Proteomic Maps of Human Gastrointestinal Stromal Tumor Subgroups*
2019; Elsevier BV; Volume: 18; Issue: 5 Linguagem: Inglês
10.1074/mcp.ra119.001361
ISSN1535-9484
AutoresYu Liu, Zhigui Li, Zhiqiang Xu, Xiuxiu Jin, Yanqiu Gong, Xuyang Xia, Yuqin Yao, Zhaofen Xu, Yong Zhou, Heng Xu, Shuangqing Li, Yong Peng, Xiaoting Wu, Lunzhi Dai,
Tópico(s)Gastric Cancer Management and Outcomes
ResumoGastrointestinal stromal tumor (GIST) is a common sarcoma of gastrointestinal tract (GIT) with high metastatic and recurrence rates, but the proteomic features are still less understood. Here we performed systematic quantitative proteome profiling of GIST from 13 patients classified into very low/low, intermediate and high risk subgroups. An extended cohort of GIST (n = 131) was used for immunohistochemical validation of proteins of interest. In total, 9177 proteins were quantified, covering 55.9% of the GIT transcriptome from The Human Protein Altas. Out of the 9177 quantified proteins, 4930 proteins were observed in all 13 cases with 517 upregulated and 187 downregulated proteins in tumorous tissues independent of risk stage. Pathway analysis showed that the downregulated proteins were mostly enriched in metabolic pathway, whereas the upregulated proteins mainly belonged to spliceosome pathway. In addition, 131 proteins showed differentially expressed patterns among GIST subgroups with statistical significance. The 13 GIST cases were classified into 3 subgroups perfectly based on the expression of these proteins. The intensive comparison of molecular phenotypes and possible functions of quantified oncoproteins, tumor suppressors, phosphatases and kinases between GIST subgroups was carried out. Immunohistochemical analysis of the phosphatase PTPN1 (n = 117) revealed that the GIST patients with high PTPN1 expression had low chances of developing metastasis. Collectively, this work provides valuable information for understanding the inherent biology and evolution of GIST. Gastrointestinal stromal tumor (GIST) is a common sarcoma of gastrointestinal tract (GIT) with high metastatic and recurrence rates, but the proteomic features are still less understood. Here we performed systematic quantitative proteome profiling of GIST from 13 patients classified into very low/low, intermediate and high risk subgroups. An extended cohort of GIST (n = 131) was used for immunohistochemical validation of proteins of interest. In total, 9177 proteins were quantified, covering 55.9% of the GIT transcriptome from The Human Protein Altas. Out of the 9177 quantified proteins, 4930 proteins were observed in all 13 cases with 517 upregulated and 187 downregulated proteins in tumorous tissues independent of risk stage. Pathway analysis showed that the downregulated proteins were mostly enriched in metabolic pathway, whereas the upregulated proteins mainly belonged to spliceosome pathway. In addition, 131 proteins showed differentially expressed patterns among GIST subgroups with statistical significance. The 13 GIST cases were classified into 3 subgroups perfectly based on the expression of these proteins. The intensive comparison of molecular phenotypes and possible functions of quantified oncoproteins, tumor suppressors, phosphatases and kinases between GIST subgroups was carried out. Immunohistochemical analysis of the phosphatase PTPN1 (n = 117) revealed that the GIST patients with high PTPN1 expression had low chances of developing metastasis. Collectively, this work provides valuable information for understanding the inherent biology and evolution of GIST. Gastrointestinal stromal tumor (GIST)1, a relatively newly defined pathologic entity, is the most common sarcoma of gastrointestinal tract (GIT), with an estimated annual incidence of 10–15 per million (1.Mucciarini C. Rossi G. Bertolini F. Valli R. Cirilli C. Rashid I. Marcheselli L. Luppi G. Federico M. Global tuberculosis report 2015 Incidence and clinicopathologic features of gastrointestinal stromal tumors. A population-based study.BMC Cancer. 2007; 7: 230Crossref PubMed Scopus (154) Google Scholar). It has been found that ∼50–60% of GIST occurs in the stomach (2.Pisters P.W. Blanke C.D. von Mehren M. Picus J. Sirulnik A. Stealey E. Trent J.C. re G.S.C. A U.S.A. registry of gastrointestinal stromal tumor patients: changes in practice over time and differences between community and academic practices.Ann. Oncol. 2011; 22: 2523-2529Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar), 30–35% in small intestine, 5% in colorectum, and less than 1% in esophagus, which representing 0.1–3% of gastrointestinal malignancies (3.Lewis J.J. Brennan M.F. Soft tissue sarcomas.Current Problems Surg. 1996; 33: 817-872Crossref PubMed Google Scholar). Approximately 10–20% of patients develop metastasis at the time of diagnosis (4.Woodall 3rd, C.E. Brock G.N. Fan J. Byam J.A. Scoggins C.R. McMasters K.M. Martin 2nd., R.C. An evaluation of 2537 gastrointestinal stromal tumors for a proposed clinical staging system.Arch. 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For operable GIST, recurrences mostly occurred within the first 5 years after surgery, and roughly 40% of patients with GIST developed metastasis during the 15 year follow-up period after surgery (7.Joensuu H. Vehtari A. Riihimäki J. Nishida T. Steigen S.E. Brabec P. Plank L. Nilsson B. Cirilli C. Braconi C. Bordoni A. Magnusson M.K. Linke Z. Sufliarsky J. Federico M. Jonasson J.G. Dei Tos A.P. Rutkowski P. Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.Lancet Oncol. 2012; 13: 265-274Abstract Full Text Full Text PDF PubMed Scopus (644) Google Scholar). The mutations of mast/stem cell growth factor receptor Kit (KIT or CD117) (8.Hirota S. Isozaki K. Moriyama Y. Hashimoto K. Nishida T. Ishiguro S. Kawano K. Hanada M. Kurata A. Takeda M. Muhammad Tunio G. Matsuzawa Y. Kanakura Y. Shinomura Y. Kitamura Y. 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Lasota J. DOG1 antibody in the differential diagnosis of gastrointestinal stromal tumors: a study of 1840 cases.Am. J. Surg. Pathol. 2009; 33: 1401-1408Crossref PubMed Scopus (330) Google Scholar). GIST without KIT and PDGFRA mutation are considered to as wild type, which shelter mutations of other genes, such as succinate dehydrogenase (SDH) and serine/threonine-protein kinase B-raf (BRAF) (12.Pasini B. McWhinney S.R. Bei T. Matyakhina L. Stergiopoulos S. Muchow M. Boikos S.A. Ferrando B. Pacak K. Assie G. Baudin E. Chompret A. Ellison J.W. Briere J.J. Rustin P. Gimenez-Roqueplo A.P. Eng C. Carney J.A. Stratakis C.A. Clinical and molecular genetics of patients with the Carney-Stratakis syndrome and germline mutations of the genes coding for the succinate dehydrogenase subunits SDHB, SDHC, and SDHD.Eur. J. Human Gen. 2008; 16: 79-88Crossref PubMed Scopus (362) Google Scholar, 13.Agaram N.P. Wong G.C. Guo T. Maki R.G. Singer S. Dematteo R.P. Besmer P. Antonescu C.R. Novel V600E BRAF mutations in imatinib-naive and imatinib-resistant gastrointestinal stromal tumors.Genes, Chromosomes Cancer. 2008; 47: 853-859Crossref PubMed Scopus (291) Google Scholar). Presently, several risk stratification criteria, such as the National Institute of Health (NIH) consensus criteria (14.Fletcher C.D.M. Berman J.J. Corless C. Gorstein F. Lasota J. Longley B.J. Miettinen M. O'Leary T.J. Remotti H. Rubin B.P. Shmookler B. Sobin L.H. Weiss S.W. Diagnosis of gastrointestinal stromal tumors: A consensus approach.Hum. Pathol. 2002; 33: 459-465Crossref PubMed Scopus (2836) Google Scholar, 15.Joensuu H. Risk stratification of patients diagnosed with gastrointestinal stromal tumor.Hum. Pathol. 2008; 39: 1411-1419Crossref PubMed Scopus (833) Google Scholar), the modified NIH consensus criteria (16.Rutkowski P. Bylina E. Wozniak A. Nowecki Z.I. Osuch C. Matlok M. Świtaj T. Michej W. Wroński M. Głuszek S. Kroc J. Nasierowska-Guttmejer A. Joensuu H. Validation of the Joensuu risk criteria for primary resectable gastrointestinal stromal tumour – The impact of tumour rupture on patient outcomes.Eur. J. Surg. Oncol. 2011; 37: 890-896Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar), and the Armed Forces Institute of Pathology (AFIP) criteria (17.Miettinen M. Lasota J. Gastrointestinal stromal tumors: Pathology and prognosis at different sites.Seminars Diagnostic Pathol. 2006; 23: 70-83Crossref PubMed Scopus (1416) Google Scholar), have been used to evaluate the malignant potential of GIST. Generally, the large tumor size, high mitosis count, nongastric site, presence of rupture, and male sex were found to be associated with unfavorable outcomes (6.Joensuu H. Vehtari A. Riihimaki J. Nishida T. Steigen S.E. Brabec P. Plank L. Nilsson B. Cirilli C. Braconi C. Bordoni A. Magnusson M.K. Linke Z. Sufliarsky J. Federico M. Jonasson J.G. Dei Tos A.P. Rutkowski P. Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.Lancet Oncol. 2012; 13: 265-274Abstract Full Text Full Text PDF PubMed Scopus (499) Google Scholar, 7.Joensuu H. Vehtari A. Riihimäki J. Nishida T. Steigen S.E. Brabec P. Plank L. Nilsson B. Cirilli C. Braconi C. Bordoni A. Magnusson M.K. Linke Z. Sufliarsky J. Federico M. Jonasson J.G. Dei Tos A.P. Rutkowski P. Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.Lancet Oncol. 2012; 13: 265-274Abstract Full Text Full Text PDF PubMed Scopus (644) Google Scholar). NIH consensus classification criteria stratify GIST patients into four subgroups (very low, low, intermediate, and high risk) based on the tumor size and mitotic rates (15.Joensuu H. Risk stratification of patients diagnosed with gastrointestinal stromal tumor.Hum. Pathol. 2008; 39: 1411-1419Crossref PubMed Scopus (833) Google Scholar). Except for small GIST with a diameter no more than 1 cm, GIST with large tumor size and high mitosis count are more prone to recurrence and metastasis typically in the liver and abdominal cavity (18.Joensuu H. Fletcher C. Dimitrijevic S. Silberman S. Roberts P. Demetri G. Management of malignant gastrointestinal stromal tumours.Lancet Oncol. 2002; 3: 655-664Abstract Full Text Full Text PDF PubMed Scopus (511) Google Scholar). Large retrospective studies have also validated that patients with different risk stage stratification always have different disease-free survival ratios, increasing from very low-risk to high-risk GIST patients (7.Joensuu H. Vehtari A. Riihimäki J. Nishida T. Steigen S.E. Brabec P. Plank L. Nilsson B. Cirilli C. Braconi C. Bordoni A. Magnusson M.K. Linke Z. Sufliarsky J. Federico M. Jonasson J.G. Dei Tos A.P. Rutkowski P. 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In this study, we performed large-scale quantitative analysis of proteome between tumorous tissues (N) and paired adjacent nontumorous tissues (T) of GIST, including 3 paired NIH consensus criteria based very low/low risk (NIH-L), 5 paired NIH consensus criteria based intermediate risk (NIH-I), and 5 paired NIH consensus criteria based high risk (NIH-H) GIST samples. Out of the 9177 quantified proteins in GIST proteome, 4930 proteins were quantified with good reproducibility (student-t test, p > 0.1), and 517 proteins upregulated, and 187 proteins downregulated in all 13 GIST tumorous tissues were found. Clustering and clustering enrichment analysis of 131 differentially expressed proteins clearly showed some distinctive biological processes and pathways enriched in GIST subgroups, and partial least squares discrimination analysis (PLS-DA) confirmed the distinctive expression patterns of these 131 proteins in GIST subgroups. We further systematically assessed the expression patterns of oncoproteins, tumor suppressors (TSs), kinases, and phosphatases, and discussed their potential functions in GIST. Immunohistochemical analysis of phosphatases tyrosine-protein phosphatase nonreceptor type 1 (PTPN1 or PTP1B) and serine/threonine-protein phosphatase 2A catalytic subunit beta isoform (PPP2CB) in an extended cohort of GIST indicated that the GIST patients with high PTPN1 expression had low chances of developing metastasis. Collectively, this work is the first large-scale quantitative characterization of GIST proteome and will present valuable information for understanding the etiology of GIST progression. We performed in-depth proteomic analysis between tumorous and paired adjacent nontumorous tissues of GIST, including 3 paired NIH-L, 5 paired NIH-I and 5 NIH-H GIST samples, using tandem mass tag (TMT) 10plex labeling, respectively. To reduce the influence of data-dependent acquisition, all samples were run in duplicate. To minimize the protein abundance difference among individuals, the protein intensity of tumor was normalized to that of its corresponding nontumorous tissue. We used the p value to assess the reproducibility of the technical duplicates based on student-t test. If the protein ratios quantified in duplicates had p values (student-t test) more than 0.1, which meant that the quantified ratio difference between duplicates was small, the quantification of proteins was reproducible, and these proteins were used for data mining. One hundred thirty-one GIST patients were recruited from the department of gastrointestinal surgery, West China Hospital, with ethical approval from the Biomedical Ethics Committee of West China Hospital (Permission Number: 2017–254). All these cases were diagnosed as GIST by two independent pathologists according to Chinese consensus guidelines for diagnosis and management of GIST. To avoid protein degradation, all the tissues were frozen in liquid nitrogen 30 min after surgery. The criteria for GIST sample collection is as follows. Out of the 131 cases, 13 cases had tumors with paired adjacent nontumorous tissues, and 118 cases had only tumorous tissues. All tumorous and adjacent nontumorous specimens obtained from these patients were stored in West China Hospital Biobank of Sichuan University. The GIST tissues were shredded and lysed in RIPA buffer (1% NP-40, 0.5% (w/v) sodium deoxycholate, 150 mm NaCl, 50 mm Tris (pH = 7.5)) containing protease inhibitor, and then homogenized by Gentle-MACS (Miltenyi Biotec GmbH) under the procedure "protein 01. 01" twice, followed by 5 min sonication of 0.3 s on and 1.7 s off with 195 watt of JY92-IIN (NingBoXinYi, China). The lysate was centrifuged at 20,000 rcf for 30 min, and the supernatant was transferred to a new tube. The protein concentration was measured by Bradford assay. Extracted proteins (50 μg) from each sample was reduced by 0.5 μmol Tris (2-carboxyethyl) phosphine (TCEP) with a final concentration of 10 mm at 56 °C for 1 h, alkylated with 1.0 μmol iodoacetamide with a final concentration of 20 mm in the dark at room temperature for additional 30 min, and then precipitated with methanol, chloroform, and water (CH3OH:CHCl3:H2O = 4:1:3). The precipitate was air-dried and digested using sequence grade trypsin in triethylammonium bicarbonate (TEAB) buffer. The tryptic peptides of each sample were labeled with TMT (Thermo Fisher Scientific) reagents according to the manufacturer's protocol. After quenching with 5% hydroxylamine, TMT-labeled peptides of each risk grade GIST were mixed and fractionated by reverse-phase C18 column. The TMT-labeled peptides were fractionated using reversed phase high performance liquid chromatography (RP-HPLC, agilent-1260) under basic pH. The mobile phase was composed of buffer A (98% H2O with 2% ACN, 10 mm ammonium formate, pH = 10) and buffer B (90% ACN with 10% H2O, 10 mm ammonium formate, pH = 10). A standard 120 min LC gradient run was used and showed below: 0–10 min, 0–8% Buffer B; 10–80 min, 8–35% Buffer B; 80–95 min, 35–60% Buffer B; 95–105 min, 60–70% Buffer B; 105–120 min, 70–100% Buffer B, and the flow rate was 1 ml/min. The peptide mixture was separated into 120 fractions and combined into 40 fractions. The combined fractions were dried in the speed vacuum and used for mass spectrometry analysis. The desalted peptides were lyophilized and resuspended in buffer A (2% ACN, 0.1% FA), LC-MS/MS analysis was performed using an EASY-nanoLC 1000 nanoflow LC instrument coupled to a high-resolution mass spectrometer (Q Exactive Plus, Thermo Fisher Scientific). A 100 μm (inner diameter) × 2 cm (length) trap column and a 75 μm (inner diameter) × 12 cm (length) analytical column were pulled and packed in-house with C18 particle (DIKMA). Data dependent acquisition (DDA) was performed in positive ion mode at the flow rate of 300 nL/min. MS spectra were acquired from 350 m/z to 1600 m/z with a resolution of 70,000 at m/z = 200. The automatic gain control (AGC) value was set at 3e6, with maximum injection time of 20 ms. For MS/MS scans, the top 15 most intense parent ions were selected with 1.6 m/z isolation window and fragmented with normalized collision energy (NCE) of 30%. The AGC value for MS/MS was set to a target value of 1e5, with a maximum injection time of 100 ms and a resolution of 35,000. Parent ions with a charge state of z = 1 or with unassigned charge states were excluded from fragmentation and the intensity threshold for selection was set to 2e5. All the raw files were searched against the Swiss-Prot human protein sequence database (20413 entries, 2017/01/14) in Maxquant (version 1.6). The precursor peptide mass tolerance was 10 ppm and a fragment ion mass tolerance was 0.02 Da. Two missed trypsin cleavages were allowed. Cysteine carbamidomethylation was set as a fixed modification. Oxidation of methionine and protein N-terminal acetylation were set as variable modifications. Peptides with 0.1). Supplemental Table S5 included 4930 quantified proteins in all GIST subgroups with good technical reproducibility (student-t test, p > 0.1). Supplemental Table S6 contained 517 upregulated proteins and 187 downregulated proteins in all subgroups. Supplemental Table S7 contains the GIT-specific and -unspecific genes in each GIST subgroup. Supplemental Table S8 contains the differentially expressed proteins between tumorous and paired adjacent nontumorous tissues in each subgroup. Supplemental Table S9 showed Gene Ontology Cellular Component (GOCC) enriched results of differentially expressed proteins in each subgroup. Supplemental Table S10 included the differentially expressed proteins with statistical significance between GIST subgroups (student-t test, p < 0.05) and a small difference within a GIST subgroup. Supplemental Table S11 included cluster-specific enrichment results of Gene Ontology Biological Processes (GOBP), GOCC and Kyoto Encyclopedia of Genes and Genomes (KEGG). Supplemental Table S12 included all oncoproteins and TSs simultaneously quantified in all GIST subgroups based on 138 well-annotated cancer driver genes (24.Vogelstein B. Papadopoulos N. Velculescu V.E. Zhou S. Diaz Jr, L.A. Kinzler K.W. Cancer genome landscapes.Science. 2013; 339: 1546-1558Crossref PubMed Scopus (5206) Google Scholar). Supplemental Table S13 included all phosphatases and kinases simultaneously quantified in all GIST subgroups referring to Eukaryotic Kinase and Phosphatase Database (EKPD, 2019/1/1). Supplemental Table S14 included the immunohistochemical results of both PPP2CB and PTPN1. Supplemental Table S15 contained the statistics of PPP2CB and PTPN1 positive rates in GIST subgroups. Gene Annotation, including biological process, molecular function and cellular component, and KEGG pathway were performed using DAVID 6.8 and gene set enrichment analysis (GSEA). Unsupervised-clustering and PLS-DA was applied to evaluate the difference of GIST subgroups. Pearson correlation coefficient analysis was used to confirm the difference and similarity among GIST subgroups, as well as the technical reproducibility in duplicates of each subgroup. The fold change statistics of kinases and phosphatases were performed in Excel. Volcano Plot was applied to show the significantly changed proteins in each subgroup. All tumor specimens diagnosed as GIST by two independent pathologists were made into the donor paraffin blocks. Tumor microarray and immunohistochemical staining were performed as our previous study (25.Li Z. Xu Z. Huang Y. Zhao R. Cui Y. Zhou Y. Wu X. The predictive value and the correlation of peripheral absolute monocyte count, tumor-associated macrophage and microvessel density in patients with colon cancer.Medicine. 2018; 97: e10759Crossref PubMed Scopus (12) Google Scholar). The antibodies for immunohistochemistry were purchased from HuaBio (China, anti-PTPN1 antibody, Cat#RT1521; anti-PPP2CB antibody, Cat#ET1611–54). Image-Pro Plus 6.0 software was used to evaluate the intensity of protein expression. We collected 131 GIST cases deposited in the tumor biobank of West China Hospital. All 131 GIST cases were diagnosed by immunohistochemical staining of KIT, DOG1 and hematopoietic progenitor cell antigen CD34 (CD34), as well as histopathological review by two independent pathologists. Out of these, 13 tumors with their paired adjacent nontumorous tissues were used for the following proteome profiling to study the molecular phenotypes during GIST progression (Table I), whereas the remaining 118 cases with only tumorous tissues were used for immunohistochemical staining. In the 13 GIST cases, 3 patients were diagnosed with very low/low risk (tumor size < 2 cm, or mitotic count < 3/50 HPFs (high-power fields)) GIST, 5 patients with intermediate risk (tumor size 5–10 cm and mitotic count < 5/50 HPFs, or mitotic count 5–10/50 HPFs) GIST, and 5 patients with high risk (mitotic count > 10/50 HPFs, or tumor size > 10 cm) GIST, according to NIH standard classification system (Table I). It should be mentioned that one case belonging to NIH-I subgroup was KIT negative and included in the samples used for proteome profiling.Table IClinicopathologic features of the proteomic set samplesPatient NoAgeGenderSiteTumor size in largest dimension (cm)Mitosis count (per 50HFPs)Risk classification*156MStomach712High261FStomach1510High370FStomach5.57High463FSmall intestine1016High554FSmall intestine69High643FStomach58Intermediate774FStomach74Intermediate852FStomach63Intermediate959FStomach54Intermediate1060MStomach37Intermediate1155FStomach41Low1250MStomach2.83Low1358MStomach1.81LowClinicopathologic features of the collected GIST cases for proteome profiling. Thirteen GIST cases were selected for quantitative proteomic studies. All these tissues were frozen in liquid nitrogen 30 min after surgery. Among them, three cases were diagnosed as very low/low risk, five cases as intermediate risk, and five cases as high risk.Note: M: male; F: female.*Prognostic classification based on tumor size and mitosis count. Open table in a new tab Clinicopathologic features of the collected GIST cases for proteome profiling. Thirteen GIST cases were selected for quantitative proteomic studies. All these tissues were frozen in liquid nitrogen 30 min after surgery. Among them, three cases were diagnosed as very low/low risk, five cases as intermediate risk, and five cases as high risk. Note: M: male; F: female. *Prognostic classification based on tumor size and mitosis count. Quantitative proteomic analysis using isobaric isotope reagents such as TMT have been widely used to determine the relative protein ratios between different samples. In this study, to obtain the changing patterns of proteome between tumorous and nontumorous tissues in GIST subgroups, we performed quantitative proteomics using TMT-10plex isobaric label reagent, with five biological repeats applied for both NIH-I and NIH-H GIST, as well as three biological repeats for NIH-L GIST (Fig. 1A). To increase the throughput and minimize the co-isolated peptide contamination, the combined TMT labeled peptides were separated into 40 fractions by reversed phase high-performance liquid chromatography (RP-HPLC) under basic pH, and further analyzed by mass spectrometer with two technical replicates. The raw mass spectrometry (MS) data were searched using MaxQuant (version 1.6) against the Swiss-Prot human database (20413 entries, 2017/01/14), with protein false discovery rate (FDR) of 1% and MS2 tolerance of 10 ppm at peptide level (Fig. 1A). After removing reverse, contaminant and zero intensity proteins, as well as proteins with single-peptide identification, a total number of 9177 proteins, representing about 55.9% of all GIT coding genes (26.Uhlen M. Fagerberg L. Hallstrom B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson A. Kampf C. Sjostedt E. Asplund A. Olsson I. Edlund K. Lundberg E. Navani S. Szigyarto C.A. Odeberg J. Djureinovic D. Takanen J.O. Hober S. Alm T. Edqvist P.H. Berling H. Tegel H. Mulder J. Rockberg J. Nilsson P. Schwenk J.M. Hamsten M. von Feilitzen K. Forsberg M. Persson L. Johansson F. Zwahlen M. von Heijne G. Nielsen J. Ponten F. Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (7240) Google Scholar), were quantified in GIST samples (Fig. 1B, and supplemental Table S1). The median protein coverage was ∼21.33% and the median number of unique peptides for each protein was 10.1. Specifically, 8650, 8287, and 7797 proteins were quantified in NIH-H, NIH-I, and NIH-L GIST subgroups, respectively (supplemental Fig. S1A–S1D, and supplemental Table S2). Good reproducibility was observed between technical repeats (supplemental Fig. S1E–S1G). Besides, 7230 quantified proteins were overlapped in all GIST samples (Fig. 1C, and supplemental Table S3). Then, we applied student-t test for the T/N ratios in duplicates and revealed that the p values of 4930 proteins were greater than 0.1 between technical duplicates, indicating good reproducibility of protein quantification in between duplicates of each GIST subgroup, which were used for further data mining (supplemental Fig. S1H, and supplemental Table S4–S5). To further demonstrate the reliability of our proteomic data, we first examined some well-known overexpressed proteins in GIST, such as KIT, protein PML (PML), tyrosin
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