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Breast cancer risk prediction using a clinical risk model and polygenic risk score

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case–control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69–3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57–0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53–0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ≥3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS’s role in decision-making around screening and prevention strategies is merited.

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Abbreviations

AUROC:

Area under the receiver operating characteristic curve

BCSC:

Breast Cancer Surveillance Consortium

BIRADS:

Breast Imaging Reporting and Data System

BMI:

Body mass index

CCR:

California Cancer Registry

CPMC:

California Pacific Medical Center

GWAS:

Genome-wide association study

LR:

Likelihood ratio

OR:

Odds ratio

PRS:

Polygenic risk score

SFMR:

San Francisco Mammography Registry

SNPs:

Single nucleotide polymorphisms

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Acknowledgments

We are grateful to Sarah D. Sawyer, PhD for her assistance with the polygenic risk score. The collection of cancer data was supported in part by the California Cancer Registry. For a full description, please see: http://breastscreening.cancer.gov/work/acknowledgement.html. We thank the participating women, mammography facilities, and radiologists for the data they have provided for this study.

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Authors and Affiliations

  1. Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, Box 0320, 1545 Divisadero Street, San Francisco, CA, 94143, USA

    Yiwey Shieh, Donglei Hu, Scott Huntsman, Jeffrey A. Tice & Elad Ziv

  2. University of California, San Francisco, Box 1793, 550 16th Street, San Francisco, CA, 94158, USA

    Lin Ma

  3. Department of Economics, Applied Statistics, and International Business, New Mexico State University, MSC 3CQ, P.O. Box 30001, Las Cruces, NM, 88003, USA

    Charlotte C. Gard

  4. Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1350, Houston, TX, 77030, USA

    Jessica W. T. Leung

  5. Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, 200 First Street SW, Charlton Building 6-239, Rochester, MN, 55905, USA

    Celine M. Vachon

  6. San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Box 0560, 550 16th Street, 2nd Floor, San Francisco, CA, 94159, USA

    Steven R. Cummings

  7. Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA

    Karla Kerlikowske

  8. General Internal Medicine Section, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, Mailing Code 111A1, San Francisco, CA, 94121, USA

    Karla Kerlikowske

Authors
  1. Yiwey Shieh
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  2. Donglei Hu
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  3. Lin Ma
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  4. Scott Huntsman
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  5. Charlotte C. Gard
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  6. Jessica W. T. Leung
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  8. Celine M. Vachon
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  9. Steven R. Cummings
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  10. Karla Kerlikowske
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  11. Elad Ziv
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Corresponding author

Correspondence to Yiwey Shieh.

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Funding

K. Kerlikowske received support for the Breast Cancer Surveillance Consortium from the National Cancer Institute, grant P01 CA154292. E. Ziv received support from the National Cancer Institute under grant K24 CA169004. S.R. Cummings received support for the collection of blood specimens from the DaCosta Fund for the Prevention of Breast Cancer, the Clinical Research in Clinical Care (CRCLE) funds provided by the California Pacific Medical Center, and by a grant from the Eli Lilly Foundation. Y. Shieh was supported by a National Research Service Award through the National Institutes of Health T32 HP19025. Data collection for this work was supported by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Conflict of interest

Dr. Jessica Leung receives consultation fees from Hologic, Inc. related to the development of contrast-enhanced mammography.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Specifically, each registry in the Breast Cancer Surveillance Consortium and the Statistical Coordinating Center (SCC) have received institutional review board approval for either active or passive consenting processes or a waiver of consent to enroll participants, link data, and perform analytic studies. All procedures are Health Insurance Portability and Accountability Act (HIPAA) compliant and all registries and the SCC have received a Federal Certificate of Confidentiality and other protection for the identities of women, physicians, and facilities who are subjects of this research.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Drs. Kerlikowske and Ziv are co-senior authors.

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Shieh, Y., Hu, D., Ma, L. et al. Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat 159, 513–525 (2016). https://doi.org/10.1007/s10549-016-3953-2

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