. 2016 Oct;159(3):513-25.

doi: 10.1007/s10549-016-3953-2. Epub 2016 Aug 26.

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

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@ucsf.edu.
² Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, Box 0320, 1545 Divisadero Street, San Francisco, CA, 94143, USA.
³ University of California, San Francisco, Box 1793, 550 16th Street, San Francisco, CA, 94158, USA.
⁴ Department of Economics, Applied Statistics, and International Business, New Mexico State University, MSC 3CQ, P.O. Box 30001, Las Cruces, NM, 88003, USA.
⁵ Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1350, Houston, TX, 77030, USA.
⁶ Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, 200 First Street SW, Charlton Building 6-239, Rochester, MN, 55905, USA.
⁷ San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Box 0560, 550 16th Street, 2nd Floor, San Francisco, CA, 94159, USA.
⁸ Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA.
⁹ General Internal Medicine Section, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, Mailing Code 111A1, San Francisco, CA, 94121, USA.

PMID: 27565998
PMCID: PMC5033764
DOI: 10.1007/s10549-016-3953-2

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

Yiwey Shieh et al. Breast Cancer Res Treat. 2016 Oct.

. 2016 Oct;159(3):513-25.

doi: 10.1007/s10549-016-3953-2. Epub 2016 Aug 26.

Authors

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@ucsf.edu.
² Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, Box 0320, 1545 Divisadero Street, San Francisco, CA, 94143, USA.
³ University of California, San Francisco, Box 1793, 550 16th Street, San Francisco, CA, 94158, USA.
⁴ Department of Economics, Applied Statistics, and International Business, New Mexico State University, MSC 3CQ, P.O. Box 30001, Las Cruces, NM, 88003, USA.
⁵ Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1350, Houston, TX, 77030, USA.
⁶ Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, 200 First Street SW, Charlton Building 6-239, Rochester, MN, 55905, USA.
⁷ San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Box 0560, 550 16th Street, 2nd Floor, San Francisco, CA, 94159, USA.
⁸ Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA.
⁹ General Internal Medicine Section, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, Mailing Code 111A1, San Francisco, CA, 94121, USA.

PMID: 27565998
PMCID: PMC5033764
DOI: 10.1007/s10549-016-3953-2

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.

Keywords: Breast cancer; Cancer surveillance and screening; Risk assessment; Single nucleotide polymorphisms.

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Figures

**Fig. 1**
The receiver operating characteristic curves for the polygenic risk score (PRS), fitted BCSC model (fitted-BCSC), and the fitted BCSC model plus polygenic risk score (fitted-BCSC-PRS) are shown. The p-value shown corresponds to the null hypothesis that the AUROCs between all models are equal

**Fig. 2**
The receiver operating characteristic curves and corresponding area under the curve (AUC) with 95% confidence interval are shown for (A) the Asian-specific, 76-SNP polygenic risk score in East Asians, (B) the general, 83-SNP polygenic risk score in East Asians, and (C) the general, 83-SNP polygenic risk score in Caucasians

**Fig. 3**
The percentage of cases and controls within 5-year risk strata according to estimates from two models are shown: (A) the BCSCv2 model, and (B) the BCSCv2-PRS model. The USPSTF recommends consideration of chemoprevention in women with a 5-year risk ≥3%, represented by the dashed line

See this image and copyright information in PMC

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

Affiliations

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

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