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Predicting Response To Platin Chemotherapy Agents With Biochemically-Inspired Machine Learning, Peter Rogan, Eliseos J. Mucaki, Dan Lizotte

Biochemistry Publications

Selection of effective genes that accurately predict chemotherapy response could improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin response in the same cell lines, and respectively validate each with cancer patient data. Supervised support vector machine learning was used to derive gene sets whose expression was related to cell line GI₅₀ 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 vs. median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce dependence …