Workflow Comparison for Label-Free, Quantitative Secretome

Jan 19, 2010 - Figure 1. Workflow comparison for identification of cancer cell line secretome proteins. (A) Overview of first-dimension separation wor...
1 downloads 0 Views 1MB Size
Download PDF
Workflow Comparison for Label-Free, Quantitative Secretome Proteomics for Cancer Biomarker Discovery: Method Evaluation, Differential Analysis, and Verification in Serum Sander R. Piersma,*,†,# Ulrike Fiedler,‡,# Simone Span,† Andreas Lingnau,‡ Thang V. Pham,† Steffen Hoffmann,‡ Michael H. G. Kubbutat,‡ and Connie R. Jime´nez† OncoProteomics Laboratory, Department of Medical Oncology, VUmc-Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands, and ProQinase GmbH, Freiburg, Germany Received November 23, 2009

The cancer cell secretome has emerged as an attractive subproteome for discovery of candidate bloodbased biomarkers. To choose the best performing workflow, we assessed the performance of three first-dimension separation strategies prior to nanoLC-MS/MS analysis: (1) 1D gel electrophoresis (1DGE), (2) peptide SCX chromatography, and (3) tC2 protein reversed phase chromatography. 1DGE using 4-12% gradient gels outperformed the SCX and tC2 methods with respect to number of identified proteins (1092 vs 979 and 580, respectively), reproducibility of protein identification (80% vs 70% and 72%, respectively, assessed in biological N ) 3). Reproducibility of protein quantitation based on spectral counting was similar for all 3 methods (CV: 26% vs 24% and 24%, respectively). As a proof-of-concept of secretome proteomics for blood-based biomarker discovery, the gradient 1DGE workflow was subsequently applied to identify IGF1R-signaling related proteins in the secretome of mouse embryonic fibroblasts transformed with human IGF1R (MEF/Toff/IGF1R). VEGF and osteopontin were differentially detected by LC-MS/MS and verified in secretomes by ELISA. Follow-up in serum of mice bearing MEF/ Toff/IGF1R-induced tumors showed an increase of osteopontin levels paralleling tumor growth, and reduction in the serum of mice in which IGF1R expression was shut off and tumor regressed. Keywords: secretome • spectral counting • biomarker • osteopontin

Introduction Cell secretome (cell-conditioned medium) is composed of proteins that are found in the extracellular growth medium. The secretome consists of proteins that are secreted, shed from the cell surface and intracellular proteins released into the supernatant due to cell lysis, apoptosis and necrosis. These proteins may end up in the bloodstream, and thereby may have a potential use as noninvasive biomarkers. Moreover, the secretome has the added analytical advantage of being of medium-complexity compared to total cell lysate or plasma/ serum. For these reasons, the cancer cell secretome has emerged as an attractive starting point for biomarker discovery.1 Proteome analysis of several cancer cell secretomes has been reported including breast cancer,2–5 lung cancer,6,7 prostate cancer,4,8 cervical cancer,9 ovarian10 and bladder4 cancer cell lines. In most studies, the number of identified proteins per cell line secretome ranges from 250 to 700.2–4,11 Most recent analyses use two dimensions of separation, with nano-LC online coupled to MS being the second dimensionseparation, to deal with the complexity of the secretome.2–4,11 * To whom correspondence should be addressed. E-mail: s.piersma@ vumc.nl. † VU University Medical Center. ‡ ProQinase GmbH. # Both authors contributed equally to this work. 10.1021/pr901072h

 2010 American Chemical Society

Several (chromatographic) first-dimension separation techniques have been reported for secretome analysis with peptide strong cation exchange (SCX) as the most popular technique.2,3 However, a comprehensive evaluation of workflows for secretome analysis, including analysis of reproducibility, is lacking. For a comprehensive comparison of workflows for secretome analysis, we selected three first-dimension separation techniques relying on different separation principles, prior to nanoLC-MS/MS analysis. As first-dimension separation, 1D gel electrophoresis (1DGE) (10% and 4-12% acrylamide), protein reversed chromatography (RPC) (tC2 cartridges), and peptide SCX chromatography (HPLC) were chosen. 1D gel electrophoresis and peptide SCX are widely used as first-dimension separation steps in proteomics analysis of complex protein mixtures. The tC2 method has been reported previously for secretome analysis11 and was therefore included in the method assessment. For each method, three lung cancer cell line secretomes were analyzed in 10 fractions per secretome sample. The most optimal first-dimension separation technique combines ease-of-use with maximum number of identified proteins, high reproducibility of protein identification and spectral counts per protein, and acceptable throughput. As a proof-of concept of quantitative secretome analysis as a strategy for candidate blood-based biomarker discovery, the best performing workflow (GeLC MS/MS) was applied to the differential analysis of the secretome of engineered mouse Journal of Proteome Research 2010, 9, 1913–1922 1913 Published on Web 01/19/2010

research articles fibroblasts with controlled expression of IGF1R (Mef/Toff/ IGF1R). Insulin and Insulin-like growth factors are well-known as regulators of energy metabolism and growth. There is increasing evidence that both molecules play also a key critical role in neoplasias, which are, for example, diabetes and obesity, but also in cancer.12 IGF1R expression was detected in human cancers and showed to have transforming activities.13,14 IGF1R signaling influences expression and signaling of numerous growth factors, like VEGF and EGF but is also involved in the induction of hyperinsulinaemia which might be a mediator of adverse effects in cancer.12,15 Using the MEF/Toff/IGF1R model, selected candidate IGF1R activity-related proteins were verified in cell secretomes as well as in mouse sera by ELISA.

Materials and Methods Materials. All chemicals were obtained from Sigma (Sigma Aldrich, Zwijndrecht, The Netherlands). HPLC solvents, LCMS grade water, acetonitrile (ACN), trifluoroacetic acid (TFA) and formic acid (FA) were obtained from Biosolve (Biosolve B.V., Valkenswaard, The Netherlands). Porcine sequence-grade modified trypsin was obtained from Promega (Promega Benelux B.V., Leiden, The Netherlands). RPMI 1640 medium was from BioWhittaker (BioWhittaker, Walkersville, MD) and penicillinstreptomycin was from Invitrogen (Invitrogen, Breda, The Netherlands). Secretome Sample Preparation. H460 non-small cell lung cancer cells were grown to 70% confluency in 10% FCScontaining Phenol Red-free RMPI 1640 medium on 10 cm plates. The FCS-containing medium was removed and adherent cells were washed 4× in serum-free medium (20 mM HEPES, 1 mg/mL glucose, 4 mM L-glutamine, amino acids, and 0.75× PBS, pH 7.6) and incubated for another 24 h in FCS-free, phenol red free medium. Three plates of H460 cells were grown for each biological replicate of each evaluated fractionation method. Moreover, the cells were grown in parallel, plated at the same density, and therefore, the number of cells used in each comparison (the amount of input material) was completely comparable. In addition, the secretome equivalent of one plate was loaded for each first-dimension separation workflow and was separated in 10 fractions. Mouse Embryonic Fibroblasts: MEF/Toff and MEF/Toff/ IGF1R were cultured in DMEM 4,500 mg/L Glucose and Glutamax-1 (Gibco) with 10% FCS (Invitrogen Corporation, Karlsruhe, Germany). MEF/Toff/IGF1R cells were generated by stable co-transfection of MEF/Toff cells (Clontech, Heidelberg, Germany) with the plasmid pTRE IGF1R-2myc6his and pSVhygro. Clones were selected with hygromycin and subclones were selected by picking clones that form colonies when grown in soft agar (Graeser et al., unpublished). The resulting MEF/ Toff/IGF1R cells express the human IGF1R cDNA under the control of a Tet-inducible promoter. IGF1R is expressed in the absence of doxycycline and expression is inhibited in the presence of doxycycline. Mef/Toff and MEF/Toff/IGF1R cells were grown to 70% confluency in the presence and absence of doxycycline and in the presence and absence of IGF1 to stimulate IGF1R signaling for 3 days. After 3 days, the FCScontaining medium was removed and adherent cells were washed 4× in serum-free, phenol red-free medium and incubated for 16 h in FCS-free and phenol-red-free medium in the presence and absence of doxycycline and in the presence and absence of IGF1. In all Mef/Toff and MEF/Toff/IGF1R experiments, supernatants of two independent experiments were collected. The material from one experiment was analyzed by 1914

Journal of Proteome Research • Vol. 9, No. 4, 2010

Piersma et al. LC-MS/MS. The material from both experiments was analyzed by ELISA to verify candidate biomarkers. Secretome was harvested by aspiration and was passed through a 0.45 µm filter (Millex-HV, Millipore, Amsterdam, The Netherlands) to remove detached cells. For solution digest (SCX workflow) and SDS-PAGE (GeLC workflow), the secretome volume (3 plates; 9 mL) was reduced to 50 µL using a 5K MWCO centrifugal concentrator (Amicon ultra-4, Millipore, Amsterdam, The Netherlands) operated at 2800g. The 5K spin filters were used to concentrate the secretome proteins. For the tC2 protein reversed-phase workflow, the pH was lowered by adding 4% (TFA) to a final concentration of 0.1%. Protein concentrations were determined using the BioRad protein assay (BioRad, Hercules, CA). Protein Reversed-Phase Fractionation. Filtered, acidified secretome solution was applied manually to a 1 mL Seppak tC2 reversed-phase cartridge (Waters, Milford, MA) activated prior to use with ACN and equilibrated with 0.1% TFA. After protein binding, the cartridge was washed with 1 mL of 0.1% TFA; subsequently, protein fractions were eluted sequentially by applying 0.5 mL aliquots of 0.1% TFA + 30% ACN, 35% ACN, 40% ACN, 45% ACN, 50% ACN, 55% ACN, 60% ACN, 65% ACN, and finally 70% ACN. Eluted protein fractions were dried in a vacuum centrifuge and were further processed for solution digestion. Gel Electrophoresis. The equivalent of one 10 cm plate H460 secretome was analyzed by SDS-PAGE using a Mini-Protean 3 system (BioRad, Hercules, CA) (10% acrylamide, Tris-Glycine) or a NuPAGE SDS-PAGE system (Invitrogen, Breda, The Netherlands) (4-12% acrylamide, Bis-Tris with MES running buffer). After electrophoresis, the gels were fixed in 50% ethanol containing 3% phosphoric acid and stained with Coomassie R-250. After staining, the gels were washed in Milli-Q water and stored at 4 °C until processing for in-gel digestion. In-Gel Digestion. The gel lanes corresponding to the H460 secretomes were cut in 10 slices, and each slice was processed for in-gel digestion according to the method of Shevchenko.16 Briefly, slice were washed/dehydrated three times in 50 mM ABC (ammonium bicarbonate pH 7.9)/50 mM ABC + 50% ACN (acetonitrile). Subsequently, cysteine bonds were reduced with 10 mM dithiothreitol for 1 h at 56 °C and alkylated with 50 mM iodoacetamide for 45 min at room temperature (RT) in the dark. After two subsequent wash/dehydration cycles, the slices were dried 10 min in a vacuum centrifuge (ThermoFisher, Breda, The Netherlands) and incubated overnight with 6.25 ng/ µL trypsin in 50 mM ABC at 25 °C. Peptides were extracted once in 100 µL of 1% formic acid and subsequently twice in 100 µL of 50% ACN in 5% formic acid. The volume was reduced to 50 µL in a vacuum centrifuge prior to LC-MS/MS analysis. In-Solution Digestion. After centrifugal concentration, the secretome solution was brought to dryness in a vacuum centrifuge. The pellet was dissolved in 10 µL of 8 M urea and 10 mM DTT and was incubated for 1 h at 56 °C. Ten microliters of 50 mM iodoactamide was added and incubated in the dark for 45 min at RT. Twenty microliters of 50 mM ABC was added to the reduced and alkylated secretome proteins to reduce the urea concentration to 2 M. Subsequently, 10 µL of trypsin solution (10 ng/µL in 50 mM ABC) was added and incubated overnight at 25 °C. After incubation, the digestion was stopped by adding 1% formic acid. Strong Cation Exchange Chromatography (SCX). Tryptic peptides from the solution digestion were desalted on a Seppak tC18 cartridge (Waters, Milford, MA) activated with 1 mL of

research articles

Workflow Comparison for Secretome Proteomics ACN and equilibrated with 1 mL of 0.05% FA prior to use. Peptides were diluted to 500 µL with 0.05% FA and were manually loaded on the cartridge. Bound peptides were washed with 1 mL of 0.05% FA and eluted with 0.5 mL of 60% ACN/ 0.05% FA. Desalted tryptic peptides were dried in a vacuum centrifuge and dissolved in SCX buffer A (10 mM KH2PO4, pH 2.9, and 20% ACN). Peptides were separated by SCX using a polysulfethyl aspertamide column (100 mm × 2.1 mm, 3 µm, 300 Å) (PolyLC, Columbia, MD) operated at 0.2 mL/min. After injection, peptides were eluted using Buffer B (10 mM KH2PO4, pH 2.9, 20% ACN, and 500 mM KCl) using the following gradient: 0-20 min, 0-60% buffer B; 20-25 min, 60-100% buffer B; 25-30 min, 100% buffer B; 30-50 min, 100% A. Fractions were collected every 2 min yielding 12 peptide containing fractions for each biological sample. All fractions were dried in a vacuum centrifuge and were dissolved in 0.1% formic acid. Peptides were desalted on a 96 well Empore Universal Resin extraction disk plate (3M) according to instructions of the manufacturer. The volume of the desalted peptide fractions was reduced to 50 µL in a vacuum centrifuge prior to LC-MS/MS analysis. Nano-LC Separation. Peptides were separated using an Ultimate 3000 nanoLC system (Dionex LC-Packings, Amsterdam, The Netherlands) equipped with a 20 cm × 75 µm i.d. fused silica column custom packed with 3 µm 120 Å ReproSil Pur C18 aqua (Dr. Maisch, GMBH, Ammerbuch-Entringen, Germany). After injection, peptides were trapped at 30 µL/min on a 5 mm × 300 µm i.d. Pepmap C18 cartridge (Dionex LCPackings, Amsterdam, The Netherlands) at 2% buffer B (buffer A, 0.05% formic acid in MQ; buffer B, 80% ACN and 0.05% formic acid in MQ) and separated at 300 nL/min in a 10-40% buffer B gradient in 60 min. Mass Spectrometry. Eluting peptides were ionized at 1.7 kV in a Nanomate Triversa Chip-based nanospray source using a Triversa LC coupler (Advion, Ithaca, NJ). Intact peptide mass spectra and fragmentation spectra were acquired on a LTQFT hybrid mass spectrometer (Thermo Fisher, Bremen, Germany). Intact masses were measured at a resolution of 50 000 in the ICR cell using a target value of 1 × 106 charges. In parallel, following an FT prescan, the top 5 peptide signals (charge-states 2+ and higher) were submitted to MS/MS in the linear ion trap (3 amu isolation width, 30 ms activation, 35% normalized activation energy, Q-value of 0.25 and a threshold of 5000 counts). Dynamic exclusion was applied with a repeat count of 1 and an exclusion time of 30 s. Database Searching. MS/MS spectra were searched against the human IPI database 3.31 (67 511 entries) or mouse IPI 3.31 (56 555 entries) using Sequest (version 27, rev 12), which is part of the BioWorks 3.3 data analysis package (Thermo Fisher, San Jose, CA). MS/MS spectra were searched with a maximum allowed deviation of 10 ppm for the precursor mass and 1 amu for fragment masses. Methionine oxidation and cysteine carboxamidomethylation were allowed as variable modifications, two missed cleavages were allowed and the minimum number of tryptic termini was 1. After database searching, the DTA and OUT files were imported into Scaffold (versions 1.07 and 2.01) (Proteomesoftware, Portland, OR). Scaffold was used to organize the data and to validate peptide identifications using the PeptideProphet algorithm, and only identifications with a probability >95% were retained. Subsequently, the ProteinProphet algorithm was applied and protein identifications with a probability of >99% with 2 peptides or more in at least one of the samples were retained.17,18 Proteins that contained

similar peptides and could not be differentiated based on MS/ MS analysis alone were grouped. For each protein identified, the number of spectral counts (the number of MS/MS associated with an identified protein) was exported to Excel. For quantitative analysis across samples, spectral counts were normalized on the sum of the spectral counts per biological sample (see eq 1).

SpCnormi )

SpCi

(1)

n

∑ SpC

i

i)1

Each sample was separated in 10 fractions that were subjected to nanoLC-MS/MS. Spectral counts for identified proteins in a sample were summed across all fractions for each sample and were normalized on the total sum of spectral counts for that sample (a similar approach has been used in ref 19). This gives the relative spectral count (SpC) contribution of protein i to all spectral counts in the sample (eq 1).20 When comparing different data sets, these normalized spectral counts are used to calculate ratios. Differential analysis of MEF secretomes was performed using the BetaBinominal test20 comparing MEF/ Toff/IGF1R-Dox+IGF1 with MEF/Toff+Dox, MEF/Toff-Dox, MEF/Toff-Dox+IGF1, MEF/Toff/IGF1R-Dox MEF/Toff/IGF1R+ Dox, and MEF/Toff/IGF1R+Dox+IGF1. Data Mining. Secretion signal peptide prediction was performed using SignalP 3.0 (http://www.cbs.dtu.dk/services/ SignalP/)21 using FASTA sequences of the identified proteins. Subcellular protein localization (GO-annotations) and protein network analysis was performed using Ingenuity pathway analysis (IPA, Ingenuity Systems, Inc.). Ingenuity pathway analysis mapped 549 IPI accessions of the secretome-enriched proteins (out of a total of 568 proteins), and for the lysate, 1695 IPI accessions (out of a total of 1726 proteins). Significance analysis of workflows was calculated using a two-tailed t test. Mouse Tumor Experiments. MEF/Toff/IGF1R cells form tumors when implanted in mice. These tumors regress if IGF1R expression is switched off by administration of doxycycline via the drinking water. Specifically, 2 million cells were implanted subcutaneously into female NMRI nu/nu mice. After a tumor of a size of 0.4 cm3 had been established, doxycycline was added into the drinking water for 10 days. The mice have been grouped in four groups: mice in Group A had an established tumor and received no doxycycline in the drinking water; mice in Group B had an established tumor and received doxycycline into the drinking water for 10 days; mice in Group C had no tumor and received no doxycycline into the drinking water; and mice in Group D had no tumor and received doxycycline into the drinking water. Groups A and B consisted of 8 mice each, groups C and D consisted of four mice each. A total of 200 µL of blood was taken from Groups A and B at the day doxycycline was applied to the drinking water (t ) 1) and 2 days after mice received doxycycline (t ) 2) to collect serum samples for proteomics. All mice were sacrificed at the end of the experiment and serum samples were collected (t ) 3) just prior to sacrifice. The tumor volume and body weight of the mice was monitored every 2-3 days. Two independent tumor experiments were performed and serum samples were analyzed for mouse VEGF levels and mouse osteopontin levels by ELISA. ELISA. The amount of secreted mouse VEGF and osteopontin was determined either in secretomes and mouse sera using ELISA assays according to the manufacturer’s protocol [ELISA Journal of Proteome Research • Vol. 9, No. 4, 2010 1915

research articles

Piersma et al.

Figure 1. Workflow comparison for identification of cancer cell line secretome proteins. (A) Overview of first-dimension separation workflows. For each workflow, 3 biological replicates of H460 secretomes were analyzed. Venn diagrams of identified proteins using different first-dimension separations workflows coupled to nanoLC-MS/MS including total number of identified proteins, reproducibility of identification (%) and reproducibility of protein quantitation as expressed by the %CV of spectral counts for proteins detected in 3 replicate experiments (intersection, %). (B) 10% SDS PAGE; (C) tC2 protein RPC; (D) SCX; and (E) 4-12% SDS PAGE.

for determination of mouse VEGF, MMV00; ELISA for determination of mouse osteopontin, MOST00] (RnDSystems, Heidelberg, Germany). Briefly, secretome samples were diluted to 0.2 µg/mL total protein in PBS. A total of 50 µL of the sample was directly applied to the mouse osteopontin-ELISA and 50 µL of a 1:5 dilution of the samples diluted in dilution buffer RD5T was applied to the mouse VEGF ELISA. A total of 50 µL of the serum samples was directly applied to the mouse VEGFELISA and 50 µL of serum diluted 1:100 in RD6-12 (RnDSystems, Heidelberg, Germany) was applied to the mouse osteopontin-ELISA. All samples were measured as duplicates. The samples of the two independent secretome experiments and the two independent mouse tumor experiments were analyzed.

Results Workflow Comparison To Select Optimal Method for Secretome Analysis. Success of cell secretome analysis using fractionation coupled to LC-MS/MS depends on the quality of the input secretome. For each cell line, the serum-free incubation time needs to be optimized. For human H460 cells, minimal cell death (3× higher spectral counts in the secretome (62%) vs cell lysate. (13%). (D) Spectral counts ((SD, N ) 3) for example secretome proteins (IGD, 4-12% SDS PAGE). The label ‘sp’ denotes the presence of a signal peptide as detected by SignalP.

The peptide SCX method could potentially be automated to a large extent since it is an all-LC method. The desalting steps prior and after the SCX-separation, however, make the workflow time and labor-intensive. The GeLC methods are intrinsically time and labor-intensive but do yield reproducible and rich protein ID data sets, with the added value of protein Mw information from the gel slice. Several important secretome proteins are detected in the low Mw range, including interleukin 8 (IL8), cystatin B and CXCL2, all around 10 kDa. To resolve this region on gels, our overall preferred first-dimension separation method for secretome analyis is 1DGE with 4-12% acrylamide gradient gels. Protein and peptide identifications are listed in Supplementary Tables 1A and 1B, respectively. Identification of Secretome Enriched Proteins: Comparison of Secretome and Cell Lysate. To identify secretome-enriched proteins and to filter out proteins resulting from cell lysis and cell death, a cell lysate of H460 cells was analyzed using gel fractionation (10 bands) coupled to 1DGE LC-MS/MS similar to the secretome. A total of 1726 H460 proteins were identified in the cell lysate. In Figure 2A, the Venn diagram for GeLC analyses of secretome and cell lysate is shown. A total of 585 of the 1092 secretome proteins were both detected in H460 cell lysate and secretome, 507 proteins were excusively detected in the secretome and 1141 proteins were exclusively detected in the lysate. Next, the spectral counts for the cell lysate and

the secretome were normalized on the sum of spectral counts for each sample; for replicate samples, normalized counts were averaged. Subsequently, the spectral count ratio (secretome/ cell lysate) was calculated for each protein. A 3-fold cutoff was applied to the gradient 1DGE LC-MS/MS data set yielding a list of 568 secretome-enriched proteins (including 507 proteins found exclusively in the secretome) with normalized spectral counts >3× higher than the corresponding protein in the cell lysate data set (Supplementary Table 2). The list of secretomeenriched and cell lysate proteins was analyzed with respect to (sub-)cellular localization and the presence of a predicted signal peptide for protein export. In Figure 2B, the subcellular localization distribution of the 568 secretome-enriched proteins and the 1726 cell lysate proteins is shown. The cellular component ontologies ‘extracellular space’ and ‘plasma membrane’ were enriched in the secretome (28% and 18%, respectively, vs 2% and 7% in the cell lysate). In Figure 2C, the signal peptide prediction using SignalP is shown for secretome (62%) and cell lysate (13%). The enrichment for proteins containing a secretion signal peptide in the secretome is evident. Finally, in Figure 2D, examples of secretome-enriched proteins in the gradient 1DGE LC-MS/MS data set (N ) 3) are shown with their average spectral counts and standard deviations across the spectral count dynamic range. Spectral count ratios and Signal P predictions for H460 secretome and cell lysate are listed in Journal of Proteome Research • Vol. 9, No. 4, 2010 1917

research articles

Piersma et al.

Table 1. Up-Regulated MEF Secretome Proteins from MEF Cells Containing IGF1R, Stimulated by IGF1 in the Absence of Doxycycline, Compared with the Six MEF Control Secretomes (p < 0.01) protein.name

Prss22 Protease, serine, 22 Gba Glucosylceramidase precursor Sod3 Extracellular superoxide dismutase [Cu-Zn] precursor Spp1 Osteopontin precursor Chid1 Isoform 1 of Chitinase domain-containing protein 1 precursor Myadm Myeloid-associated differentiation marker Inhba Inhibin beta A chain precursor Stch Isoform 1 of Stress 70 protein chaperone microsome-associated 60 kDa protein precursor Galnt4 Polypeptide N-acetylgalactosaminyltransferase 4 Prss23 Serine protease 23 precursor Npm1 Nucleophosmin Npc2 Epididymal secretory protein E1 precursor Naga Alpha-N-acetylgalactosaminidase precursor Igsf8 Immunoglobulin superfamily member 8 precursor Pgm1;Pgm2 Phosphoglucomutase 2 Pi16 protease inhibitor 16 Vegfa Isoform VEGF-1 of Vascular endothelial growth factor A precursor Xylt2 Xylosyltransferase 2 Tgfb1 Transforming growth factor beta-1 precursor Lman1 Bone marrow macrophage cDNA, RIKEN full-length enriched library, clone:I830043L04 product:lectin, mannose-binding, 1, full insert sequence Prcp Lysosomal Pro-X carboxypeptidase precursor Gnai3 Guanine nucleotide-binding protein G Efna1 Ephrin-A1 precursor Msln Mesothelin precursor P4ha1 Isoform 2 of Prolyl 4-hydroxylase subunit alpha-1 precursor Tgfbr3 TGF-beta receptor type III precursor Rnaset2a;Rnaset2b Ribonuclease T2 precursor Col2a1 Isoform 6 of Collagen alpha-1(II) chain precursor Txndc4 Thioredoxin domain-containing protein 4 precursor Crtap Cartilage-associated protein precursor Acpl2 Acid phosphatase-like 2 Adam10 ADAM 10 precursor C1rl Adult male thymus cDNA, RIKEN full-length enriched library, clone:5830476A12 product:complement component 1, r subcomponent-like, full insert sequence Fgf7 Keratinocyte growth factor precursor Tspan9 Tetraspanin-9 Slc1a5 Solute carrier family 1 (Neutral amino acid transporter), member 5 Dcn Decorin precursor Gcnt2 glucosaminyl (N-acetyl) transferase 2 isoform C Ggta1 Isoform 2 of N-acetyllactosaminide alpha-1,3galactosyltransferase Slc2a1 Solute carrier family 2, facilitated glucose transporter member 1 Chst11 Carbohydrate sulfotransferase 11 Rab21 Ras-related protein Rab-21 Sema7a Semaphorin-7A precursor Lgmn Legumain precursor Metrnl Isoform 1 of Meteorin-like protein precursor Dnajc3a;Dnajc3b DnaJ homologue subfamily C member 3 Calu Calumenin precursor Gpc4 Glypican-4 precursor Timp1 Metalloproteinase inhibitor 1 precursor Ctsa Lysosomal protective protein precursor Il1rl1 Isoform A of Interleukin-1 receptor-like 1 precursor Tnc Isoform 1 of Tenascin precursor Oaf Out at first protein homologue precursor Ctsl Cathepsin L precursor Col6a3 similar to alpha 3 type VI collagen isoform 5 precursor isoform 5 Angptl2 Angiopoietin-related protein 2 precursor Lamb1-1 laminin B1 subunit 1 Pgcp plasma glutamate carboxypeptidase Sdc4 Syndecan-4 precursor Spon2 Spondin-2 precursor Lamc1 Laminin subunit gamma-1 precursor Timp2 Metalloproteinase inhibitor 2 precursor Ctsb Cathepsin B precursor 1918

Journal of Proteome Research • Vol. 9, No. 4, 2010

Fca

p-value

Signal Pb

IPI00227985 IPI00108811 IPI00114319 IPI00309133, IPI00625970 IPI00467127

unique unique unique unique unique

0.0015 0.0020 0.0020 0.0022 0.0022

Y Y Y Y Y

IPI00132938 IPI00112347 IPI00222937

unique unique unique

0.0029 0.0033 0.0033

Y Y Y

IPI00116332 IPI00318017 IPI00127415, IPI00129186 IPI00315593 IPI00321348, IPI00649253 IPI00113610, IPI00402915,

unique unique unique unique unique unique unique unique unique

0.0037 0.0037 0.0045 0.0045 0.0045 0.0045 0.0045 0.0045 0.0045

Y Y N Y Y Y N Y N

IPI00228810 IPI00114457 IPI00132475

unique unique unique

0.0045 0.0045 0.0045

Y Y Y

IPI00132020 IPI00338854 IPI00130752 IPI00121279 IPI00399959

unique unique unique unique unique

0.0056 0.0056 0.0056 0.0056 0.0056

Y N Y Y Y

IPI00314779 IPI00625328, IPI00849527 IPI00622890, IPI00828653, IPI00828753 IPI00134058 IPI00111370 IPI00221818 IPI00131881 IPI00380781

unique unique unique unique unique unique unique unique

0.0056 0.0056 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075

Y Y Y Y Y Y Y N

IPI00121803 IPI00222480, IPI00830878 IPI00229548

unique unique unique

0.0075 0.0075 0.0075

Y Y N

IPI00123196 IPI00396807 IPI00226278, IPI00624194, IPI00626100

unique unique unique

0.0075 0.0075 0.0075

Y Y Y

IPI00308691

unique

0.0075

Y

IPI00312605 IPI00337980 IPI00315280 IPI00130627 IPI00123793 IPI00459033 IPI00135186 IPI00312407 IPI00114403 IPI00137177, IPI00658539 IPI00129750, IPI00319792 IPI00403938 IPI00154044 IPI00128154 IPI00785427

unique unique unique 46.1 42.3 38.5 20.0 15.8 15.5 12.1 11.6 10.4 10.4 9.2 6.8

0.0075 0.0075 0.0027 0.0068 0.0075 0.0085 0.0091 0.0016 0.0050 0.0089 0.0060 0.0015 0.0057 0.0028 0.0033

Y N Y Y N Y Y Y Y Y Y Y Y Y Y

IPI00330567 IPI00338785 IPI00126050, IPI00761184 IPI00136382 IPI00309041 IPI00400016 IPI00113863, IPI00649059 IPI00113517

6.5 4.9 4.7 4.6 4.1 4.0 4.0 3.6

0.0079 0.0010 0.0003 0.0010 0.0034 0.0008 0.0040 0.0012

Y N Y Y Y Y Y Y

IPI mouse accession.numbers

IPI00515155, IPI00849626 IPI00752565 IPI00775822, IPI00831472 IPI00467967, IPI00830961

research articles

Workflow Comparison for Secretome Proteomics Table 1. Continued protein.name

IPI mouse accession.numbers

Fca

p-value

Signal Pb

Fstl1 Follistatin-related protein 1 precursor Col3a1 Collagen alpha-1(III) chain precursor Sgce Brain epsilon-sarcoglycan Col5a2 Collagen alpha-2(V) chain precursor 4632419I22Rik 4632419I22Rik protein Snx22;Ppib peptidylprolyl isomerase B Plod1 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 precursor Serpinf1 Pigment epithelium-derived factor precursor Cpe Carboxypeptidase E precursor

IPI00556878 IPI00129571 IPI00623136, IPI00750388, IPI00850636 IPI00121120 IPI00226497 IPI00135686 IPI00127407

3.5 3.1 3.0 3.0 2.7 2.6 2.4

0.0037 0.0041 0.0085 0.0012 0.0083 0.0006 0.0082

Y Y Y Y Y Y Y

IPI00331088 IPI00119152

2.3 1.9

0.0053 0.0067

Y Y

a Fc: Fold change. If Fc is unique, this indicates black-and-white regulation (the protein is detected only in the MEF/Toff/IGF1R-Dox+IGF1 sample and not in either of the controls). b SignalP: Y, signal peptide predicted; N, no signal peptide predicted by Signal P.

Supplementary Table 2. The gradient 1DGE LC-MS/MS method was selected as the most appropriate method for secretome analysis and was applied to identify IGF1R-signaling related proteins in secretomes of IGF1R transformed mouse fibroblasts. Selected candidates were followed-up in sera of mice bearing these tumors. Differential Secretome Analysis of MEF/TOFF/IGF1R Cells. To identify IGF1R signaling-specific biomarkers, we applied the 1DGE LC-MS/MS workflow to secretomes of mouse embryonic fibroblasts (MEFs) transformed with human IGF1R, in which the protein kinase expression can be switched on and off using a TET-inducible system. By stable transfection of human IGF1R into MEF/Toff, cells become transformed and form colonies when grown in soft agar and grow tumors in mice (Graeser et al, unpublished). For differential secretome analyses, MEF/Toff/ IGF1R cells were grown in the presence and absence of doxycycline to modulate IGF1R expression, and the presence and absence of IGF1 to stimulate IGF1R signaling. As controls, the parental cell line MEF/Toff was grown in the presence and absence of doxycycline and in the absence of doxycycline but in the presence of IGF1. This allowed us to filter out non-IGF1R signaling related doxycycline and IGF1-effects. Cell secretomes were analyzed using the 1DGE LC-MS/MS workflow described above. In total, 1435 proteins were identified in the seven different secretome samples (MEF/Toff+Dox, MEF/Toff-Dox, MEF/Toff-Dox+IGF1, MEF/Toff/IGF1R-Dox MEF/Toff/IGF1R+ Dox, MEF/Toff/IGF1R-Dox+IGF1, MEF/Toff/IGF1R+Dox+IGF1). The spectral counts for identified proteins were normalized per sample and analyzed using the Beta Binominal test.20 Comparative analysis of MEF/Toff cells expressing IGF1R and stimulated with IGF1 to initiate IGF1R signaling with the six control samples yielded 136 regulated proteins, including 72 up- and 64 down-regulated proteins (p < 0.01). In Table 1, the up-regulated proteins are listed. We used the tool Ingenuity Pathway Analysis to assign the differential proteins to diseases and functions. A majority of differentially up- and downregulated secreted/shed proteins were classified to the following diseases and functions: cancer (41 proteins), cellular growth and proliferation (34 proteins), cellular movement (31 proteins), and cellular development (26 proteins). Examples of upregulated, secreted proteins from MEF/Toff/IGF1R cells during IGF1R signaling, associated with these diseases and functions, are laminin, VEGF, cathepsin B, TGFß, TIMP1, TIMP2, cathepsin L, carboxypeptidase E, and osteopontin (also see Table 1). Protein and peptide identifications are listed in Supplementary Tables 3A and 3B, respectively. Verification of Selected Secretome Candidate Biomarkers in Cell Secretomes. The up-regulation of two selected proteins in MEF/Toff/IGF1R during IGF1R signaling was further ana-

Figure 3. Mouse VEGF and mouse osteopontin ELISA of the MEF/ Toff/IGF1R secretome. Secretomes of MEF/Toff/IGF1R cells stimulated with and without IGF1 and cultured in the presence and absence of doxycycline and secretome of MEF/Toff cells grown in the presence and absence of doxycycline were analyzed by ELISA to determine the levels of (A) murine VEGF and (B) murine osteopontin.

lyzed in ELISA assays (Figure 3). VEGF (unique, p-value 0.0022) and osteopontin (unique, p-value 0.0045) were selected for follow-up because of their presence among the top-20 upregulated proteins and the availability of high-quality ELISA assays. The analysis showed that both VEGF and osteopontin levels were significantly increased in secretomes of MEF/Toff/IGF1R cells cultured in the absence of doxcycycline and presence of IGF1, which reflects IGF1R signaling (Figure 3). Moreover, both VEGF and osteopontin levels were low if IGF1R expression was repressed by the addition of doxcycycline to the culture Journal of Proteome Research • Vol. 9, No. 4, 2010 1919

research articles

Piersma et al.

Figure 4. Proof-of-concept of quantitative secretome proteomics as a candidate blood-based biomarker discovery strategy in a mouse tumor model. (A) MEF/Toff/IGF1R cells were grown in NMRI nu/nu mice. After establishment of tumors, half of the tumor bearing mice received doxycycline via the drinking water for 10 days to inhibit IGF1R expression in the tumors. (B) The tumor regressed. A total of 200 µL of blood was taken at day 32 (t ) 1), before addition of doxycycyline to the drinking water, at day 34 (t ) 2) when tumors went into regression phase, and at the end of the experiment when mice were sacrificed (t ) 3). In addition, sera of mice without tumor and of mice without tumor but treated with doxycycline for 10 days were collected. (C) Serum levels of murine VEGF and (D) murine osteopontin were analyzed by ELISA.

medium. These findings corroborate the results of the secretome 1DGE LC-MS/MS analyses. Verification of Secretome Candidate Biomarkers in Mouse Serum Collected from Mice with MEF/Toff/IGF1R-Dependent Tumors. To explore the use of secretome proteins as candidate serum biomarkers, we performed in vivo tumor experiments with MEF/Toff/IGF1R cells and analyzed serum samples for the expression of mouse VEGF and mouse osteopontin at different time points of tumor growth by ELISA (Figure 4). Specifically, MEF/Toff/IGF1R cells were implanted into nu/nu mice. After formation of tumors with an average size of 0.4 cm3, doxycycline was added into the drinking water for 10 days to one group (Group B). The other group (Group A) received no doxcycycline into the drinking water. Administration of doxycycline to the drinking water resulted in inhibition of tumor growth (Figure 4B). Blood samples were taken from Groups A and B at the day doxycycline was applied to the drinking water (t ) 1), at 2 days after mice received doxycycline (t ) 2), and at the end of the experiment (t ) 3). Moreover, blood samples were taken from mice having no tumor (Group C) and mice having no tumor but receiving doxycycline for 10 days (Group D). The analysis of mouse VEGF levels in serum showed no significant difference in the VEGF levels (Figure 4C). This might be due to the fact that VEGF165 or VEGF164 in mice is not 1920

Journal of Proteome Research • Vol. 9, No. 4, 2010

freely diffusible due to its high capacity to bind to tumor stroma components23 or absorption by platelets, respectively.24 Osteopontin levels, however, increased in mouse serum paralleling tumor growth and were reduced in the serum of mice in which IGF1R expression was shut off by addition of doxcycycline into the drinking water (Figure 4D). These results show that proteins that are identified as upregulated in the in vitro secretome 1DGE LC-MS/MS analysis can be detected in serum of mice in which the same tumor cell line was grown to an established tumor in vivo.

Discussion and Conclusions Secretome First-Dimension Separation Method Assessment. In this study, we assessed three (chromatographic) firstdimension separation methods prior to nanoLC-MS/MS for comprehensive cell secretome analysis. 1DGE coupled to LCMS/MS was superior compared to peptide SCX and protein RPC in terms of high number of identified proteins (1092-1100), high reproducibility (80-84% overlap between IDs in 3 replicate analyses), acceptable coefficient of variance for spectral counts (23-26%), acceptable throughput and convenience. Although being a partially manual procedure, GeLC gives the richest and most consistent protein identification data, with the added

research articles

Workflow Comparison for Secretome Proteomics advantage of information on molecular weight. To capture proteins down to 10 kDa, 4-12% acrylamide (Bis-Tris with MES running buffer) gradient gels are our preferred first-dimension separation method. The peptide SCX chromatography is a popular first-dimension separation that has been applied to secretome analysis,2,3 though in many studies assessment of reproducibility was missing. One study reported for 3 breast cancer cell lines, MCF-10, BT474 and MDA-MB-468, 632, 505, and 723 identified proteins, respectively, with a reproducibility of 73-76% using 8 SCX fractions per sample.3 This reproducibility is similar to our results, whereas the number of identified proteins is lower. The tC2 protein reversed phase separation first-dimension separation, described in Mbeunkui et al.,11 reported 250 proteins per cell line in 3 breast cancer cell lines.5 No overall reproducibility was reported for the tC2 method; however, reproducibility of the number of identified peptides was illustrated for 5 proteins in 4 replicate analyses. In our hands, the protein RPC method underperformed in number of identified proteins (580) by a factor of 2, compared to the SCX (979) and GeLC (1100, 1092) methods, but with acceptable reproducibility (72%). One large-scale secretome analysis applying a gel-based first-dimension separation has been reported recently.4 In this study of Lawlor et al., secretome proteins were run into the gel for 5 mm (‘stacking gels’) and 3 slivers were cut for IGD to achieve an increase in throughput for secretome analysis.4 Using the 3-sliver ‘stacking gels’, 625 proteins were identified in six cell lines. The throughput is similar as our 10slices-1DGE experiment, since 3 technical replicates/sample were performed, but goes at the expense of the number of identified proteins per sample. Comparison of the H460 secretome to H460 cell lysate after applying a 3× enrichment threshold (spectral counts) yielded 568 secretome-enriched proteins. This data filtering yielded a data set that is highly enriched for secreted and shed proteins. GO analysis with respect to (sub)-cellular localization and the presence of a secretion signal peptide (Figure 2B,C) illustrate that the secretome-enriched proteins are enriched for the annotations extracellular space (28%) and plasma membrane (18%) and the presence of a signal peptide (62%). The presence of proteins with nuclear and cytosolic annotation in the secretome-enriched fraction cannot be explained solely by (minor) cell death. One alternative explanation for the detection of nuclear and cytosolic proteins in the secretome is secretion of intracellular proteins via microvesicles (exosomes).25 Differential Secretome Analysis for Discovery of IGF1R-Related Signaling Markers. Having established a reproducible and sensitive workflow for quantitative secretome analysis that yields a subproteome data set enriched for secreted proteins, we applied the method for discovery of IGF1R signaling markers in mouse fibroblast cells inducibly overexpressing IGF1R and verified the results in serum of mice carrying tumors generated from IGF1R-positive tumor cells. Secretome analysis of MEF/Toff/IGF1R cells identified proteins that are IGF1R signaling-dependent and are secreted and/or shed by the cells. The identified proteins are stroma-interacting proteins, proteins linked to apoptosis and cell death, and others. Moreover, VEGF and osteopontin could be identified as secreted proteins. It has been shown previously that IGF1 stimulation of granulosa cells results in VEGF secretion,26 but nothing is known so far about the link between IGF1R signaling and osteopontin secretion. Both the up-regulation of VEGF and osteopontin in the

secretome of IGF1 stimulated MEF/TOFF/IGF1R cells could be verified in ELISA assays (Figure 3). No increased VEGF levels could be found in serum of mice harboring MEF/Toff/ IGF1R tumors, whereas osteopontin levels increased in serum in dependence of tumor growth. The lack of detection of tumor-derived VEGF in blood may be explained by binding to extracellular matrix components23 and sequestration by platelets.24,27,28 Importantly, osteopontin levels in serum of mice with MEF/Toff/IGF1R cells-induced tumors were proportional with the tumor-volume. Osteoponin (bone sialoprotein-1 or secreted phosphoprotein-1, SPP1) is a secreted, phosphorylated and N- and O-glycosylated protein involved in biomineralization and cell-adhesion and acts as cytokine by enhancing production of interferon-γ and interleukin-12. Moreover, it reduces production of interleukin10. Currently, both VEGF and osteopontin are being explored as biomarkers for tumor growth and antitumoral therapies.29,30 High levels of osteopontin expression correlate with tumor invasion, progression or metastasis in multiple cancers31 and the protein has been associated with inhibition of apoptosis.32 Osteopontin could serve as candidate biomarker for inhibition of IGF1R-dependent signaling in small molecule IGF1R inhibitor screens. Whether there is a direct mechanistic correlation between osteopontin secretion and IGF1R signaling remains to be clarified. The verification of osteopontin in serum in an in vivo IGF1R model system illustrates the potential of secretome proteomics in model systems for candidate biomarker discovery. Additionally, it shows that the label-free 1DGE LC-MS/MS strategy yields rich and reproducible data that can be followed-up successfully in a targeted analysis of candidate biomarkers.

Acknowledgment. U.F. acknowledges Ralph Graeser, Sarah Umber, Anna Jasper, and Norbert Esser. This project was carried out with financial support of the FP6 EU Angiotargeting project (Contract No. 504743). S.R.P. and C.R.J. acknowledge the input of Dr. Frank Kruyt in the initial phase of this project. Avanti-STR is acknowledged for financial support of T.V.P. Supporting Information Available: Tables including protein and peptide identifications, spectral count ratios and the presence of signal peptides are available as Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Jimenez, C. R.; Piersma, S. R.; Pham, T. V. Biomarkers Med. 2007, 1, 541–565. (2) Jacobs, J. M.; Waters, K. M.; Kathmann, L. E.; David, G.; Wiley, H. S.; Smith, R. D.; Thrall, B. D. J. Proteome Res. 2008, 7, 558–569. (3) Kulasingam, V.; Diamandis, E. P. Mol. Cell. Proteomics 2007, 6, 1997–2011. (4) Lawlor, K.; Nazarlan, A.; Lacomis, L.; Tempst, P.; Villanueva, J. J. Proteome Res. 2009, 8, 1489–1503. (5) Mbeunkui, F.; Metge, B. J.; Shevde, L. A.; Pannell, L. K. J. Proteome Res. 2007, 6, 2993–3002. (6) Huang, L. J.; Chen, S. X.; Huang, Y.; Luo, W. J.; Jiang, H. H.; Hu, Q. H.; Zhang, P. F.; Yi, H. Lung Cancer 2006, 54, 87–94. (7) Xiao, T.; Ying, W. T.; Li, L.; Hu, Z.; Ma, Y.; Jiao, L. Y.; Ma, J. F.; Cai, Y.; Lin, D. M.; Guo, S. P.; Han, N. J.; Di, X. B.; Li, M.; Zhang, D. C.; Su, K.; Yuan, J. S.; Zheng, H. W.; Gao, M. X.; He, J.; Shi, S. S.; Li, W. J.; Xu, N. Z.; Zhang, H. S.; Liu, Y.; Zhang, K. T.; Gao, Y. N.; Qian, X. H.; Cheng, S. J. Mol. Cell. Proteomics 2005, 4, 1480–1486. (8) Martin, D. B.; Gifford, D. R.; Wright, M. E.; Keller, A.; Yi, E.; Goodlett, D. R.; Aebersold, R.; Nelson, P. S. Cancer Res. 2004, 64, 347–355. (9) Li, H. Y.; DeSouza, L. V.; Ghanny, S.; Li, W.; Romaschin, A. D.; Colgan, T. J.; Siu, K. W. M. J. Proteome Res. 2007, 6, 2615–2622.

Journal of Proteome Research • Vol. 9, No. 4, 2010 1921

research articles (10) Gunawardana, C. G.; Kuk, C.; Smith, C. R.; Batruch, I.; Soosaippillai, A.; Diamandis, E. P. J. Proteome Res. 2009, 8, 4705–4713. (11) Mbeunkui, F.; Fodstad, O.; Pannell, L. K. J. Proteome Res. 2006, 5, 899–906. (12) Pollak, M. Nat. Rev. Cancer 2008, 8, 915–928. (13) Sell, C.; Rubini, M.; Rubin, R.; Liu, J. P.; Efstratiadis, A.; Baserga, R. Proc. Natl. Acad. Sci. U.S.A. 1993, 90, 11217–11221. (14) Yang, X. F.; Beamer, W. G.; Huynh, H.; Pollak, M. Cancer Res. 1996, 56, 1509–1511. (15) Tao, Y.; Pinzi, V.; Bourhis, J.; Deutsch, E. Nat. Clin. Pract. Oncol. 2007, 4, 591–602. (16) Shevchenko, A.; Wilm, M.; Vorm, O.; Mann, M. Anal. Chem. 1996, 68, 850–858. (17) Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Anal. Chem. 2002, 74, 5383–5392. (18) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. Anal. Chem. 2003, 75, 4646–4658. (19) Sardiu, M. E.; Cai, Y.; Jin, J. J.; Swanson, S. K.; Conaway, R. C.; Conaway, J. W.; Florens, L.; Washburn, M. P. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 1454–1459. (20) Pham, T. V.; Piersma, S. R.; Warmoes, M.; Jimenez, C. R. Bioinformatics [Epub ahead of print]. DOI: 10.1093/bioinformatics/ btp677. Published Online: Dec 9, 2009. (21) Nielsen, H.; Engelbrecht, J.; Brunak, S.; vonHeijne, G. Protein Eng. 1997, 10, 1–6.

1922

Journal of Proteome Research • Vol. 9, No. 4, 2010

Piersma et al. (22) Liu, H. B.; Sadygov, R. G.; Yates, J. R. Anal. Chem. 2004, 76, 4193– 4201. (23) Carmeliet, P. Oncology 2005, 69, 4–10. (24) Klement, G. L.; Yip, T. T.; Cassiola, F.; Kikuchi, L.; Cervi, D.; Podust, V.; Italiano, J. E.; Wheatley, E.; bou-Slaybi, A.; Bender, E.; Almog, N.; Kieran, M. W.; Folkman, J. Blood 2009, 113, 2835–2842. (25) Mathivanan, S.; Lim, J. W.; Tauro, B. J.; Ji, H.; Moritz, R. L.; Simpson, R. J. Mol. Cell. Proteomics [MCP Papers in Press]. DOI: 10.1074/mcp.M900152-MCP200. (26) Schams, D.; Kosmann, M.; Berisha, B.; Amselgruber, W. M.; Miyamoto, A. Exp. Clin. Endocrinol. Diabetes 2001, 109, 155–162. (27) Kut, C.; Mac Gabhann, F.; Popel, A. S. Br. J. Cancer 2007, 97, 978– 985. (28) Verheul, H. M. W.; Hoekman, K.; Bakker, S. L. D.; Eekman, C. A.; Folman, C. C.; Broxterman, H. J.; Pinedo, H. M. Clin. Cancer Res. 1997, 3, 2187–2190. (29) Jain, S.; Chakraborty, G.; Bulbule, A.; Kaur, R.; Kundu, G. C. Expert Opin. Ther. Targets 2007, 11, 81–90. (30) Pathak, A. P.; Hochfeld, W. E.; Goodman, S. L.; Pepper, M. S. Angiogenesis 2008, 11, 321–335. (31) Wai, P. Y.; Kuo, P. C. Cancer Metastasis Rev. 2008, 27, 103–118. (32) Zhao, J.; Dong, L.; Liu, B.; Wu, G. B.; Xu, D. M.; Chen, J. J.; Li, K.; Tong, X.; Dai, J. X.; Yao, S.; Wu, M. C.; Guo, Y. J. Gastroenterology 2008, 135, 956–968.

PR901072H