• Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
• Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
• Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
• Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska Univ Hospital, Stockholm, Sweden.
• Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA.
• Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
• 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.
• Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.
• Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA.
• Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA.

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.