• Joellen M Schildkraut, Edwin S Iversen, Lucy Akushevich, Regina Whitaker, Rex C Bentley, Andrew Berchuck, and Jeffrey R Marks.
• Authors' Affiliations: Departments of Community and Family Medicine, Statistical Science, Obstetrics and Gynecology, Division of Gynecologic Oncology, Pathology, and Surgery, Duke Cancer Institute, and Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina.
• Cancer Epidemiol. Biomarkers Prev. 2013 Oct 1; 22 (10): 1709-21.

BackgroundSix gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas (TCGA) project subsequently replicated these subtypes and identified a signature predictive of survival in high-grade serous (HGS) cancers. We previously validated these signatures for use in formalin-fixed paraffin-embedded tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease.MethodsWe modeled signature-specific tumor characteristics and epidemiologic risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer.ResultsWe observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (P = 0.0008) and race (P = 0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (P = 0.024) and first-degree family history of breast or ovarian cancer (P = 0.034) among the 106 HGS cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature.ConclusionsThese data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer.ImpactThis study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.

23 keywords...

hide…