Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement

Hatef Darabi, Kamila Czene, Wanting Zhao, Jianjun Liu, Per Hall, Keith Humphreys, Hatef Darabi, Kamila Czene, Wanting Zhao, Jianjun Liu, Per Hall, Keith Humphreys

Abstract

Introduction: Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.

Methods: We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.

Results: The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.

Conclusions: Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.

Figures

Figure 1
Figure 1
Distributions of estimated absolute risk by case-control status using the Swe-Gail model and the full model (with displayed proportions of women with five-year absolute risks greater than (multiples of 2.5%).
Figure 2
Figure 2
Observed versus predicted proportions of cases for deciles of risk score for the Swe-Gail model and the full model.
Figure 3
Figure 3
Proportion of breast cancer cases explained by the proportion of the population at highest risk of the disease, for the Swe-Gail model and the full model.

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