Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings. [Display omitted] •Integrating protein interaction data with metabolic models expands multiomic mapping.•Proteomic profiling of tumors and cell lines reveals altered metabolic-related signatures.•Metabolite measurements validate pathway alterations in cancer cell lines and tumors. Metabolism is recognized as an important driver of complex diseases, but global metabolite profiling remains a challenge. Protein expression is a poor proxy because pathway enrichment models provide an incomplete mapping between the proteome and metabolism. We developed MOMENTA, a multiomic network approach for interrogating metabolic pathways from proteomics data. Analysis of data from cancer cell lines and human tumors reveals metabolic network rewiring and oncogene connections. The metabolic networks altered in cancer are linked to clinical outcomes.