Genetic Variants as Predictors of the Success of Colorectal Cancer Treatments

SIMPLE SUMMARY: Some colorectal cancer (CRC) outcomes are partially associated with genetics, and different studies have proposed several genetic variants as predictors. However, analysis of their performance in other populations is limited. Thus, our objectives were to assess their use in our cohort and to find additional genetic variants associated with CRC outcomes. We found that some of the genetic variants proposed as predictors could be used in our cohort, although the addition of clinical data improved the performance. In addition, we found additional genetic variants that could be useful to predict the CRC manifestations in our population. Our findings will help to refine the use of genetic polymorphisms to predict CRC outcomes in our population, and we expect that our findings could be useful for other populations. ABSTRACT: Background: Some genetic polymorphisms (SNPs) have been proposed as predictors for different colorectal cancer (CRC) outcomes. This work aims to assess their performance in our cohort and find new SNPs associated with them. Methods: A total of 833 CRC cases were analyzed for seven outcomes, including the use of chemotherapy, and stratified by tumor location and stage. The performance of 63 SNPs was assessed using a generalized linear model and area under the receiver operating characteristic curve, and local SNPs were detected using logistic regressions. Results: In total 26 of the SNPs showed an AUC > 0.6 and a significant association (p < 0.05) with one or more outcomes. However, clinical variables outperformed some of them, and the combination of genetic and clinical data showed better performance. In addition, 49 suggestive (p < 5 × 10(−6)) SNPs associated with one or more CRC outcomes were detected, and those SNPs were located at or near genes involved in biological mechanisms associated with CRC. Conclusions: Some SNPs with clinical data can be used in our population as predictors of some CRC outcomes, and the local SNPs detected in our study could be feasible markers that need further validation as predictors.