Database-augmented Mass Spectrometry Analysis of Exosomes Identifies Claudin 3 as a Putative Prostate Cancer Biomarker
2017; Elsevier BV; Volume: 16; Issue: 6 Linguagem: Inglês
10.1074/mcp.m117.068577
ISSN1535-9484
AutoresThomas Stefan Worst, Jost von Hardenberg, Julia Christina Gross, Philipp Erben, Martina Schnölzer, Ingrid Haußer, Peter Bugert, Maurice Stephan Michel, Michael Boutros,
Tópico(s)Cancer-related molecular mechanisms research
ResumoIn prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer.Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry data sets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized (n = 58; 42 with Gleason score 6–7, 16 with Gleason score ≥8) and metastatic prostate cancer (n = 11) compared with patients with benign prostatic hyperplasia (n = 15) and healthy individuals (n = 15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups (p = 0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared with patients with benign prostatic hyperplasia (p = 0.012) and Gleason 6–7 tumors (p = 0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC = 0.705; p = 0.016) and did not correlate with serum PSA.By using the described workflow claudin 3 was identified and validated as a potential blood-based biomarker in prostate cancer. Furthermore this workflow could serve as a template to be used in other cancer entities. In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer. Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry data sets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized (n = 58; 42 with Gleason score 6–7, 16 with Gleason score ≥8) and metastatic prostate cancer (n = 11) compared with patients with benign prostatic hyperplasia (n = 15) and healthy individuals (n = 15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups (p = 0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared with patients with benign prostatic hyperplasia (p = 0.012) and Gleason 6–7 tumors (p = 0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC = 0.705; p = 0.016) and did not correlate with serum PSA. By using the described workflow claudin 3 was identified and validated as a potential blood-based biomarker in prostate cancer. Furthermore this workflow could serve as a template to be used in other cancer entities. There is an urgent need for minimally invasively obtained biomarkers for multiple cancer entities. For each cancer entity, a biomarker must meet specific requirements. Prostate cancer (PCa) 1The abbreviations used are: PCa, prostate cancer; ADT, androgen deprivation therapy; BPH, benign prostatic hyperplasia; CLDN3, claudin 3; CM, conditioned medium; CPE, clostridium perfringens enterotoxin; EV, extracellular vesicle; NTA, nanoparticle tracking analysis; PSA, prostate specific antigen; SPECT, single photon emission computed tomography; TEM, transmission electron microscopy. 1The abbreviations used are: PCa, prostate cancer; ADT, androgen deprivation therapy; BPH, benign prostatic hyperplasia; CLDN3, claudin 3; CM, conditioned medium; CPE, clostridium perfringens enterotoxin; EV, extracellular vesicle; NTA, nanoparticle tracking analysis; PSA, prostate specific antigen; SPECT, single photon emission computed tomography; TEM, transmission electron microscopy. for instance is the most common cancer among men in industrialized countries (1.Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global cancer statistics.CA. Cancer J. Clin. 2011; 61: 69-90Crossref PubMed Scopus (30255) Google Scholar) with most cases diagnosed in early stages. Many patients have insignificant carcinoma. These patients benefit from active surveillance, sparing them from overtreatment. On the other hand, in patients suffering from disease recurrence or incurable metastatic PCa, androgen deprivation therapy (ADT) typically fails after several years of therapy. Although an increasing number of therapeutic strategies has become available during the last decade (2.Mottet N. Bellmunt J. Bolla M. Joniau S. Mason M. Matveev V. Schmid H.-P. Van der Kwast T. Wiegel T. Zattoni F. Heidenreich A. EAU guidelines on prostate cancer. Part II: Treatment of advanced, relapsing, and castration-resistant prostate cancer.Eur. Urol. 2011; 59: 572-583Abstract Full Text Full Text PDF PubMed Scopus (465) Google Scholar, 3.Yap T.A. Zivi A. Omlin A. de Bono J.S. The changing therapeutic landscape of castration-resistant prostate cancer.Nat. Rev. Clin. Oncol. 2011; 8: 597-610Crossref PubMed Scopus (123) Google Scholar), more than 250,000 men die from aggressive late stage PCa per year worldwide (1.Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global cancer statistics.CA. Cancer J. Clin. 2011; 61: 69-90Crossref PubMed Scopus (30255) Google Scholar). Therefore, an early discrimination between patients either suitable for surveillance strategies or in need of therapy is crucial to prevent overtreatment and to reduce PCa mortality. The value of prostate specific antigen (PSA), which is the most widely used screening marker, is still controversial (4.Ilic D. Neuberger M.M. Djulbegovic M. Dahm P. Screening for prostate cancer.Cochrane Database Syst. Rev. Online. 2013; 1: CD004720PubMed Google Scholar) because it lacks specificity and only gives limited information about disease aggressiveness (5.Prensner J.R. Rubin M.A. Wei J.T. Chinnaiyan A.M. Beyond PSA: The next generation of prostate cancer biomarkers.Sci. Transl. Med. 2012; 4: 127rv3Crossref PubMed Scopus (326) Google Scholar). More exact primary tumor histology needs harmful (6.Zowawi H.M. Harris P.N.A. Roberts M.J. Tambyah P.A. Schembri M.A. Pezzani M.D. Williamson D.A. Paterson D.L. The emerging threat of multidrug-resistant Gram-negative bacteria in urology.Nat. Rev. Urol. 2015; 12: 570-584Crossref PubMed Scopus (234) Google Scholar) and expensive transrectal multicore biopsies and still has a frequent rate of undergrading (7.Ploussard G. Epstein J.I. Montironi R. Carroll P.R. Wirth M. Grimm M.-O. Bjartell A.S. Montorsi F. Freedland S.J. Erbersdobler A. van der Kwast T.H. The contemporary concept of significant versus insignificant prostate cancer.Eur. Urol. 2011; 60: 291-303Abstract Full Text Full Text PDF PubMed Scopus (254) Google Scholar). Therefore markers, easily obtainable from blood or urine, that enable a precise risk prediction of PCa, are needed. Main efforts in PCa biomarker research have either focused on frequent genetic alterations like the TMPRSS2:ERG fusion gene, which has shown to be associated with worse outcome (8.Choudhury A.D. Eeles R. Freedland S.J. Isaacs W.B. Pomerantz M.M. Schalken J.A. Tammela T.L.J. Visakorpi T. The role of genetic markers in the management of prostate cancer.Eur. Urol. 2012; 62: 577-587Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar), or distinct biomarkers from blood (e.g. circulating tumor cells) or urine (like PCA3) (5.Prensner J.R. Rubin M.A. Wei J.T. Chinnaiyan A.M. Beyond PSA: The next generation of prostate cancer biomarkers.Sci. Transl. Med. 2012; 4: 127rv3Crossref PubMed Scopus (326) Google Scholar, 9.Armstrong A.J. Eisenberger M.A. Halabi S. Oudard S. Nanus D.M. Petrylak D.P. Sartor A.O. Scher H.I. Biomarkers in the management and treatment of men with metastatic castration-resistant prostate cancer.Eur. Urol. 2012; 61: 549-559Abstract Full Text Full Text PDF PubMed Scopus (145) Google Scholar). Yet, none of these markers has made it into clinical routine. Exosomes are small (40–100 nm in diameter) extracellular vesicles (EVs) which are shed by nearly all mammalian cells. They contain a varying mixture of different proteins and nucleic acid species (e.g. mRNA and miRNA) encapsulated in a lipid bilayer carrying transmembrane molecules which gives exosomes antigenic potency (10.Duijvesz D. Luider T. Bangma C.H. Jenster G. Exosomes as Biomarker Treasure Chests for Prostate Cancer.Eur. Urol. 2011; 59: 823-831Abstract Full Text Full Text PDF PubMed Scopus (219) Google Scholar, 11.Vlassov A.V. Magdaleno S. Setterquist R. Conrad R. Exosomes: current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials.Biochim. Biophys. Acta. 2012; 1820: 940-948Crossref PubMed Scopus (1424) Google Scholar). Recent findings suggest exosomes as important mediators in different physiological and pathophysiological processes (12.Raposo G. Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends.J. Cell Biol. 2013; 200: 373-383Crossref PubMed Scopus (5145) Google Scholar). Especially long-range signaling via exosomes through the blood stream is associated with metastatic niche formation (13.Alderton G.K. Metastasis. Exosomes drive premetastatic niche formation.Nat. Rev. Cancer. 2012; 12: 447Crossref PubMed Scopus (74) Google Scholar) and systemic inflammation (14.Peinado H. Alecković M. Lavotshkin S. Matei I. Costa-Silva B. Moreno-Bueno G. Hergueta-Redondo M. Williams C. García-Santos G. Ghajar C. Nitadori-Hoshino A. Hoffman C. Badal K. Garcia B.A. Callahan M.K. Yuan J. Martins V.R. Skog J. Kaplan R.N. Brady M.S. Wolchok J.D. Chapman P.B. Kang Y. Bromberg J. Lyden D. Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET.Nat. Med. 2012; 18: 883-891Crossref PubMed Scopus (2624) Google Scholar). Because of their cancer-related function, exosomes themselves, or their components, could serve as valuable markers in different types of cancer. Recently, exosomal glypican 1 was reported as very promising early detection marker for pancreatic adenocarcinoma after identification using mass spectrometry (15.Melo S.A. Luecke L.B. Kahlert C. Fernandez A.F. Gammon S.T. Kaye J. LeBleu V.S. Mittendorf E.A. Weitz J. Rahbari N. Reissfelder C. Pilarsky C. Fraga M.F. Piwnica-Worms D. Kalluri R. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer.Nature. 2015; 523: 177-182Crossref PubMed Scopus (1841) Google Scholar). In PCa Huang et al. identified exosomal miRNAs 375 and 1290 as markers for castration-resistant disease (16.Huang X. Yuan T. Liang M. Du M. Xia S. Dittmar R. Wang D. See W. Costello B.A. Quevedo F. Tan W. Nandy D. Bevan G.H. Longenbach S. Sun Z. Lu Y. Wang T. Thibodeau S.N. Boardman L. Kohli M. Wang L. Exosomal miR-1290 and miR-375 as prognostic markers in castration-resistant prostate cancer.Eur. Urol. 2014; 67: 33-41Abstract Full Text Full Text PDF PubMed Scopus (447) Google Scholar) and mRNA transcripts of the tumor associated gene AGR2 in urinary exosomes were suggested as diagnostic marker for localized PCa (17.Neeb A. Hefele S. Bormann S. Parson W. Adams F. Wolf P. Miernik A. Schoenthaler M. Kroenig M. Wilhelm K. Schultze-Seemann W. Nestel S. Schaefer G. Bu H. Klocker H. Nazarenko I. Cato A.C.B. Splice variant transcripts of the anterior gradient 2 gene as a marker of prostate cancer.Oncotarget. 2014; 5: 8681-8689Crossref PubMed Scopus (36) Google Scholar). Proteomic approaches suggest proteins like Survivin 1 (18.Khan S. Jutzy J.M.S. Valenzuela M.M.A. Turay D. Aspe J.R. Ashok A. Mirshahidi S. Mercola D. Lilly M.B. Wall N.R. Plasma-derived exosomal survivin, a plausible biomarker for early detection of prostate cancer.PloS One. 2012; 7: e46737Crossref PubMed Scopus (234) Google Scholar), CD151 and CDCP1 (19.Sandvig K. Llorente A. Proteomic analysis of microvesicles released by the human prostate cancer cell line PC-3.Mol. Cell. Proteomics. 2012; 11Abstract Full Text Full Text PDF PubMed Scopus (69) Google Scholar) as potential exosome-based biomarkers in PCa (18.Khan S. Jutzy J.M.S. Valenzuela M.M.A. Turay D. Aspe J.R. Ashok A. Mirshahidi S. Mercola D. Lilly M.B. Wall N.R. Plasma-derived exosomal survivin, a plausible biomarker for early detection of prostate cancer.PloS One. 2012; 7: e46737Crossref PubMed Scopus (234) Google Scholar, 20.Corcoran C. Rani S. O'Brien K. O'Neill A. Prencipe M. Sheikh R. Webb G. McDermott R. Watson W. Crown J. O'Driscoll L. Docetaxel-resistance in prostate cancer: evaluating associated phenotypic changes and potential for resistance transfer via exosomes.PloS One. 2012; 7: e50999Crossref PubMed Scopus (328) Google Scholar). Yet the transfer of this approach to clinical samples is still in its infancy. The main drawbacks of exosomes for their use in the clinical setting are a difficult and laborious isolation procedure and very small sample amounts. Until now, there is no established gold standard for the profiling and characterization of exosomal protein biomarkers in cancer. To integrate existing knowledge about potential protein biomarkers for PCa we combined an in vitro mass spectrometry (MS) approach with a subsequent extensive database search regarding biomarker-specific properties. The top candidate protein was validated immunologically in vitro and in clinical samples, using a facilitated approach without need for laborious exosome isolation. Supernatant for exosome isolation for MS and quality control (immunoblotting, NTA, TEM) from PC3 and PNT1A cells and control medium was generated in two biological replicates each. Quality control experiments (Western blotting, NTA, TEM) under serum free conditions were performed in three biological replicates. The scoring system for candidate generation implemented user weighted factors for assumed specific biomarker features (Fig. 1). ELISA testing of patient samples was conducted in two technical replicates of each sample. ANOVA, post hoc Turkey test and Student's t test were used to determine statistical significance in data with normal distribution. Human metastatic PC3 and benign PNT1A cells were expanded in a predefined FBS-containing modified DMEM medium (Quantum 286, GE Healthcare, Chalfort St. Giles, UK) under standard culture conditions. At a confluence of 70%, medium was discarded and cells were washed three times with sterile PBS (Life Technologies, Carlsbad, CA). Unlike other studies using serum-free or depleted medium, cells were then incubated for further 48 h with FBS-containing growth medium to generate conditioned medium (CM) for exosome isolation. This approach was chosen not to impair cellular growth conditions. Cells were detached with trypsin, harvested, washed with sterile PBS twice and stored as pellets at −80 °C. For retests PC3 and PNT1A cells were cultured and expanded under the same condition, but DMEM (Life Technologies) with no additives was used to generate CM for isolation of exosomes. PC3 and PNT1A exosomes both from FBS-containing and FBS-free CM were isolated as described by Théry et al. (21.Théry C. Amigorena S. Raposo G. Clayton A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids.Curr. Protoc. Cell Biol. Editor. Board Juan Bonifacino Al. 2006; (Chapter 3, Unit 3.22)Google Scholar) with minor modifications. Additionally, two samples of FBS-containing medium, not in contact with prostate cells were subjected to the same isolation procedure as controls. In brief medium was centrifuged at 300 × g for 10 min, followed by 2000 × g for 10 min and 12,000 × g for 40 min. Pellets from each centrifugation step were discarded. Supernatant was then subjected to ultracentrifugation at 100,000 × g for 120 min. The resulting pellet was washed in sterile-filtered PBS and again centrifuged for 120 min at 100,000 × g. The final pellet was eluted in 50 μl of sterile-filtered PBS. Five microliters of exosomes were placed on 100 Mesh formvar-coated copper grids (Plano, Wetzlar, Germany) for 5 min. Grids were then briefly rinsed three times with distilled water and negatively stained with 3% aqueous uranylacetate for 3 min. Grids were subsequently air-dried and investigated with an EM900 or EM10 transmission electron microscope (Zeiss, Jena, Germany) equipped with a CCD camera at 30 k, 50 k, and 85 k magnification. For immunogold electron microscopy, the exosomes on grids were blocked for 5 min with 50 mm glycine/PBS and for 10 min with 50 mm glycine/PBS/0.8% BSA/0.1% fish skin gelatin, incubated for 20 min in the primary antibody solution (murine anti-CLDN3 (R&D Systems, Wiesbaden, Germany) 1:10 to 1:100), rinsed 2 × 5 min with PBS, incubated for 30 min in bridging antibody rabbit-anti-mouse (Dako, Agilent Pathology Solutions, Santa Clara, CA) 1:150 in blocking solution, incubated in Protein-A-Gold 10 nm (University Utrecht, Netherlands) 1:50 in blocking solution, rinsed 2 × 5 min in PBS, fixed in 1% glutaraldehyde in PBS, rinsed 5 × 2 min in PBS and 7 × 2 min in dH2O, contrasted on ice with 1.8% aqueous uranylacetate/0.8% methylcellulose, looped out and air dried for 5 min. Samples labeled with anti-CD63 (Active Bioscience, Hamburg, Germany, 1:50) were used as positive control, in negative controls the primary antibody was omitted. Two microliters of exosomes were diluted in sterile-filtered PBS and visualized using the LM10 NTA device (Malvern Instruments, Malvern, UK). Each sample was measured 6 times for 45 s (Screen Gain 1.0, camera level 15) with at least 200 valid tracks per video to obtain particle concentration and size distribution. The protein content of exosomes and cell lysates was determined using BCA assay (Thermo Fisher Scientific, Waltham, MA). For immunoblotting sample preparation was performed both with reducing and nonreducing Laemmli buffer. Two micrograms of proteins from EVs or cell lysate were loaded on a 4–12% SDS gel for electrophoresis, followed by transfer to a PVDF membrane. Primary antibodies used were polyclonal rabbit anti-Calnexin, mouse monoclonal anti-HSC70 (clone W27), goat polyclonal anti-ALIX (all from Santa Cruz, CA), mouse monoclonal anti-CD9 (Immunotools, Friesoythe, Germany), rabbit polyclonal anti-CLDN3 and mouse monoclonal anti-beta Actin (both from Abcam, Cambridge, UK). HRP-conjugated goat anti-mouse, and goat anti-rabbit (both Jackson ImmunoResearch, West Grove, PA) or donkey anti-goat (Santa Cruz) were used as secondary antibodies. Ten micrograms of protein from PC3 and PNT1A exosomes generated from FBS-containing CM or untreated FBS-containing medium were loaded on a 4–12% SDS gel. After 1D gel electrophoresis and Coomassie staining the stained area of each sample (2 cm) was cut into 3 individual pieces. In-gel digestion, peptide extraction and MS analysis were performed as described by Aretz et al. (22.Aretz S. Krohne T.U. Kammerer K. Warnken U. Hotz-Wagenblatt A. Bergmann M. Stanzel B.V. Kempf T. Holz F.G. Schnölzer M. Kopitz J. In-depth mass spectrometric mapping of the human vitreous proteome.Proteome Sci. 2013; 11: 22Crossref PubMed Scopus (51) Google Scholar), with some modifications. In detail, gel pieces were chopped into small gel plugs and incubated with 150 μl water for 5 min at 37 °C. Proteins were reduced with 150 μl 10 mm DTT in 40 mm NH4HCO3 for 1 h at 56 °C, alkylated with 150 μl 55 mm iodoacetamide in 40 mm NH4HCO3 for 30 min at 25 °C in the dark, followed by three washing steps with 150 μl of water and water/acetonitrile at 37 °C. Gel pieces were dehydrated with 150 μl neat acetonitrile for 1 min at room temperature, dried for 15 min and subsequently rehydrated with sequencing grade porcine trypsin (Promega, Madison, WI). After overnight digestion at 37 °C the supernatant was collected while gel pieces were subjected to four further extraction steps (acetonitrile/0.1% TFA 50:50 (v/v)). The combined solutions were evaporated to dryness and redissolved in 0.1% TFA/2.5% hexafluoroisopropanol. Tryptic peptides mixtures were separated using a nanoAcquity UPLC system (Waters, Milford, MA). Peptides were trapped on a nanoAcquity C18 column (180 μm x 20 mm, particle size 5 μm) (Waters). The liquid chromatography separation was performed on a C18 column (BEH 130 C18 100 μm x 100 mm, particle size 1.7 μm, Waters) with a flow rate of 400 nL/min. The chromatography was carried out using a 3 h gradient of solvent A (98.9% water, 1% acetonitrile, 0.1% formic acid) and solvent B (99.9% acetonitrile and 0.1% μl formic acid). The nanoUPLC system was connected to a LTQ-Orbitrap XL mass spectrometer (Thermo Fisher Scientific). The mass spectrometer was operated in the sensitive mode with the following parameters: capillary voltage 2400 V; capillary temperature 200 °C, normalized collision energy 35 V, activation time 30000 ms. Data were acquired by scan cycles of one FTMS (resolution: 60000 at m/z 400; range: 370 to 2000 m/z) in parallel with six MS/MS scans in the ion trap of the most abundant precursor ions. The peak list mgf-files generated by Xcalibur software (version 2.1, Thermo Fisher Scientific) were used for database searches with the MASCOT search engine (version 2.4.0, Matrix Science, Boston, MA) against the SwissProt database (version 2015_04, species: human, 20279 sequences). Trypsin was used as digestion enzyme (cleaves peptide chains mainly at the carboxyl side of the amino acids lysine or arginine) and the maximum of missed cleavages was set at 1. Fixed modification was set to carbamidomethyl on cysteine residues and variable modifications were deamination of glutamine and asparagine and oxidation of methionine. Mass tolerance for precursor ions was 5 ppm and mass tolerance for fragments was 0.4 Da. No known contaminants were excluded during peptide matching. Using standard scoring, ion score cut-off was set at 20, the maximum significance threshold was set at 0.01 and the maximum number of hits was set at default. False discovery rate for matches above homology and identity threshold ranged between 1.49 and 4.45% per data set. Proteins identified were considered significant if at least two unique peptides had an individual ion score exceeding the MASCOT identity threshold and a cumulative Mascot score >60 was reached. The mass spectrometry data have been deposited to the ProteomeXchange Consortium (23.Vizcaíno J.A. Deutsch E.W. Wang R. Csordas A. Reisinger F. Ríos D. Dianes J.A. Sun Z. Farrah T. Bandeira N. Binz P.-A. Xenarios I. Eisenacher M. Mayer G. Gatto L. Campos A. Chalkley R.J. Kraus H.-J. Albar J.P. Martinez-Bartolomé S. Apweiler R. Omenn G.S. Martens L. Jones A.R. Hermjakob H. ProteomeXchange provides globally coordinated proteomics data submission and dissemination.Nat. Biotechnol. 2014; 32: 223-226Crossref PubMed Scopus (2071) Google Scholar) after conversion into xml-files using PRIDE Converter 2 (24.Côté R.G. Griss J. Dianes J.A. Wang R. Wright J.C. van den Toorn H.W.P. van Breukelen B. Heck A.J.R. Hulstaert N. Martens L. Reisinger F. Csordas A. Ovelleiro D. Perez-Rivevol Y. Barsnes H. Hermjakob H. Vizcaíno J.A. The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium.Mol. Cell. Proteomics MCP. 2012; 11: 1682-1689Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar) with the data set identifier PXD003651 and 10.6019/PXD003651 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD003651). A complete list of all identified peptides is given in supplemental Table S1. For better compatibility, protein accessions from all data sets were transcribed to official gene symbols. Only proteins identified in both replicates of PC3 or PNT1A exosomes were counted as valid. Proteins identified in at least one sample of untreated medium were defined as background noise and subtracted from exosomal protein data sets. The remaining exosomal protein data sets of PC3 and PNT1A were compared and proteins only present in the PC3 data set were subjected to further analysis as potential biomarkers. A custom-made scoring system incorporated information from four publicly available databases (Table I) and studies reporting proteomic mass spectrometry data sets of PCa tissue, identified from PubMed (date of search: December 12th 2016). CBioPortal is part of The Cancer Genome Atlas Project and harbors detailed clinical, mutational and transcriptional data from around hundred studies in different cancer entities (25.Cerami E. Gao J. Dogrusoz U. Gross B.E. Sumer S.O. Aksoy B.A. Jacobsen A. Byrne C.J. Heuer M.L. Larsson E. Antipin Y. Reva B. Goldberg A.P. Sander C. Schultz N. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.Cancer Discov. 2012; 2: 401-404Crossref PubMed Scopus (9402) Google Scholar). In two data sets (data set 1, Broad/Cornell, Nature Genetics 2012 (26.Barbieri C.E. Baca S.C. Lawrence M.S. Demichelis F. Blattner M. Theurillat J.-P. White T.A. Stojanov P. Van Allen E. Stransky N. Nickerson E. Chae S.-S. Boysen G. Auclair D. Onofrio R.C. Park K. Kitabayashi N. MacDonald T.Y. Sheikh K. Vuong T. Guiducci C. Cibulskis K. Sivachenko A. Carter S.L. Saksena G. Voet D. Hussain W.M. Ramos A.H. Winckler W. Redman M.C. Ardlie K. Tewari A.K. Mosquera J.M. Rupp N. Wild P.J. Moch H. Morrissey C. Nelson P.S. Kantoff P.W. Gabriel S.B. Golub T.R. Meyerson M. Lander E.S. Getz G. Rubin M.A. Garraway L.A. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer.Nat. Genet. 2012; 44: 685-689Crossref PubMed Scopus (1122) Google Scholar), microarray, n = 31 and data set 2, TCGA provisional, RNA sequencing, n = 236) only data from primary tumors were available. In one data set (data set 3, MSKCC, Cancer Cell 2010 (27.Taylor B.S. Schultz N. Hieronymus H. Gopalan A. Xiao Y. Carver B.S. Arora V.K. Kaushik P. Cerami E. Reva B. Antipin Y. Mitsiades N. Landers T. Dolgalev I. Major J.E. Wilson M. Socci N.D. Lash A.E. Heguy A. Eastham J.A. Scher H.I. Reuter V.E. Scardino P.T. Sander C. Sawyers C.L. Gerald W.L. Integrative genomic profiling of human prostate cancer.Cancer Cell. 2010; 18: 11-22Abstract Full Text Full Text PDF PubMed Scopus (2728) Google Scholar), microarray) both expression data from primary tumors (n = 131) and metastases (n = 19) were available. In data set 1 and 2 candidate gene expression was normalized to the expression in diploid tumors. In data set 3, gene expression in tumor samples was compared with benign controls. Expression data from primary tumors were pooled and resulted in a score ranging from −3 (largely underexpressed) to +3 (highly overexpressed). The same was done for metastasis data and both scores were added.Table IFour publicly available databases were used for retrieval of biomarker relevant data of 64 proteins exclusively identified in PC3 exosomes and their corresponding genesDatabaseInformationContact addressCBioPortalExpression data from primary tumors and metastaseshttp://www.cbioportal.org/public-portal/ (date of access: January 26th 2017)Peptide AtlasExpression in blood plasmahttp://www.peptideatlas.org/ (date of access: February 8st 2017)PROTTERProtein conformation, association to membranehttp://wlab.ethz.ch/protter/ (date of access: February 7nd 2017)VesiclepediaExpression in exosomeswww.microvesicles.org (date of access: February 6nd 2017) Open table in a new tab From the literature eight studies reporting proteomic mass spectrometry data of PCa tissue, either to identify proteins overexpressed in PCa compared with benign tissue or to identify proteins being associated with metastatic or high risk PCa, could be identified (28.Alaiya A.A. Al-Mohanna M. Aslam M. Shinwari Z. Al-Mansouri L. Al-Rodayan M. Al-Eid M. Ahmad I. Hanash K. Tulbah A. Bin Mahfooz A. Adra C. Proteomics-based signature for human benign prostate hyperplasia and prostate adenocarcinoma.Int. J. Oncol. 2011; 38: 1047-1057Crossref PubMed Scopus (35) Google Scholar, 29.Dunne J.C. Lamb D.S. Delahunt B. Murray J. Bethwaite P. Ferguson P. Nacey J.N. Sondhauss S. Jordan T.W. Proteins from formalin-fixed paraffin-embedded prostate cancer sections that predict the risk of metastatic disease.Clin. Proteomics. 2015; 12Crossref PubMed Scopus (15) Google Scholar, 30.Geisler C. Gaisa N.T. Pfister D. Fuessel S. Kristiansen G. Braunschweig T. Gostek S. Beine B. Diehl H.C. Jackson A.M. Borchers C.H. Heidenreich A. Meyer H.E. Knüchel R. Henkel C. Identification and validation of potential new biomarkers for prostate cancer diagnosis
Referência(s)