Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

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Abstract

Importance

Single-nucleotide polymorphisms (SNPs) have demonstrated an association with breast cancer susceptibility, but there is limited evidence on how to incorporate them into current breast cancer risk prediction models.

Objective

To determine whether a panel of 18 SNPs (SNP18) may be used to predict breast cancer in combination with classic risk factors and mammographic density.

Design, Setting, and Participants

This cohort study enrolled a subcohort of 9363 women, aged 46 to 73 years, without a previous breast cancer diagnosis from the larger prospective cohort of the PROCAS study (Predicting Risk of Cancer at Screening) specifically to evaluate breast cancer risk-assessment methods. Enrollment took place from October 2009 through June 2015 from multiple population-based screening centers in Greater Manchester, England. Follow-up continued through January 5, 2017.

Exposures

Genotyping of 18 SNPs, visual-assessment percentage mammographic density, and classic risk assessed by the Tyrer-Cuzick risk model from a self-completed questionnaire at cohort entry.

Main Outcomes and Measures

The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per interquartile range of the predicted risk.

Results

A total of 9363 women were enrolled in this study (mean [range] age, 59 [46-73] years). Of these, 466 were found to have breast cancer (271 prevalent; 195 incident). SNP18 was similarly predictive when unadjusted or adjusted for mammographic density and classic factors (odds ratios per interquartile range, respectively, 1.56; 95% CI, 1.38-1.77 and 1.53; 95% CI, 1.35-1.74), with observed risks being very close to expected (adjusted observed-to-expected odds ratio, 0.98; 95% CI, 0.69-1.28). A combined risk assessment indicated 18% of the subcohort to be at 5% or greater 10-year risk, compared with 30% of all cancers, 35% of interval-detected cancers, and 42% of stage 2+ cancers. In contrast, 33% of the subcohort were at less than 2% risk but accounted for only 18%, 17%, and 15% of the total, interval, and stage 2+ breast cancers, respectively.

Conclusions and Relevance

SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density. A combined risk is likely to aid risk-stratified screening and prevention strategies.

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