Combining Breast Cancer Risk Prediction Models

Author:

Guan Zoe1ORCID,Huang Theodore2,McCarthy Anne Marie3,Hughes Kevin4,Semine Alan5,Uno Hajime67ORCID,Trippa Lorenzo68,Parmigiani Giovanni68ORCID,Braun Danielle68

Affiliation:

1. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA

2. Vertex Pharmaceuticals, Boston, MA 02210, USA

3. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA

4. Department of Surgery, Medical University of South Carolina, Charleston, SC 29425, USA

5. Advanced Image Enhancement, Fall River, MA 02720, USA

6. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA

7. Department of Medicine, Harvard Medical School, Boston, MA 02115, USA

8. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

Abstract

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

Funder

NSERC

NIH NCI

NSF

NIH

Dana-Farber Cancer Institute Research Scientist Development Fund

American Cancer Society

Publisher

MDPI AG

Subject

Cancer Research,Oncology

Reference68 articles.

1. Cancer statistics, 2020;Siegel;CA Cancer J. Clin.,2020

2. American Cancer Society (2020, May 03). Facts and Figures 2020. Available online: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2020.html.

3. Breast cancer risk models: A comprehensive overview of existing models, validation, and clinical applications;Braun;Breast Cancer Res. Treat.,2017

4. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually;Gail;J. Natl. Cancer Inst.,1989

5. Projecting individualized absolute invasive breast cancer risk in African American women;Gail;J. Natl. Cancer Inst.,2007

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