Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
SCIE
SCOPUS
- Title
- Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
- Authors
- Kong, J; Ha, D; Kim, D; Han, SeongKyu; Lee, H; Shin, Kunyoo; KIM, SANGUK
- Date Issued
- 2020-10
- Publisher
- NATURE RESEARCH
- 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. Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. Here, the authors present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/104598
- DOI
- 10.1038/s41467-020-19313-8
- ISSN
- 2041-1723
- Article Type
- Article
- Citation
- NATURE COMMUNICATIONS, vol. 11, no. 1, 2020-10
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- There are no files associated with this item.
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