JungHo Kong¹, Heetak Lee¹, Donghyo Kim¹, Seong Kyu Han¹, Doyeon Ha¹, Kunyoo Shin^1,2,* & Sanguk Kim^1,2,*

¹Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea.
²Institute of Convergence Science, Yonsei University, Seoul 120-749, Korea.

^*Corresponding author

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.