SNPs that are associated with a small but statistically significant increased risk of developing breast cancer do not substantially improve the accuracy of existing risk-prediction models, according to an NCI researcher.
"Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy," remarks Mitchell Gail, M.D., Ph.D.
Traditionally information such as age and family history has been used to build risk models. An ideal model would provide much higher risk estimates for women who eventually develop breast cancer than for women who do not, a feature called discriminatory accuracy.
Scientists recently identified seven SNPs that are associated with a moderate increase in the risk of breast cancer. Hence, Dr. Gail decided to compare the discriminatory accuracy of these seven SNPs to that of an established risk model, called the Breast Cancer Risk Assessment Tool (BCRAT). He also tested whether the addition of the SNPs to the existing model could improve its accuracy. BCRAT uses a woman's age, age at menarche and at birth of first child, family history of breast cancer, and breast biopsy results to predict her risk.
Dr. Gail found that the seven SNPs had less discriminatory accuracy than the BCRAT model. When he added the seven characterized SNPs to BCRAT, the discriminatory accuracy of the model improved modestly. The increase, however, was less than would be gained by adding information on breast density, which is also associated with breast cancer risk.
These results were published in the July 8 online issue of the Journal of the National Cancer Institute.