Background: Adding genotypes from seven single-nucleotide polymorphisms (SNPs), which had previously been associated with breast cancer, to the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) increases the area under the receiver operating characteristic curve from 0.607 to 0.632.
Methods: Criteria that are based on four clinical or public health applications were used to compare BCRAT with BCRATplus7, which includes the seven genotypes. Criteria included number of expected life-threatening events for the decision to take tamoxifen, expected decision losses (in units of the loss from giving a mammogram to a woman without detectable breast cancer) for the decision to have a mammogram, rates of risk reclassification, and number of lives saved by risk-based allocation of screening mammography. For all calculations, the following assumptions were made: Hardy-Weinberg equilibrium, linkage equilibrium across SNPs, additive effects of alleles at each locus, no interactions on the logistic scale among SNPs or with factors in BCRAT, and independence of SNPs from factors in BCRAT.
Results: Improvements in expected numbers of life-threatening events were only 0.07% and 0.81% for deciding whether to take tamoxifen to prevent breast cancer for women aged 50-59 and 40-49 years, respectively. For deciding whether to recommend screening mammograms to women aged 50-54 years, the reduction in expected losses was 0.86% if the ideal breast cancer prevalence threshold for recommending mammography was that of women aged 50-54 years. Cross-classification of risks indicated that some women classified by BCRAT would have different classifications with BCRATplus7, which might be useful if BCRATplus7 was well calibrated. Improvements from BCRATplus7 were small for risk-based allocation of mammograms under costs constraints.
Conclusions: The gains from BCRATplus7 are small in the applications examined. Models with SNPs, such as BCRATplus7, have not been validated for calibration in independent cohort data. Additional studies are needed to validate a model with SNPs and justify its use.