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Abstract
This study presents a machine-learning framework for identifying robust drug biomarkers using network-based analyses of pharmacogenomic data from 3D organoid culture models. The identified biomarkers accurately predict drug responses in colorectal and bladder cancer patients treated with 5-fluorouracil and cisplatin, respectively. Validation using external datasets of isogenic cancer cell lines and concordance analysis with somatic mutation-based biomarkers further support the method's robustness.
Publisher
Nature Communications
Published On
Oct 30, 2020
Authors
JungHo Kong, Heetak Lee, Donghyo Kim, Seong Kyu Han, Doyeon Ha, Kunyoo Shin, Sanguk Kim
Tags
machine learning
drug biomarkers
pharmacogenomics
cancer treatment
bioinformatics
network analysis
3D organoid models

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