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Development of the Ovarian Cancer Cohort Consortium: Risk Factor Associations by Heterogeneity of Disease

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Annual rept. 30 Sep 2013-29 Sep 2014

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The objective of this application is to study the etiologic heterogeneity of ovarian cancer in multiple cohorts and to build the infrastructure of the Ovarian Cancer Cohort Consortium OC3, an international consortium of cohort studies, to address scientific aims important for understanding ovarian cancer risk, early detection, and tumor heterogeneity that are only feasible in a consortium setting. Specifically we will examine associations of risk factors with invasive ovarian cancer, including but not limited to age, OCs, tubal ligation, parity, postmenopausal hormone use, family history of ovarian cancer, BMI, height, analgesic use, and lifetime ovulatory cycles, differ by histologic subtype, tumor dominance as a surrogate for cell of origin, and tumor aggressiveness tumors fatal within three years vs. all others. Then we will determine if risk prediction models for ovarian cancer can be improved by accounting for differential associations by cancer phenotype. In addition, the proposed efforts will create an infrastructure with a core dataset of important variables for ovarian cancer epidemiology that will be available for future efforts to study ovarian cancer risk, including projects that will use prospectively collected biological specimens. Currently, 23 cohorts have agreed to participate in the OC3. We have executed data use agreements between the Brigham and Women s Hospital data coordinating center with all studies. We have received data from 20 cohorts, with 3 cohorts actively preparing data. Data harmonization is complete for the cohorts for which we have received data. Analyses of primary ovarian cancer risk factors e.g., oral contraceptive use, parity by histology are complete and a manuscript is drafted and sent to co-authors. Preliminary analyses are on-going for the manuscript examining risk factors by tumor aggressiveness and for the development of a baseline risk prediction model. Several projects have been funded to utilize this resourc

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  • Medicine and Medical Research

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