Amber N Hurson1,2, Parichoy Pal Choudhury1,3, Chi Gao4,5, Anika Hüsing6, Mikael Eriksson7, Min Shi8, Michael E Jones9, D Gareth R Evans10,11, Roger L Milne12,13,14, Mia M Gaudet15, Celine M Vachon16, Daniel I Chasman17,18, Douglas F Easton19,20, Marjanka K Schmidt21,22, Peter Kraft4,5, Montserrat Garcia-Closas1, Nilanjan Chatterjee23,24. 1. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. 2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 3. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. 4. Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 5. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 6. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 7. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska Univ Hospital, Stockholm, Sweden. 8. Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA. 9. Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. 10. Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. 11. Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester NIHR Biomedical Research Centre, Manchester University Hospitals NHS, Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. 12. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia. 13. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia. 14. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia. 15. Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA. 16. Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA. 17. Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA. 18. Harvard Medical School, Boston, MA, USA. 19. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. 20. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. 21. Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. 22. Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands. 23. Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. 24. Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Abstract
BACKGROUND: Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. METHODS: Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. RESULTS: Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. CONCLUSION: Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines. Published by Oxford University Press on behalf of the International Epidemiological Association 2021. This work is written by US Government employees and is in the public domain in the US.
BACKGROUND: Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. METHODS: Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. RESULTS: Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. CONCLUSION: Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines. Published by Oxford University Press on behalf of the International Epidemiological Association 2021. This work is written by US Government employees and is in the public domain in the US.
Entities:
Keywords:
Breast cancer; iCARE; model validation; polygenic risk score; risk prediction; risk stratification
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