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

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

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)555-569
Number of pages15
JournalAmerican journal of epidemiology
Volume184
Issue number8
DOIs
StatePublished - Oct 15 2016

Keywords

  • Genetic risk polymorphisms
  • model evaluation
  • ovarian cancer
  • risk model

ASJC Scopus subject areas

  • Epidemiology

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    Clyde, M. A., Palmieri Weber, R., Iversen, E. S., Poole, E. M., Doherty, J. A., Goodman, M. T., Ness, R. B., Risch, H. A., Rossing, M. A., Terry, K. L., Wentzensen, N., Whittemore, A. S., Anton-Culver, H., Bandera, E. V., Berchuck, A., Carney, M. E., Cramer, D. W., Cunningham, J. M., Cushing-Haugen, K. L., ... Schildkraut, J. M. (2016). Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. American journal of epidemiology, 184(8), 555-569. https://doi.org/10.1093/aje/kww091