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

JungHo Kong, Heetak Lee, Donghyo Kim, Seong Kyu Han, Doyeon Ha, Kunyoo Shin () and Sanguk Kim ()
Additional contact information
JungHo Kong: Pohang University of Science and Technology
Heetak Lee: Pohang University of Science and Technology
Donghyo Kim: Pohang University of Science and Technology
Seong Kyu Han: Pohang University of Science and Technology
Doyeon Ha: Pohang University of Science and Technology
Kunyoo Shin: Pohang University of Science and Technology
Sanguk Kim: Pohang University of Science and Technology

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-19313-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19313-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-19313-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().