Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells

Soo Min Lee; Younghyun Han; Kwang-Hyun Cho

doi:10.1016/j.isci.2023.108377

Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells

iScience. 2023 Nov 1;26(12):108377. doi: 10.1016/j.isci.2023.108377. eCollection 2023 Dec 15.

Authors

Soo Min Lee¹, Younghyun Han¹, Kwang-Hyun Cho¹

Affiliation

¹ Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

Abstract

Tumor suppressor p53 plays a pivotal role in suppressing cancer, so various drugs has been suggested to upregulate its function. However, drug resistance is still the biggest hurdle to be overcome. To address this, we developed a deep learning model called AnoDAN (anomalous gene detection using generative adversarial networks and graph neural networks for overcoming drug resistance) that unravels the hidden resistance mechanisms and identifies a combinatorial target to overcome the resistance. Our findings reveal that the TGF-β signaling pathway, alongside the p53 signaling pathway, mediates the resistance, with THBS1 serving as a core regulatory target in both pathways. Experimental validation in lung cancer cells confirms the effects of THBS1 on responsiveness to a p53 reactivator. We further discovered the positive feedback loop between THBS1 and the TGF-β pathway as the main source of resistance. This study enhances our understanding of p53 regulation and offers insights into overcoming drug resistance.

Keywords: Cancer; Cancer systems biology; Machine learning; Mathematical biosciences; Molecular biology; Pharmaceutical science.