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

  • Author(s): Clyde, Merlise A;
  • Palmieri Weber, Rachel;
  • Iversen, Edwin S;
  • Poole, Elizabeth M;
  • Doherty, Jennifer A;
  • Goodman, Marc T;
  • Ness, Roberta B;
  • Risch, Harvey A;
  • Rossing, Mary Anne;
  • Terry, Kathryn L;
  • Wentzensen, Nicolas;
  • Whittemore, Alice S;
  • Anton-Culver, Hoda;
  • Bandera, Elisa V;
  • Berchuck, Andrew;
  • Carney, Michael E;
  • Cramer, Daniel W;
  • Cunningham, Julie M;
  • Cushing-Haugen, Kara L;
  • Edwards, Robert P;
  • Fridley, Brooke L;
  • Goode, Ellen L;
  • Lurie, Galina;
  • McGuire, Valerie;
  • Modugno, Francesmary;
  • Moysich, Kirsten B;
  • Olson, Sara H;
  • Pearce, Celeste Leigh;
  • Pike, Malcolm C;
  • Rothstein, Joseph H;
  • Sellers, Thomas A;
  • Sieh, Weiva;
  • Stram, Daniel;
  • Thompson, Pamela J;
  • Vierkant, Robert A;
  • Wicklund, Kristine G;
  • Wu, Anna H;
  • Ziogas, Argyrios;
  • Tworoger, Shelley S;
  • Schildkraut, Joellen M;
  • , on behalf of the Ovarian Cancer Association Consortium
  • et al.

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.

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