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

Elke Van Veen, Adam R. Brentnall, Helen Byers, Elaine Harkness, Susan Astley, Sarah Sampson, Anthony Howell, William Newman, Jack Cuzick, Dafydd Evans

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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 classical risk factors and mammographic density. Design: A case-cohort study within a prospective cohort, set up specifically to evaluate breast cancer risk assessment methods for women attending population-based screening. Setting: Recruitment from multiple screening centres in Greater Manchester, UK. Participants: Women aged 46-73 years attending the national program for breast screening, without a previous breast cancer diagnosis, were recruited between 10/2009-06/2015 with follow-up to 01/2017. 466 cases (prevalent=271; incident=195) were included, and a sub-cohort of 8897 women. Exposures: Genotyping of 18 SNPs, visually-assessment percentage mammographic density and classical risk assessed by the Tyrer-Cuzick risk model from a self-completed questionnaire at cohort entry. Main Outcome and Measure: The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per inter-quartile range of the predicted risk. Results: SNP18 was similarly predictive when unadjusted or adjusted for mammographic density and classical factors (odds ratio per inter-quartile 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 sub-cohort to be at ≥5% 10-year risk, compared with 30% of all, 35% of interval-detected and 42% of stage 2+ cancers, respectively. In contrast, 33% of the sub-cohort were at <2% risk but accounted for only 18%, 17% and 15% of the total, interval and stage 2+ breast cancers, respectively. Conclusions and Relevance: SNP18 adds 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.
Original languageEnglish
Pages (from-to)476-482
Number of pages7
JournalJAMA oncology
Issue number4
Publication statusPublished - 18 Jan 2018


  • breast cancer
  • risk prediction
  • SNP
  • mammographic density
  • Tyrer-Cuzick


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