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Polypharmacology-based ceritinib repurposing using integrated functional proteomics

Abstract

Targeted drugs are effective when they directly inhibit strong disease drivers, but only a small fraction of diseases feature defined actionable drivers. Alternatively, network-based approaches can uncover new therapeutic opportunities. Applying an integrated phenotypic screening, chemical and phosphoproteomics strategy, here we describe the anaplastic lymphoma kinase (ALK) inhibitor ceritinib as having activity across several ALK-negative lung cancer cell lines and identify new targets and network-wide signaling effects. Combining pharmacological inhibitors and RNA interference revealed a polypharmacology mechanism involving the noncanonical targets IGF1R, FAK1, RSK1 and RSK2. Mutating the downstream signaling hub YB1 protected cells from ceritinib. Consistent with YB1 signaling being known to cause taxol resistance, combination of ceritinib with paclitaxel displayed strong synergy, particularly in cells expressing high FAK autophosphorylation, which we show to be prevalent in lung cancer. Together, we present a systems chemical biology platform for elucidating multikinase inhibitor polypharmacology mechanisms, subsequent design of synergistic drug combinations, and identification of mechanistic biomarker candidates.

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Figure 1: Ceritinib has beneficial off-target activity in ALK-negative non-small-cell lung cancer cells.
Figure 2: Ceritinib inhibits multiple previously unknown targets including FAK1, RSK1/2, FER and CAMKK2.
Figure 3: Integrated analysis of chemical and phosphoproteomics data sets.
Figure 4: Ceritinib inhibits cell viability through inhibition of IGF1R, FAK1 and RSK1/2.
Figure 5: Ceritinib strongly synergizes with the microtubule inhibitor paclitaxel.
Figure 6: FAK1 autophosphorylation may be predictive of a synergistic response to ceritinib and paclitaxel.

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Proteomics Identifications Database

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Protein Data Bank

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Acknowledgements

This work was supported by the NIH/NCI R01 CA181746 (to U.R.), the NIH/NCI F99/K00 Predoctoral to Postdoctoral Transition Award F99 CA212456 (to B.M.K), the Moffitt NIH/NCI SPORE in Lung Cancer P50 CA119997 (to E.B.H.), Moffitt Pinellas Partners, and the H. Lee Moffitt Cancer Center and Research Institute. We wish to acknowledge the Moffitt Lung Cancer Center of Excellence and the Moffitt Chemical Biology (Chemistry Unit), Proteomics, Flow Cytometry, Molecular Genomics and Analytical Microscopy Core Facilities. Moffitt Core Facilities are supported by the National Cancer Institute (Award No. P30-CA076292) as a Cancer Center Support Grant. Proteomics is also supported by the Moffitt Foundation.

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Contributions

B.M.K., L.L.R.R., J.M.K., E.B.H. and U.R. conceived and designed the project. L.L.R.R and F.K. conducted the drug screen, and B.M.K and U.R. analyzed the data. Chemistry was done by B.M.K. B.M.K. performed chemical proteomics experiments, and B.M.K and U.R. analyzed the data. Phosphoproteomics experiments were done by B.M.K. and B.F., and B.M.K., P.A.S., and U.R. analyzed the data. B.M.K., L.L.R.R. and A.T.B. performed the western blots. B.M.K. conducted bioinformatic and network analyses. SiRNA and rescue experiments were done by B.M.K. B.M.K. and L.L.R.R. performed synergy experiments. B.M.K. performed all IHC, T.A.B. scored the slides and B.M.K analyzed the data. B.M.K. and U.R. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Uwe Rix.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Tables 1–2 and Supplementary Figures 1–9. (PDF 4127 kb)

Life Sciences Reporting Summary (PDF 167 kb)

Supplementary Data Set 1

Chemical proteomics data set. Data was searched by Mascot and displayed are exclusive unique spectrum counts. A minimum of 2 exclusive unique spectra were required for protein ID. Data was filtered with 95% protein and peptide cutoffs. CT, competition. (XLSX 153 kb)

Supplementary Data Set 2

pY phosphoproteomics data set. Phosphotyrosine data was IRON normalized and filtered for PEP <= 0.1. Contaminants, reverse sequences and rows with all zero values were removed. (XLSX 116 kb)

Supplementary Data Set 3

pSTY phosphoproteomics data set. Global phosphoproteomics data was IRON normalized and filtered for PEP <= 0.1. Contaminants, reverse sequences and rows with all zero values were removed. (XLSX 737 kb)

Supplementary Data Set 4

KEGG pathways enriched in Modules 1–4. Pathway analysis was done using ClusterProfiler to search the KEGG database. (XLSX 13 kb)

Supplementary Data Set 5

ReKINect mutational profile analysis. ReKINect output was generated using H650 cell missense mutational data from the Cancer Cell Line Encyclopedia. (XLSX 15 kb)

Supplementary Data Set 6

KEGG pathways of mutated genes in Supplementary Data Set 5. Pathway analysis was done using ClusterProfiler to search the KEGG database. (XLSX 10 kb)

Supplementary Data Set 7

NetworKIN analysis for potential kinase–substrate interactions. Unfiltered NetworKIN output was generated from phosphosites present in ceritinib subnetwork in Figure 3b–d. (XLSX 278 kb)

Supplementary Data Set 8

pY peptide confirmation by extracted ion chromatogram (XIC). Phosphotyrosine peptide MS1 quantification was confirmed using skyline. (XLSX 14 kb)

Supplementary Data Set 9

pSTY peptide confirmation by XIC. Phosphopeptide MS1 quantification was confirmed using skyline. (XLSX 11 kb)

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Kuenzi, B., Remsing Rix, L., Stewart, P. et al. Polypharmacology-based ceritinib repurposing using integrated functional proteomics. Nat Chem Biol 13, 1222–1231 (2017). https://doi.org/10.1038/nchembio.2489

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