RESUMO
The complexity and incompleteness of metabolic-regulatory networks make it challenging to predict metabolomes from other omics. Using machine learning, we predicted metabolomic variation across ~1000 different cancer cell lines from matched oct-omics data: genomics, epigenomics (histone post-translational modifications (PTMs) and DNA-methylation), transcriptomics, RNA splicing, miRNA-omics, proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, while miRNAs, phosphoproteins and histone PTMs have the highest metabolic information per feature. Metabolites in peripheral metabolism are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across 4 omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.