Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model

Abstract

It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35 to 59 years and 463 controls frequency matched for age and for whom genotyping data was available. Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95% confidence interval [CI]=0.55–0.61) for BCRAT alone to 0.61 (95% CI=0.58–0.64) for BCRAT and SNP data combined (p<0.001). For women aged 35 to 39 years at interview, the corresponding improvement in AUC was from 0.61 (95% CI=0.56–0.66) to 0.65 (95% CI=0.60–0.70; p=0.03), while for women aged 40 to 49 years at diagnosis, the AUC improved from 0.61 (95% CI=0.55–0.66) to 0.63 (95% CI=0.57–0.69; p=0.04). Using previously used classifications of low, intermediate and high risk, 2.1% of cases and none of the controls aged 35 to 39 years, and 10.9% of cases and 4.0% of controls aged 40 to 49 years were classified into a higher risk group. Including information on seven SNPs associated with breast cancer risk improves the discriminatory accuracy of BCRAT for women aged 35 to 39 years and 40 to 49 years. Given the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry

“…However, given the key issue we are addressing is how much the addition of the SNP score increases the predictive ability of the models, this should not induce a substantive bias in our estimates of increased predictive performance. We used the approach of multiplying the SNP-based score by the risk factor based on a clinical model so as to be able to compare our findings with those of previous studies (19, 20). We also fitted models along the traditional line in which the SNP-based score and the (virtually independent) risk factor are separate entities (data not shown).…”

“…Adjusted risk values (with a population average risk equal to 1) were calculated as 1/ μ , OR/ μ and OR 2 / μ for the three genotypes defined by number of risk alleles (0, 1, or 2). The overall SNP-based risk score was then calculated by multiplying the adjusted risk values for each of the 77 SNPs (19). …”

“…Modelling and empirical studies have quantified how much the area under the receiver operating characteristic curve (AUC) for BCRAT can be increased by including 7 SNPs (19–21) or 10 SNPs (22). Other analyses have shown how the classification of women into high-risk groups is improved by including 7 SNPs (19, 20, 23) or 15 SNPs (24) to BCRAT.…”

“…Other analyses have shown how the classification of women into high-risk groups is improved by including 7 SNPs (19, 20, 23) or 15 SNPs (24) to BCRAT. Analyses of 18 SNPs (25) and simulations of 67 SNPs (25, 26) have demonstrated improvement in the ability of IBIS to discriminate women at high risk of breast cancer, while the addition of 76 SNPs and a measure of mammographic density has been shown to increase the AUC for the Breast Cancer Surveillance consortium risk prediction model (27).…”

“…We previously investigated combining the BCRAT risk prediction model with a risk score based on 7 SNPs (19). We now investigate the extent to which risk estimates obtained from BOADICEA, BRCAPRO, BCRAT, and IBIS can be improved, in terms of their calibration and discriminatory accuracy, by combination with a risk score based on the previously identified 77 SNPs (18).…”