Gauging the Performance of SNPs, Biomarkers, and Clinical Factors for Predicting Risk of Breast Cancer

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Abstract
Predicting risk of cancer for individuals has long been a goal of medical research. If an individual's risk could be predicted, then prevention and screening modalities could be targeted toward those at meaningfully high risk. This approach is not only more cost efficient than targeting the whole population but also more ethical, at least when interventions are burdensome to the individual. The quest for risk predictors has been revitalized with the emergence of technologies that measure genetic information and other molecular and physiological attributes of the individual. In this issue of the Journal, Gail ( 1 ) asks to what extent newly discovered associations between seven single-nucleotide polymorphisms (SNPs) and incidence of breast cancer can improve assessment of breast cancer risk. Comparisons are made with models that employ standard clinical factors to evaluate the incremental value of the SNPs for prediction over the standard clinical information. Using estimated relative risks and allele frequencies, Gail finds that the SNPs are expected to have a small effect on the capacity of prediction models to distinguish women who will and will not develop breast cancer. Because he assumes best-case scenarios, his results probably provide upper limits on expected increments in risk prediction with SNPs. He postulates that many more SNPs with these levels of association with breast cancer will need to be discovered to substantially improve risk prediction. Gail's arguments demonstrate that the sample sizes needed to discover an adequate number of SNPs will need to be very large indeed. Although his calculations are based on many assumptions, they provide a good place from which to start the discussion about what types of markers and studies will be needed to make progress in this field.