211701 Value of adding single nucleotide polymorphism data to a model that predicts breast cancer risk

Tuesday, November 10, 2009: 4:30 PM

Mitchell Gail, MD, PhD , Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
Eleven single nucleotide polymorphisms (SNPs) have recently been confirmed to be associated with breast cancer. I assessed the value of adding these SNPs to the 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. The model with these SNPs (BCRATplus11) had an area under the receiver operating characteristic curve (AUC) of 0.637, compared to 0.607 for BCRAT. This improvement is less than from adding mammographic density to BCRAT. I also assessed how much BCRATplus11 reduced expected losses in deciding whether a woman should take tamoxifen to prevent breast cancer and in deciding whether a woman should have a mammogram. In addition, I examined whether BCRATplus11 was more effective than BCRAT in allocating a scarce public health resource, such as access to mammography, based on ranking women on their breast cancer risk and allocating the resource to those at highest risk. In none of these applications did BCRATplus11 perform substantially better than BCRAT. I conclude that the available SNPs do not improve the performance of models to estimate breast cancer risk enough to warrant their use outside the research setting.

Learning Objectives:
To discuss the usefulness of genetics markers for predicting disease risk

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have an MD and a PhD in statistics and have worked as a medical statistician and epidemiologist at the National Cancer Institute since 1972. I am an expert in modeling the absolute risk of disease, and my model is the basis of the National Cancer Institute’s Breast Cancer Risk Assessment Tool, which is widely used for counseling women and designing prevention studies, such as the STAR trial. I have become familiar with studies of single nucleotide polymorphisms, which are an area of emphasis in the Division of Cancer Epidemiology and Genetics, where I work. I have recently written three peer reviewed papers on the topic above, namely: Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst. 2008 Jul 16;100(14):1037-41. Epub 2008 Jul 8. Gail, MH. Applying the Lorenz curve to disease risk to optimize health benefits under cost constraints. Statistics and Its Interface 2009; 2:117-121 Gail, MH. Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst. 2009;101:959-963
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.