Improvement of a clinical colorectal cancer risk prediction model integrating polygenic risk.

81 Background: Improving colorectal cancer risk prediction and stratification is pivotal for implementing better screening and prevention programs in public health and for enabling a personalised approach for assessing patients’ colorectal cancer risk. Methods: In this study, we used the UK Biobank to compare the performance of a risk prediction model incorporating two different polygenic risk scores – one comprising 45 SNPs and the other comprising 140 SNPs. The clinical component of the risk prediction model included a simple measure of first-degree family history. We used age- and sex-specific population incidence rates to calculate full-lifetime risks. Results: The model using the 140-SNP PRS showed an improvement in discrimination, calibration and risk stratification over the model using the 45-SNP PRS for full-lifetime risk: discrimination was 0.706 (95% CI 0.697–0.715) and 0.674 (95% CI 0.664–0.683), respectively, and the P for difference was < 0.001. The 140-SNP model was well calibrated and showed a small overestimation of risk 0.951 (95% CI 0.918–0.986). Standard incidence ratios compared to population incidence rates showed that, for the 140-SNP model, the top quintile of risk shows a 27% improvement compared to the 45-SNP model. Furthermore, there was a 3-fold difference in colorectal cancer incidence between adults identified in the top quintile compared to the bottom quintile of risk using the 140-SNP model versus the 45-SNP model. Conclusions: This updated risk prediction score with a 140–SNP PRS and a simple measure of family history, improves risk prediction and risk stratification in the general population compared with a similar model with a 45-SNP PRS, and will ultimately assist in colorectal cancer disease prevention in the clinic.