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Identifying Prognostic Markers and Therapeutic Targets for Serous Ovarian Cancer

Susan J Ramus

10 Collaborator(s)

Funding source

National Cancer Institute (NIH)
High-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and has very poor survival, as the tumors quickly become resistant to the current drug treatments. The Cancer Genome Atlas (TCGA) project has performed tumor profiling of ~500 HGSOC cases. From the expression analysis TCGA has recently identified a panel of genes that can be used to predict survival. Although the panel of genes validate in other studies many of the individual genes do not show consistent results across studies. To identify validated prognostic markers suitable to use as drug targets will require discovery in a larger set of tumors followed by validation in a very large series of samples. We have established the Ovarian Tumor Tissue Analysis (OTTA) consortium, an international collaboration of 30 studies with approximately 8,000 ovarian tumors and extensive clinical data. The majority of these tumors is from cases participating in the Ovarian Cancer Association Consortium (OCAC) and therefore have genotyping data on 200,000 single nucleotide polymorphisms (SNPs) and epidemiological data. We have identified SNPs that are associated with survival from our genome wide association study (GWAS) and preliminary data suggests that these germline changes may help to identify novel genes that may also be important somatic prognostic factors. We will utilize these existing datasets from TCGA, OTTA and OCAC to identify candidate prognostic genes and validate these genes in samples from OTTA. These biomarkers could be useful in the clinical setting for type of treatment and decision-making in recurrence and they will also provide a much-needed understanding of the biology of the disease. The specific aims of the proposal are: Aim 1: To identify candidate prognostic genes from a meta-analysis of expression data including subgroups of HGSOC and from an analysis of our survival GWAS. Aim 2: To perform expression analysis of 400 genes using the Nanostring platform on thousands of primary tumors to identify prognostic markers and to subtype the tumors. Aim 3: To reanalyze the survival GWAS data using the subgroups of the tumors and identify novel changes associated with survival. Aim 4: To test the identified genes and pathways in novel ovarian cancer models to determine if they could be used as targets for new drug treatments.

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