Proteomics Analysis of Cellular Imatinib Targets and their Candidate

Proteomics Analysis of Cellular Imatinib Targets and their Candidate Downstream Effectors Susanne B. Breitkopf,†,‡,§ Felix S. Oppermann,†,‡,| Gyo ¨ rgy Ke´ri,⊥,# Markus Grammel,†,¶ and ,†,O Henrik Daub* Department of Molecular Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany, Vichem Chemie Ltd., Herman Otto´ u. 15., Budapest, 1022, Hungary, Pathobiochemistry Research Group of the Hungarian Academy of Science, Semmelweis University, Puskin u. 9., Budapest, 1088, Hungary, and Kinaxo Biotechnologies GmbH, Am Klopferspitz 19, 82152 Martinsried, Germany Received August 20, 2010

Inhibition of deregulated protein kinases by small molecule drugs has evolved into a major therapeutic strategy for the treatment of human malignancies. Knowledge about direct cellular targets of kinaseselective drugs and the identification of druggable downstream mediators of oncogenic signaling are relevant for both initial therapy selection and the nomination of alternative targets in case molecular resistance emerges. To address these issues, we performed a proof-of-concept proteomics study designed to monitor drug effects on the pharmacologically tractable subproteome isolated by affinity purification with immobilized, nonselective kinase inhibitors. We applied this strategy to chronic myeloid leukemia cells that express the transforming Bcr-Abl fusion kinase. We used SILAC to measure how cellular treatment with the Bcr-Abl inhibitor imatinib affects protein binding to a generic kinase inhibitor resin and further quantified site-specific phosphorylations on resin-retained proteins. Our integrated approach indicated additional imatinib target candidates, such as flavine adenine dinucleotide synthetase, as well as repressed phosphorylation events on downstream effectors not yet implicated in imatinib-regulated signaling. These included activity-regulating phosphorylations on the kinases Btk, Fer, and focal adhesion kinase, which may qualify them as alternative target candidates in Bcr-Abldriven oncogenesis. Our approach is rather generic and may have various applications in kinase drug discovery. Keywords: kinase inhibitors • affinity purification • SILAC • phosphoproteomics • protein kinases • imatinib • chronic myeloid leukemia

Introduction Protein kinases are critical regulators in human cancer and play major roles in tumor cell proliferation, migration and survival.1 Aberrant kinase activity has been identified as a major factor contributing to disease progression in various human malignancies.2 The targeted inhibition of protein kinases has therefore emerged as a major therapeutic approach and fueled the development of various kinase-selective drugs, such as cellpermeable small molecule inhibitors, with the potential to address currently unmet medical needs in cancer therapy.3,4 * To whom correspondence should be addressed. E-mail: daub@ biochem.mpg.de. † Max Planck Institute of Biochemistry. ‡ These author contributed equally to this work. § Present address: Beth Israel Deaconess Medical Center, Harvard Medical School, 3 Blackfan Circle, Boston, MA 02115. | Present address: Kinaxo Biotechnologies GmbH, Am Klopferspitz 19, 82152 Martinsried, Germany. ⊥ Vichem Chemie Ltd. # Semmelweis University. ¶ Present address: Laboratory of Chemical Biology and Microbial Pathogenesis, The Rockefeller University, 1230 York Avenue, New York, NY 10065. O Kinaxo Biotechnologies GmbH. 10.1021/pr1008527

 2010 American Chemical Society

Imatinib (also known as imatinib mesylate, Gleevec, Glivec or STI571) was one of the first small molecule inhibitors developed for the targeted inactivation of kinases in human cancer. Imatinib efficiently blocks the activities of several tyrosine kinases including Abl, Kit and the platelet-derived growth factor receptor and has demonstrated impressing clinical efficacy in human malignancies such as chronic myeloid leukemia (CML).5 In most cases, CML pathogenesis results from the Philadelphia (Ph) chromosome translocation which generates the causative BCR-ABL oncogene.6 By selective interference with the deregulated Bcr-Abl kinase activity imatinib treatment results in impressive and long-lasting responses in chronic-phase CML patients.5 However, CML patients in advanced disease states such as the accelerated and blast crisis phases typically relapse and acquire resistance to imatinib within several months.7 In the majority of these cases, resistance formation is due to mutations in the kinase domainencoding region of the BCR-ABL oncogene, which selectively interfere with imatinib binding without abrogating the catalytic activity of Abl tyrosine kinase.7-9 Molecular resistance of the targeted Bcr-Abl oncoprotein in relapsed CML patients demonstrates its continued requirement for disease progression. Journal of Proteome Research 2010, 9, 6033–6043 6033 Published on Web 09/27/2010

research articles Structural data revealed that imatinib selectively interacts with an inactive conformation of the Abl kinase, which is destabilized by many imatinib resistance-conferring mutations.10 These mechanistic insights provided a rational basis for the development of second-generation inhibitors, such as the small molecule drugs bosutinib and dasatinib, which target the active kinase conformation and thereby overcome imatinib resistance in many Abl kinase variants.7-9 However, similar to imatinib, these second-generation drugs lack inhibitory activity against the frequently occurring Thr-315 to Ile mutation in the Abl kinase domain, which directly interferes with drug binding irrespective of the kinase conformation. Drug development and clinical efforts are ongoing with the goal to address all possible drug-resistant Abl kinase mutants.9 Alternative to targeted inhibition of the mutated, causative oncoprotein, therapeutic intervention might also be directed against essential downstream mediators of Bcr-Abl. The Bcr-Abl fusion protein possesses constitutive tyrosine kinase activity and assembles multiprotein complexes that trigger proliferative and antiapoptotic signaling as well as regulation of the actin cytoskeleton.11,12 Previous investigations have mostly been hypothesis-driven and placed known signal transducing modules in Bcr-Abl signaling, such as the Ras/mitogen-activated protein kinase (MAPK) cascades, phosphatidylinositol-3 kinase/Akt signaling and the signal transducer and activator of transcription (STAT) pathway,7 and their concomitant activation is implicated in the malignant transformation in Bcr-Abl-expressing leukemia cells. In addition to these well documented pathways, Bcr-Abl might engage additional signal transducers that are regulated by reversible phosphorylation events and have not been revealed by previous studies. Recent developments in proteomics, including the availability of rapid, sensitive and highly accurate hybrid ion trap-orbitrap mass spectrometers, improved phosphopeptide fractionation procedures and breakthroughs in MS data processing and quantification, make MS-based phosphoproteomics the method-of-choice for unbiased signal transduction analyses.13-20 Quantitative phosphorylation analyses enabled by stable isotope labeling by amino acids in cell culture (SILAC) has been used to identify imatinib-induced tyrosine phosphorylation changes in K562 cells21 and, more recently, for a global survey of phosphoproteome regulation upon dasatinib treatment of the same cell line.22 The identification of downstream protein kinases with essential roles in Bcr-Abl signal transmission would be of particular interest, as such knowledge might define alternative, small molecule-tractable targets in case of relapse due to drug-insensitive Abl kinase mutants. In addition to phosphorylating their cellular substrates, protein kinases regulate each other by reversible phosphorylation events and, moreover, many protein kinases undergo autophosphorylation upon cellular activation.23 Thus, comprehensive monitoring of imatinib-induced phosphorylation changes on protein kinases might allow for the identification of druggable downstream targets in Bcr-Abl signaling and thus contribute to the nomination of new candidates for therapeutic intervention. To analyze small molecule-tractable protein kinases with high analytical sensitivity, we and others have previously developed affinity chromatography procedures that employ combinations of immobilized kinase inhibitors for selective kinase prefractionation from total cell extracts.24-26 This approach combined with SILAC enabled us to quantify the cell cycle regulation of more than 200 protein kinases and to detect more than 1000 distinct phosphorylation events on these key 6034

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signaling enzymes. A similar kinase enrichment strategy reported by Bantscheff et al. was used to identify cellular targets of the clinical Bcr-Abl kinase inhibitors imatinib, dasatinib and bosutinib as well as downstream signaling elements upon treatment of K562 cells with these drugs.26 To further expand the knowledge obtained in this previous work, in particular to identify additional kinase candidates downstream of direct imatinib targets, we revisited the imatinib paradigm in our present study. Using imatinib-treated K562 CML cells as a model system, we here present proof-of-concept for an integrated proteomics strategy that quantitatively assesses both direct drug targets and their downstream signal transducers, and report previously unknown target protein candidates falling in both categories.

Experimental Section Cell Culture. For all SILAC experiments, the human CML cell line K562 (ATCC, # CCL-243) was cultured in suspension in RPMI1640 medium (Invitrogen) containing 10% dialyzed fetal bovine serum (Invitrogen), 1% (10,000 units/ml) penicillin/ (10 mg/mL) streptomycin (Invitrogen) and either 45 mg/L unlabeled L-arginine and 76 mg/L unlabeled L-lysine (Arg0, Lys0) or equimolar amounts of L-[U-13C6]arginine and L-[2H4]lysine (Arg6, Lys4) or L-[U-13C6,15N4] and L-[U-13C6,15N2]lysine (Arg10, Lys8) (Cambridge Isotope Laboratories or Sigma). K562 cells were grown in SILAC medium for five cell doublings on 15 cm dishes and then transferred to spinner flasks into 500 mL fresh SILAC medium at a cell density of 0.25 × 106 cells/mL. After two further rounds of cell division, K562 cells were either treated with 1 or 10 µM imatinib mesylate (ACC Corporation) or control-incubated with DMSO for 90 min before harvesting by centrifugation. Our cell culture strategy yielded total cell numbers of 5 × 108 per labeling condition. Harvested K562 cells were washed once with ice-cold PBS, snap-frozen in liquid nitrogen and stored at -80 °C until cell lysis. Cell Lysis and Kinase Enrichment. Kinase inhibitor resins containing the immobilized compounds VI16832, purvalanol B, bisindolylmaleimide X, AX14596 and SU6668 were essentially prepared as described previously,24,25,27 with the only differences that 2 volumes of 1.5 mM (instead of previously 0.75 mM)24 VI16832 solution and 2 volumes of 5 mM (instead of 10 mM)25 bisindolylmaleimide X were coupled to 1 volume of aspirated epoxy-activated Sepharose 6B for immobilization. For each of the two replicate experiments, we prepared a mixed kinase inhibitor resin containing 0,5 mL of the VI16832 and purvalanol B resins and 0,33 mL of the bisindolylmaleimide X, AX14596 and SU6668 resins. Frozen cell pellets from differentially encoded and treated K562 cell populations were solubilized with 9 mL of lysis buffer containing 50 mM Hepes-NaOH, pH 7.5, 150 mM NaCl, 0.5% Triton X-100, 1 mM EDTA, 1 mM EGTA, 1 mM phenylmethylsulfonyl fluoride, 10 mM NaF, 2.5 mM Na3VO4, 50 ng/mL calyculin A (Alexis Biochemicals, San Diego, CA), 10 µg/mL aprotinin, 10 µg/mL leupeptin, and 1% phosphatase inhibitor mixtures 1 and 2 (Sigma) for 1 h at 4 °C. Cell debris was removed by centrifugation (20 min at 13,000 rpm) and by further filtering through 0.22-µm mixed esters of cellulose membranes (Millipore). Protein concentration was measured using the BCA assay (Pierce). 55 mg of each of the three differentially labeled K562 lysates were adjusted to a final NaCl concentration of 1 M and a final volume of 10 mL. SILACencoded samples were then subjected to parallel in vitro associations with 0.7 mL mixed kinase inhibitor resin for 2 h at 4 °C in the dark. Beads were then washed three times with

Quantitative Kinase Drug Proteomics 10 mL lysis buffer adjusted to 1 M NaCl and twice with lysis buffer containing 150 mM NaCl. For elution of bound proteins, mixed kinase inhibitor beads were repeatedly incubated for 10 min with 1.4 mL elution buffer (20 mM Tris-HCl pH 7.5, 5 mM DTT, 0.5% SDS) at 50 °C. Aliquots of the resulting elution fractions were analyzed by SDS-PAGE and silver staining. Protein-containing elution fractions were pooled and lyophilized and then resuspended with water in 1/10 of the initial volume prior to protein precipitation according to the protocol by Wessel and Flu ¨ gge.28 Sample Preparation for Mass Spectrometry. In each of the two replicate analyses, 25% of the kinase-enriched fraction was solubilized in 20 mM HEPES buffer (pH 7.5) containing 7 M urea, 2 M thiourea, 1% n-octylglucoside and then reduced, alkylated and sequentially digested with the endoprotease Lys-C (Wako) and modified trypsin (sequencing grade, Promega) as described previously.13 The resulting peptide samples were then separated by strong cation exchange chromatogra¨ KTA explorer system into a flow-through and 6 phy on an A elution fractions using a 1 mL Resource S column (GE Healthcare) according to a published protocol.13 The remaining protein pellets were dissolved in 1.5x LDS buffer and separated on a 10% NuPage Bis-Tris gel (Invitrogen) according to the manufacturer’s instructions. Proteins were stained using the Collodial Blue staining kit (Invitrogen). In both SILAC experiments, the gel was cut into 16 slices followed by in-gel digestion with trypsin.29 Twenty percent of the resulting peptide mixtures were mixed with an equal volume of 1% TFA, 5% ACN and then loaded on C18 StageTips.30 After washing twice with buffer containing 0.5% acetic acid and 0.1% TFA, bound peptides were eluted with buffer containing 0.5% acetic acid and 80% ACN and then concentrated in a SpeedVac prior to further analysis. The larger part of 80% of each fraction from the tryptic ingel digests were subjected to phosphopeptide enrichment using titanium dioxide (TiO2) microspheres.31,32 The TiO2 beads (GL Science, Tokyo, Japan) were first equilibrated by consecutive incubations with 20 mM NH4OH in 20% acetonitrile (ACN), pH 10.5, washing buffer (0.1% TFA, 50% ACN) and loading buffer (5 g/L 2,5-dihydrobenzoic acid in 55% ACN). Trypsin digests from adjacent gel slides were combined to a total of 8 peptide samples for further phosphopeptide enrichment. Each of them was adjusted to a final concentration of 30% ACN, 2 M urea and incubated with 5 mg equilibrated TiO2 beads for 30 min at room temperature on a rotating wheel. Afterward, beads were washed once with 100 µL of loading buffer, three times with 1.5 mL of washing buffer, and phosphopeptides were eluted by incubating twice with 30 µL of 20 mM NH4OH in 20% ACN, pH 10.5. Elution fractions were combined and passed through a C8 StageTip followed by a 30-µL rinse with 80% ACN, 0.5% acetic acid. After adjusting to a pH of 6, samples were concentrated to 3 µL and mixed with an equal volume of 4% ACN, 0.2% TFA. We further performed phosphopeptide purifications with TiO2 microspheres from the seven SCX chromatography fractions of in-solution digested, kinase-enriched samples in each of the two replicate experiments. Additionally, total peptide extractions with C18 StageTips were done with 20% aliquots of the SCX fractions in experiment 2. Mass Spectrometry Analysis. MS analyses were done as described previously.13,24 Briefly, peptide separations were done on 15-cm analytical columns (75-µm inner diameter) in-house packed with 3-µm C18 beads (Reprosil-AQ Pur, Dr. Maisch) using a nanoflow high pressure liquid chromatography system

research articles (Agilent Technologies 1100). Peptides were eluted with 2 h gradients from 5 to 40% ACN in 0.5% acetic acid and directly electrosprayed into a LTQ-Orbitrap mass spectrometer (Thermo Fisher Scientific) by a nanoelectrospray ion source (Proxeon Biosystems). The LTQ-Orbitrap was operated in the datadependent mode to automatically switch between full scan MS in the orbitrap analyzer (with resolution r ) 60 000 at m/z 400) and the fragmentation of the five most intense multiply charged peptide ions by either MS/MS or multistage activation in the LTQ part of the instrument, the latter being triggered upon neutral losses of 97.97, 48.99, or 32.66 m/z.33 For all full scan measurements in the orbitrap detector a lock-mass strategy was used for internal calibration as described.34 Typical mass spectrometric conditions were: spray voltage, 2.4 kV; no sheath and auxiliary gas flow; heated capillary temperature, 150 °C; normalized collision energy 35% for MSA in LTQ. The ion selection threshold was 500 counts for MS2. An activation q ) 0.25 and activation time of 30 ms were used. Peptide Identification, Quantification, and Data Analysis. All MS raw files from both biological replicate analyses were collectively processed with the MaxQuant software suite (version 1.0.13.12), which performs peak list generation, SILACbased quantification, estimation of false discovery rates, peptide to protein group assembly, and data filtration and presentation as described.20 Data were searched against a concatenated forward and reversed version of the human International Protein Index (IPI) database version 3.37 containing 69141 protein entries and 175 frequently detected contaminants (such as porcine trypsin, human keratins and LysC) using the Mascot search engine (Matrix Science; version 2.2.04). Cysteine carbamidomethylation was set as a fixed modification and methionine oxidation, protein N-acetylation, loss of ammonia from N-terminal glutamine as well as phosphorylation of serine, threonine and tyrosine residues were allowed as variable modifications. Spectra resulting from isotopically labeled peptides, as revealed by presearch MaxQuant analysis of SILAC partners, were searched with the fixed modifications Arg6 and Lys4 or Arg10 and Lys8, respectively, whereas spectra for which a SILAC state could not be assigned before database searching were searched with Arg6, Arg10, Lys4 and Lys8 as variable modifications. The accepted mass tolerance was set to 7 p.p.m for precursor ions and to 0.5 Da for fragment ions. The minimum required peptide length was 6 amino acids and up to two missed cleavage sites and three isotopically labeled amino acids were permitted. The accepted FDR was 1% for both protein and peptide identifications, and the cutoff for the posterior error probability (PEP) of peptides was set to 10%. Phosphorylation site assignments were performed by a modified version of the PTM scoring algorithm13 implemented in MaxQuant. Phosphorylation site assignments were classified as class I sites in case of a localization probability of at least 0.75 and a score difference of at least 5 to the second most likely assignment. Network Analysis. All IPI identifiers of all quantified protein groups were matched to respective Ensembl entries using BioMart and then uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (version 8.2).35 We retrieved interactions that were of at least high confidence (score g 0.7) based exclusively on experimental and database knowledge while excluding all other prediction methods implemented in STRING (such as textmining and coexpression). The resulting networks were visualized using Cytoscape.36 Additionally, we randomly selected subsets of the Journal of Proteome Research • Vol. 9, No. 11, 2010 6035

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IPI database that contained the same number of entries as present in our experimental data. This was repeated five times to determine the average numbers of network nodes and edges in random protein selections by STRING analysis. Data Availability. All raw data files from this study have been up-loaded to the Tranche file-sharing system (ProteomeCommons. org, hash: 1jgzdG97a2b6CKjEqKrhQr2xSu6uyff7dft5qynd/cBqaOTO37Re2Ys7GHbDiOX5J+J8FQjtVQZ799wYWADqviLesoIAAAAAAABHsA))). Furthermore, annotated phosphopeptide spectra for all identified class I sites have been deposited (hash: G3KHiKwaE7x1r164vN4p1uMmbNrRDbVozsgmlmLi1qsAnXFPV2mcHTeyJJpQIqNtygeQbnZe3WtmmrsknUeyVmXkMh8AAAAAAAfdqQ))).

Results and Discussion Experimental Strategy. As judged by initial immunoblot analysis of total K562 cell lysates, maximal repression of cellular tyrosine phosphorylation was evident after 45 min treatment with 10 µM imatinib (data not shown). We further reasoned that serine/threonine phosphorylation events located downstream in imatinib-regulated signaling might exhibit slower dephosphorylation kinetics than direct tyrosine kinase substrates and therefore opted for 90 min treatment in subsequent SILAC experiments. We also decided against longer stimulation times of several hours used in earlier studies,12,26 as such treatment schemes might bear an increased risk of accumulating secondary changes far away from the initial sites of imatinib action. To enable sensitive and unbiased detection of imatinib effects on the kinase inhibitor-tractable subproteome, we implemented SILAC for K562 leukemia cells grown in spinner flasks. We differently encoded three populations of K562 cells by culturing them in medium containing either normal arginine and lysine (Arg0 and Lys0) or combinations of heavier isotopic variants of the two amino acids (Arg6 and Lys4, or Arg10 and Lys8) (Figure 1). Differently labeled cells were treated for 90 min with either 1 µM or 10 µM imatinib, or control-incubated with solvent prior to cell lysis. We subjected each of the resulting lysates to a separate in vitro association with a mixture of five kinase inhibitor resins. This affinity purification strategy was designed for comprehensive enrichment of drug-interacting protein kinases along with their associating factors, and we used our previously established incubation conditions to ensure preservation of cellular protein phosphorylation states.24,25 Bound proteins were eluted from the resin mixtures, and we pooled the kinase-enriched fractions from differentially encoded and treated K562 cells prior to further sample processing. Three fourth of the combined material was resolved by gel electrophoresis and in-gel digested with trypsin, followed by StageTip extractions of total peptide samples and phosphopeptide purification with TiO2 beads. The remaining inhibitor resin-enriched material was digested with trypsin in-solution prior to SCX chromatography and TiO2 enrichment of phosphorylated peptides (Figure 1). Thus, our combined prefractionation strategy exploited the different fractionation principles of gel-based and gel-free approaches for more comprehensive phosphopeptide coverage than possible with either approach alone.24 All peptide and phosphopeptide fractions were analyzed by nanoscale liquid chromatography-tandem MS (LC-MS/MS) analysis on a linear ion trap/orbitrap (LTQ-Orbitrap) hybrid mass spectrometer. Moreover, to assess the biological reproducibility of quantitative MS data, we repeated the whole analysis in a replicate experiment with a modified SILAC scheme for the different treatment conditions. All resulting raw data files were then collectively processed with the MaxQuant software suite for 6036


Figure 1. Schematic illustration of the experimental strategy. Three populations of K562 CML cells were SILAC-encoded with normal or isotopically labeled arginine and lysine and either 1 µM imatinib, 10 µM imatinib, or DMSO before lysis. Two biological replicate experiments were performed with different SILAC schemes. In both experiments, each of the three lysates was subjected to separate affinity purification with a mixture of five kinase inhibitor resins. Resin-bound proteins were eluted and pooled. Subsequently, fractions of the pooled kinase-enriched material were separated by gel electrophoresis prior to or by SCX chromatography after digestion with trypsin. Phosphopeptides were enriched with titanium dioxide beads and total peptide samples were prepared by desalting with C18-StageTips from all peptide fractions. All resulting samples were analyzed by LC-MS/MS on an LTQ-Orbitrap.

the integrated analysis of both site-specific phosphorylation changes and protein binding to the kinase inhibitor resin upon imatinib treatment. Our goal was to demonstrate proof-of-concept for a proteomics approach designed to identify both regulated effector proteins, which are downstream of imatinib-inhibited kinases and therefore exhibit repressed site-specific phosphorylations without changes in protein abundance, and possible direct cellular imatinib targets, which are prevented from resin interactions by bound imatinib. Analysis of the Kinase Inhibitor-Enriched Subproteome. In total, we identified more than 2000 inhibitor resin-retained proteins with an accepted false-discovery rate of less than 1%. Protein ratios for imatinib versus control-treated K562 cells could be determined for 1275 distinct proteins, of which 683 were quantified in both biological replicate experiments (Figure 2A, Supplementary Table 1, Supporting Information). Due to

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research articles group prone to higher interexperimental variability. Both experiments combined, we quantified as many as 868 distinct phosphorylation events on K562 cell-derived protein kinases which account for three times as many phosphorylation sites on protein kinases compared to an earlier study.26 Thus, our current study considerably expands previous knowledge on phospho-modifications in the expressed K562 cell kinome. While protein and phosphorylation site ratios were determined for only 23% of all quantified proteins, this was possible for the majority (64%) of the 213 quantified protein kinases (Figure 2B). About 81% of all identified site-specific phosphorylations were located on serine residues, whereas phosphorylated threonines and tyrosines accounted for about 13 and 6% of all phosphorylation sites, respectively (Figure 2C).

Figure 2. Overview of results from SILAC experiments. (A) Comparison of the two independent SILAC experiments regarding the quantified proteins and quantified phosphorylation sites with confident site localization (class I sites with p g 0.75). Numbers are separately shown for all proteins and for protein kinases. (B) Numbers of all proteins and protein kinases for which protein ratios and class I phosphorylation site ratios were quantified. The overlapping regions indicate the protein and protein kinase numbers for which both protein and phosphorylation site ratios were obtained within this study. (C) Numbers and distribution of serine, threonine and tyrosine phosphorylation for all quantified class I phosphorylation sites are shown for each of the two biological replicate experiments and the overlap between the two experiments.

the kinase enrichment strategy we obtained such quantitative data for more than 170 members of the protein kinase superfamily, which indicate substantial enrichment considering that the kinome accounts for only 1.7% of the human genome. Our affinity purifications with a mixture of broadly selective, ATP competitive inhibitors fractionated for other likely direct binders that did not belong to the protein kinase superfamily, for example other types of nucleotide-utilizing enzymes. We detected many proteins falling into such categories, including various dehydrogenases and lipid kinases. In addition to protein quantification based on unphosphorylated peptides, phosphopeptide enrichment allowed for quantification of more than 13 500 identified phosphopeptides, which harbored 1842 distinct phosphorylation sites that could be localized to specific serine, threonine or tyrosine residues with high confidence (class I sites with a localization probability g0.75) (Supplementary Tables 2 and 3, Supporting Information). For 898 class I phosphorylation sites ratios were determined in both biological replicate experiments, and, notably, with 504 more than half of all repeatedly quantified sites were detected on protein kinases (Figure 2A). The overlap of phosphorylation sites quantified in both experiments was higher for protein kinases compared to all other proteins, which might be due to a certain subset of nonspecific binders in the latter

Furthermore, cellular interaction partners of direct inhibitor targets were also expected to be captured by subsequent MS analysis in case such interactions were preserved during cell lysis and affinity purification. In the present study, we used 1 M NaCl-containing buffer to promote protein kinase selectivity in the enrichment step.37,38 Although such high salt concentrations can disrupt hydrophilic protein-protein binding, the majority of protein kinase interactions with other proteins are apparently not suppressed according to our recent comparison of kinase enrichment in low and high salt conditions.38 Therefore, we reasoned that the current data sets can be used to get an impression of the overall relationships within the affinity-purified subproteome. We used STRING to retrieve known interactions among all proteins for which quantitative data was available from both biological replicate experiments. Out of these 900 proteins submitted to STRING 489 reappeared within a complex network, in which we specified all nodes depending on whether they were identified as phosphoproteins and/or represented protein kinases (Supplementary Figure 1, Supplementary Table 4, Supporting Information). Notably, we detected as many as 3435 edges within the STRING-derived network, which were almost 20-fold more than obtained for the same number of randomly selected IPI database identifiers (Supplementary Figure 1). This indicated a high degree of network connectivity within the enriched subproteome, which was in part due to the identification of many known kinase interactors including several cyclins, SH2-domain containing proteins and regulatory kinase subunits (Supplementary Figure 1, Supplementary Table 4, Supporting Information). Moreover, we identified prominent modules of proteins involved in translation, RNA processing and proteasomal protein degradation. Detection of these rather abundant proteins might result from unspecific binding or sedimentation in the inhibitor affinity purification step instead of specific interactions with coupled inhibitors or bound inhibitor targets. However, we found the corresponding gene ontology (GO) biological process categories highly overrepresented in the proteins detected upon kinase inhibitor enrichment compared to those identified in a parallel analysis of K562 total cell extracts (data not shown), thus pointing to preferred detection of these protein machineries as a byproduct of our chemical proteomics strategy. Identification of Imatinib-Interacting Proteins. Due to the SILAC strategy combined with parallel inhibitor affinity purifications we could identify proteins that exhibited decreased resin binding upon prior imatinib treatment of K562 cells. To obtain reliable data, we filtered all SILAC-based quantifications for proteins that were recorded in both biological replicate experiments. Moreover, we considered only those proteins as target candidates which were reproducibly quantified with Journal of Proteome Research • Vol. 9, No. 11, 2010 6037

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Figure 3. Quantified K562 proteins exhibiting dose-dependent suppression of kinase inhibitor resin binding upon cellular imatinib treatment. Average values are shown for K562 cell proteins quantified in both replicate experiments whose interaction with the immobilized kinase inhibitors was reduced by at least 50% upon 10 µM imatinib treatment of K562 cells.

SILAC ratios below 0.5 for affinity resin-retained proteins from 10 µM imatinib versus control incubated cells. (Supplementary Table 1, Supporting Information). Evidently, proteins with such properties comprise direct cellular imatinib targets as well as their interaction partners, as monitored for Bcr-Abl and its associated signal transducers Grb2 and SHIP2. Grb2 is known to bind to Bcr-Abl in a phosphotyrosine-dependent manner via its SH2 domain, whereas SHIP2 was reported to bind to the SH3 domain of Abl.7,11 In accordance with the reported high imatinib affinity of Bcr-Abl, its binding to the multiinhibitor resin was already fully suppressed upon exposure of K562 cells to 1 µM imatinib (Figure 3, Supplementary Figure 2A, Supporting Information).5 We detected similar resin binding properties for discoidin domain receptor 1 (DDR1), a receptor tyrosine kinase recently identified as a high affinity target by Bantscheff and colleagues.26 We further monitored imatinibdependent competition for quinone reductase 2 (NQO2) and the tyrosine kinase Syk (Figure 3). These two enzymes have been previously characterized as additional imatinib targets.26,39,40 While the known high affinity for NQO2 was reflected by its nearly complete competition at both imatinib concentrations of 1 µM and 10 µM, Syk binding was prevented in a dose-dependent manner, with less than 40% still retained at the higher imatinib concentration as indicated by an average binding ratio of 0.36 (Figure 3, Supplementary Figure 2A, Supporting Information). Our results regarding Syk were consistent with earlier biochemical data, which identified Syk as a low-affinity target with a reported Ki value of 5 µM for imatinib.39 Furthermore, we identified two phosphatidylinositol-4-kinase type-2 isoforms R and γ (PIP4K2A and PIP4K2C) as potential new imatinib targets (Figure 3, Supplementary Figure 2B). Although it cannot be formally excluded that these lipid kinases interacted indirectly with immobilized inhibitors and imatinib, direct binding appears more likely due to their structural and functional similarities to protein kinases. Imatinib-dependent displacement of these lipid kinases was similar as observed for Syk, and the moderate effect of imatinib argues against their substantial cellular inhibition at therapeutically relevant drug concentrations (Figure 3). However, as even minor structural differences can significantly alter drug affinities, our data might warrant further testing of phosphatidylinositol4-kinases against related drugs such as nilotinib, INNO-406 or development compounds based on the phenylaminopyridine 6038


scaffold of imatinib. Additionally, we identified the flavine adenine dinucleotide (FAD) synthetase encoded by the FLAD1 gene as a new potential target of imatinib (Figure 3, Supplementary Figure 2B). FAD synthetase is a key metabolic enzyme which catalyzes the formation of FAD by adenylation of flavin mononucleotide.41 FAD represents a redox cofactor of many flavoproteins and is therefore essential for many biological processes, suggesting that pharmacological inactivation of FAD synthetase might cause toxicity. Notably, FAD is present as a prosthetic group in the described imatinib target NQO2 and functions in the electron transfers catalyzed by this oxidoreductase. Thus, our identification of FAD synthetase might point to common structural features that determine imatinib binding to some FAD-utilizing or -containing enzymes. Alternatively, imatinib might selectively interact with the ATP site of FAD synthetase. Binding ratios of FAD synthetase were 0.55 and 0.21 for 1 and 10 µM imatinib-treated versus control incubated cells, respectively, indicating that almost 80% of the enzyme was not retained by the affinity beads at the higher imatinib dose. Thus, imatinib interfered with resin binding of FAD synthetase to a lesser extent than observed for Bcr-Abl and NQO2, but had a more pronounced effect on this enzyme than on Syk and PI4 kinases (Figure 3). Identification of Imatinib-regulated Downstream Kinases. Our enrichment strategy enabled the sensitive detection and quantification of protein kinases-derived phosphopeptides. To identify biologically reproducible effects induced by imatinib, we filtered our data for phosphorylation sites that could be quantified and confidently assigned to specific residues in both replicate experiments (Supplementary Table 3, Supporting Information). As shown in Figure 4A, most quantified phosphorylation sites were not affected by imatinib and were found in ratios close to one in both experiments. Our goal was to obtain proof-of-concept that the identification of potentially drug-regulated sites is feasible by our approach. We considered imatinib-induced changes as relevant for further inspection in case phosphorylation sites were either consistently down- or up-regulated by more than 2-fold in both experiments, or exhibited an average regulation of at least 2-fold with both ratios differing by a factor of less than two. However, these data have to be seen as preliminary in a sense that additional biological replicates would be needed to enable the statistical evaluation of individual site ratios. Biologically reproducible down-regulation according to the aforementioned criteria was observed for 70 distinct phosphorylation sites upon cellular incubation with 10 µM imatinib (Supplementary Table 3, Supporting Information). Notably, regulation on tyrosine residues was far more prominent than their prevalence among all phosphosites quantified in kinase-enriched K562 cell fractions. We detected as many as 15 distinct tyrosine phosphorylated residues and a similar number of Ser/Thr sites mapping to the Bcr-Abl oncoprotein. These sites were found at very low ratios upon cellular imatinib treatment which reflects the combined effect of cellular dephosphorylation and near complete prevention of Bcr-Abl protein binding due to imatinib binding. However, these very low SILAC ratios obviate reliable quantification of phosphorylation versus protein changes, and we therefore did not further consider Bcr-Abl phosphorylation sites in our quantitative analysis. In case of all other regulated phosphoproteins for which protein ratios were measured normalization of phosphorylation changes was possible due to either less dramatic (as observed for the direct target Syk) or no imatinib effect on the amount of resin-bound protein.


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Figure 4. Quantitative phosphoproteomics of imatinib effects on kinase inhibitor beads-captured proteins. (A) Scatter plot comparison of log2 transformed class I phosphorylation site ratios upon 10 µM cellular imatinib treatment. Sites that were consistently downregulated (up-regulated) in both biological replicate experiments are highlighted in red (green). Moreover, phosphorylation ratios for Tyr-714 of FER and for Tyr-313 of PKCδ are indicated. (B) Examples of imatinib-regulated downstream kinases identified by quantitative MS. SILAC spectra are shown for imatinib-regulated, pTyr-containing phosphopeptides (left panels) and unchanged, nonphosphorylated peptides (right panels) derived from the protein kinases Fer and PKCδ in experiment 1. Values for measured and pooling error-corrected (normalized) SILAC ratios are shown. Journal of Proteome Research • Vol. 9, No. 11, 2010 6039

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Table 1. Selected Imatinib-Regulated Phosphorylation Sites on Downstream Signal Transducers average pSTY site ratio

average protein ratio

IPI protein ID

protein name

residue

1 µM imatinib /ctrl.




IPI00018597 IPI00018597 IPI00018597 IPI00029263 IPI00029132 IPI00289342 IPI00413961 IPI00384562 IPI00384562 IPI00013981 IPI00442025 IPI00181703

Tyrosine-protein kinase SYK Tyrosine-protein kinase SYK Tyrosine-protein kinase SYK Proto-oncogene tyrosine-protein kinase FER Tyrosine-protein kinase BTK Ephrin type-B receptor 4 Focal adhesion kinase 1 Protein kinase C delta Protein kinase C delta Proto-oncogene tyrosine-protein kinase Yes Activated CDC42 kinase 1 Mitogen-activated protein kinase kinase kinase 3 90 kDa ribosomal protein S6 kinase 1 90 kDa ribosomal protein S6 kinase 1 Casein kinase II subunit alpha′ Serine/threonine-protein kinase PCTAIRE-1 FYVE finger-containing phosphoinositide kinase III

Y348 Y352 Y323 Y714 Y551 Y774 Y883a Y313 Y334 Y32a Y905a S368

0.51 0.70 0.69 0.60 0.59 0.78 0.50 0.71 0.77 0.78 0.62 0.47

0.06 0.10 0.16 0.29 0.33 0.27 0.26 0.27 0.31 0.34 0.45 0.30

0.74 0.74 0.74 1.01 0.93 1.20 0.95 0.99 0.99 1.09 n/a 0.84

0.36 0.36 0.36 1.05 0.97 1.21 1.02 1.01 1.01 1.06 n/a 0.97

T359 S363 S18 S71a S307

0.45 0.43 0.66 0.25 0.69

0.24 0.27 0.46 0.19 0.34

0.92 0.92 1.02 0.85 n/a

0.95 0.95 0.92 0.90 n/a

IPI00017305 IPI00017305 IPI00020602 IPI00549858 IPI00396145

a Phosphorylation site position according to the identified IPI database entries. The corresponding positions according to the UniProtKB entry names/ accessions are: FAK1 _HUMAN, Q05397, Y861; ACK 1_HUM AN, Q07912, Y827; YES _HUMAN, P07949, Y31; PCTK1_HUMAN, Q00536, S65.

Notably, most imatinib-regulated phosphorylations have not been reported in earlier studies (Supplementary Table 3, Supporting Information), including all down-regulated sites listed in Table 1 and discussed in the following part. Because Syk phosphorylation site ratios were on average 3-fold more strongly reduced upon 10 µM imatinib compared to Syk protein binding, our results indicated cellular dephosphorylation at residues such as Tyr-323, Tyr-348 and Tyr-352. Previous reports revealed that Syk phosphorylation on these sites creates binding sites for signaling proteins such as phospholipase Cγ, the guanine nucleotide exchange factor Vav, phosphatidylinositol 3-kinase and c-Cbl.42-45 Interestingly, while Tyr-348 and Tyr-352 (or the corresponding residues in mouse Syk) were shown to positively contribute to cellular Syk function, Tyr-317 was characterized as a negative regulatory site in Ag and Fc receptor signaling.45-47 By extension, our results point to possible functional modulation of Syk-mediated signaling at the higher imatinib dose in CML cells. The major goal of our phosphoproteomics analysis was the identification of protein kinases that transduce signals emanating from direct imatinib interactors, as a strategy to identify potential alternative targets in case of imatinib resistance formation due to Bcr-Abl mutations or overexpression. In contrast to direct cellular imatinib targets, imatinib treatment would not affect inhibitor resin binding of such downstream signaling kinases, but instead selectively repress site-specific phosphorylations in our experimental approach. To identify such imatinib-induced effects, we focused on phosphorylation sites that could be quantified and confidently assigned to specific residues in both replicate experiments. Notably, we recorded effects on a number of phosphorylation sites with reported regulatory functions. According to our analysis, imatinib treatment exerted a dose-dependent effect on the phosphorylation of the cytoplasmic tyrosine kinase BTK at Tyr-551, with a 3-fold down-regulation measured for the 10 µM imatinib concentration (Table 1). Quantification of nonphosphorylated peptides from BTK revealed that comparable protein amounts were retained by the inhibitor resin from imatinib-treated cells. We observed similar regulation patterns for Tyr-714 of the 6040


cytoplasmic tyrosine kinase Fer and Tyr-774 of the receptor tyrosine kinase EphB4 (Figure 4B, Table 1). Our evidence of selective dephosphorylation in the absence of protein changes points to these three tyrosine kinases as potential downstream signaling elements of direct imatinib targets. Notably, these imatinib-repressed phosphorylations occurred in the activation loop regions of the BTK, Fer and EphB4, at a conserved position that stabilizes the active kinase conformation in a phosphorylation-dependent manner.23,48 Thus, our data further suggest cellular inhibition of BTK, Fer and EphB4 kinase activities upon imatinib, identifying them as candidate signal transducers of Bcr-Abl-mediated and imatinib-sensitive leukemia cell transformation. It is noteworthy that in case of BTK earlier data argue against an essential function in Bcr-Abl signaling, based on evidence that BTK inactivation showed no inhibitory effect in Bcr-Abl-transformed murine cells.49 However, despite this data, cell-type specific requirements for BTK are conceivable, for example in case of reduced signaling capacity of Bcr-Abl due to endogenous expression at considerably lower levels compared to ectopic overexpression in murine model cell lines. In our experiments, imatinib treatment of K562 cells markedly decreased the tyrosine phosphorylation of focal adhesion kinase (FAK) at Tyr-883 according to the assigned IPI database identifier (Table 1), which corresponds to Tyr-861 in the commonly used UniProt knowledgebase entry FAK1_HUMAN. Phosphorylation of FAK at Tyr-861 is up-regulated in Rastransformed cells and required for Ras-mediated transformation.50 As constitutive Ras activation represents a hallmark of Bcr-Abl transformation, our data point to FAK Tyr-861 phosphorylation as a previously unknown switch point in CML cell signaling. We further detected dose-dependent dephosphorylation of protein kinase Cδ (PKCδ) at Tyr-313 and Tyr-334 upon imatinib treatment (Figure 4B, Table 1). These tyrosine residues reside in the hinge region between the regulatory and catalytic domains of PKCδ and have been implicated in the regulation of apoptosis in glioma cells.51,52 Phosphorylation at Tyr-313 plays a role in diverse signaling responses including keratinocyte differentiation and thromboxane A2 generation in platelets.53,54 Thus, our identification of these regulation events

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Figure 5. Interactome of imatinib-regulated proteins. All proteins that either exhibited reduced kinase inhibitor resin binding or which harbored imatinib-repressed phosphorylation sites were used for STRING analysis. The resulting network was visualized with Cytoscape.

in imatinib-treated CML cells may reflect a role of PKCδ in the propagation of Bcr-Abl-induced signals and warrant further examination of this kinase in leukemia cell transformation. We also detected imatinib-sensitive tyrosine phosphorylation events in the N-terminal region of the Src family kinase Yes and the C-terminal part of the cytoplasmic tyrosine kinase ACK. As these modifications have not been functionally characterized yet, their possible roles in Bcr-Abl signaling cannot be inferred from published data. In addition to imatinib effects on tyrosine phosphorylation levels we detected inhibitor-repressed serine and threonine phosphorylations on protein kinases such as mitogen-activated protein kinase kinase kinase 3 (MAP3K3), 90 kDa ribosomal protein S6 kinase (RSK1), casein kinase 2 (CK2R2) and PCTAIRE1, with the latter exhibiting as much as 75% reduction of Ser-71 phosphorylation even at the low inhibitor dose of 1 µM imatinib (Table 1). To explore potential relationships among all proteins that were either prevented from resin binding or harbored down-regulated phosphorylation sites upon imatinib treatment, we mapped them on known protein interactions by STRING analysis (Figure 5). Notably, imatinib-regulated phosphoproteins such as BTK, Fer, FAK and others were extensively connected to direct imatinib targets like Bcr-Abl and Syk, further emphasizing their putative roles as downstream signal transducers. Surprisingly, imatinib treatment of cells not only caused sitespecific reduction of but also triggered a subset of phosphorylations in the kinase inhibitor-enriched subproteome (Supplementary Table 3, Supporting Information). These were exclusively found on serine and threonine residues, thus contrasting the high prevalence of tyrosine among the imatinib-repressed phosphorylations. Almost half of all proteins with imatinibinduced phosphorylation sites have reported cell cycle functions. For example, we detected increased phosphorylation on the protein kinases polo-like kinase 1 (Plk1), TTK, Wee1 and Myt1, which have well established functions in the entry and progression through mitosis. Plk1, which is highly expressed in many leukemia cell lines, has recently been described as a promising target for therapeutic intervention in hematological

malignancies.55 Notably, our analysis revealed Plk1 phosphorylation occurring at Thr-210 in the activation loop, thus indicating enzymatic activation of the kinase upon short-term imatinib treatment. Although the statistical significance of this regulation needs to be verified and the underlying molecular mechanisms remain to be elucidated, Plk1 activation may be part of a cellular response to counteract the immediate cellular consequences of Bcr-Abl kinase suppression. Thus, our results might warrant investigations how or whether therapeutic Plk1 inhibition synergizes with cellular Bcr-Abl inactivation regarding the antiproliferative and apoptotic effects on CML cells.

Conclusions and Outlook Targeted intervention strategies with kinase inhibitors have already made an enormous impact on the treatment of several human cancers. The role of such therapies is likely to increase in the years to come, considering the large number of kinaseselective drugs currently in preclinical and clinical development.4 Proteomics approaches can contribute valuable information to such efforts, including drug selectivity assessments in relevant biological systems and the identification of alternative molecular targets for pharmacological intervention. Target flexibility in cancer treatment is desirable, given that the selective pressure imposed on cancer cells frequently leads to drug resistance due to desensitizing mutations in the targeted, disease-causing oncogenes.7,8 This has been extensively documented for the transforming Bcr-Abl tyrosine kinase in imatinib-treated chronic myeloid leukemia (CML) patients, but represents a pervading theme as evident from, for example, the occurrence of drug-resistant epidermal growth factor receptor (EGFR) kinase mutants in nonsmall cell lung cancer therapy with the EGFR inhibitors gefitinib and erlotinib.56-58 One potential strategy to overcome resistance could involve inhibition of protein kinases, which are essential downstream mediators of oncogenic signaling emanating from kinases such as Bcr-Abl. We have implemented a chemical phosphoproteomics strategy to identify such transducers upon imatinib exposure, by targeted analysis of a pharmacologically tractable Journal of Proteome Research • Vol. 9, No. 11, 2010 6041

research articles subproteome isolated by affinity purification with nonselective kinase inhibitors. These proof-of-concept experiments provide preliminary evidence for so far unknown kinases in imatinibregulated K562 cell signaling, which represent candidates for further validation and functional studies. Our proteomics strategy is generic and can be applied to other kinase inhibitors. For example, comprehensive analysis of essential and druggable mediators of EGFR signaling in nonsmall cell lung cancer cells might define therapeutic back-up strategies to overcome the frequent EGFR resistance upon prolonged gefitinib or erlotinib treatment. Moreover, concomitant inhibition of both primary oncogenic kinases and their essential signal transducers might effectively counteract resistance formation, as individual target mutations would not suffice to evade from such polypharmacological regimens. In addition to the phosphoproteomic identification of signaling factors situated downstream of direct imatinib targets, our integrated approach recapitulated known imatinib targets from K562 cells, as these were competed from the generic kinase inhibitor resin in lysates from imatinib treated cells.26 Notably, this part of our analysis was not only confirmatory but also identified previously unknown imatinib-interacting proteins such as the key metabolic enzyme FAD synthetase. Our identification of such a key metabolic enzyme as off-target raises the issue of potential dose-limiting toxicity resulting from its likely pharmacological inhibition. We think further in vitro and cellular studies are warranted to verify enzymatic inhibition of FAD synthetase by imatinib and by related compounds in clinical development. Taken together, our sensitive proteomics approach that integrates kinase inhibitor selectivity analysis with phosphoproteome quantification in the kinase-enriched subproteome should have considerable utility for discovery and development efforts aiming for improved targeted intervention strategies in human malignancies.

Acknowledgment. We thank Axel Ullrich for his generous support of our work. We thank Matthias Mann and Ju ¨ rgen Cox for early access to the MaxQuant software. We further thank Jesper Olsen and Kirti Sharma for help and advice. This work was supported by a grant from the Novartis-Stiftung fu ¨ r therapeutische Forschung. Supporting Information Available: Supplementary Table 1, list of all protein groups identified in this study. Supplementary Table 2, peptide evidence data for all identified peptides. Supplementary Table 3, all identified class I phosphorylation sites. Supplementary Table 4, STRING-derived interactions among all kinase inhibitor resin-bound proteins that were quantified with protein ratios and/or class I phosphorylation site ratios in both biological replicate experiments. Supplementary Table 5, STRING-derived interactions among all proteins with either imatinib-reduced kinase inhibitor resin binding or imatinib-inhibited phosphorylation sites. Supplementary Figure 1, interaction network constituted by inhibitor resin-bound proteins. Supplementary Figure 2, representative MS spectra of K562 cell proteins exhibiting reduced inhibitor resin binding upon cellular imatinib treatment. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Blume-Jensen, P.; Hunter, T. Oncogenic kinase signalling. Nature 2001, 411 (6835), 355–65. (2) Krause, D. S.; Van Etten, R. A. Tyrosine kinases as targets for cancer therapy. N. Engl. J. Med. 2005, 353 (2), 172–87.

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PR1008527

Journal of Proteome Research • Vol. 9, No. 11, 2010 6043

Proteomics Analysis of Cellular Imatinib Targets and their Candidate

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