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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 isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

SUBMITTER: Kong J

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

REPOSITORIES: biostudies-literature

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

Kong JungHo J Lee Heetak H Kim Donghyo D Han Seong Kyu SK Ha Doyeon D Shin Kunyoo K Kim Sanguk S

Nature communications 20201030 1

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 ...[more]

PMID: 33127883

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Dataset Information

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

Publications

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

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