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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.
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.

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