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.

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Description

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Citation

Published Version (Please cite this version)

10.1093/aje/kww091

Publication Info

Clyde, Merlise A, Rachel Palmieri Weber, Edwin S Iversen, Elizabeth M Poole, Jennifer A Doherty, Marc T Goodman, Roberta B Ness, Harvey A Risch, et al. (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. Am J Epidemiol, 184(8). pp. 579–589. 10.1093/aje/kww091 Retrieved from https://hdl.handle.net/10161/12934.

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Scholars@Duke

Clyde

Merlise Clyde

Professor of Statistical Science

Model uncertainty and choice in prediction and variable selection problems for linear, generalized linear models and multivariate models. Bayesian Model Averaging. Prior distributions for model selection and model averaging. Wavelets and adaptive kernel non-parametric function estimation. Spatial statistics. Experimental design for nonlinear models. Applications in proteomics, bioinformatics, astro-statistics, air pollution and health effects, and environmental sciences.

Iversen

Edwin Severin Iversen

Research Professor of Statistical Science

Bayesian statistical modeling with application to problems in genetic
epidemiology and cancer research; models for epidemiological risk
assessment, including hierarchical methods for combining related
epidemiological studies; ascertainment corrections for high risk
family data; analysis of high-throughput genomic data sets.

Berchuck

Andrew Berchuck

James M. Ingram Distinguished Professor of Gynecologic Oncology

Dr. Andrew Berchuck is Director of the Duke Division of Gynecologic Oncology and holds the James M. Ingram Distinguished Professorship. He is a practicing oncologist who is actively involved in the surgical and chemotherapy management of women with ovarian, endometrial and lower genital tract cancers. This includes minimally invasive laparoscopic surgical approaches. He also has developed a research program that focuses on the molecular-genetic alterations involved in malignant transformation of the ovarian and endometrial epithelium. He has published over 300 peer-reviewed papers in these areas. The objectives of his research include 1) identification of ovarian cancer susceptibility polymorphisms through a population-based case-control molecular epidemiologic study, and 2) use of genomic approaches  to elucidate the molecular heterogenetity of ovarian cancer. Thirty fellows and residents have worked in his lab, several of whom are now funded investigators. His research efforts have been recognized nationally and he has received awards for best oral presentation at the annual meetings of both the Society of Gynecologic Oncology and the International Gynecologic Cancer Society. Dr. Berchuck was awarded the Barbara Thomason Ovarian Cancer Research Professorship by the American Cancer Society in 2006. He has served as editor of several books in the field including Principles and Practice of Gynecologic Oncology. Dr. Berchuck also has a major commitment to national activities, and was President of the Society of Gynecologic Oncology in 2008. He served as chair of the scientific advisory committee of the Ovarian Cancer Research Fund (http://www.ocrf.org) in New York City. Finally, he is also head of the steering committee of the international Ovarian Cancer Association Consortium (OCAC), a group of 50 case-control studies that are working together to identify ovarian cancer susceptibility polymorphisms (www.srl.cam.ac.uk/consortia/ocac/index.html).

Schildkraut

Joellen Martha Schildkraut

Professor Emeritus in Family Medicine and Community Health

Dr. Schildkraut is an epidemiologist whose research includes the molecular epidemiology of ovarian, breast and brain cancers. Dr. Schildkraut's research interests include the study of the interaction between genetic and environmental factors. She is currently involved in a large study of genome wide association and ovarian cancer risk and survival. Some of her work is also focused on particular genetic pathways including the DNA repair and apoptosis pathways. She currently leads a study of African American women diagnosed with ovarian cancer. She is also collaborating in a large a case-control study of meningioma risk factors and with which a genome wide association analysis is about to commence.


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