Adding seven SNPs previously associated with breast cancer to the Breast Cancer Risk Assessment Tool (BCRAT) provided only a small improvement in accuracy, according to Mitchell H. Gail, M.D., Ph.D., of the division of cancer epidemiology and genetics, NCI.
Dr. Gail published his results in the June 17 advance access issue of the Journal of the National Cancer Institute. The paper is titled “Value of Adding Single-Nucleotide Polymorphism Genotypes to a Breast Cancer Risk Model.”
The seven SNPs were located in FGFR2, TNRC9, MAP3K1 , LSP1 , CASP8 , chromosomal region 8q, and chromosomal region 2q35. BCRAT is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations.
Dr. Gail compared the performance of BCRAT with BCRATplus7 (the standard tool plus the seven SNPs) in the following applications: deciding which women could benefit from tamoxifen to prevent breast cancer, deciding which should have a mammogram, assessing the extent of reclassification of breast cancer risk, and allocating access to screening mammograms under cost constraints.
Gail found that for all applications, the value added with BCRATplus7 compared with BCRAT was very small. 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%.
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.
“The gains from BCRATplus7 are small in the applications examined,” Dr. Gail notes. “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.”
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