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Graphical Abstract
Highlights
Anticancer activity of midostaurin but not other PKC inhibitors in NSCLC cells.
Mechanism of action by network-based integration of chemo- and phosphoproteomics.
Midostaurin polypharmacology by simultaneous inhibition of TBK1, PDPK1 and AURKA.
Design of synergistic drug combination with PLK1 inhibitor by pathway validation.
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.
Footnotes
Author contributions: C.C., B.M.K., E.B.H., J.M.K., and U.R. designed research; C.C., V.P., B.M.K., B.F., N.J.S., V.I., S.N., F.K., and L.L.R.R. performed research; C.C. and U.R. analyzed data; C.C. and U.R. wrote the paper.
↵* This work was supported by the NIH/NCI R01 CA181746 (to U.R.), the Austrian Marshall Plan Scholarships (to C.C.), the NIH/NCI F99/K00 Predoctoral to Postdoctoral Transition Award F99 CA212456 (to B.M.K.), the Moffitt NIH/NCI SPORE in Lung Cancer (Award No. P50 CA119997), and the H. Lee Moffitt Cancer Center and Research Institute. Moffitt Core Facilities are supported by the National Cancer Institute (Award No. P30-CA076292) as a Cancer Center Support Grant. Proteomics and Metabolomics is also supported by the Moffitt Foundation.
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This article contains supplemental Figures and Tables.
- Received February 28, 2018.
- Revision received August 16, 2018.
- © 2018 Ctortecka et al.
Published under exclusive license by The American Society for Biochemistry and Molecular Biology, Inc.