Risk Prediction Models for Colorectal Cancer Incorporating Common Genetic Variants: A Systematic Review

• Published in: Cancer epidemiology, biomarkers & prevention biomarkers & prevention, 2019-10, Vol.28 (10), p.1580-1593
• Main Authors: McGeoch, Luke, Saunders, Catherine L, Griffin, Simon J, Emery, Jon D, Walter, Fiona M, Thompson, Deborah J, Antoniou, Antonis C, Usher-Smith, Juliet A
• Format: Article
• Language: English
• Subjects: Biomarkers, Tumor - genetics; Colorectal Neoplasms - diagnosis; Colorectal Neoplasms - genetics; Early Detection of Cancer - methods; Genome-Wide Association Study - methods; Humans; Models, Statistical; Polymorphism, Single Nucleotide; Predictive Value of Tests; Prognosis; Risk Assessment - methods; Risk Factors
• ISSN: 1055-9965; 1538-7755
• DOI: 10.1158/1055-9965.EPI-19-0059

Colorectal cancer screening reduces colorectal cancer incidence and mortality. Risk models based on phenotypic variables have relatively good discrimination in external validation and may improve efficiency of screening. Models incorporating genetic variables may perform better. In this review, we updated our previous review by searching Medline and EMBASE from the end date of that review (January 2014) to February 2019 to identify models incorporating at least one SNP and applicable to asymptomatic individuals in the general population. We identified 23 new models, giving a total of 29. Of those in which the SNP selection was on the basis of published genome-wide association studies, in external or split-sample validation the AUROC was 0.56 to 0.57 for models that included SNPs alone, 0.61 to 0.63 for SNPs in combination with other risk factors, and 0.56 to 0.70 when age was included. Calibration was only reported for four. The addition of SNPs to other risk factors increases discrimination by 0.01 to 0.06. Public health modeling studies suggest that, if determined by risk models, the range of starting ages for screening would be several years greater than using family history alone. Further validation and calibration studies are needed alongside modeling studies to assess the population-level impact of introducing genetic risk-based screening programs.