Abstract CN07-03: Discriminatory accuracy from Single Nucleotide Polymorphisms in models of absolute breast cancer risk

Abstract

Abstract CN07-03 One purpose for seeking single nucleotide polymorphisms (SNPs) that are associated with disease is to improve models for projecting individualized disease risk. Seven SNPs have recently been confirmed to be associated with breast cancer. I used published estimates of relative risks and allele frequencies to calculate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574 ) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), which is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven SNPs improve the discriminatory accuracy of BCRAT only modestly. I used other criteria to compare BCRAT with a model that added the seven SNPs to BCRAT. These criteria included the proportion of cases in the portion of the population with the highest 90% of risk, the expected losses when using risk models to decide which women should be given a mammogram, and the expected losses when using a risk model to decide which women should take tamoxifen to prevent breast cancer. The seven SNPs provided very little improvement over BCRAT for any of these applications. Hundreds of SNPs would be required to achieve high discriminatory accuracy. Citation Information: Cancer Prev Res 2008;1(7 Suppl):CN07-03.