Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer

Author summaryProtein post-translational modifications (PTMs), such as phosphorylation, ubiquitination, and acetylation, are extensively employed by cell signaling pathways that regulate cell division, differentiation, migration, and cancer. We used machine learning to identify PTM clusters that represent functional modules in cell signaling pathways. These clusters were used to identify protein-protein interactions, and interactions between cell signaling pathways, that were active in lung cancer cells that were treated with anti-cancer drugs. We model these interactions as networks at three levels of granularity at the pathway, protein-protein interaction, and PTM levels. Interrogation of these networks yielded insights into molecular interactions between cell signaling pathways activated by oncogenes, transmembrane transport of small molecules, and glycolysis and gluconeogenesis. These analyses identify previously unappreciated mechanisms of crosstalk among signaling pathways between oncogenic tyrosine kinase signaling and proteins that regulate metabolic reprogramming in lung cancer, revealing new potential drug targets for combination therapy. We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy.

Automated summary

This study used machine learning to identify PTM clusters that represent functional modules in cell signaling pathways and discover protein-protein interactions and interactions between cell signaling pathways in lung cancer cells treated with anti-cancer drugs. The analysis revealed previously unappreciated mechanisms of crosstalk among signaling pathways and identified new potential drug targets for combination therapy.