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

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

Standard

Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction. / Van Veen, Elke; Brentnall, Adam R.; Byers, Helen; Harkness, Elaine; Astley, Susan; Sampson, Sarah; Howell, Anthony; Newman, William; Cuzick, Jack; Evans, Dafydd.

In: JAMA oncology, Vol. 4, No. 4, 18.01.2018, p. 476-482.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{05aae4abd34e4b8f84260b12c7e36a1c,
title = "Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction",
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 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. ",
keywords = "breast cancer, risk prediction, SNP, mammographic density, Tyrer-Cuzick",
author = "{Van Veen}, Elke and Brentnall, {Adam R.} and Helen Byers and Elaine Harkness and Susan Astley and Sarah Sampson and Anthony Howell and William Newman and Jack Cuzick and Dafydd Evans",
note = "Funding Information: Published Online: January 18, 2018. doi:10.1001/jamaoncol.2017.4881 Author Affiliations: Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, England (van Veen, Byers, Newman, Evans); Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, England (Brentnall, Cuzick); Prevention Breast Cancer Centre and Nightingale Breast Screening Centre, Manchester University Hospital Foundation Trust, Manchester, England (Harkness, Astley, Sampson, Howell, Evans); Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, England (Harkness, Astley); Manchester Academic Health Science Centre, University of Manchester, Manchester, England (Harkness, Astley); Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, England (Astley, Howell, Newman, Evans); The Christie NHS Foundation Trust, Manchester, United Kingdom (Howell, Evans); Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom (Newman, Evans). Funding Information: Funding/Support: This work was supported by Prevent Breast Cancer (GA09-002 and GA11-002) and the National Institute for Health Research (NF-SI-0513-10076 to D.G.R.E.). Publisher Copyright: {\textcopyright} 2018 American Medical Association. All rights reserved. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.",
year = "2018",
month = jan,
day = "18",
doi = "10.1001/jamaoncol.2017.4881",
language = "English",
volume = "4",
pages = "476--482",
journal = "JAMA oncology",
issn = "2374-2437",
publisher = "American Medical Association",
number = "4",

}

RIS

TY - JOUR

T1 - Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

AU - Van Veen, Elke

AU - Brentnall, Adam R.

AU - Byers, Helen

AU - Harkness, Elaine

AU - Astley, Susan

AU - Sampson, Sarah

AU - Howell, Anthony

AU - Newman, William

AU - Cuzick, Jack

AU - Evans, Dafydd

N1 - Funding Information: Published Online: January 18, 2018. doi:10.1001/jamaoncol.2017.4881 Author Affiliations: Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, England (van Veen, Byers, Newman, Evans); Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, England (Brentnall, Cuzick); Prevention Breast Cancer Centre and Nightingale Breast Screening Centre, Manchester University Hospital Foundation Trust, Manchester, England (Harkness, Astley, Sampson, Howell, Evans); Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, England (Harkness, Astley); Manchester Academic Health Science Centre, University of Manchester, Manchester, England (Harkness, Astley); Manchester Breast Centre, Manchester Cancer Research Centre, University of Manchester, Manchester, England (Astley, Howell, Newman, Evans); The Christie NHS Foundation Trust, Manchester, United Kingdom (Howell, Evans); Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom (Newman, Evans). Funding Information: Funding/Support: This work was supported by Prevent Breast Cancer (GA09-002 and GA11-002) and the National Institute for Health Research (NF-SI-0513-10076 to D.G.R.E.). Publisher Copyright: © 2018 American Medical Association. All rights reserved. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2018/1/18

Y1 - 2018/1/18

N2 - 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.

AB - 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.

KW - breast cancer

KW - risk prediction

KW - SNP

KW - mammographic density

KW - Tyrer-Cuzick

UR - http://www.scopus.com/inward/record.url?scp=85046491446&partnerID=8YFLogxK

U2 - 10.1001/jamaoncol.2017.4881

DO - 10.1001/jamaoncol.2017.4881

M3 - Article

C2 - 29346471

VL - 4

SP - 476

EP - 482

JO - JAMA oncology

JF - JAMA oncology

SN - 2374-2437

IS - 4

ER -