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

Abstract

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. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). 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.

Keywords: genetic risk polymorphisms; model evaluation; ovarian cancer; risk model.

Figure 1.

Invasive epithelial ovarian cancer relative risk prediction model from the Ovarian Cancer Association Consortium, 1992–2010. Receiver operating characteristic curve for models A) without and B) with single nucleotide polymorphisms (SNPs). The receiver operating characteristic (ROC) curve plots the true positive fraction (i.e., sensitivity) versus the false positive fraction (i.e., 1-specificity) at various threshold settings. The ROC curve in A represents the relative risk prediction model containing age, study site, and 17 risk factors; the ROC curve in B represents the full relative risk prediction model containing the variables in A plus 17 confirmed genetic susceptibility variants. For each model, 3 ROC curves are presented for women grouped by age: all ages, women younger than 50 years of age, and women 50 years of age or older. The area under the curve, a measure of discriminatory power equivalent to the C statistic in binary models, is presented for each ROC curve. A fourth hypothetical target ROC curve is depicted based on adding additional hypothetical SNPs with a minor allele frequency of 0.20 and log odds ratio of 0.15 (similar to the current data) until the area under the curve is 0.75 or more; on average, 58 additional SNPs would be needed (95% confidence interval: 39, 79).

Figure 2.

Calibration plots for risk scores from the invasive epithelial ovarian cancer relative risk prediction model from the Ovarian Cancer Association Consortium, 1992–2010. The calibration plot represents the agreement between the average predicted probability of epithelial ovarian cancer (i.e., risk score) and observed outcomes (i.e., relative frequency of cases) in the full risk prediction model containing age, study site, 17 risk factors, and 17 confirmed genetic susceptibility variants for women included in the analysis. Women were divided into 10 bins determined by increasing risk (0.10 long). The vertical and horizontal bars reflect uncertainty in the average predicted risk and mean under a Bernoulli model, respectively.