Functional assessment of genes and common genetic risk variants in ovarian cancer

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PROJECT SUMMARY (See instructions): 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.