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

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Elke Van Veen
  • Adam R. Brentnall
  • Sarah Sampson
  • Jack Cuzick
  • Dafydd Evans


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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)476-482
Number of pages7
JournalJAMA oncology
Issue number4
Publication statusPublished - 18 Jan 2018

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