Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Polypharmacology-based ceritinib repurposing using integrated functional proteomics

Nature Chemical Biology volume 13, pages 1222–1231 (2017)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

Accession codes

Primary accessions

Proteomics Identifications Database

Referenced accessions

Protein Data Bank

References

  1. Sawyers, C.L. et al. Imatinib induces hematologic and cytogenetic responses in patients with chronic myelogenous leukemia in myeloid blast crisis: results of a phase II study. Blood 99, 3530–3539 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. Kwak, E.L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Flanagan, M.E. et al. Discovery of CP-690,550: a potent and selective Janus kinase (JAK) inhibitor for the treatment of autoimmune diseases and organ transplant rejection. J. Med. Chem. 53, 8468–8484 (2010).

    Article  CAS  PubMed  Google Scholar 

  4. Palla, G., Derényi, I., Farkas, I. & Vicsek, T. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Farkas, I.J. et al. Network-based tools for the identification of novel drug targets. Sci. Signal. 4, pt3 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Paraiso, K.H.T. et al. Recovery of phospho-ERK activity allows melanoma cells to escape from BRAF inhibitor therapy. Br. J. Cancer 102, 1724–1730 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Knight, Z.A., Lin, H. & Shokat, K.M. Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer 10, 130–137 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lombardo, L.J. et al. Discovery of N-(2-chloro-6-methyl- phenyl)-2-(6-(4-(2-hydroxyethyl)- piperazin-1-yl)-2-methylpyrimidin-4- ylamino)thiazole-5-carboxamide (BMS-354825), a dual Src/Abl kinase inhibitor with potent antitumor activity in preclinical assays. J. Med. Chem. 47, 6658–6661 (2004).

    Article  CAS  PubMed  Google Scholar 

  11. Rubbi, L. et al. Global phosphoproteomics reveals crosstalk between Bcr-Abl and negative feedback mechanisms controlling Src signaling. Sci. Signal. 4, ra18 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Frett, B. et al. Fragment-based discovery of a dual pan-RET/VEGFR2 kinase inhibitor optimized for single-agent polypharmacology. Angew. Chem. Int. Edn Engl. 54, 8717–8721 (2015).

    Article  CAS  Google Scholar 

  13. Davis, M.I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Godl, K. et al. An efficient proteomics method to identify the cellular targets of protein kinase inhibitors. Proc. Natl. Acad. Sci. USA 100, 15434–15439 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bantscheff, M. et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat. Biotechnol. 25, 1035–1044 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Ong, S.-E. et al. Identifying the proteins to which small-molecule probes and drugs bind in cells. Proc. Natl. Acad. Sci. USA 106, 4617–4622 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Remsing Rix, L.L. et al. GSK3 alpha and beta are new functionally relevant targets of tivantinib in lung cancer cells. ACS Chem. Biol. 9, 353–358 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Marsilje, T.H. et al. Synthesis, structure-activity relationships, and in vivo efficacy of the novel potent and selective anaplastic lymphoma kinase (ALK) inhibitor 5-chloro-N2-(2-isopropoxy-5-methyl-4-(piperidin-4-yl)phenyl)-N4-(2-(isopropylsulfonyl)phenyl)pyrimidine-2,4-diamine (LDK378) currently in phase 1 and phase 2 clinical trials. J. Med. Chem. 56, 5675–5690 (2013).

    Article  CAS  PubMed  Google Scholar 

  19. Sabbatini, P. et al. GSK1838705A inhibits the insulin-like growth factor-1 receptor and anaplastic lymphoma kinase and shows antitumor activity in experimental models of human cancers. Mol. Cancer Ther. 8, 2811–2820 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Shaw, A.T. et al. Ceritinib in ALK-rearranged non-small-cell lung cancer. N. Engl. J. Med. 370, 1189–1197 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nishio, M. et al. Phase I study of ceritinib (LDK378) in Japanese patients with advanced, anaplastic lymphoma kinase-rearranged non-small-cell lung cancer or other tumors. J. Thorac. Oncol. 10, 1058–1066 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Friboulet, L. et al. The ALK inhibitor ceritinib overcomes crizotinib resistance in non-small cell lung cancer. Cancer Discov. 4, 662–673 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fisher, T.L. & Blenis, J. Evidence for two catalytically active kinase domains in pp90rsk. Mol. Cell. Biol. 16, 1212–1219 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bjørbaek, C., Zhao, Y. & Moller, D.E. Divergent functional roles for p90rsk kinase domains. J. Biol. Chem. 270, 18848–18852 (1995).

    Article  PubMed  Google Scholar 

  25. Vik, T.A. & Ryder, J.W. Identification of serine 380 as the major site of autophosphorylation of Xenopus pp90rsk. Biochem. Biophys. Res. Commun. 235, 398–402 (1997).

    Article  CAS  PubMed  Google Scholar 

  26. Romeo, Y., Zhang, X. & Roux, P.P. Regulation and function of the RSK family of protein kinases. Biochem. J. 441, 553–569 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

    Article  Google Scholar 

  29. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Creixell, P. et al. Kinome-wide decoding of network-attacking mutations rewiring cancer signaling. Cell 163, 202–217 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wang, J., Chen, G., Li, M. & Pan, Y. Integration of breast cancer gene signatures based on graph centrality. BMC Syst. Biol. 5 (Suppl. 3), S10 (2011).

  32. Linding, R. et al. NetworKIN: a resource for exploring cellular phosphorylation networks. Nucleic Acids Res. 36, D695–D699 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Clauset, A., Newman, M.E.J. & Moore, C. Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004).

    Article  CAS  Google Scholar 

  34. Andersson, S., D'Arcy, P., Larsson, O. & Sehat, B. Focal adhesion kinase (FAK) activates and stabilizes IGF-1 receptor. Biochem. Biophys. Res. Commun. 387, 36–41 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Kang, Y. et al. Role of focal adhesion kinase in regulating YB-1-mediated paclitaxel resistance in ovarian cancer. J. Natl. Cancer Inst. 105, 1485–1495 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shiota, M. et al. Targeting ribosomal S6 kinases/Y-box binding protein-1 signaling improves cellular sensitivity to taxane in prostate cancer. Prostate 74, 829–838 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Sumi, N.J., Kuenzi, B.M., Knezevic, C.E., Remsing Rix, L.L. & Rix, U. Chemoproteomics reveals novel protein and lipid kinase targets of clinical CDK4/6 inhibitors in lung cancer. ACS Chem. Biol. 10, 2680–2686 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ott, G.R. et al. Discovery of clinical candidate CEP-37440, a selective inhibitor of focal adhesion kinase (FAK) and anaplastic lymphoma kinase (ALK). J. Med. Chem. 59, 7478–7496 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Konstantinidou, G. et al. RHOA-FAK is a required signaling axis for the maintenance of KRAS-driven lung adenocarcinomas. Cancer Discov. 3, 444–457 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. de Hoon, M.J.L., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Ritz, C., Baty, F., Streibig, J.C. & Gerhard, D. Dose-response analysis using R. PLoS One 10, e0146021 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Ong, S.-E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

    Article  CAS  PubMed  Google Scholar 

  43. Cox, J. et al. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat. Protoc. 4, 698–705 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Tyanova, S., Mann, M. & Cox, J. MaxQuant for in-depth analysis of large SILAC datasets. Methods Mol. Biol. 1188, 351–364 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  46. Welsh, E.A., Eschrich, S.A., Berglund, A.E. & Fenstermacher, D.A. Iterative rank-order normalization of gene expression microarray data. BMC Bioinformatics 14, 153 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  47. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zybailov, B. et al. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J. Proteome Res. 5, 2339–2347 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Kuenzi, B.M. et al. APOSTL: an interactive galaxy pipeline for reproducible analysis of affinity proteomics data. J. Proteome Res. 15, 4747–4754 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bastian, M., Heymann, S. & Jacomy Gephi: an open source software for exploring and manipulating networks. (Association for the Advancement of Artificial Intelligence, 2009).

  51. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal. Complex Syst. 2006, 1695 (2006).

    Google Scholar 

  53. Hanson, B. HiveR: 2D and 3D Hive Plots for R. R Package Version 0255 (2016).

  54. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York) 2009.

  55. Landthaler, M. et al. Molecular characterization of human Argonaute-containing ribonucleoprotein complexes and their bound target mRNAs. RNA 14, 2580–2596 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pratt, D. et al. NDEx, the network data exchange. Cell Syst. 1, 302–305 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Drug Discovery, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

    Brent M Kuenzi, Lily L Remsing Rix, Annamarie T Bryant & Uwe Rix

  2. Cancer Biology PhD Program, University of South Florida, Tampa, Florida, USA

    Brent M Kuenzi

  3. Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

    Paul A Stewart, Fumi Kinose, Theresa A Boyle & Eric B Haura

  4. Proteomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

    Bin Fang

  5. Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

    John M Koomen

Authors
  1. Brent M Kuenzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lily L Remsing Rix
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul A Stewart
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fumi Kinose
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Annamarie T Bryant
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Theresa A Boyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. John M Koomen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eric B Haura
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Uwe Rix
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

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)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio.2489

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer