Precise metabolic dependencies of cancer through deep learning and validations

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.