A new genome-scale model enables prediction of cancer metabolic dependencies

Abstract

Cancer cells rewire metabolism to support proliferation. Intriguingly, divergent metabolic choices are made to attain this common goal. Identifying the unique metabolic requirements for a specific cell has profound implications for cancer biology and precision medicine. Genome-scale metabolic models (GEMs) have emerged as powerful tools to systematically characterize, understand, and predict metabolism of cells and tissues. Despite being comprehensive, the current GEMs remain limited in their predictive power. Here, we present a new GEM of human cells, in silico Human Metabolic Essentiality (iHME), that significantly improves the prediction of metabolic dependencies at a reduced computational cost. Wse rationally downsized, curated, and corrected previous models to remove unsupported metabolic redundancies, which led to a slim model containing 4,377 reactions, 3,241 metabolites, and 1,825 genes. When used to reconstruct metabolic networks of 1,103 cancer cell lines, iHME recalled on average 84.6% of experimental essential genes, which is two-fold increase over previous models. Cholesterol biosynthesis was revealed to be the most reliably predicted pathway with alternative dependencies. Finally, we applied the model to reconstruct individualized networks and predict essential gene profiles for 8,384 patient tumor samples. Glucose transporter SLC2A1 (GLUT1) was identified as a context-specific dependency for head and neck cancers and ovarian cancer. Likewise, CDP-diacylglycerol synthase CDS2 was identified for skin cancer. Overall, iHME is a new genome-scale model for prediction of metabolic dependency at higher accuracy and computational efficiency.