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

Claudia Ctortecka,Vinayak Palve,Brent M Kuenzi,Bin Fang,Natalia J Sumi,Victoria Izumi,Silvia Novakova,Fumi Kinose,Lily L Remsing Rix,Eric B Haura,John Matthew Koomen,Uwe Rix

doi:10.1074/mcp.ra118.000713

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.

Highlights

Functional Proteomics and Deep Network Interrogation Reveal a Complex Mechanism of Action of Midostaurin in Lung Cancer Cells*□S
The screen revealed potent activity of the protein kinase C (PKC) inhibitor midostaurin compared with other PKC inhibitors, such as sotrastaurin and ruboxistaurin, in several non-small cell lung cancer (NSCLC) cell lines (Fig. 1A)
Despite tremendous advances in genomics and the associated success of precision medicine, the majority of cancers, including many NSCLC tumors, currently does not display actionable mutations illustrating the urgent need for new anticancer targets [3]

Summary

Introduction

Functional Proteomics and Deep Network Interrogation Reveal a Complex Mechanism of Action of Midostaurin in Lung Cancer Cells*□S. 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 multikinase inhibitors, actionable targets for drug discovery and cancer vulnerabilities. There is an entire array of driver mutations and gene fusions in NSCLC [3], most of these are rare events and the majority of lung cancers currently does not show actionable mutations illustrating the urgent need for new anticancer targets Some of these needed targets may not be detectable using genomic methods as they promote oncogenicity without being genetically altered [4, 5]. Using multi-targeted compounds with unexplained anticancer activity as research tools to identify previously unrecognized cancer vulnerabilities constitutes an intriguing novel modality for drug development This strategy can lead to new therapeutic approaches via drug repurposing, if these compounds are already approved therapeutics, or to new drug discovery efforts to develop inhibitors for the responsible targets. The integration of phosphoproteomics, which can describe proteome-wide drug effects on the oncogenic signaling network [10, 11], and chemical proteomics, which can identify direct drug targets that serve as entry points into this network [12,13,14], allows for deep network mining and is a powerful method to dissect complex kinase inhibitor MoAs [15, 16]

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Conclusion