Identifying factors associated with ovarian cancer recurrence using a population-based approach

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Ovarian cancer is the fifth leading cause of cancer related mortality in the United States. Despite advances in surgical approaches and treatment regimens, overall survival has improved only marginally over the past thirty years. Although nearly 80% of ovarian cancer patients will achieve complete clinical remission through surgery and systemic therapy at their initial diagnosis, more than 50% will experience a recurrence by five years after diagnosis. However, little is known about factors contributing to risk of ovarian cancer recurrence. Ovarian cancer is a heterogeneous disease with distinct histotypes that inform prognosis. High grade serous carcinoma is the most common histotype, comprising ~70% of all ovarian cancer diagnoses. Recently, three robust gene expression signatures have been developed that have the potential to inform patient prognosis and biomarker- driven therapeutic approaches. These tumor gene expression signatures include: a) the Milstein prognostic score that distinguishes individuals with high and low probability of survival; b) the PrOTYPE classifier, which categorizes four biologic subtypes; and c) the Oxford classifier, which identifies a poor prognosis epithelial-to- mesenchymal transition score. Each signature correlates to differential with survival, suggesting that the signatures may have clinical utility in informing patient prognosis; however, the scores have yet to be evaluated in a population-based setting. Thus, the overarching goal of this proposal is to understand patient demographic, clinicopathologic, and molecular features associated with patterns of ovarian cancer recurrence and mortality. To do this, I will leverage the robust resources through the Utah Population Database to achieve the following study aims: (1) Characterize patterns of ovarian recurrence and mortality by patient and clinicopathologic characteristics; and (2) Compare the performance of three prognostic tumor gene expression signatures with (a) mortality and (b) recurrence among high-grade serous ovarian cancer patients. The primary training experience will focus on three areas: first, to develop expertise in the development and validation of an algorithm to identify recurrence using multiple data streams; second, to develop expertise in transcriptomics and data analysis pipelines for gene expression profiling; and third, to foster professional and career development through leadership, scientific communication, and then transitioning to independence. The research and training will be supported by an interdisciplinary mentorship team led by Dr. Jennifer Doherty, and comprised of experts in ovarian cancer and genetic epidemiology, computational biology, and biostatistics. The results from these aims will expand our understanding of factors contributing to risk and timing of ovarian cancer recurrence and provide evidence on how gene expression signatures of high-grade serous ovarian cancer can be incorporated into clinical risk assessment. Cumulatively, information gleaned from this work could lead to a personalized approach to ovarian cancer disease management through inclusion of prognostic markers in clinical care and the development of biomarker-driven therapies.