorption in the body, tumor DNA may be found at earlier stages and at higher concentrations compared to ovarian cancers that are located in the peritoneal cavity. Besides the mechanisms of tumor DNA release into the serum/plasma, the clearance mechanisms are also not fully understood. It was reported that plasma nucleases do not play an important role in the degradation [110]. Other potential mechanisms are via hepatic and/ or renal clearance. One study showed the presence of tumor and fetal DNA in the urine samples of cancer patients and pregnant women, 98 respectively [25]. Whatever the mechanism, evidence supports a rapid clearance of circulating DNA [110, 180]. Such basic understandings determine whether blood-based biomarkers could hold a promise in early detection of cancer. Understanding the nature of tumor DNA molecule in the blood (from active release, necrosis, or apoptosis) may affect assay design to increase detection sensitivity. Urine samples, or other bodily fluids (e.g. vaginal washes) may show greater potentials for ovarian cancer screening. Another challenge is in the development of markers that can detect ovarian cancer at an early stage. The promise of early detection is to detect cancers at an early stage where therapeutic intervention is useful and result in reducing mortality. This means, an ideal ovarian cancer marker should be able to detect cancer prior to development of clinical symptoms. To select such marker(s), ideally, one should use samples from patients diagnosed at early stages without any clinical symptoms. As discussed earlier, majority of ovarian cancer patients are diagnosed at late stages, because earlier stages do not show any clear symptoms. This has been a limiting factor in obtaining adequate number of samples at early stages, both for marker detection and for understanding the natural history of the disease. In our study, majority of the tumors (for which the histopathological data was available to us) were collected from patients of higher stages who had, most likely, presented with clinical symptoms. While some abnormal tumor-specific DNA methylation may be early events in tumorigenesis, tumors may accumulate more abnormal DNA methylation either as byproducts or for selective advantages. Therefore, markers that are found to be hypermethylated in late stages might be cancer-specific but not useful for early stage diagnosis. When interpreting data using 99 these cancer-specific markers, for example in comparing the pooled control sera and pooled patient sera, it is important to realize that even the limited success of our markers, was in distinguishing controls from patients who already presented with clinical symptoms. Many validation studies using early stage tumors are required in order to find markers that can detect cancer in patients without clinical symptoms. This is expected to remain a challenge considering the current limited success of markers in discriminating even between patients and controls. It is possible that despite the efforts in optimizing marker discovery strategies, advancements in understanding tumor DNA release and clearance, and selection of the best remote media, DNA methylation markers alone would not hold a promise for early detection of ovarian cancer. This remains to be evaluated. 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HUGO gene name Reaction No. HB No. Assay Tissue type Approach BC# 8_ C(t) BC# 6_ C(t) FOXE1 FOXE1-M1 HB- 417 Methylation based Primary tumors Candidate gene 50.0 37.1 CLDN5 CLDN5-M1 HB- 415 Methylation based Primary tumors Candidate gene 50.0 50.0 UQCRH UQCRH-M1 HB- 224 Methylation based Primary tumors Candidate gene 50.0 50.0 SCGB3A1 SCGB3A1-M1 HB- 194 Methylation based Primary tumors Candidate gene 42.7 43.3 PYCARD PYCARD-M1 HB- 228 Methylation based Primary tumors Candidate gene & Experimantal screens 50.0 50.0 KL KL-M1 HB- 175 Methylation based Primary tumors Candidate gene 50.0 50.0 RUNX3 RUNX3-M1 HB- 181 Methylation based Primary tumors Candidate gene 50.0 50.0 MGMT2 MGMT2-M2 HB- 160 Methylation based Primary tumors Candidate gene 39.3 47.2 GPR88 GPR88-M1 HB- 391 Methylation based Primary tumors Candidate gene 49.1 43.1 DRD1 DRD1-M1 HB- 252 Methylation based Primary tumors Candidate gene 41.2 39.1 HCK HCK-M1 HB- 519 Methylation based Primary tumors Experimantal screens 50.0 50.0 TJP2 TJP2-M1 HB- 418 Methylation based Primary tumors Candidate gene 50.0 50.0 LAMB1 LAMB1-M1 HB- 393 Methylation based Primary tumors Candidate gene 38.3 37.2 SFRP4 SFRP4 HB- 281 Methylation based Primary tumors Candidate gene 45.7 37.1 GDNF GDNF-M1 HB- 221 Methylation based Primary tumors Candidate gene 39.2 37.2 DRD DRD-M1 HB- 253 Methylation based Primary tumors Candidate gene 40.1 36.1 PGR PGR-M1 HB- 149 Methylation based Primary tumors Candidate gene 50.0 39.2 DKFZp667BO210 DKFZp667BO210- M1 HB- 229 Methylation based Primary tumors Candidate gene 47.5 37.1 TNFRSF10C TNFRSF10C HB- 308 Methylation based Primary tumors Candidate gene 50.0 50.0 HOXA10 HOXA10-M2 HB- 271 Methylation based Primary tumors Candidate gene 50.0 40.2 GATM GATM-M1 HB- 401 Methylation based Primary tumors Candidate gene 50.0 39.1 TERT TERT HB- 074 Methylation based Primary tumors Candidate gene 50.0 50.0 EPM2AIP1 EPM2AIP1-M1 HB- 152 Methylation based Primary tumors Candidate gene 50.0 50.0 RARRES1 RARRES1 HB- 322 Methylation based Primary tumors Candidate gene 50.0 36.2 TIMP3 TIMP3-M1 HB- 167 Methylation based Primary tumors Candidate gene 38.1 35.1 DAPK DAPK-M1 HB- 046 Methylation based Primary tumors Candidate gene 39.2 36.0 SORBS3 SORBS3-M1 HB- 064 Methylation based Primary tumors Candidate gene 50.0 50.0 CYP1B1 CYP1B1-M1 HB- 078 Methylation based Primary tumors Candidate gene 50.0 38.2 122 SLC6A20 SLC6A20- HB- 079 Methylation based Primary tumors Candidate gene 50.0 39.0 BCL2 BCL2-M1 HB- 140 Methylation based Primary tumors Candidate gene 50.0 50.0 TSHR TSHR-M1 HB- 141 Methylation based Primary tumors Candidate gene 50.0 50.0 MLH1 MLH1-M2 HB- 150 Methylation based Primary tumors Candidate gene 50.0 50.0 CTNNB1 CTNNB1-M1 HB- 170 Methylation based Primary tumors Candidate gene 50.0 50.0 TP73 TP73-M1 HB- 177 Methylation based Primary tumors Candidate gene 50.0 50.0 RBP1 RBP1 HB- 185 Methylation based Primary tumors Candidate gene 47.1 50.0 MT3 MT3-M1 HB- 207 Methylation based Primary tumors Candidate gene 38.2 38.1 TITF1 TITF1-M1 HB- 213 Methylation based Primary tumors Candidate gene 37.2 35.2 DLC1 DLC1-M1 HB- 218 Methylation based Primary tumors Candidate gene 38.1 35.1 NEUROG1 NEUROG1-M1 HB- 261 Methylation based Primary tumors Candidate gene 47.7 38.1 CLDN7 CLDN7-M1 HB- 303 Methylation based Primary tumors Candidate gene 50.0 50.0 TNFRSF10D TNFRSF10D HB- 309 Methylation based Primary tumors Candidate gene 50.0 50.0 SMAD9 SMAD9 HB- 315 Methylation based Primary tumors Candidate gene 50.0 50.0 ITGA4 ITGA4-M1 HB- 321 Methylation based Primary tumors Candidate gene 50.0 50.0 GATA3 GATA3-M1 HB- 327 Methylation based Primary tumors Candidate gene 50.0 39.1 BNIP3 BNIP3-M1 HB- 363 Methylation based Primary tumors Candidate gene 50.0 43.1 ECH1 ECH1-M1 HB- 402 Methylation based Primary tumors Candidate gene 50.0 50.0 WNT7A WNT7A-M1 HB- 421 Methylation based Primary tumors Candidate gene 36.2 37.2 CDKN1B CDKN1B-M1 HB- 455 Methylation based Primary tumors Candidate gene 50.0 50.0 NR3C NR3C-M11 HB- 067 Methylation based Primary tumors Candidate gene 50.0 50.0 CCND1 CCND1-M1 HB- 146 Methylation based Primary tumors Candidate gene 50.0 50.0 PTEN PTEN-M1 HB- 157 Methylation based Primary tumors Candidate gene 50.0 50.0 CACNA1G CACNA1G-M1 HB- 158 Methylation based Primary tumors Candidate gene 50.0 50.0 STK11 STK11-M1 HB- 182 Methylation based Primary tumors Candidate gene 50.0 50.0 ARF/CDKN2A ARF/CDKN2A- M1 HB- 196 Methylation based Primary tumors Candidate gene 50 50 CHFR CHFR-M1 HB- 190 Methylation based Primary tumors & Cell lines Candidate gene & Experimantal screens 42.1 50.0 DPH1 DPH1-M1 HB- 049 Methylation based Primary tumors Candidate gene 39.7 50.0 CAV1 CAV1-M1 HB- 469 Expression based Cell lines Experimantal screens 50.0 50.0 AQP3 AQP3-M1 HB- 527 Methylation based Cell lines Experimantal screens 50.0 50.0 HOXA1 HOXA1 HB- 268 Methylation based Primary tumors Candidate gene 39.6 50.0 HLA-F HLA-F-M1 HB- 518 Methylation based Primary tumors Experimantal screens 39.1 39.1 BRAC1 BRAC1-M1 HB- 045 Methylation based Primary tumors Candidate gene 39.2 36.0 123 CDH13 CDH13-M1 HB- 075 Methylation based Primary tumors Candidate gene 50.0 35.1 TWIST1 TWIST1-M1 HB- 047 Methylation based Primary tumors Candidate gene 38.1 35.1 MLH3 MLH3-M1 HB- 099 Methylation based Primary tumors Candidate gene 50.0 42.2 ESR2 ESR2 HB- 165 Methylation based Primary tumors Candidate gene 38.2 35.1 GRIN2B GRIN2B HB- 250 Methylation based Primary tumors Candidate gene 40.1 36.1 IGF2 IGF2-M2 HB- 319 Methylation based Primary tumors Candidate gene 50.0 36.1 TFPI2 TFPI2-M1 HB- 361 Methylation based Primary tumors Candidate gene 46.5 40.2 IGFBP7 IGFBP7-M1 HB- 523 Expression based Cell lines Experimental screens 43.0 36.1 CYP24A1 CYP24A1-M1 HB- 525 Expression based Cell lines Experimental screens 38.0 35.1 RASSF1 RASSF1-M1 HB- 044 Methylation based Primary tumors Candidate gene & Experimental screens 50.0 50.0 ASCL2 ASCL2-M1 HB- 530 Methylation based Primary tumors Experimental screens 39.1 36.4 DCC DCC-M1 HB- 178 Methylation based Primary tumors Candidate gene 38.2 36.1 2C64/ BC031882 2C64/ BC031882- M1 HB- 395 Methylation based Primary tumors Candidate gene 37.0 36.7 GSTP1 GSTP1-M1 HB- 172 Methylation based Primary tumors Candidate gene 50.0 48.6 CXCR4 CXCR4-M1 HB- 362 Methylation based Primary tumors Candidate gene 46.4 44.2 CDH1 CDH1-M2 HB- 050 Methylation based Primary tumors Candidate gene 39.3 37.1 ALPL ALPL-M1 HB- 536 Methylation based Primary tumors Experimental screens 36.0 36.0 GP1BB GP1BB-M1 HB- 398 Methylation based Primary tumors Candidate gene 36.6 37.1 SOCS1 SOCS1-M1 HB- 042 Methylation based Primary tumors Candidate gene 45.2 50.0 APC APC HB- 153 Methylation based Primary tumors Candidate gene 50.0 37.5 MYOD1 MYOD1-M1 HB- 154 Methylation based Primary tumors Candidate gene 37.6 35.2 IGSF4 IGSF4-M1 HB- 069 Methylation based Primary tumors Candidate gene 39.1 37.1 HSD17B4 HSD17B4-M1 HB- 066 Methylation based Primary tumors Candidate gene 36.6 50.0 CDH1 CDH1-M1 HB- 171 Methylation based Primary tumors Candidate gene 39.2 35.1 MINT32 MINT32-M1 HB- 364 Methylation based Primary tumors Candidate gene 37.2 36.1 RTEL1 RTEL1-M1 HB- 511 Expression based Cell lines Experimantal screens 39.1 40.1 MINT31 MINT31-M1 HB- 162 Methylation based Primary tumors Candidate gene 40.3 38.3 PITX2 PITX2-M2 HB- 235 Methylation based Primary tumors & Cell lines Candidate gene & Experimental screens 46.6 39.2 DLEC1 DLEC1 HB- 225 Methylation based Primary tumors Candidate gene 48.3 42.3 CDKN2A CDKN2A-M2 HB- 081 Methylation based Primary tumors Candidate gene 50.0 39.6 PTPM6 PTPM6-M4 HB- 350 Methylation based Primary tumors Candidate gene 40.1 37.4 ONECUT2 ONECUT2-M1 HB- 242 Methylation based Primary tumors Candidate gene 38.1 36.2 124 ESR1 ESR1-M1 HB- 164 Methylation based Primary tumors Candidate gene 41.6 36.2 TNFRSF25 TNFRSF25-M1 HB- 080 Methylation based Primary tumors Candidate gene 50.0 48.3 CDKN1C CDKN1C-M2 HB- 329 Methylation based Primary tumors Candidate gene 42.1 37.1 SEZ6L SEZ6L-M1 HB- 184 Methylation based Primary tumors Candidate gene 38.2 36.1 HOXA11 HOXA11-M1 HB- 272 Methylation based Primary tumors Candidate gene 36.7 35.0 CCND2 CCND2-M1 HB- 040 Methylation based Primary tumors Candidate gene 50.0 50.0 PSAT1 PSAT1-M1 HB- 231 Methylation based Primary tumors Candidate gene 50.0 50.0 LDLR LDLR-M1 HB- 219 Methylation based Primary tumors Candidate gene 35.7 36.7 VDR VDR-M1 HB- 068 Methylation based Primary tumors Candidate gene 36.3 36.6 SHARBEY SHARBEY-M1 HB- 389 Methylation based Primary tumors Candidate gene 50.0 33.1 COMP COMP-M2 HB- 406 Methylation based Primary tumors Candidate gene 36.1 33.1 MT1G MT1G HB- 204 Methylation based Primary tumors Candidate gene 36.6 34.1 CDX2 CDX2-M1 HB- 353 Methylation based Primary tumors Candidate gene 38.1 34.2 ONECUT2 ONECUT2-M3 HB- 446 Methylation based Primary tumors Candidate gene 36.2 33.0 CRABP1 CRABP1-M1 HB- 197 Methylation based Primary tumors Candidate gene 38.2 34.0 THRB THRB-M1 HB- 216 Methylation based Primary tumors Candidate gene 38.5 33.4 CDH3 CDH3-M1 HB- 422 Methylation based Primary tumors Candidate gene 36.4 34.2 DIO3 DIO3-M1 HB- 494 Methylation based Primary tumors Experimental screens 41.1 33.2 HNT HNT-M1 HB- 410 Methylation based Primary tumors Candidate gene 35.1 30.2 OPCML OPCML-M2 HB- 409 Methylation based Primary tumors Candidate gene 36.2 32.2 CXCL1 CXCL1-M1 HB- 509 Expression based Cell lines Experimental screens 41.1 34.2 SFRP1 SFRP1-M1 HB- 201 Methylation based Primary tumors Candidate gene 37.3 32.1 CD81 CD81-M1 HB- 537 Methylation based Primary tumors Experimental screens 37.0 34.0 LHX1 LHX1-M1 HB- 414 Methylation based Primary tumors Candidate gene 36.6 33.3 DIO3OS DIO3OS-M1 HB- 496 Methylation based Primary tumors Experimental screens 35.5 32.0 NEFL NEFL-M2 HB- 528 Methylation based Primary tumors Experimental screens 37.1 33.9 HS3ST2 HS3ST2-M1 HB- 517 Methylation based Primary tumors Experimental screens 40.1 34.6 COMP COMP-M1 HB- 405 Methylation based Primary tumors Candidate gene 36.1 34.1 SPARC SPARC-M1 HB- 419 Methylation based Primary tumors Candidate gene 42.7 33.2 CXCL3 CXCL3-M1 HB- 524 Expression based Cell lines Experimental screens 36.1 32.3 SFRP2 SFRP2-M2 HB- 280 Methylation based Primary tumors Candidate gene 39.1 34.2 TFAP2A TFAP2A-M1 HB- 314 Methylation based Primary tumors Candidate gene 43.4 34.2 MINT2 MINT2-M1 HB- 187 Methylation based Primary tumors Candidate gene 39.2 34.5 BDNF BDNF-M2 HB- 258 Methylation based Primary tumors Candidate gene 50.0 33.6 125 SOX1 SOX1-M1 HB- 513 Methylation based Primary tumors Experimental screens 39.2 32.9 HIC1 HIC1-M1 HB- 168 Methylation based Primary tumors Candidate gene 36.0 34.2 NEUROD1 NEUROD1-M1 HB- 259 Methylation based Primary tumors Candidate gene 50.0 34.2 SFRP5 SFRP5 HB- 282 Methylation based Primary tumors Candidate gene 37.1 34.5 GATA5 GATA5-M1 HB- 326 Methylation based Primary tumors Candidate gene 35.7 32.2 2C35 2C35-M1 HB- 394 Methylation based Primary tumors Candidate gene 39.2 32.3 HOXA9 HOXA9-M1 HB- 516 Methylation based Primary tumors Experimental screens 39.3 34.1 GATA4 GATA4-M1 HB- 323 Methylation based Primary tumors Candidate gene 36.2 34.1 PAX6 PAX6-M1 HB- 533 Methylation based Cell lines Experimental screens 37.1 34.1 DNAJC15 DNAJC15-M1 HB- 048 Methylation based Primary tumors & Cell lines Candidate gene & Experimental screens 36.4 31.3 MOS MOS-M1 HB- 515 Methylation based Primary tumors Experimental screens 37.1 34.1 TMEFF2 TMEFF2-M1 HB- 274 Methylation based Primary tumors & Cell lines Candidate gene & Experimental screens 35.0 33.1 PENK PENK-M1 HB- 163 Methylation based Primary tumors Candidate gene 35.0 30.2 CALCA CALCA-M1 HB- 166 Methylation based Primary tumors Candidate gene 35.1 33.1 GABRA2- GABRA2-M1 HB- 254 Methylation based Primary tumors Candidate gene 36.2 33.9 NEUROD1 NEUROD1-M1 HB- 260 Methylation based Primary tumors Candidate gene 35.5 31.5 LOC375323 LOC375323-M1 HB- 390 Methylation based Primary tumors Candidate gene 42.6 33.5 UCHL1 UCHL1-M1 HB- 512 Expression based Cell lines Experimental screens 36.1 31.1 PGR PGR-M2 HB- 169 Methylation based Primary tumors Candidate gene 35.0 34.1 PTGS2 PTGS2-M1 HB- 065 Methylation based Primary tumors Candidate gene 35.2 33.1 RARB RARB-M1 HB- 176 Methylation based Primary tumors Candidate gene 39.9 33.5 SOX1 SOX1-M2 HB- 514 Methylation based Primary tumors Experimental screens 35.1 32.1 FGF8 FGF8-M1 HB- 532 Methylation based Cell lines Experimental screens 35.0 31.0 COL1A2 COL1A2-M1 HB- 193 Methylation based Primary tumors Candidate gene 36.1 31.8 SYK SYK-M2 HB- 241 Methylation based Primary tumors Candidate gene & Experimental screens 45.3 34.5 GAD1 GAD1-M2 HB- 256 Methylation based Primary tumors Candidate gene 36.1 31.6 PTPRN2 PTPRN2-M1 HB- 392 Methylation based Primary tumors Candidate gene 42.6 33.2 DIRAS3 DIRAS3-M1B HB- 043 Methylation based Primary tumors Candidate gene 27.1 27.1 ABCB1 ABCB1-M1B HB- 051 Methylation based Primary tumors Candidate gene 27.8 29.1 S100A2 S100A2-M1B HB- 061 Methylation based Primary tumors Candidate gene 33.3 33.5 LTB4R LTB4R-M1B HB- Methylation Primary Candidate 32.3 31.1 126 070 based tumors gene RNR1 RNR1-M1B HB- 071 Methylation based Primary tumors Candidate gene 19.3 21.2 ICAM1 ICAM1-M1B HB- 076 Methylation based Primary tumors Candidate gene 28.1 28.2 TFF1 TFF1-M1B HB- 145 Methylation based Primary tumors Candidate gene 29.3 29.2 MINT1 MINT1-M1B HB- 161 Methylation based Primary tumors Candidate gene 31.4 32.1 SFN SFN-M1B HB- 174 Methylation based Primary tumors Candidate gene 25.1 25.3 SERPINB5 SERPINB5-M1B HB- 208 Methylation based Primary tumors Candidate gene 26.1 26.2 CYP27B1 CYP27B1-M1B HB- 223 Methylation based Primary tumors Candidate gene 32.1 31.1 CDKN2A CDKN2A-M3B HB- 269 Methylation based Primary tumors Candidate gene 34.1 31.1 GDF15 GDF15-M1B HB- 357 Methylation based Primary tumors Candidate gene 31.5 30.5 TCF21 TCF21-M1B HB- 359 Methylation based Primary tumors Candidate gene 30.4 28.3 RPRM RPRM-M1B HB- 416 Methylation based Primary tumors Candidate gene 34.1 30.1 NPTX2 NPTX2-M1B HB- 420 Methylation based Primary tumors Candidate gene 33.4 30.1 MAGEA1 MAGEA1-M1B HB- 423 Methylation based Primary tumors Candidate gene 25.8 26.6 MAEL MAEL-M1B HB- 510 Expression based Cell lines Experimental screens 27.4 28.1 TNFRSF6B TNFRSF6B-M1B HB- 508 Expression based Cell lines Experimental screens 33.0 34.5 REC8L1 REC8L1-M1B HB- 526 Expression based Cell lines Experimental screens 33.0 31.1 HOXA5 HOXA5-M1B HB- 531 Methylation based Cell lines Experimental screens 28.1 27.0 HHIP HHIP-M2B HB- 535 Methylation based Cell lines Experimental screens 32.0 32.2 SEPT9 SEPT9-M1M HB- 547 Methylation based Primary tumors Experimental screens 29.0 28.0 The assay, approach and tissue type that was used to select each of these markers are provided. The C(t) value of the two control buffy coat samples BC#6 and BC#8 are listed for each marker.
Abstract (if available)
Abstract
Epigenetics refers to the changes in gene expression that are not accounted for by the changes in DNA sequence. DNA methylation is one of the main epigenetic mechanisms in mammals. It contributes to various biological processes such as cellular differentiation, gametogenesis, and cancer. We investigated DNA methylation abnormalities in the male and female reproductive tract. During gametogenesis germ cells undergo epigenetic reprogramming, defects of which may lead to compromised spermatogenesis. Using MethyLight and Illumina GoldenGate assays we found a broad abnormal epigenetic defect associated with abnormal semen parameters. We propose that the underlying mechanism may be improper erasure of DNA methylation during germline reprogramming.
Linked assets
University of Southern California Dissertations and Theses
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Asset Metadata
Creator
Houshdaran, Sahar
(author)
Core Title
DNA methylation as a biomarker in human reproductive health and disease
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Biochemistry and Molecular Biology
Degree Conferral Date
2009-08
Publication Date
08/10/2011
Defense Date
03/25/2009
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
DNA methylation,epigenetics,male infertility,OAI-PMH Harvest,ovarian cancer,ovarian carcinoma,sperm epigenetics
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Laird, Peter W. (
committee chair
), Laird-Offringa, Ite A. (
committee member
), Shibata, Darryl K. (
committee member
)
Creator Email
houshdar@usc.edu,sahar.hooshdaran@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-m2560
Unique identifier
UC1457364
Identifier
etd-Houshdaran-2835 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-254394 (legacy record id),usctheses-m2560 (legacy record id)
Legacy Identifier
etd-Houshdaran-2835.pdf
Dmrecord
254394
Document Type
Dissertation
Rights
Houshdaran, Sahar
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Repository Name
Libraries, University of Southern California
Repository Location
Los Angeles, California
Repository Email
cisadmin@lib.usc.edu
Tags
DNA methylation
epigenetics
male infertility
ovarian cancer
ovarian carcinoma
sperm epigenetics