Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies

abstract:
Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

 Abbreviations: AUC, area under the curve; COGS, Collaborative Oncological Gene-Environment Study; EOC, epithelial ovarian cancer; GWAS, genome-wide association study; MCMC, Markov chain Monte Carlo; MHT, menopausal hormone therapy; OC, oral contraceptive; OCAC, Ovarian Cancer Association Consortium; ROC, receiver operating characteristics; SNP, single nucleotide polymorphism.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. ....... The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.