Decision curve analysis for Tyrer–Cuzick and Gail 10-year risk scores with and without PRSBC in test data (N = 25,359). Plots show net benefit calculated in full available follow-up time. The gray line indicates net benefit if all women were screened, and the black line indicates net benefit if no women were screened.
ARTICLE ABSTRACT
Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI).
We analyzed data from 126,490 post-menopausal women of “White British” ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer–Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected.
The Harrell's C statistic of the 10-year risk from the Tyrer–Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053–0.104) and 0.051 (95% CI, 0.030–0.073), respectively], with negligible impact on controls.
The addition of a PRS for breast cancer to the well-established Tyrer–Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification.
These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.
