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

Nat Commun. 2020 Oct 30;11(1):5485. doi: 10.1038/s41467-020-19313-8.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antineoplastic Agents / therapeutic use*
  • Biomarkers, Tumor / genetics
  • Biomarkers, Tumor / metabolism
  • Cell Line, Tumor
  • Cisplatin / therapeutic use
  • Colorectal Neoplasms / drug therapy*
  • Colorectal Neoplasms / genetics
  • Colorectal Neoplasms / metabolism
  • Drug Development / methods
  • Fluorouracil / therapeutic use
  • Gene Expression Regulation, Neoplastic
  • Gene Regulatory Networks / drug effects
  • Humans
  • Machine Learning*
  • Organoids / drug effects
  • Organoids / metabolism*
  • Protein Interaction Maps / drug effects
  • Transcriptome
  • Urinary Bladder / drug effects
  • Urinary Bladder / metabolism*
  • Urinary Bladder Neoplasms / drug therapy*
  • Urinary Bladder Neoplasms / genetics
  • Urinary Bladder Neoplasms / metabolism


  • Antineoplastic Agents
  • Biomarkers, Tumor
  • Cisplatin
  • Fluorouracil