Functional Proteomics and Deep Network Interrogation Reveal a Complex Mechanism of Action of Midostaurin in Lung Cancer Cells

Abstract

Lung cancer is associated with high prevalence and mortality, and despite significant successes with targeted drugs in genomically defined subsets of lung cancer and immunotherapy, the majority of patients currently does not benefit from these therapies. Through a targeted drug screen, we found the recently approved multi-kinase inhibitor midostaurin to have potent activity in several lung cancer cells independent of its intended target, PKC, or a specific genomic marker. To determine the underlying mechanism of action we applied a layered functional proteomics approach and a new data integration method. Using chemical proteomics, we identified multiple midostaurin kinase targets in these cells. Network-based integration of these targets with quantitative tyrosine and global phosphoproteomics data using protein-protein interactions from the STRING database suggested multiple targets are relevant for the mode of action of midostaurin. Subsequent functional validation using RNA interference and selective small molecule probes showed that simultaneous inhibition of TBK1, PDPK1 and AURKA was required to elicit midostaurin's cellular effects. Immunoblot analysis of downstream signaling nodes showed that combined inhibition of these targets altered PI3K/AKT and cell cycle signaling pathways that in part converged on PLK1. Furthermore, rational combination of midostaurin with the potent PLK1 inhibitor BI2536 elicited strong synergy. Our results demonstrate that combination of complementary functional proteomics approaches and subsequent network-based data integration can reveal novel insight into the complex mode of action of multi-kinase inhibitors, actionable targets for drug discovery and cancer vulnerabilities. Finally, we illustrate how this knowledge can be used for the rational design of synergistic drug combinations with high potential for clinical translation.

Effect of Praziquantel on Schistosoma mekongi Proteome and Phosphoproteome

“…PZQ at 40 μg/mL killed 46.7% of adult worms and was defined as IC 50 ( Figure 1 ). In previous studies, the IC 50 of PZQ varied from 0.03 to 80 μg/mL [ 29 , 32 , 33 , 34 ]. Duration of exposure could have an effect on IC 50 .…”

“…Changes in phosphorylation levels can critically impact the functions proteins thereby reducing the survivability of organisms. Modern mass spectrometric (MS) technologies and phosphoprotein enrichment methods have been successfully used to elucidate the mechanism of action of a number of drugs, such as gemcitabine [28], midostaurin [29] and pyrazolo [3,4-d] pyrimidine [30]. The aim of this study was to apply proteomic and phosphoproteomic approaches to understand the mechanism of action of PZQ in S. mekongi.…”

Analysis of Protein–Protein Functional Associations by Using Gene Ontology and KEGG Pathway

“…Therefore, genes that participate in such molecular function may tend to be more probable to actually contribute to PPIs. Similarly, the other molecular function GO term, named GO: 0097159, which describes organic cyclic compound binding, also involves various PPIs, like interactions among TBK1, PDPK1, and AURKA [64]. As for the last term, GO: 0003676, it describes the nucleic acid binding.…”

Investigation of the Potential Mechanism Governing the Effect of the Shen Zhu San on COVID‐19 by Network Pharmacology

“…In order to better understand protein interactions systematically, the associated targets were inputted into Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, Version: 11.0) ( https://string-db.org/ ) to obtain the relevant information on protein interaction, for which the genes were determined as nodes and the interactions as lines in a network [ 34 ]. STRING is an online database of functional protein association networks, providing associations between proteins according to experimentally determined data, literature mining, databases, and gene associations like neighborhood, fusions, co-occurrence, coexpression, or protein homology [ 110 ]. To ensure high interaction confidence, the minimum score was set to the highest confidence value of 0.9 to include the broad scope of protein interactions.…”