Risk prediction for epithelial ovarian cancer in 11 United States-based case-control studies: Incorporation of epidemiologic risk factors and 17 confirmed genetic loci Journal Article
| Authors: | Clyde, M. A.; Weber, R. P.; 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.; Edwards, R. P.; Fridley, B. L.; Goode, E. L.; Lurie, G.; McGuire, V.; Modugno, F.; Moysich, K. B.; Olson, S. H.; Pearce, C. L.; Pike, M. C.; Rothstein, J. H.; Sellers, T. A.; Sieh, W.; Stram, D.; Thompson, P. J.; Vierkant, R. A.; Wicklund, K. G.; Wu, A. H.; Ziogas, A.; Tworoger, S. S.; Schildkraut, J. M.; on behalf of the Ovarian Cancer Association Consortium |
| Article Title: | 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: | ovarian cancer; breast-cancer; women; models; metaanalysis; variants; replacement therapy; susceptibility locus; single-nucleotide polymorphisms; menopausal hormone-therapy; tubal-ligation; risk model; genetic risk polymorphisms; model evaluation |
| Journal Title: | American Journal of Epidemiology |
| Volume: | 184 |
| Issue: | 8 |
| ISSN: | 0002-9262 |
| Publisher: | Oxford University Press |
| Date Published: | 2016-10-15 |
| Start Page: | 555 |
| End Page: | 569 |
| Language: | English |
| ACCESSION: | WOS:000386552900001 |
| DOI: | 10.1093/aje/kww091 |
| PROVIDER: | wos |
| PMCID: | PMC5065620 |
| PUBMED: | 27698005 |
| Notes: | Article -- Source: Wos |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work