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

Medicine and Health

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

J. Kong, H. Lee, et al.

Discover a groundbreaking machine-learning framework developed by JungHo Kong, Heetak Lee, Donghyo Kim, Seong Kyu Han, Doyeon Ha, Kunyoo Shin, and Sanguk Kim that identifies robust drug biomarkers through innovative network-based analyses of pharmacogenomic data. This research promises to enhance drug response predictions in colorectal and bladder cancer treatments, verified by extensive validation against external datasets.

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Playback language: English
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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