Genetics and breast cancer risk prediction--are we there yet?

Whether information on genetic variants can be used for better prediction of disease risk is of current clinical interest. Previous studies have shown that mutations in the BRCA1 and BRCA2 genes are associated with high risk of breast cancer (1). Theoretical considerations suggest that further work will lead to the discovery of more genes that are involved in breast carcinogenesis (2). However, it requires more than proof of association to assess improvement in risk prediction by including genetic information. In this issue of the Journal, Mealiffe et al. (3) use data from the Women ' s Health Initiative Clinical Trial to provide additional support for including genetic information in risk prediction models for breast cancer. This study finds that adding a small number of single-nucleotide polymorphisms to the well-known and established Gail model can improve the prediction of breast cancer. The authors (3) indicate that previous studies that added single-nucleotide polymorphisms to the Gail model for breast cancer risk prediction suffer from methodologic al limitations or are merely theoretical in nature. What statistical methods are appropriate to use in comparing risk prediction models has been subject to much recent debate. Standard analyses comparing receiver operating characteristic curves have been criticized as being insensitive to meaningful changes in predicted risk and not well suited to prospective data (4 , 5). Mealiffe et al. (3) use newer methods based on reclassifi cation, or changes in risk strata, which have become relatively widespread in the clinical literature. A reclassifi cation table sorts the predicted probabilities from each risk prediction model into clinically relevant risk categories and tabulates one against the other. The extent and accuracy of any reclassifi cation, or change in risk category, indicate whether a model can generate improvements in risk stratifi cation, and ultimately, decisions regarding therapies. The authors compute the net reclas-sifi cation improvement (NRI), which compares the proportion of case patients appropriately moving up vs down a risk category with the corresponding proportions for control patients (6). There are several questions regarding how these methods perform and how they are affected by study design, some of which are addressed in this article (3). First, how should relevant risk categories be defi ned? It is known that reclassifi cation statistics, such as the percent reclassifi ed or the NRI, can vary according to how the risk strata are defi ned (7). For cardiovascular disease, an expert panel has established …