N.2: Project 2 Ovarian cancer is a lethal gynecologic malignancy and its development is poorly understood. We have performed four independent genome wide association studies (GWAS) in ovarian cancer and have identified highly significant, replicated single nucleotide polymorphisms (SNPs) associated with ovarian cancer risk. In Project 1, these GWAS will be combined to identify additional susceptibility loci and genetic variants associated with risk. Here we will evaluate the functional significance of candidate genes and SNPs at these loci.
The specific aims are as follows: 1. To evaluate the role of candidate genes at susceptibility loci in ovarian cancer. We will use bioinformatics tools to extract publicly available data describing a role for candidate genes in cancer. Next, we will assess differences in transcript/protein expression between ovarian cancer cell lines and primary tumours, and normal ovarian epithelia. Then we will determine whether candidate genes have acquired somatic genetic changes in primary ovarian cancers. 2. To determine the functional significance of candidate SNPs in the susceptibility regions. Bioinformatics tools will be employed to determine whether a SNP's DNA location can predict functional impact. We will also correlate SNP genotype and copy number variants (CNVs) with differential germline expression and methylation status. 3. To evaluate the role of candidate SNPs located distant from known Open Reading Frames. We expect several SNP associations to fall in """"""""gene deserts"""""""". Bioinformatics tools will be used to predict microRNAs or distant regulatory regions, and to identify conserved elements. We will look for functional evidence of regulatory elements correlated with SNP location using chromatin immunoprecipitation and sequencing analysis (ChlP-Seq). 4. To perform detailed functional characterization of candidate genes and SNPs. We will evaluate the biological significance of candidate genes using three-dimensional culture models of ovarian cancers and normal ovaries. We will modulate their expression using cDNA or shRNA expression mediated by lentlviral transduction. Bioinformatics predictions of SNP function will be tested using specific functional assays that will depend on the nature of the candidate gene and SNP. This will include mobility shift DNA binding, reporter and DNAse I hypersensitivity assays. The knowledge gained from this large collaborative study will significantly contribute to our understanding of the functional rationale underlying genetic susceptibility and survival in women diagnosed with ovarian cancer.

Public Health Relevance

Determining the functional mechanism for genetic variants that cause ovarian cancer will improve our understanding of the underlying biology of the disease. This will also enhance the ability to identify women at greatest risk, and potentially lead to the development of more effective, individualized therapies. The studies may also inform the research community about the cellular origins of epithelial ovarian cancers, which remains an unresolved clinical and research question.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program--Cooperative Agreements (U19)
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Special Emphasis Panel (ZCA1-SRLB-4 (J1))
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H. Lee Moffitt Cancer Center & Research Institute
United States
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Lu, Yingchang; Beeghly-Fadiel, Alicia; Wu, Lang et al. (2018) A Transcriptome-Wide Association Study Among 97,898 Women to Identify Candidate Susceptibility Genes for Epithelial Ovarian Cancer Risk. Cancer Res 78:5419-5430
Chenevix-Trench, Georgia; Beesley, Jonathan; Pharoah, Paul D P et al. (2018) The importance of using public data to validate reported associations. PLoS Genet 14:e1007416
Painter, Jodie N; O'Mara, Tracy A; Morris, Andrew P et al. (2018) Genetic overlap between endometriosis and endometrial cancer: evidence from cross-disease genetic correlation and GWAS meta-analyses. Cancer Med 7:1978-1987
Mancuso, Nicholas; Gayther, Simon; Gusev, Alexander et al. (2018) Large-scale transcriptome-wide association study identifies new prostate cancer risk regions. Nat Commun 9:4079
Liu, Gang; Mukherjee, Bhramar; Lee, Seunggeun et al. (2018) Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. Am J Epidemiol 187:366-377
Yang, Yaohua; Wu, Lang; Shu, Xiang et al. (2018) Genetic data from nearly 63,000 women of European descent predicts DNA methylation biomarkers and epithelial ovarian cancer risk. Cancer Res :
Ong, Jue-Sheng; Hwang, Liang-Dar; Cuellar-Partida, Gabriel et al. (2018) Assessment of moderate coffee consumption and risk of epithelial ovarian cancer: a Mendelian randomization study. Int J Epidemiol 47:450-459
Zuber, Verena; J├Ânsson, Erik G; Frei, Oleksandr et al. (2018) Identification of shared genetic variants between schizophrenia and lung cancer. Sci Rep 8:674
Earp, Madalene; Tyrer, Jonathan P; Winham, Stacey J et al. (2018) Variants in genes encoding small GTPases and association with epithelial ovarian cancer susceptibility. PLoS One 13:e0197561
O'Mara, Tracy A; Glubb, Dylan M; Amant, Frederic et al. (2018) Identification of nine new susceptibility loci for endometrial cancer. Nat Commun 9:3166

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