Abstract B025: Integrative omics analysis decodes cross-talk between signaling pathways to understand the anti-cancer mechanism of TKIs in NSCLC

Abstract

Abstract Dynamic signaling complexes employ multiple post-translational modifications (PTMs) to convey intracellular messages that govern cell division. We seek to model these mechanisms to understand the action of tyrosine kinase inhibitors (TKIs) as anti-cancer drugs. Advanced proteomics and informatics approaches allow proteome-wide dissection of TKI-associated signaling complexes and decoding the cross-talk among PTM signaling network. Sequential enrichment of PTM proteomics and MaxQuant were used to quantitatively characterize phosphorylation, ubiquitination and acetylation in ten non-small cell cancer (NSCLC) cell lines including H3122, H2228, STE-1, PC9, HCC827, HCC4006, HCC78, H1781,H2286, H366, H3122 and PC9 under conditions where cells were treated with the TKIs crizotinib (targets ALK and ROS1), erlotinib (targets EGFR), Afatinib (targets HER2), Dasatinib (targets DDR2) and the proteasome inhibitor PR171. We used t-distributed stochastic neighbor embedding (t-SNE) to identify PTM clusters, and filtered known protein-protein interactions using these clusters. We identified 5941 unique phosphorylation tyrosine sites, 5643 unique ubiquitinated sites and 2893 acetylated sites. We hypothesize that PTM clusters that contain proteins known to interact with one another are likely to represent cross-talk among functionally interactive signaling pathways. We identified 826 clusters containing at least 3 unique PTM sites in each cluster. The data suggest functional interactions between ALK, EGFR and enzymes that act on different PTMs, including kinases, phosphatases, acetyltransferases, and E3 ubiquitin ligases. PTM sites of co-clustered enzymes are associated with distinct signaling modules that respond to drug in concert with the known drug target in NSCLC. Clusters of PTMs were used to filter specific functional interactions, some of which represent cell signaling pathways. The goal is to filter interactions that are active in lung cancer cell lines from the many possible interactions identified in PPI networks. These pathways associated with TKI form a signaling network identified by various PTM clusters providing broad and deep insights into TKI mechanisms. The data suggest that particular kinases (MET, PKM, EPHA2, PRKDC, LYN and ROS1), E3 ligase (ZNF451), E1 ubiquitin activating enzyme (UBA1), deubiquitinase (USP5) and acetyltransferases (FASN and EP300) are common regulators of TKI signaling pathways. Targeting these proteins provides an opportunity to enhance TKI drug efficacy or overcome drug resistance in NSCLC. Collectively, our study identifies individual PTM sites that are responsible for cross-talk within TKI signaling pathways, and provides insight into TKI mechanisms with an eye towards a co-targeting strategy for improving TKI-based therapy in NSCLC. Citation Format: Guolin Zhang, Karen Ross, Cuneyt Akcora, Katia Smirnova, Bin Fang, John M. Koomen, Eric B. Haura, Mark Grimes. Integrative omics analysis decodes cross-talk between signaling pathways to understand the anti-cancer mechanism of TKIs in NSCLC [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr B025. doi:10.1158/1535-7163.TARG-19-B025