Modeling Risk of Breast Cancer and Decisions about Genetic Testing

Abstract

Recent advances in the understanding of genetic susceptibility to breast cancer, notably iden-tiication of the BRCA1 and BRCA2 genes, and the advent of genetic testing, raise important questions for clinicians, patients and policy makers. Answers to many of these questions hinge on accurate assessment of the risk of breast cancer. In particular, it is important to predict genetic susceptibility based on easy-to-collect data about family history of breast and related cancers, to predict risk of developing cancer based on both family history and additional well recognized risk factors, and to use such predictions to provide women facing testing and preventive treatment decisions with relevant, individualized information. In this paper we give an overview of several speciic research projects that together address these goals. These studies are being carried out within the context of two interdisciplinary research programs at Duke University: the Specialized Program of Research Excellence (SPORE) in breast cancer and the Cancer Prevention Research Unit (CPRU) on improving risk communication. In both programs we found Bayesian modeling useful in addressing speciic scientiic questions. We illustrate this in detail and convey how the Bayesian paradigm provides a framework for modeling the concerns and substantive knowledge of the diverse elds involved in this research. These elds include clinical oncology, human genetics, epidemiology, medical decision making, and patient counseling.