Breast cancer risk prediction using clinical models and 77 independent risk-associated SNPs for women aged under 50 years: Australian Breast Cancer Family Registry.

Dite GS1, MacInnis RJ1, Bickerstaffe A1, Dowty JG1, Allman R2, Apicella C1, Milne RL3, Tsimiklis H4, Phillips KA5, Giles GG3, Terry MB6, Southey MC4, Hopper JL7.

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Abstract

BACKGROUND:

The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility single nucleotide polymorphisms (SNPs) is not known.

METHODS:

Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the odds ratio per adjusted standard deviation for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement.

RESULTS:

The odds ratios for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding odds ratios were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score.

CONCLUSIONS:

By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%.

IMPACT:

Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.