Objective
To create a tool that considers clinical risk factors for colorectal cancer and polygenic risk scores (PRSs) derived from large-scale genomic data in patient risk stratification to predict the risk of individuals contracting CRC with improved discriminatory accuracy between both incident and prevalent case and control subjects. This study has three main objectives: (1) develop meta & ancestry-specific PRSs for UK Biobank and All of US cohorts, (2) create a Cox-clinical risk score for CRC using identified risk factors, (3) computing an integrated risk prediction tool (IRT) using both clinical data and genetic information to improve risk stratification and this tool would be made available for use on the R Shiny App, (4) finding the gain in accuracy of prediction when genetic data are added to clinical risk factors.
Background
Colorectal cancer (CRC) is among the most prevalent and preventable forms of cancer worldwide. There is increased awareness of a strong genetic component to CRC risk, with the identification of several high penetrance alleles that predict increased CRC susceptibility. [1] Although risk factors often influence cancer development, most do not directly cause cancer. Over the past decade, genome-wide association studies (GWASs) for sporadic CRC, which constitutes most cases, have identified ~60 association signals at over 50 loci.
Predictive genetic testing is the use of a genetic test in an asymptomatic person to predict future risk of disease. These tests represent a new and growing class of medical tests, differing fundamentally from conventional medical diagnostic tests. The hope underlying such testing is that early identification of individuals at risk of a specific condition will lead to reduced morbidity and mortality through targeted screening, surveillance, and prevention. [2]