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Grant Details

Grant Number: 4R01CA168758-04 Interpret this number
Primary Investigator: Rossing, Mary
Organization: Fred Hutchinson Cancer Research Center
Project Title: Epidemiologic Factors and Survival By Molecular Subtypes of Ovarian Cancer
Fiscal Year: 2016


Abstract

DESCRIPTION (provided by applicant): New research that improves prospects for prevention or treatment of ovarian cancer is essential to reduce the burden of this disease. Although only one-eighth as common as breast cancer, ovarian cancer accounts for a disproportionately large number of deaths, due to its typical presentation in an advanced stage with little chance for cure. Epithelial ovarian cancer is now considered not as a single disease, but rather as a diverse group of tumors with subtypes that can best be classified based on molecular genetic features. We will apply this model to assess the association of tumor subgroups with known or suspected ovarian cancer risk and preventive factors and with disease outcome. As much as 75% of epithelial ovarian cancer is now regarded as high-grade serous (HGSC), and accounts for 90% of disease mortality. This provides strong incentive to employ novel methods to identify and assess biologically relevant subgroups of HGSC. Identifying subtypes with true etiologic differences has important implications for prevention and for improved, targeted therapy. In the proposed study, we will follow up on intriguing findings of The Cancer Genome Atlas Research Network and related research that has identified four robust subtypes of HGSC based on patterns of mRNA expression. In two population-based studies of 2240 invasive ovarian cancer cases and 2900 controls (with detailed information on reproductive, lifestyle and medical histories, and on germline genetic variation), we propose to: 1. a. Classify tumors as HGSC, low-grade serous (LGSC), endometrioid (EC), clear cell (CCC) or mucinous (MC), using protein (IHC) and mRNA (NanoString) based classification schemes; b. Sub-classify HGSC into four robust and reproducible subgroups according to mRNA expression patterns, and describe the prevalence of each subtype in our population-based samples; 2. Examine whether associations with known or putative epidemiologic and genetic risk and protective factors differ by protein and mRNA expression subtype; 3. Examine whether survival differs by protein and mRNA expression subtype An integral strength of our approach is the examination of novel, molecularly-defined and biologically meaningful subtypes of epithelial ovarian cancer. We will use the NanoString nCounter platform, a highly sensitive and accurate multiplex assay, to directly measure mRNA expression levels from representative sections of formalin-fixed paraffin-embedded tumor specimens. Notably, we will examine epidemiologic differences across four subgroups of HGSC, which has not previously been done. Our findings can importantly influence the development of more effective strategies for disease prevention and treatment.



Publications

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).
Authors: Talhouk A. , George J. , Wang C. , Budden T. , Tan T.Z. , Chiu D.S. , Kommoss S. , Leong H.S. , Chen S. , Intermaggio M.P. , et al. .
Source: Clinical cancer research : an official journal of the American Association for Cancer Research, 2020-10-15; 26(20), p. 5411-5423.
EPub date: 2020-06-17.
PMID: 32554541
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Prognostic gene expression signature for high-grade serous ovarian cancer.
Authors: Millstein J. , Budden T. , Goode E.L. , Anglesio M.S. , Talhouk A. , Intermaggio M.P. , Leong H.S. , Chen S. , Elatre W. , Gilks B. , et al. .
Source: Annals of oncology : official journal of the European Society for Medical Oncology, 2020 09; 31(9), p. 1240-1250.
EPub date: 2020-05-28.
PMID: 32473302
Related Citations

Molecular Classification of Epithelial Ovarian Cancer Based on Methylation Profiling: Evidence for Survival Heterogeneity.
Authors: Bodelon C. , Killian J.K. , Sampson J.N. , Anderson W.F. , Matsuno R. , Brinton L.A. , Lissowska J. , Anglesio M.S. , Bowtell D.D.L. , Doherty J.A. , et al. .
Source: Clinical cancer research : an official journal of the American Association for Cancer Research, 2019-10-01; 25(19), p. 5937-5946.
EPub date: 2019-05-29.
PMID: 31142506
Related Citations

Invasive Epithelial Ovarian Cancer Survival by Histotype and Disease Stage.
Authors: Peres L.C. , Cushing-Haugen K.L. , Köbel M. , Harris H.R. , Berchuck A. , Rossing M.A. , Schildkraut J.M. , Doherty J.A. .
Source: Journal of the National Cancer Institute, 2019-01-01; 111(1), p. 60-68.
PMID: 29718305
Related Citations

Histotype classification of ovarian carcinoma: A comparison of approaches.
Authors: Peres L.C. , Cushing-Haugen K.L. , Anglesio M. , Wicklund K. , Bentley R. , Berchuck A. , Kelemen L.E. , Nazeran T.M. , Gilks C.B. , Harris H.R. , et al. .
Source: Gynecologic oncology, 2018 10; 151(1), p. 53-60.
EPub date: 2018-08-16.
PMID: 30121132
Related Citations

Association of p16 expression with prognosis varies across ovarian carcinoma histotypes: an Ovarian Tumor Tissue Analysis consortium study.
Authors: Rambau P.F. , Vierkant R.A. , Intermaggio M.P. , Kelemen L.E. , Goodman M.T. , Herpel E. , Pharoah P.D. , Kommoss S. , Jimenez-Linan M. , Karlan B.Y. , et al. .
Source: The journal of pathology. Clinical research, 2018 10; 4(4), p. 250-261.
EPub date: 2018-09-21.
PMID: 30062862
Related Citations

Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses.
Authors: Harrington L.X. , Way G.P. , Doherty J.A. , Greene C.S. .
Source: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018; 23, p. 157-167.
PMID: 29218878
Related Citations

Current Gaps in Ovarian Cancer Epidemiology: The Need for New Population-Based Research.
Authors: Epidemiology Working Group Steering Committee, Ovarian Cancer Association Consortium Members of the EWG SC, in alphabetical order: , Doherty J.A. , Jensen A. , Kelemen L.E. , Pearce C.L. , Poole E. , Schildkraut J.M. , Terry K.L. , Tworoger S.S. , Webb P.M. , et al. .
Source: Journal of the National Cancer Institute, 2017-10-01; 109(10), .
PMID: 29117355
Related Citations

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes.
Authors: Doherty J.A. , Peres L.C. , Wang C. , Way G.P. , Greene C.S. , Schildkraut J.M. .
Source: Current epidemiology reports, 2017 Sep; 4(3), p. 211-220.
EPub date: 2017-07-10.
PMID: 29226065
Related Citations

Epidemiologic paradigms for progress in ovarian cancer research.
Authors: Tworoger S.S. , Doherty J.A. .
Source: Cancer causes & control : CCC, 2017 05; 28(5), p. 361-364.
PMID: 28299511
Related Citations

Pelvic Inflammatory Disease and the Risk of Ovarian Cancer and Borderline Ovarian Tumors: A Pooled Analysis of 13 Case-Control Studies.
Authors: Rasmussen C.B. , Kjaer S.K. , Albieri V. , Bandera E.V. , Doherty J.A. , Høgdall E. , Webb P.M. , Jordan S.J. , Rossing M.A. , Wicklund K.G. , et al. .
Source: American journal of epidemiology, 2017-01-01; 185(1), p. 8-20.
EPub date: 2016-12-09.
PMID: 27941069
Related Citations

Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes.
Authors: Way G.P. , Rudd J. , Wang C. , Hamidi H. , Fridley B.L. , Konecny G.E. , Goode E.L. , Greene C.S. , Doherty J.A. .
Source: G3 (Bethesda, Md.), 2016-12-07; 6(12), p. 4097-4103.
EPub date: 2016-12-07.
PMID: 27729437
Related Citations

Assessment of variation in immunosuppressive pathway genes reveals TGFBR2 to be associated with risk of clear cell ovarian cancer.
Authors: Hampras S.S. , Sucheston-Campbell L.E. , Cannioto R. , Chang-Claude J. , Modugno F. , Dörk T. , Hillemanns P. , Preus L. , Knutson K.L. , Wallace P.K. , et al. .
Source: Oncotarget, 2016-10-25; 7(43), p. 69097-69110.
PMID: 27533245
Related Citations

Leveraging global gene expression patterns to predict expression of unmeasured genes.
Authors: Rudd J. , Zelaya R.A. , Demidenko E. , Goode E.L. , Greene C.S. , Doherty J.A. .
Source: BMC genomics, 2015-12-15; 16, p. 1065.
EPub date: 2015-12-15.
PMID: 26666289
Related Citations

Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.
Authors: Kar S.P. , Tyrer J.P. , Li Q. , Lawrenson K. , Aben K.K. , Anton-Culver H. , Antonenkova N. , Chenevix-Trench G. , Australian Cancer Study , Australian Ovarian Cancer Study Group , et al. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2015 Oct; 24(10), p. 1574-84.
EPub date: 2015-07-24.
PMID: 26209509
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