Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning.

ABSTRACT: 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 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 and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values

SUBMITTER: Mucaki EJ 

PROVIDER: S-EPMC6329797 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature