Digital patient modeling identifies predictive biomarkers of regorafenib response in elderly metastatic colorectal cancer

Abstract

In silico clinical trials that simulate individualized mechanisms of action offer a powerful approach to assess drug efficacy across large and diverse patient populations, while also enabling the identification of predictive biomarkers. In this study, we conducted an in silico clinical trial of first-line, single-agent regorafenib in 399 elderly patients with metastatic colorectal cancer (mCRC). Individualized network-based models were constructed using patient-specific differential transcriptomic profiles and employed to simulate the target-specific effects of regorafenib. From this analysis, we identified both predictive and mechanistic biomarkers of treatment response. Notably, four proteins-MARK3, RBCK1, LHCGR, and HSF1-emerged as dual biomarkers, showing associations with both response mechanisms and predictive potential. Three of these (MARK3, RBCK1, and HSF1) were validated in an independent cohort of mCRC patients and were also found to be targets of previously reported regorafenib-predictive miRNAs. This study demonstrates a novel systems biology strategy for evaluating drug response in silico, leveraging transcriptomic data to simulate individual treatment outcomes and uncover clinically relevant biomarkers. Our findings suggest that such approaches may serve as valuable complements to traditional clinical trials for assessing drug efficacy and guiding precision oncology.

Keywords: In silico clinical trial; machine learning; metastatic colorectal cancer; predictive biomarkers; regorafenib; transcriptomics data.