Precise metabolic dependencies of cancer through deep learning and validations.

Journal: Cell reports
Published Date: Jul 3, 2025

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.

Authors

  • Tao Wu
    Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Xiangyu Zhao
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Die Qiu
    School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China.
  • Kaixuan Diao
    School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China.
  • Dongliang Xu
    Department of Urology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China.
  • Weiliang Wang
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Xiaopeng Xiong
    Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang, P.R. China; NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma (Jiangxi Cancer Hospital of Nanchang University), Nanchang, Jiangxi, P.R. China.
  • Xinxiang Li
    Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Xuhui, Shanghai 200032, China.
  • Xue-song Liu

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