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• W2101976670 abstract "Malignant gliomas (glioblastoma multiforme) have a poor prognosis with an average patient survival under current treatment regimens ranging between 12 and 14 months. The tumors are characterized by rapid cell growth, extensive neovascularization, and diffuse cellular infiltration of normal brain structures. We have developed a human glioblastoma xenograft model in nude rats that is characterized by a highly infiltrative non-angiogenic phenotype. Upon serial transplantation this phenotype will develop into a highly angiogenic tumor. Thus, we have developed an animal model where we are able to establish two characteristic tumor phenotypes that define human glioblastoma (i.e. diffuse infiltration and high neovascularization). Here we aimed at identifying potential biomarkers expressed by the non-angiogenic and the angiogenic phenotypes and elucidating the molecular pathways involved in the switch from invasive to angiogenic growth. Focusing on membrane-associated proteins, we profiled protein expression during the progression from an invasive to an angiogenic phenotype by analyzing serially transplanted glioma xenografts in rats. Applying isobaric peptide tagging chemistry (iTRAQ) combined with two-dimensional LC and MALDI-TOF/TOF mass spectrometry, we were able to identify several thousand proteins in membrane-enriched fractions of which 1460 were extracted as quantifiable proteins (isoform- and species-specific and present in more than one sample). Known and novel candidate proteins were identified that characterize the switch from a non-angiogenic to a highly angiogenic phenotype. The robustness of the data was corroborated by extensive bioinformatics analysis and by validation of selected proteins on tissue microarrays from xenograft and clinical gliomas. The data point to enhanced intercellular cross-talk and metabolic activity adopted by tumor cells in the angiogenic compared with the non-angiogenic phenotype. In conclusion, we describe molecular profiles that reflect the change from an invasive to an angiogenic brain tumor phenotype. The identified proteins could be further exploited as biomarkers or therapeutic targets for malignant gliomas. Malignant gliomas (glioblastoma multiforme) have a poor prognosis with an average patient survival under current treatment regimens ranging between 12 and 14 months. The tumors are characterized by rapid cell growth, extensive neovascularization, and diffuse cellular infiltration of normal brain structures. We have developed a human glioblastoma xenograft model in nude rats that is characterized by a highly infiltrative non-angiogenic phenotype. Upon serial transplantation this phenotype will develop into a highly angiogenic tumor. Thus, we have developed an animal model where we are able to establish two characteristic tumor phenotypes that define human glioblastoma (i.e. diffuse infiltration and high neovascularization). Here we aimed at identifying potential biomarkers expressed by the non-angiogenic and the angiogenic phenotypes and elucidating the molecular pathways involved in the switch from invasive to angiogenic growth. Focusing on membrane-associated proteins, we profiled protein expression during the progression from an invasive to an angiogenic phenotype by analyzing serially transplanted glioma xenografts in rats. Applying isobaric peptide tagging chemistry (iTRAQ) combined with two-dimensional LC and MALDI-TOF/TOF mass spectrometry, we were able to identify several thousand proteins in membrane-enriched fractions of which 1460 were extracted as quantifiable proteins (isoform- and species-specific and present in more than one sample). Known and novel candidate proteins were identified that characterize the switch from a non-angiogenic to a highly angiogenic phenotype. The robustness of the data was corroborated by extensive bioinformatics analysis and by validation of selected proteins on tissue microarrays from xenograft and clinical gliomas. The data point to enhanced intercellular cross-talk and metabolic activity adopted by tumor cells in the angiogenic compared with the non-angiogenic phenotype. In conclusion, we describe molecular profiles that reflect the change from an invasive to an angiogenic brain tumor phenotype. The identified proteins could be further exploited as biomarkers or therapeutic targets for malignant gliomas. Glioblastoma multiforme (GBM) 1The abbreviations used are:GBMglioblastoma multiformeiTRAQisobaric tags for relative and absolute quantitationTMAtissue microarrayEGFRepidermal growth factor receptorSCXstrong cation exchangeCIconfidence intervalNCBINational Center for Biotechnology InformationCAcorrespondence analysis2Dtwo-dimensionalFDRfalse discovery rateVEGFvascular endothelial growth factorSsupernatantPpatientIDidentificationAnxAnnexinGOgene ontologyMHCmajor histocompatibility complex.1The abbreviations used are:GBMglioblastoma multiformeiTRAQisobaric tags for relative and absolute quantitationTMAtissue microarrayEGFRepidermal growth factor receptorSCXstrong cation exchangeCIconfidence intervalNCBINational Center for Biotechnology InformationCAcorrespondence analysis2Dtwo-dimensionalFDRfalse discovery rateVEGFvascular endothelial growth factorSsupernatantPpatientIDidentificationAnxAnnexinGOgene ontologyMHCmajor histocompatibility complex. is the prevalent and most fatal brain tumor in adults with an average patient survival time between 12 and 14 months under current treatment regimens. Invasion and angiogenesis are two defining hallmarks of GBM that are largely responsible for the aggressive nature of the disease (1Louis D.N. Ohgaki H. Wiestler O.D. Cavenee W.K. Burger P.C. Jouvet A. Scheithauer B.W. Kleihues P. The 2007 WHO classification of tumours of the central nervous system.Acta Neuropathol. 2007; 114: 97-109Crossref PubMed Scopus (8020) Google Scholar). Invasion is likely triggered by signals that prompt tumor cells to egress from the tumor mass, including those that are activated by an acidic and hypoxic environment (e.g. hypoxia-inducible factor) (2Hoelzinger D.B. Demuth T. Berens M.E. Autocrine factors that sustain glioma invasion and paracrine biology in the brain microenvironment.J. Natl. Cancer Inst. 2007; 99: 1583-1593Crossref PubMed Scopus (290) Google Scholar). These highly infiltrative glioma cells escape neurosurgical resection and are the seeds for tumor recurrence. Oxygen limitation in the tumor microenvironment is also responsible for the active recruitment of new blood vessels from preexisting vessels, a process termed angiogenesis. Absence of angiogenesis is considered a rate-limiting factor in solid tumors. Although high grade gliomas show extensive infiltration of the normal brain they are also among the neoplasms with the highest degree of vascularization (3Carmeliet P. Jain R.K. Angiogenesis in cancer and other diseases.Nature. 2000; 407: 249-257Crossref PubMed Scopus (7385) Google Scholar, 4Jain R.K. di Tomaso E. Duda D.G. Loeffler J.S. Sorensen A.G. Batchelor T.T. Angiogenesis in brain tumours.Nat. Rev. Neurosci. 2007; 8: 610-622Crossref PubMed Scopus (1045) Google Scholar, 5Reiss Y. Machein M.R. Plate K.H. The role of angiopoietins during angiogenesis in gliomas.Brain Pathol. 2005; 15: 311-317Crossref PubMed Scopus (92) Google Scholar). Antiangiogenic treatment is considered a promising therapeutic strategy against malignant brain tumors and is currently being evaluated in clinical trials (6Reardon D.A. Wen P.Y. Desjardins A. Batchelor T.T. Vredenburgh J.J. Glioblastoma multiforme: an emerging paradigm of anti-VEGF therapy.Expert Opin. Biol. Ther. 2008; 8: 541-553Crossref PubMed Scopus (76) Google Scholar). glioblastoma multiforme isobaric tags for relative and absolute quantitation tissue microarray epidermal growth factor receptor strong cation exchange confidence interval National Center for Biotechnology Information correspondence analysis two-dimensional false discovery rate vascular endothelial growth factor supernatant patient identification Annexin gene ontology major histocompatibility complex. glioblastoma multiforme isobaric tags for relative and absolute quantitation tissue microarray epidermal growth factor receptor strong cation exchange confidence interval National Center for Biotechnology Information correspondence analysis two-dimensional false discovery rate vascular endothelial growth factor supernatant patient identification Annexin gene ontology major histocompatibility complex. In solid tumors the angiogenic switch is thought to occur when the balance between proangiogenic and antiangiogenic molecules is shifted in favor of angiogenesis, permitting rapid tumor growth and subsequent development of invasive and metastatic properties (7Hanahan D. Folkman J. Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis.Cell. 1996; 86: 353-364Abstract Full Text Full Text PDF PubMed Scopus (6037) Google Scholar). Thus, aggressive tumor growth depends on a successful adaptation of the tumor cells to the host microenvironment. In brain tumors no biomarkers are currently available that define different cell populations within human GBMs (for instance tumor cells that show infiltrative growth and those that trigger angiogenesis) or that predict the propensity of low grade (non-angiogenic) gliomas to develop into malignant angiogenic gliomas. We have recently generated a xenograft model for human GBM that displays a highly invasive phenotype and stem cell characteristics (8Sakariassen P.Ø. Prestegarden L. Wang J. Skaftnesmo K.O. Mahesparan R. Molthoff C. Sminia P. Sundlisaeter E. Misra A. Tysnes B.B. Chekenya M. Peters H. Lende G. Kalland K.H. Øyan A.M. Petersen K. Jonassen I. van der Kogel A. Feuerstein B.G. Terzis A.J. Bjerkvig R. Enger P.Ø. Angiogenesis-independent tumor growth mediated by stem-like cancer cells.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 16466-16471Crossref PubMed Scopus (190) Google Scholar). By serial transplantation in nude rats new cell clones eventually develop that generate a more rapidly growing aggressive, angiogenesis-dependent phenotype. The transition to an angiogenic phenotype is accompanied by a reduced infiltrative growth (8Sakariassen P.Ø. Prestegarden L. Wang J. Skaftnesmo K.O. Mahesparan R. Molthoff C. Sminia P. Sundlisaeter E. Misra A. Tysnes B.B. Chekenya M. Peters H. Lende G. Kalland K.H. Øyan A.M. Petersen K. Jonassen I. van der Kogel A. Feuerstein B.G. Terzis A.J. Bjerkvig R. Enger P.Ø. Angiogenesis-independent tumor growth mediated by stem-like cancer cells.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 16466-16471Crossref PubMed Scopus (190) Google Scholar). Thus, we are able to initiate two distinct phenotypes from human GBMs that classify their growth and progression. Our model is extremely useful for identifying mechanisms causing the switch from angiogenesis-independent to angiogenesis-dependent tumor growth. This work was aimed at identifying cell membrane markers and molecular pathways that characterize the two phenotypes and may underlie the angiogenic switch. Such markers may represent potential therapeutic targets toward specific cellular subsets within GBMs. Here we applied iTRAQ peptide labeling on membrane-enriched tumor fractions followed by MALDI-TOF/TOF protein identification and bioinformatics analysis to quantify large scale species-specific protein expression over four consecutive generations of the glioma xenograft model. In a search for disease biomarkers, there has been a rapid development of quantitative protein expression technologies including isobaric peptide tagging (iTRAQ) combined with multidimensional LC and MS/MS analysis (9Rajcevic U. Niclou S.P. Jimenez C.R. Proteomics strategies for target identification and biomarker discovery in cancer.Front. Biosci. 2009; 14: 3292-3303Crossref Google Scholar). This approach allows for sample multiplexing (currently 4- or 8-plex at the time). iTRAQ is particularly powerful when applied on a subfraction of the proteome, thereby increasing the possibility of identifying less abundant proteins (10Li K.W. Miller S. Klychnikov O. Loos M. Stahl-Zeng J. Spijker S. Mayford M. Smit A.B. Quantitative proteomics and protein network analysis of hippocampal synapses of CaMKIIalpha mutant mice.J. Proteome Res. 2007; 6: 3127-3133Crossref PubMed Scopus (46) Google Scholar). Because more than a third of all known biomarkers as well as more than two-thirds of known and potential antitumor protein targets are membrane-related proteins (11Hopkins A.L. Groom C.R. Target analysis: a priori assessment of druggability.Ernst Schering Res. Found. Workshop. 2003; 42: 11-17Google Scholar, 12Josic D. Clifton J.G. Mammalian plasma membrane proteomics.Proteomics. 2007; 7: 3010-3029Crossref PubMed Scopus (111) Google Scholar, 13Josic D. Clifton J.G. Kovac S. Hixson D.C. Membrane proteins as diagnostic biomarkers and targets for new therapies.Curr. Opin. Mol. Ther. 2008; 10: 116-123PubMed Google Scholar, 14Rabilloud T. Membrane proteins ride shotgun.Nat. Biotechnol. 2003; 21: 508-510Crossref PubMed Scopus (87) Google Scholar), we focused on membrane-enriched fractions of the tumor xenografts. In four different iTRAQ experiments we were able to identify over 7000 (redundant) proteins of which 1460 proteins were extracted based on quantifiable and species-specific expression. Correspondence analysis and unsupervised cluster analysis confirmed consistent protein expression profiles in the different xenograft phenotypes generated from different patient samples. The expression of a selection of identified candidates was confirmed by immunohistochemical methods on tissue microarrays (TMAs) from a large number of xenograft tumors and patient gliomas. The differentially expressed proteins identified in the two phenotypes represent unique candidate biomarkers that may represent novel therapeutic targets in GBMs. The information generated also provides novel insight into the molecular networks governing the infiltrative and the angiogenic tumor properties and reveals new mechanisms involved in the angiogenic switch in GBMs. Human tumor spheroids derived from GBM biopsies were cultured for 10–14 days on agar-coated flasks in serum-containing medium and transplanted into the brain of nude rats as described previously (8Sakariassen P.Ø. Prestegarden L. Wang J. Skaftnesmo K.O. Mahesparan R. Molthoff C. Sminia P. Sundlisaeter E. Misra A. Tysnes B.B. Chekenya M. Peters H. Lende G. Kalland K.H. Øyan A.M. Petersen K. Jonassen I. van der Kogel A. Feuerstein B.G. Terzis A.J. Bjerkvig R. Enger P.Ø. Angiogenesis-independent tumor growth mediated by stem-like cancer cells.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 16466-16471Crossref PubMed Scopus (190) Google Scholar, 15Mahesparan R. Read T.A. Lund-Johansen M. Skaftnesmo K.O. Bjerkvig R. Engebraaten O. Expression of extracellular matrix components in a highly infiltrative in vivo glioma model.Acta Neuropathol. 2003; 105: 49-57Crossref PubMed Scopus (142) Google Scholar, 16Bjerkvig R. Tønnesen A. Laerum O.D. Backlund E.O. Multicellular tumor spheroids from human gliomas maintained in organ culture.J. Neurosurg. 1990; 72: 463-475Crossref PubMed Scopus (171) Google Scholar). First generation rats developed highly invasive tumors that were lethal within 3–4 months. Tumor spheroids generated from the first generation xenografts were transplanted into subsequent generations of rats. After serial passaging in rats over 4–6 generations, the tumors gradually underwent an adaptation to the host characterized by a more aggressive angiogenic phenotype (see Fig. 1). Brain tumor xenografts thus generated over four generations of rats from two different patient GBMs (patients 6 and 17) were collected, flash frozen in liquid nitrogen, and stored at −80 °C until further processing. The handling of the animals and the surgical procedures were performed in accordance with the Norwegian Animal Act, and the local ethics committee approved the protocol. The biopsy material was obtained from the Department of Neurosurgery, Haukeland University Hospital, Bergen, Norway. All biopsies were primary GBMs (patient 6 (P6), 81-year-old female; P7, 64-year-old male) with epidermal growth factor receptor (EGFR) amplification. The collection of tumor tissue was approved by the regional ethics committee at Haukeland University Hospital, Bergen, Norway. Membrane fractions were prepared as described previously (17Li K. Hornshaw M.P. van Minnen J. Smalla K.H. Gundelfinger E.D. Smit A.B. Organelle proteomics of rat synaptic proteins: correlation-profiling by isotope-coded affinity tagging in conjunction with liquid chromatography-tandem mass spectrometry to reveal post-synaptic density specific proteins.J. Proteome Res. 2005; 4: 725-733Crossref PubMed Scopus (69) Google Scholar). Briefly tissue samples were homogenized with a Polytron homogenizer (Kinematica, Lucerne, Switzerland) in 10 ml of 0.32 m sucrose, 5 mm HEPES, pH 7.4, protease inhibitor mixture (Amersham Biosciences)/g of tissue. The homogenate was centrifuged for 10 min at 1000 × g at 4 °C to remove the cell debris. Supernatant 1 (S1) was collected and centrifuged for 30 min at 4 °C on a tabletop centrifuge at 16,000 × g. An aliquot of it was saved for quality control (see Fig. 2). The resulting pellet was homogenized in the same buffer (0.32 m sucrose, 5 mm HEPES, pH 7.4, protease inhibitor mixture (Amersham Biosciences)) and centrifuged for 30 min at 4 °C at 20,000 × g. The resulting pellet (the crude membrane fraction) was resuspended in a buffer containing 0.32 m sucrose, 5 mm NaH2PO4, pH 8.1, protease inhibitor mixture (Amersham Biosciences), and supernatant 2 (S2) was saved for quality control. A sucrose step gradient was formed in a 17-ml ultracentrifuge tube (Beckman Coulter, Fullerton, CA) with 1.4 m sucrose in water at the bottom followed by a layer of 1.1 m sucrose. The crude membrane fraction was applied at the top. The tubes were centrifuged at 85,000 × g (Beckmann SW 40 rotor, Beckman Coulter) for 2 h at 4 °C. The two layers between 0.32 and 1.1 m sucrose (light membranes) and between 1.1 and 1.4 m sucrose (plasma membranes), respectively, were carefully collected and centrifuged at 86,000 × g (SW31T1–1 rotor, Beckman Coulter) at 4 °C for 2 h. The two pelleted membrane fractions were resuspended in 5 mm HEPES, pH 8.1. Protein concentration in the homogenate, S1, S2, and membrane suspensions was estimated using Bradford reagent (Bio-Rad) and absorbance measurement at 595 nm. Proteins samples dissolved in SDS sample buffer were separated by SDS-PAGE and transferred to a PVDF membrane (Bio-Rad). EGFR (Chemicon, Temecula, CA) and actin antibody binding was revealed with the ECL Plus Western Blotting Detection system (Amersham Biosciences) according to the manufacturer's instructions. Sample labeling with isobaric tagging reagents was carried out according to the manufacturer's instructions with modifications (iTRAQ® Reagents Multiplex kit; Applied Biosystems/MDS Sciex, Foster City, CA). Briefly 100 µg of membrane suspensions was vacuum-dried, resuspended in 25 µl of Rapigest detergent solution (Waters) dissolved in 125 µl of Dissolution buffer from the iTRAQ kit per vial of detergent, and vortexed. 2 µl of reducing agent (Applied Biosystems/MDS Sciex) was added followed by incubation with vortexing for 2 h at room temperature. After centrifugation, cysteine-blocking agent was added and incubated for 10 min at room temperature followed by addition of 2 µg of trypsin (modified sequencing grade; Promega, Madison, WI) and incubation for 12 h at 37 °C. Digested samples were labeled with four different iTRAQ reagents dissolved in 80 µl of ethanol and vortexed at room temperature for 4 h. After the labeling, the four samples were pooled, 400 µl of 1% (v/v) TFA was added to cleave the Rapigest detergent, and samples were vortexed for 1 h at room temperature and centrifuged. The supernatant was removed and vacuum-dried. Four separate quadruplex iTRAQ experiments were performed. 1) In iTRAQ1 light membranes from early and late generations of two different GBM patients (P6 and P17) were analyzed with P6 first and last labeled with iTRAQ 114 and 115, respectively, and P17 first and last labeled with iTRAQ 116 and 117, respectively. 2) In iTRAQ2 plasma membranes of the same samples were analyzed. 3) and 4) In iTRAQ3 and iTRAQ4 plasma membranes from four consecutive generations of xenografts originating from the two GBM patients (P6 and P17, respectively) were analyzed. Samples were labeled as follows: iTRAQ 114, first (early); iTRAQ 115, second; iTRAQ 116, third; and iTRAQ 117, fourth (late) generation. The experimental flowchart is summarized in Fig. 2. The iTRAQ labeled peptide pool was resuspended in 170 µl of buffer A (10 mm KH2PO4, 20% (v/v) acetonitrile, pH 2.9) and separated on a strong cation exchange (SCX) column (PolySULFOETHYL Aspartamide, 100 × 2.1 mm, 3 µm, 300 Å (Poly LC, Columbia, MD)) under the following conditions: flow, 200 µl, 20-min gradient, 0–60% B (A + 500 mmol KCl); 5 min, 60–100% B; 4 min, 100% B. Minute fractions were collected in a fraction collector and vacuum-dried. 20 peak SCX fractions were dissolved in 30–60 µl of 0.1% (v/v) TFA and further separated on a microcapillary reverse phase column (ReproSil-Pur C18 Aqua 3-µm material, 100-µm-inner diameter, 15-cm column) under the following conditions: flow, 400 µl; buffer A, 5% (v/v) acetonitrile, 0.05% (v/v) TFA; buffer B, 80% (v/v) acetonitrile, 0.04% (v/v) TFA; 6 min, 5% B; 45 min, 5–45% B; 1 min, 45–90% B; 5 min, 90% B; 1 min, 90–5% B. The reverse phase fractions were premixed with the matrix (6 mg/ml α-cyanohydroxycinnaminic acid in 33.3% (v/v) acetonitrile, 1 mm ammonium citrate) and spotted on line onto a MALDI target plate using a Probot Microfraction Collector (Dionex, Sunnyvale, CA). 192 spots were spotted per fraction. For all measurements, a 4800 MALDI-TOF/TOF Analyzer (Applied Biosystems/MDS Sciex) operated under 4000 Series Explorer v.3.5.1 software (Applied Biosystems/MDS Sciex) was used. The instrument was equipped with a 20-µJ neodymium-doped yttrium aluminium garnet (Nd-YAG) laser operating at 355 nm and 200 Hz and was used in reflector-positive ion mode. For calibration and tuning purposes, 4700 Cal Mix (Applied Biosystems/MDS Sciex) containing six calibrant peptides in the m/z range of 904–3658 were spotted on 13 dedicated calibration spot locations distributed over each MALDI target plate. Before each job run, the instrument was retuned (including deflector, mirror, time ion selector, and laser settings), and MS and MS/MS default calibration was updated. Automatic jobs were created for two 384-spot MALDI target plates at a time. In MS analyses, 1250 shots (25 subspectra at randomized locations over the whole spot, 50 shots/spectrum) were acquired and averaged over an m/z window of 800–4000. For each spot, up to 30 of the strongest precursor peaks with a minimal signal-to-noise ratio of 35 were automatically selected for MS/MS analysis using a job-wide interpretation method that excludes multiple analyses of the same precursor with a 200-ppm tolerance. In MS/MS analyses, acquisition was from the weakest to the strongest selected precursor for each spot. For each precursor, 2500 shots (50 subspectra at randomized locations over the whole spot, 50 shots/spectrum) were acquired and averaged. All MS/MS fragmentation (collision-induced dissociation) was performed with air at medium pressure (∼1 × 10−6 torr) and using 2-keV collision energy. MS/MS spectra were searched against databases with trypsin specificity, allowing one missed cleavage and fixed iTRAQ modifications at lysine residues and the N termini of the peptides, using GPS Explorer v.3.6. (Applied Biosystems/MDS Sciex), which incorporates the Mascot (v.2.1.) search algorithm (Matrix Science Inc., Boston, MA). Mass tolerance was set to 100 ppm for precursor ions and 0.5 Da for fragment ions. The Mascot confidence interval (CI) in percent was calculated by GPS Explorer (Applied Biosystems/MDS Sciex) based on the Mascot ion scores. Because peptides could have been derived from rat (host) or human (tumor) proteins, spectra were annotated twice using the peptides with the highest Mascot CI, once according to the rat Swiss-Prot database and once according to the human Swiss-Prot database (UniProtKB/Swiss-Prot Release 9.6 of February 6, 2007 (Swiss-Prot Release 51.6)). If a spectrum was not annotated using the well curated rat or human Swiss-Prot databases, Mascot searches were performed in the larger but more redundant NCBI rat and human databases (NCBInr release of March 29, 2005). Next the precursor protein sequences of all peptides were retrieved from the respective databases, and NCBI sequences that shared more than 90% similarity over 85% of the sequence length with a Swiss-Prot sequence were clustered together as a single protein cluster (blastclust). Clustering of Swiss-Prot sequences was not permitted, thereby preventing the clustering of human and rat sequences. Next the sequences of the peptides were compared with the sequence of the protein clusters. Peptides were automatically compared with a protein cluster using the VBA script in Excel. Using a text search, the peptide sequences were searched in the sequences of all proteins of a cluster. Peptides were only matched to a protein cluster if the protein sequence was preceded by an Arg or Lys (trypsin cleavage sites) or if the peptide sequence started with a Met and matched to the beginning of a protein sequence. Peptides that matched the sequence of more than one cluster were considered species- or isoform-nonspecific. These nonspecific peptides were not considered for protein identification and quantification. Peak areas for each iTRAQ signature peak (m/z 114.1, 115.1, 116.1, and 117.1) were obtained directly from the Oracle database of the mass spectrometer, corrected according to the manufacturer's instructions to account for the differences in isotopic overlap, and log2-transformed. Possible systematic differences in the starting amounts or labeling efficiencies between samples were normalized by subtracting the mean peak area of a sample from each individual iTRAQ signature peak. Low signature peaks generally have larger variation, which may compromise the quantitative analysis of the proteins. Therefore, peptides with iTRAQ signature peaks less than 10.97 (log2) were not considered for quantification. To compare the abundance of peptides across multiple iTRAQ experiments, within each experiment peptide quantity values were standardized to scores around zero by subtracting the mean peak area of all four samples. These normalized and standardized peptide quantity values were used to calculate mean protein quantity values. The mean protein quantity values were imported into J-Express v.2.8 (18Dysvik B. Jonassen I. J-Express: exploring gene expression data using Java.Bioinformatics. 2001; 17: 369-370Crossref PubMed Scopus (279) Google Scholar, 19Stavrum A.K. Petersen K. Jonassen I. Dysvik B. Analysis of gene-expression data using J-Express.Curr. Protoc. Bioinformatics. 2008; (Chapter 7, Unit 7.3)Crossref PubMed Scopus (59) Google Scholar) for further analysis. Only proteins quantified by at least two peptides in all four iTRAQ signature peaks and having at least one peptide with better Mascot CI value of 95% were imported for quantitative analysis. Note that because data sets from four different iTRAQ experiments were combined, one particular protein can be present up to four times in the final (redundant) data set. Except to calculate mean -fold changes, the separate entry points for one protein were not combined to be able to attribute each data point to the corresponding iTRAQ experiment from which it was derived. A protein quantity value matrix was formed having four columns representing the following xenograft samples: 1) early generation P6, 2) late generation P6, 3) early generation P17, and 4) late generation P17. Quality filtered data from iTRAQ1 and iTRAQ2 fitted directly into this matrix with different proteins on separate rows. From iTRAQ3 and iTRAQ4 (P6 and P17, respectively) the standardized early and late generation xenograft samples (labels 113 and 117, respectively) protein quantity values were added as additional rows in the protein quantity matrix. To identify consistent differences in protein abundance levels in both P6 and P17 between the early and late generation, we applied the RankProduct method (20Breitling R. Armengaud P. Amtmann A. Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.FEBS Lett. 2004; 573: 83-92Crossref PubMed Scopus (1207) Google Scholar) implemented in J-Express. RankProduct is a robust approach designed for experiments with few replicates and provides false discovery rate-equivalent q values as confidence estimates to counter for the issues of multiple testing in high throughput experiments. To visualize a global view of how the samples and proteins relate to each other, the CA approach (21Fellenberg K. Hauser N.C. Brors B. Neutzner A. Hoheisel J.D. Vingron M. Correspondence analysis applied to microarray data.Proc. Natl. Acad. Sci. U.S.A. 2001; 98: 10781-10786Crossref PubMed Scopus (263) Google Scholar) calculates the principal components of both the sample co-variance matrix and the protein co-variance matrix in such a manner that the resulting most significant principal components can be used to plot both samples and proteins in the same biplot. The biplot thus provides a 2D estimated global view of sample-sample, sample-protein, and protein-protein relationships found in the original higher order dimension data set. The quality filtered data of iTRAQ3 and iTRAQ4, representing the four xenograft generations of P6 and P17, respectively, were imported as two separate data sets in J-Express. Unsupervised analysis approac" @default.
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• W2101976670 date "2009-11-01" @default.
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• W2101976670 title "iTRAQ-based Proteomics Profiling Reveals Increased Metabolic Activity and Cellular Cross-talk in Angiogenic Compared with Invasive Glioblastoma Phenotype" @default.
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• W2101976670 doi "https://doi.org/10.1074/mcp.m900124-mcp200" @default.
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