Incremental impact of breast cancer SNP panel on risk classification and screening.

Abstract

1522 Background: Accurate breast cancer risk prediction could improve the performance of breast cancer screening. However, current models are limited, particularly among minorities. Multiple single nucleotide polymorphisms (SNPs) associated with breast cancer have been validated in multiple studies. While each SNP confers a relatively small increase in risk, a multiplicative allelic risk model generates relative risk estimates ranging from 0.45 to 3.77, which appear independent of other risk factors. Methods: Using a prospective cohort study, standard risk information and buccal swab specimen are being collected at the point of screening mammography. A 12 SNP panel was performed by deCODE Genetics. Minor allele frequencies were compared to reported frequencies and the total genetic risk ratio was calculated using reported risk ratios assuming independence. 5 year and lifetime risks incorporating SNPs were calculated by multiplying estimated Gail risk by genotype risk ratio. Concordance between the Gail model and the Gail+SNP model in identifying high risk women (5 year risk > 1.7, lifetime risk > 20) was measured using the kappa statistic. Results: SNP results were available for 601 women (39% AA, 61% W). Mean genetic risk ratios were 1.07 in W and 1.31 in AA women. High risk allele frequencies for W women were the same as previously reported for 8 SNPs and higher than previously reported for 4 SNPs. For AA women, high risk allele frequencies were higher in 8 SNPs, lower in 3 SNPs, and the same for 1 SNP. There was relatively low agreement between the Gail model and Gail +SNP model (kappa of 0.53 for 5-year risk and 0.435 for lifetime risk). Addition of SNP information had the greatest effect among AA women, with 12% of AA women identified as 5 year high risk by Gail alone, but 35% by Gail +SNPs, and 0.4% of AA women identified as lifetime high risk by Gail alone but 7% by Gail + SNPs. Conclusions: The addition of SNP information to traditional breast cancer risk prediction models reclassifies the high risk status of some women undergoing screening mammography, particularly among AA women. Further research is needed to determine the clinical validity and utility of this reclassification in breast cancer risk prediction, screening and prevention.