Prospective Evaluation of a Breast Cancer Risk Model Integrating Classical Risk Factors and Polygenic Risk in 15 Cohorts from Six Countries

BACKGROUND
Rigorous evaluation of 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 237 632 women (7529 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 younger than 50 years, 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 or older, 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 US) ranged from 7.0% in Germany (~841,000 of 12 million) to 17.7% in the US (~5.3 of 30 million). At this threshold, 14.7% of US women were re-classified by adding PRS to classical risk factors, with identification of 12.2% of additional future cases.
CONCLUSIONS
Integrating a 313-variant PRS with classical risk factors can improve identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.