Colon to Rectum

BMJ. 2022;379:e071707

Briggs SEW, Law P, East JE, Wordsworth S, Dunlop M, Houlston R, Hippisley-Cox J, Tomlinson I

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: Population-based cohort study


Objective: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer, CRC) prediction model for non-genetic risk to identify people at highest risk of CRC.
Design: Population-based cohort study.
Setting: Data from the UK Biobank study, collected between March 2006 and July 2010.
Participants: 434,587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts.
Main outcome measures: Prediction of CRC diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, 6 different polygenic risk scores for CRC were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women.
Results: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval [CI]: 1.536–1.633), and top age- and sex-adjusted C statistic of 0.733 (95% CI: 0.710–0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (95% CI: 0.720–0.741) and explained variation of 28.2% (95% CI: 26.3–30.1), compared with 0.693 (95% CI: 0.682–0.704) and 21.0% (95% CI: 18.9–23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (95% CI: 0.673–0.702) and explained variation was 21.0% (95% CI: 18.7–23.7), compared with 0.645 (95% CI: 0.631–0.659) and 12.4% (95% CI: 10.3–14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting CRC diagnosis was 47.8% and 80.3%, respectively in men, and 42.7% and 80.1%, respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone.

Conclusions: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost-effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real-life screening setting before implementation in the general population.

Prof. Dr. I. Tomlinson, Cancer Research, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK,
E-Mail: ian.tomlinson@igmm.ed.ac.uk

DOI: DOI: 10.1136/bmj-2022-071707

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