Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

ABSTRACT: Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant

SUBMITTER: Kong J 

PROVIDER: S-EPMC7599252 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature