Machine-Learning Tool Improves Cancer Drug Response Prediction

Applying a machine learning technique using algorithms that learn transcriptome information from artificial organoids derived from actual patients instead of animal models, researchers from the Pohang University of Science and Technology (POSTECH) in South Korea say they have successfully increased the accuracy of anti-cancer drug response predictions. The team, led by Sanguk Kim, Ph.D., in the life sciences department, published its findings "Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients" in Nature Communications "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," write the investigators. "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 isogenic cancer cell lines.