Integrating Gene and Clinical Data to Overcome Chemoresistance in Colorectal Cancer

Abstract

Colorectal cancer remains a major health challenge due to frequent drug resistance. Current treatment plans vary widely, partly because tumors can behave differently based on the genetic alterations present in the tumor. The inconsistency makes it harder to find the best treatment for individuals. Common treatments usually involve chemotherapy drug combinations such as FOLFOX (5-fluorouracil, leucovorin, and oxaliplatin) and FOLFIRI (5-fluorouracil, leucovorin, and irinotecan). Here, we enhance a gene expression panel that predicts how colorectal cancer patients will respond to drugs by stratifying gene signatures for samples from patients receiving different drug combination treatments and those with varying tumor locations. The model demonstrates strong predictive accuracy in categorizing patients into groups based on their likelihood of responding (or not) to various chemotherapy drugs, highlighting how a patient's geographical location can influence the drugs they receive and the model's effectiveness in this context. The results of our work suggest that integrating gene expression profiling with real-world clinical factors can improve personalized treatment strategies, resulting in better outcomes by overcoming drug resistance in colorectal cancer.