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Phosphoproteomic Analysis of Leukemia Cells under Basal and Drug-treated Conditions Identifies Markers of Kinase Pathway Activation and Mechanisms of Resistance

2012; Elsevier BV; Volume: 11; Issue: 8 Linguagem: Inglês

10.1074/mcp.m112.017483

ISSN

1535-9484

Autores

Maria P. Alcolea, Pedro Casado, Juan‐Carlos Rodríguez‐Prados, Bart Vanhaesebroeck, Pedro R. Cutillas,

Tópico(s)

Plant biochemistry and biosynthesis

Resumo

Protein kinase signaling is fundamental to cell homeostasis and is deregulated in all cancers but varies between patients. Understanding the mechanisms underlying this heterogeneity is critical for personalized targeted therapies. Here, we used a recently established LC-MS/MS platform to profile protein phosphorylation in acute myeloid leukemia cell lines with different sensitivities to kinase inhibitors. The compounds used in this study were originally developed to target Janus kinase, phosphatidylinositol 3-kinase, and MEK. After further validation of the technique, we identified several phosphorylation sites that were inhibited by these compounds but whose intensities did not always correlate with growth inhibition sensitivity. In contrast, several hundred phosphorylation sites that correlated with sensitivity/resistance were not in general inhibited by the compounds. These results indicate that markers of pathway activity may not always be reliable indicators of sensitivity of cancer cells to inhibitors that target such pathways, because the activity of parallel kinases can contribute to resistance. By mining our data we identified protein kinase C isoforms as one of such parallel pathways being more active in resistant cells. Consistent with the view that several parallel kinase pathways were contributing to resistance, inhibitors that target protein kinase C, MEK, and Janus kinase potentiated each other in arresting the proliferation of multidrug-resistant cells. Untargeted/unbiased approaches, such as the one described here, to quantify the activity of the intended target kinase pathway in concert with the activities of parallel kinase pathways will be invaluable to personalize therapies based on kinase inhibitors. Protein kinase signaling is fundamental to cell homeostasis and is deregulated in all cancers but varies between patients. Understanding the mechanisms underlying this heterogeneity is critical for personalized targeted therapies. Here, we used a recently established LC-MS/MS platform to profile protein phosphorylation in acute myeloid leukemia cell lines with different sensitivities to kinase inhibitors. The compounds used in this study were originally developed to target Janus kinase, phosphatidylinositol 3-kinase, and MEK. After further validation of the technique, we identified several phosphorylation sites that were inhibited by these compounds but whose intensities did not always correlate with growth inhibition sensitivity. In contrast, several hundred phosphorylation sites that correlated with sensitivity/resistance were not in general inhibited by the compounds. These results indicate that markers of pathway activity may not always be reliable indicators of sensitivity of cancer cells to inhibitors that target such pathways, because the activity of parallel kinases can contribute to resistance. By mining our data we identified protein kinase C isoforms as one of such parallel pathways being more active in resistant cells. Consistent with the view that several parallel kinase pathways were contributing to resistance, inhibitors that target protein kinase C, MEK, and Janus kinase potentiated each other in arresting the proliferation of multidrug-resistant cells. Untargeted/unbiased approaches, such as the one described here, to quantify the activity of the intended target kinase pathway in concert with the activities of parallel kinase pathways will be invaluable to personalize therapies based on kinase inhibitors. Protein kinase signaling networks control cell proliferation, survival, motility, and metabolism and are deregulated in diseases such as cancer (1Manning B.D. Challenges and opportunities in defining the essential cancer kinome.Sci. Signal. 2009; 2: pe15Crossref PubMed Scopus (42) Google Scholar, 2Hanahan D. Weinberg R.A. Hallmarks of cancer: The next generation.Cell. 2011; 144: 646-674Abstract Full Text Full Text PDF PubMed Scopus (42734) Google Scholar). Inhibitors that primarily target the HER2, vascular endothelial growth factor receptor, epidermal growth factor receptor, and BCR-Abl kinases have been approved for the treatment of specific cancers (3Baselga J. Targeting tyrosine kinases in cancer: The second wave.Science. 2006; 312: 1175-1178Crossref PubMed Scopus (399) Google Scholar, 4Baselga J. Albanell J. Mechanism of action of anti-HER2 monoclonal antibodies.Ann. 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Kaminker J.S. Zhang Z. Croshaw R. Willis J. Dawson D. Shipitsin M. Willson J.K. Sukumar S. Polyak K. Park B.H. Pethiyagoda C.L. Pant P.V. Ballinger D.G. Sparks A.B. Hartigan J. Smith D.R. Suh E. Papadopoulos N. Buckhaults P. Markowitz S.D. Parmigiani G. Kinzler K.W. Velculescu V.E. Vogelstein B. The genomic landscapes of human breast and colorectal cancers.Science. 2007; 318: 1108-1113Crossref PubMed Scopus (2565) Google Scholar, 18Jones S. Zhang X. Parsons D.W. Lin J.C. Leary R.J. Angenendt P. Mankoo P. Carter H. Kamiyama H. Jimeno A. Hong S.M. Fu B. Lin M.T. Calhoun E.S. Kamiyama M. Walter K. Nikolskaya T. Nikolsky Y. Hartigan J. Smith D.R. Hidalgo M. Leach S.D. Klein A.P. Jaffee E.M. Goggins M. Maitra A. Iacobuzio-Donahue C. Eshleman J.R. Kern S.E. Hruban R.H. Karchin R. Papadopoulos N. Parmigiani G. Vogelstein B. Velculescu V.E. Kinzler K.W. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses.Science. 2008; 321: 1801-1806Crossref PubMed Scopus (3101) Google Scholar, 19Forbes S.A. Tang G. Bindal N. Bamford S. Dawson E. Cole C. Kok C.Y. Jia M. Ewing R. Menzies A. Teague J.W. Stratton M.R. Futreal P.A. COSMIC (the Catalogue of Somatic Mutations in Cancer): A resource to investigate acquired mutations in human cancer.Nucleic Acids Res. 2010; 38: D652-D657Crossref PubMed Scopus (445) Google Scholar, 20Greenman C. Stephens P. Smith R. Dalgliesh G.L. Hunter C. Bignell G. Davies H. Teague J. Butler A. Stevens C. Edkins S. O'Meara S. Vastrik I. Schmidt E.E. Avis T. Barthorpe S. Bhamra G. Buck G. Choudhury B. Clements J. Cole J. Dicks E. Forbes S. Gray K. Halliday K. Harrison R. Hills K. Hinton J. Jenkinson A. Jones D. Menzies A. Mironenko T. Perry J. Raine K. Richardson D. Shepherd R. Small A. Tofts C. Varian J. Webb T. West S. Widaa S. Yates A. Cahill D.P. Louis D.N. Goldstraw P. Nicholson A.G. Brasseur F. Looijenga L. Weber B.L. Chiew Y.E. DeFazio A. Greaves M.F. Green A.R. Campbell P. Birney E. Easton D.F. Chenevix-Trench G. Tan M.H. Khoo S.K. Teh B.T. Yuen S.T. Leung S.Y. Wooster R. Futreal P.A. Stratton M.R. Patterns of somatic mutation in human cancer genomes.Nature. 2007; 446: 153-158Crossref PubMed Scopus (2377) Google Scholar). Although cancer may ultimately result from the collective set of genetic mutations, establishing a functional connection between specific mutations and kinase signaling pathway activation is difficult with our current knowledge of the network components and how these are wired (21Haber D.A. Settleman J. Cancer: Drivers and passengers.Nature. 2007; 446: 145-146Crossref PubMed Scopus (154) Google Scholar, 22Weinstein I.B. Cancer. Addiction to oncogenes: The Achilles heal of cancer.Science. 2002; 297: 63-64Crossref PubMed Scopus (1477) Google Scholar). Methods for direct and systematic quantification of kinase activation should therefore complement genetic studies. Correlating these activities with biological end points (e.g., with the sensitivity to inhibitors or to RNA interference probes) or with genetic mutations could then be used to investigate kinase pathways at the system level, within their given molecular, biological, and physiological contexts. Advances in MS and separation technology have resulted in the development of several techniques that can be used for global and systematic analysis of signaling pathways. For example, previous studies have shown the potential of MS to quantify the activities of selected kinases in a multiplex manner (23Cutillas P.R. Khwaja A. Graupera M. Pearce W. Gharbi S. Waterfield M. Vanhaesebroeck B. Ultrasensitive and absolute quantification of the phosphoinositide 3-kinase/Akt signal transduction pathway by mass spectrometry.Proc. Natl. Acad. Sci. U. S. 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Analytical strategies for phosphoproteomics.Proteomics. 2009; 9: 1451-1468Crossref PubMed Scopus (394) Google Scholar, 27Huang P.H. Mukasa A. Bonavia R. Flynn R.A. Brewer Z.E. Cavenee W.K. Furnari F.B. White F.M. Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma.Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 12867-12872Crossref PubMed Scopus (320) Google Scholar, 28Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2807) Google Scholar, 29Wolf-Yadlin A. Hautaniemi S. Lauffenburger D.A. White F.M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks.Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 5860-5865Crossref PubMed Scopus (430) Google Scholar, 30Ballif B.A. Roux P.P. Gerber S.A. MacKeigan J.P. 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In this study, we have applied quantitative phosphoproteomics to analyze global phosphorylation in acute myeloid leukemia (AML) cell lines with different sensitivities to inhibitors originally developed to target PI3K, MEK, and JAK pathways. Our aim was to investigate two alternative hypotheses to account for the resistance of these cells to kinase inhibitors: namely that (i) activation of the inhibitor target(s) may be the main determinant of conferring sensitivity to the compounds (as stated by the theory of oncogene addiction (34Sawyers C.L. Shifting paradigms: The seeds of oncogene addiction.Nat. Med. 2009; 15: 1158-1161Crossref PubMed Scopus (76) Google Scholar)) or (ii) resistance to kinase inhibitors can be due to the activity of parallel pathways in the kinase network, as sometimes (but not always) observed in other cancer types (35Liu P. Cheng H. Santiago S. Raeder M. Zhang F. Isabella A. Yang J. Semaan D.J. Chen C. Fox E.A. Gray N.S. Monahan J. Schlegel R. Beroukhim R. Mills G.B. Zhao J.J. Oncogenic PIK3CA-driven mammary tumors frequently recur via PI3K pathway-dependent and PI3K pathway-independent mechanisms.Nat. Med. 2011; 17: 1116-1120Crossref PubMed Scopus (209) Google Scholar, 36Muellner M.K. Uras I.Z. Gapp B.V. Kerzendorfer C. Smida M. Lechtermann H. Craig-Mueller N. Colinge J. Duernberger G. Nijman S.M. A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer.Nat. Chem. Biol. 2011; 7: 787-793Crossref PubMed Scopus (132) Google Scholar). For this, we first identified (by LC-MS/MS) phosphorylation sites inhibited by three different kinase inhibitors in three different pairs of resistant/sensitive cell lines. We then asked whether those phosphorylation sites (which are markers of the kinase activities inhibited by the compounds) would correlate with sensitivity to the inhibition of proliferation by the compounds. We found that cells that differed in their relative sensitivity to kinase inhibitors had markedly different patterns in basal global phosphorylation in the absence of inhibitors, with some phosphorylation patterns correlating with sensitivity/resistance. The intensities of activity markers did not correlate with inhibition of cell proliferation, arguing that readouts of pathway activity are not necessarily reliable indicators of cellular sensitivities to inhibitors targeting such pathways. These findings also indicate that some cancer cells are not addicted to a single overactive kinase pathway and are consistent with the emerging view that susceptibility to an inhibitor may be not only due to activation of the target kinase but also to activation of parallel pathways (37Knight Z.A. Lin H. Shokat K.M. Targeting the cancer kinome through polypharmacology.Nat. Rev. Cancer. 2010; 10: 130-137Crossref PubMed Scopus (554) Google Scholar). By mining our data, we found that several PKC isoforms were more phosphorylated at sites known to correlate with their activity in AML resistant to kinase inhibitors, suggesting a role for PKCs in resistance to kinase inhibitors. A pharmacological approach was then used to test whether combining different kinase inhibitors affected the proliferation of sensitive and resistant AML cells. These results were consistent with the phosphoproteomics data and showed that multidrug-resistant AML cells proliferated using different kinase pathways parallel to the ones targeted by the drugs. AML cell lines included in the COSMIC cancer genome project (19Forbes S.A. Tang G. Bindal N. Bamford S. Dawson E. Cole C. Kok C.Y. Jia M. Ewing R. Menzies A. Teague J.W. Stratton M.R. Futreal P.A. COSMIC (the Catalogue of Somatic Mutations in Cancer): A resource to investigate acquired mutations in human cancer.Nucleic Acids Res. 2010; 38: D652-D657Crossref PubMed Scopus (445) Google Scholar) (AML-193, CMK, CTS, HEL, Kasumi-1, KG-1, MV4-11, and P31/FUJ) and murine NIH 3T3 fibroblasts were routinely cultured in Dulbecco's modified Eagle's medium at 37 °C in a humidified atmosphere of 5% CO2 in medium supplemented with 10% bovine serum, 100 units/ml penicillin, and 100 μg/ml streptomycin. AML cell lines were maintained at ∼5–10 × 105 cell/ml in RPMI supplemented with 50 μm β-mercaptoethanol. When indicated, the effect of kinase inhibitors on phosphorylation was determined by incubating cells with the respective inhibitor (1 μm PI-103, 500 nm MEK I inhibitor, or 500 nm JAK I inhibitor (catalog numbers 528100, 444937, and 420099 from Calbiochem, respectively)) for 1 h before cell lysis. AML cells were seeded at 5 × 105 cells/ml in fresh medium the day before the experiment. Each culture contained 5 × 106 cells in 10 ml, and three independent cell cultures were used per cell line and condition. The cells were harvested by centrifugation at 300 × g for 10 min and washed twice with ice-cold PBS containing 1 mm pV and 1 mm sodium fluoride. Lysis was performed using a denaturing buffer (20 mm HEPES, pH 8.0, 8 m urea, 1 mm pV, 1 mm sodium fluoride, 2.5 mm sodium pyrophosphate, 1 mm β-glycerolphosphate) at a concentration of 10 × 106 cells/ml. Further protein solubilization was achieved by sonication. Lysate debris was cleared by centrifugation at 20,000 × g for 10 min, and protein concentration of the supernatants was determined by Bradford assay. The samples were then kept frozen at −80 °C until further analysis. Validation of label-free quantitative phosphoproteomic was carried out in NIH 3T3 cells using an approach for assessing quantification accuracy (38Casado P. Cutillas P.R. A self-validating quantitative mass spectrometry method for assessing the accuracy of high-content phosphoproteomic experiments.Mol. Cell. Proteomics. 2011; 10.1074/mcp.M110.003079Abstract Full Text Full Text PDF PubMed Scopus (45) Google Scholar). Briefly, the cells were seeded at ∼35% confluency and cultured for 24 h, when cells reached ∼70% confluency. After preincubation at 37 °C for 30 min with the phosphatase inhibitor pV at a final concentration of 1 mm, the cells were then washed twice and trypsinized off the flask following the harvesting protocol described above. Extracts of pV-treated cells were mixed with decreasing amounts of extracts of nontreated cells (100, 90, 70, 50, 30, 10, and 0%) so that each experimental point contained 0.5 mg of protein to obtain a complex phosphopeptide titration curve to test matrix contribution to the performance of the LC-MS/MS quantitative method. Eight cell lines (AML-193, CMK, CTS, HEL, Kasumi-1, KG-1, MV4-11, and P31/FUJ) were seeded in 96-well plates at 105 cell/ml in triplicate for each condition. After a recovery period of 2 h, the cells were treated with increasing concentrations (1 nm, 10 nm, 100 nm, 1 μm, and 10 μm) of MEK I inhibitor (Calbiochem), JAK I inhibitor (Calbiochem), PI-103 (Calbiochem), and GF 109203X (Sigma). As controls, the cells were both treated with the vehicle (DMSO) and left untreated. After 48 or 72 h treatment, cell viability was assessed by MTS assay (CellTiter 96® AQueous One Solution cell proliferation assay; Promega Corporation, Madison, WI) and standard curves used to calculate cell numbers. Cell proliferation was calculated as the number of cells after inhibitor treatment minus number of cells seeded. According to the suppliers, the IC50 values of these inhibitors are 12 nm for MEK (MEK I inhibitor), 15 nm for JAK1 (JAK I inhibitor), 2 nm and 5–200 nm for PI3K isoforms and mTORC1 (PI-103), respectively, and 8–20 nm for PKC isoforms (GF 109203X). Sample reduction and alkylation were performed with 4.1 mm DTT and 8.3 mm iodoacetamide in the dark and at room temperature for 15 min each. After diluting the samples to 2 m urea with HEPES, pH 8.0, trypsinization was performed using 10 TAME (p-toluene-sulfonyl-L-arginine methyl ester) units of immobilized 1-chloro-3-tosylamido-7-amino-2-heptanone-trypsin/5 × 106 cells for 16 h at 37 °C. Digestion was stopped by adding TFA at a final concentration of 1%. The resultant peptide solutions were desalted using Sep-Pak C18 columns (Waters UK Ltd., Manchester, UK) according to the manufacturer's guidelines. Peptide elution was carried out with 5 ml of 50% ACN, 0.1% TFA. Phosphopeptide separation was achieved using an adapted immobilized metal ion affinity chromatography enrichment protocol (38Casado P. Cutillas P.R. A self-validating quantitative mass spectrometry method for assessing the accuracy of high-content phosphoproteomic experiments.Mol. Cell. Proteomics. 2011; 10.1074/mcp.M110.003079Abstract Full Text Full Text PDF PubMed Scopus (45) Google Scholar). In short, each sample was incubated for 30 min at room temperature with 300 μl of Fe(III)-coated-Sepharose high performance beads used as a 50% slurry in 50% ACN, 0.1% TFA. Unbound peptides were discarded, and beads were washed with 300 μl of 50% ACN, 0.1% TFA twice. The enriched phosphopeptide fraction was eluted with 300 μl of 1.5% ammonia water, pH 11. A second elution using 50 μl of 1.5% ammonia water, pH 11, containing 50% ACN allowed further enrichment. Eluted peptides were acidified by adding 10% formic acid and finally dried in a SpeedVac and stored at −80 °C. For phosphoproteomic experiments, dried phosphopeptides enriched samples were dissolved in 10 μl of 0.1% TFA and analyzed in a LC-MS/MS system. The latter consisted of an LTQ-Orbitrap XL mass spectrometer (Thermo Fisher Scientific) connected online to a nanoflow ultrahigh pressure liquid chromatography (nanoAcquity, Waters/Micromass) that delivered a flow rate of 5 μl/min (loading) and 400 nl/min (elution) with an operating back pressure of ∼3,000 psi. Separations were performed in a BEH 100-μm × 100-mm column (Waters/Micromass). The mobile phases were solution A, 0.1% formic acid in LC-MS grade water and solution B, 0.1% FA in LC-MS grade ACN. Gradient runs were from 1% B to 35% B in 100 min followed by a 5-min wash at 85% B and a 7-min equilibration step at 1% B. Full scan survey spectra (m/z 350–1600) were acquired in the LTQ-Orbitrap XL with a resolution of 60000 at m/z 400. A data-dependent analysis in multistage acquisition mode was employed in which the five most abundant multiply charged ions present in the survey spectrum were automatically mass-selected and fragmented by collision-induced dissociation (normalized collision energy 35%). MS scans were followed by five MS/MS scans (m/z 50–2000), which allow the acquisition of at least 15 data points/chromatographic peak. Dynamic exclusion was enabled with the exclusion list restricted to 500 entries, exclusion duration of 40 s, and mass window of 10 ppm. LTQ-Orbitrap MS/MS data were smoothed and centroided using Mascot Distiller. The processed files were searched against the mouse sequence library in the international protein index (IPI Mouse v3.49, 165169 sequences) or SwissProt (version 09-2010 containing 20,286 human entries) using the Mascot search engine. Searches were automated with Mascot Daemon (v2.2.2; Matrix Science, London, UK). The parameters included, choosing trypsin as digestion enzyme with one missed cleavage allowed, carbamidomethyl (C) was set as fixed modification, and pyro-glu (N-terminal), oxidation (M), and phospho (STY) were variable modifications. The data sets were searched with a mass tolerance of ± 7 ppm and a fragment mass tolerance of ± 800 mmu. The hits were considered significant when the Mascot expectation value was <0.05. The false positive rate as estimated by searches against a decoy database was less than 2%. Sites of modification are reported when they had delta scores <20. Delta scores were calculated as described (39Savitski M.M. Lemeer S. Boesche M. Lang M. Mathieson T. Bantscheff M. Kuster B. Confident phosphorylation site localization using the Mascot Delta Score.Mol. Cell. Proteomics. 2011; 10.1074/mcp.M110.003830Abstract Full Text Full Text PDF Scopus (222) Google Scholar). Otherwise the site of modification was deemed to be ambiguous; in such cases phosphopeptides are reported as the start-end residues within the protein sequence. Phosphopeptides identified by Mascot with a statistical significant threshold were placed in a database of peptides quantifiable by LC-MS and quantified by PESCAL. This program, written in-house

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