Year: 2025
Type: article
Abstract: Cancer cells exhibit metabolic reprogramming to sustain proliferation, creating metabolic vulnerabilities absent in normal cells. While prior studies identified specific metabolic dependencies, systematic insights remain limited. Here, we build a graph deep learning-based metabolic vulnerability prediction model, "DeepMeta," which can accurately predict the dependent metabolic genes for cancer samples based on transcriptome and metabolic network information. The performance of DeepMeta has been extensively validated with independent datasets. The metabolic vulnerability of "undruggable" cancer-driving alterations has been systematically explored using The Cancer Genome Atlas (TCGA) dataset. Notably, CTNNB1 T41A-activating mutations showed experimentally confirmed vulnerability to purine/pyrimidine metabolism inhibition. TCGA patients with the predicted pyrimidine metabolism dependency show a dramatically improved clinical response to chemotherapeutic drugs that block this pyrimidine metabolism pathway. This study systematically uncovers the metabolic dependency of cancer cells and provides metabolic targets for cancers driven by genetic alterations that are originally undruggable on their own.
Source: Cell Reports
Authors: Tao Wu, Xiangyu Zhao, Yu Zhang, Die Qiu, Kaixuan Diao, Dongliang Xu, Weiliang Wang, Xiaopeng Xiong, Xinxiang Li, Xue-Song Liu
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,620
Funders: National Natural Science Foundation of China, Shanghai Educational Development Foundation, Science and Technology Commission of Shanghai Municipality, National Health and Family Planning Commission of the People's Republic of China, Shanghai Jiao Tong University, ShanghaiTech University, National Health Commission of the People's Republic of China, School of Medicine, Shanghai Jiao Tong University
OpenAlex ID: W4411988749