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

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.

Exon Skipping in the RET Gene Encodes Novel Isoforms That Differentially Regulate RET Protein Signal Transduction.

Rearranged during transfection (RET), a receptor tyrosine kinase that is activated by the glial cell line-derived neurotrophic factor family ligands (GFLs), plays a crucial role in the development and function of the nervous system and additionally is required for kidney development and spermatogenesis. RET encodes a transmembrane receptor that is 20 exons long and produces two known protein isoforms differing in C-terminal amino acid composition, referred to as RET9 and RET51. Studies of human pheochromocytomas identified two additional novel transcripts involving the skipping of exon 3 or exons 3, 4, and 5 and are referred to as RET(Δ) (E3) and RET(Δ) (E345), respectively. Here we report the presence of Ret(Δ) (E3) and Ret(Δ) (E345) in zebrafish, mice, and rats and show that these transcripts are dynamically expressed throughout development of the CNS, peripheral nervous system, and kidneys. We further explore the biochemical properties of these isoforms, demonstrating that, like full-length RET, RET(ΔE3) and RET(ΔE345) are trafficked to the cell surface, interact with all four GFRα co-receptors, and have the ability to heterodimerize with full-length RET. Signaling experiments indicate that RET(ΔE3) is phosphorylated in a similar manner to full-length RET. RET(ΔE345), in contrast, displays higher baseline autophosphorylation, specifically on the catalytic tyrosine, Tyr(905), and also on one of the most important signaling residues, Tyr(1062) These data provide the first evidence for a physiologic role of these isoforms in RET pathway function.

The non-canonical target PARP16 contributes to polypharmacology of the PARP inhibitor talazoparib and its synergy with WEE1 inhibitors.

PARP inhibitors (PARPis) display single-agent anticancer activity in small cell lung cancer (SCLC) and other neuroendocrine tumors independent of BRCA1/2 mutations. Here, we determine the differential efficacy of multiple clinical PARPis in SCLC cells. Compared with the other PARPis rucaparib, olaparib, and niraparib, talazoparib displays the highest potency across SCLC, including SLFN11-negative cells. Chemical proteomics identifies PARP16 as a unique talazoparib target in addition to PARP1. Silencing PARP16 significantly reduces cell survival, particularly in combination with PARP1 inhibition. Drug combination screening reveals talazoparib synergy with the WEE1/PLK1 inhibitor adavosertib. Global phosphoproteomics identifies disparate effects on cell-cycle and DNA damage signaling thereby illustrating underlying mechanisms of synergy, which is more pronounced for talazoparib than olaparib. Notably, silencing PARP16 further reduces cell survival in combination with olaparib and adavosertib. Together, these data suggest that PARP16 contributes to talazoparib's overall mechanism of action and constitutes an actionable target in SCLC.