An iterative supervised learning method identifying two subgroups of FOLFOX resistance patterns and predicting FOLFOX response in colorectal cancer patients

Abstract Background FOLFOX is a combination of drugs that is widely used to treat colorectal cancer. The response rate of FOLFOX in colorectal cancer(CRC) is 30-50%. We develop a method that analyzes mechanisms of FOLFOX resistance and predicts whether a patient will benefit from FOLFOX. Methods Gene expression data of 83 stage IV CRC tumor samples (FOLFOX responder n=42, non-responder n=41) were used to develop a supervised learning method IML and analyze subgroups of FOLFOX resistance mechanism. Datasets of 32 FOLFOX treated stage IV CRC patients and 55 FOLFOX treated stage III CRC patients were used as independent validations. Results An iterative supervised learning (IML) method identified two distinct subgroups of CRC patients who resist FOLFOX. Each subgroup relies on different types of DNA damage repair proteins and they are mutually exclusive. Protein-protein networks showed the main mechanism might be the synergistic effect of resisting apoptosis and an altered cell cycle. IML method was validated in two independent validation sets, one FOLFOX treated stage IV CRC patients(HR=2.6, p-value=0.02, 3-years survival rate of the predicted responder group 61.9%, predicted nonresponder group 18.8%) and one FOLFOX treated stage III CRC patients (estimated HR=2.36, p-value=0.02). A subgroup of mesenchymal subtype patients shows the pattern as FOLFOX responders. Conclusions IML method reflects the underlying biology of FOLFOX resistance and predicts FOLFOX response.