Predicting Responses to Platin Chemotherapy Agents with Biochemically-inspired Machine Learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: , , , , , , , , , , , , , , and ; carboplatin: , , , , , , , , , , , , , , and and oxaliplatin: , , , , , , , , , , and . Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free;  = 19), and 79% recurrence in smokers (62% disease-free;  = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.