Grant Number:	1R01CA200854-01 Interpret this number
Primary Investigator:	Doherty, Jennifer
Organization:	Dartmouth College
Project Title:	Characterizing Molecular Subtypes of Ovarian Cancer in African-American Women
Fiscal Year:	2016

¿ DESCRIPTION (provided by applicant): Epithelial ovarian cancer (EOC) accounts for 5% of malignancies and while it is the eighth most common cancer in US women, it is the fifth leading cause of cancer deaths. It is now considered not as a single disease, but rather as a diverse group of tumors with distinct origins, histotypes, mutation profiles, protein and gene expression profiles, and prognosis. Over 75% of EOC is high-grade serous (HGSC), accounting for 90% of disease-specific mortality. Systematic molecular characterization of HGSC by the Cancer Genome Atlas (TCGA) and others has demonstrated robust gene expression subtypes of HGSC. HGSC subtypes have been studied almost exclusively in individuals of European ancestry, and it is thus unknown whether these or other subtypes exist in populations with different genetic backgrounds and exposures. Indeed five-year relative survival from EOC is only 36.4% for African American (AA) women, compared to 44.3% for European American (EA) women. For breast cancer, aggressive subtypes are more common among AA women than EA women, and this partially explains survival differences. It is possible that aggressive subtypes of HGSC are overrepresented in AA women, but analogous research in EOC is in its infancy because it is one ninth as common as breast cancer, and molecular subtypes have only recently been identified. The African American Cancer Epidemiology Study (AACES) is an ongoing multi-site population-based study which will ultimately include 850 AA women with newly diagnosed EOC and their age-matched controls. This study will examine socioeconomic, epidemiologic, genetic and clinical factors and ovarian cancer risk and survival. As the only epidemiologic study of EOC in AA women to date, it provides a rich resource to examine molecular features of this disease. For 300 AACES participants, we propose to generate whole transcriptome data from their formalin-fixed paraffin-embedded HGSC ovarian tumors and exome sequencing data from these tumors and corresponding peripheral blood to: determine gene expression-based, and mutation-based, subtypes of HGSC tumors in AA women, compare the relative frequencies of subtypes in AA and EA populations, and evaluate whether subtype-specific survival differs by population; and characterize similarities and differences in gene expression patterns and mutational spectra across each of the molecular subtypes in AA and EA women to define key subtype-specific and population-specific pathways. The results from this study will provide key information to understand survival differences in AA women, and will more precisely define molecular subtypes allowing for future functional characterization and therapeutic development.




Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms.
Authors: Davidson N.R. , Barnard M.E. , Hippen A.A. , Campbell A. , Johnson C.E. , Way G.P. , Dalley B.K. , Berchuck A. , Salas L.A. , Peres L.C. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2024-05-23 00:00:00.0; , .
EPub date: 2024-05-23 00:00:00.0.
PMID: 38780898
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Molecular subtypes of high grade serous ovarian cancer across racial groups and gene expression platforms.
Authors: Davidson N.R. , Barnard M.E. , Hippen A.A. , Campbell A. , Johnson C. , Way G.P. , Dalley B.K. , Berchuck A. , Salas L.A. , Peres L.C. , et al. .
Source: Biorxiv : The Preprint Server For Biology, 2023-11-04 00:00:00.0; , .
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Source: International Journal Of Cancer, 2019-08-30 00:00:00.0; , .
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