Classification of rare missense variants in disease susceptibility genes as neutral or disease-causing is important for genetic counseling. Different criteria are used to help classify such variants in BRCA1 and BRCA2; however, the strongest evidence tends to come from segregation analysis and observed cooccurrence with known pathogenic mutations, which both require information that is not readily available in most circumstances. A likelihood-based model has been developed, integrating most of the data currently used to classify these variants. We have adapted the original model, including only that information that could be more easily obtained from a cancer genetics laboratory, such as loss of heterozygosity (LOH), grade, and immunohistochemical analysis to assess estrogen receptor (ER) status for the tumors of carriers of these variants. We also considered summary family history (personal or first-degree family history of bilateral breast or ovarian cancer), which was not incorporated into the original model. To test the ability of the modified model to classify missense variants in BRCA1, we analyzed 17 variants, of which 10 have previously been classified as pathogenic mutations or neutral polymorphisms. We also included a prior step consisting of the screening of the variants among 1,000 controls, with which we were able to classify five as neutral, based solely on their observed frequency. We found that combining this relatively easily collected information can be sufficient to classify variants as pathogenic or neutral if tumors from at least three carriers of the same variant can be collected and analyzed.
2007 Wiley-Liss, Inc.