BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors |
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| Affiliation: | 1. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, The Strangeways Research Laboratory, University of Cambridge, Cambridge, UK;;2. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA;;3. The Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, UK;;4. Hereditary Cancer Program, Epidemiology Unit and Girona Cancer Registry, Catalan Institute of Oncology, Girona Biomedical Research Institute (IdiBGi), Girona, Spain;;5. Centre Hospitalier Universitaire de Québec–Université Laval Research Center, Québec City, QC, Canada;;6. Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands;;7. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA;;8. Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA;;9. Department of Medical Genetics and National Institute for Health Research, Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK;;10. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. |
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| Abstract: | PurposeBreast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs).MethodsBOADICEA incorporates the effects of truncating variants inBRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information.ResultsAmong all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with14.7% of women predicted to have a lifetime risk of ≥17–<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of ≥30% (high risk).ConclusionThis comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening. |
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