Peritoneal carcinomatosis (PC) present a ubiquitous clinical conundrum in all intra-abdominal malignancies. Via functional and transcriptomic experiments of ascites-treated PC cells, we identify STAT3 as a key signaling pathway. Integrative analysis of publicly available databases and correlation with clinical cohorts (n = 7,359) reveal putative clinically significant activating ligands of STAT3 signaling. We further validate a 3-biomarker prognostic panel in ascites independent of clinical covariates in a prospective study (n = 149). Via single-cell sequencing experiments, we uncover that PAI-1, a key component of the prognostic biomarker panel, is largely secreted by fibroblasts and mesothelial cells. Molecular stratification of ascites using PAI-1 levels and STAT3 activation in ascites-treated cells highlight a therapeutic opportunity based on a phenomenon of paracrine addiction. These results are recapitulated in patient-derived ascites-dependent xenografts. Here, we demonstrate therapeutic proof of concept of direct ligand inhibition of a prognostic target within an enclosed biological space.
At first sight, haemophilia A would appear to be an ideal candidate for treatment by gene therapy. There is a single gene defect; cells in different parts of the body, but especially the liver, produce Factor VIII, and only 5% of normal levels of Factor VIII are necessary to prevent the serious symptoms of bleeding. This review attempts to outline the status of gene therapy at present and efforts that have been made to overcome the difficulties and remaining problems that require solving. Undoubtedly, success will be achieved, but it is likely that considerably more work will be necessary before experimental models can be introduced into the clinic with any likelihood of success. The most successful results in animals that may have clinical application were from introducing the Factor VIII gene to newborn animals before antibodies are produced, presumably inducing a state of tolerance.
Hypoxia is an environmental cue that is associated with multiple tumorigenic processes such as immunosuppression, angiogenesis, cancer invasion, metastasis, drug resistance, and poor clinical outcomes. When facing hypoxic stress, cells initiate several adaptive responses such as cell cycle arrest to reduce excessive oxygen consumption and co-activation of oncogenic factors. In order to identify the critical novel proteins for hypoxia responses, we used pulsed-SILAC method to trace the active cellular translation events in A431 cells. Proteomic discovery data and biochemical assays showed that cancer cells selectively activate key glycolytic enzymes and novel ER-stress markers, while protein synthesis is severely suppressed. Interestingly, deprivation of oxygen affected the expression of various epigenetic regulators such as histone demethylases and NuRD (nucleosome remodeling and deacetylase) complex in A431 cells. In addition, we identified PHF14 (the plant homeodomain finger-14) as a novel hypoxia-sensitive epigenetic regulator that plays a key role in cell cycle progress and protein synthesis. Hypoxia-mediated inhibition of PHF14 was associated with increase of key cell cycle inhibitors, p14ARF, p15INK4b, and p16INK4a, which are responsible for G1-S phase transition and decrease of AKT-mTOR-4E-BP1/pS6K signaling pathway, a master regulator of protein synthesis, in response to environmental cues. Analysis of TCGA colon cancer (n=461) and skin cancer (n=470) datasets revealed a positive correlation between PHF14 expression and protein translation initiation factors, eIF4E, eIF4B, and RPS6. Significance of PHF14 gene was further demonstrated by in vivo mouse xenograft model using PHF14 KD cell lines.
<div>Abstract<p>Next-generation sequencing has uncovered thousands of long noncoding RNAs (lncRNA). Many are reported to be aberrantly expressed in various cancers, including hepatocellular carcinoma (HCC), and play key roles in tumorigenesis. This review provides an in-depth discussion of the oncogenic mechanisms reported to be associated with deregulated HCC-associated lncRNAs. Transcriptional expression of lncRNAs in HCC is modulated through transcription factors, or epigenetically by aberrant histone acetylation or DNA methylation, and posttranscriptionally by lncRNA transcript stability modulated by miRNAs and RNA-binding proteins. Seventy-four deregulated lncRNAs have been identified in HCC, of which, 52 are upregulated. This review maps the oncogenic roles of these deregulated lncRNAs by integrating diverse datasets including clinicopathologic features, affected cancer phenotypes, associated miRNA and/or protein-interacting partners as well as modulated gene/protein expression. Notably, 63 deregulated lncRNAs are significantly associated with clinicopathologic features of HCC. Twenty-three deregulated lncRNAs associated with both tumor and metastatic clinical features were also tumorigenic and prometastatic in experimental models of HCC, and eight of these mapped to known cancer pathways. Fifty-two upregulated lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer, whereas 22 downregulated lncRNAs have tumor-suppressive properties. Aberrantly expressed lncRNAs in HCC exert pleiotropic effects on miRNAs, mRNAs, and proteins. They affect multiple cancer phenotypes by altering miRNA and mRNA expression and stability, as well as through effects on protein expression, degradation, structure, or interactions with transcriptional regulators. Hence, these insights reveal novel lncRNAs as potential biomarkers and may enable the design of precision therapy for HCC.</p></div>
Abstract A novel approach for phenotype prediction is developed for mass spectrometric data. First, the data-independent acquisition (DIA) mass spectrometric data is converted into a novel file format called “DIA tensor” (DIAT) which contains all the peptide precursors and fragments information and can be used for convenient DIA visualization. The DIAT format is fed directly into a deep neural network to predict phenotypes without the need to identify peptides or proteins. We applied this strategy to a collection of 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in classifying malignant from benign samples. We further applied refined model to 492 samples of thyroid nodules to predict thyroid cancer; and achieved a predictive accuracy of 91.7% in an independent cohort of 216 test samples. In conclusion, DIA tensor enables facile 2D visualization of DIA proteomics data as well as being a new approach for phenotype prediction directly from DIA-MS data.
Cancer cells with MET overexpression are paradoxically more sensitive to MET inhibition than cells with baseline MET expression. The underlying molecular mechanisms are incompletely understood. Here, we have traced early responses of SNU5, a MET-overexpressing gastric cancer cell line, exposed to sublethal concentration of PHA-665752, a selective MET inhibitor, using iTRAQ-based quantitative proteomics. More than 1900 proteins were quantified, of which >800 proteins were quantified with at least five peptides. Proteins whose expression was perturbed by PHA-665752 included oxidoreductases, transfer/carrier proteins, and signaling proteins. Strikingly, 38% of proteins whose expression was confidently assessed to be perturbed by MET inhibition were mitochondrial proteins. Upon MET inhibition by a sublethal concentration of PHA-665752, mitochondrial membrane potential increased and mitochondrial permeability transition pore was inhibited concomitant with widespread changes in mitochondrial protein expression. We also showed the presence of highly activated MET in mitochondria, and striking suppression of MET activation by 50 nm PHA-665752. Taken together, our data indicate that mitochondria are a direct target of MET kinase inhibition, in addition to plasma membrane MET. Effects on activated MET in the mitochondria of cancer cells that are sensitive to MET inhibition might constitute a novel and critical noncanonical mechanism for the efficacy of MET-targeted therapeutics. Cancer cells with MET overexpression are paradoxically more sensitive to MET inhibition than cells with baseline MET expression. The underlying molecular mechanisms are incompletely understood. Here, we have traced early responses of SNU5, a MET-overexpressing gastric cancer cell line, exposed to sublethal concentration of PHA-665752, a selective MET inhibitor, using iTRAQ-based quantitative proteomics. More than 1900 proteins were quantified, of which >800 proteins were quantified with at least five peptides. Proteins whose expression was perturbed by PHA-665752 included oxidoreductases, transfer/carrier proteins, and signaling proteins. Strikingly, 38% of proteins whose expression was confidently assessed to be perturbed by MET inhibition were mitochondrial proteins. Upon MET inhibition by a sublethal concentration of PHA-665752, mitochondrial membrane potential increased and mitochondrial permeability transition pore was inhibited concomitant with widespread changes in mitochondrial protein expression. We also showed the presence of highly activated MET in mitochondria, and striking suppression of MET activation by 50 nm PHA-665752. Taken together, our data indicate that mitochondria are a direct target of MET kinase inhibition, in addition to plasma membrane MET. Effects on activated MET in the mitochondria of cancer cells that are sensitive to MET inhibition might constitute a novel and critical noncanonical mechanism for the efficacy of MET-targeted therapeutics. Recent improvements in survival of some malignancies owe much to advances in uncovering aberrantly active molecular pathways, against which molecularly targeted agents have been developed as new strategies to control cancers (1.Sharma S.V. Settleman J. Oncogene addiction: setting the stage for molecularly targeted cancer therapy.Genes Dev. 2007; 21: 3214-3231Crossref PubMed Scopus (343) Google Scholar, 2.Sawyers C. Targeted cancer therapy.Nature. 2004; 432: 294-297Crossref PubMed Scopus (892) Google Scholar). However, molecular mechanisms underlying the curious dependence of some cancer cells, which contain multiple genomic, genetic, and epigenetic abnormalities, on a single oncogenic molecule (the phenomenon of "oncogene addiction") are incompletely understood (3.Weinstein I.B. Cancer. Addiction to oncogenes–the Achilles heal of cancer.Science. 2002; 297: 63-64Crossref PubMed Scopus (1477) Google Scholar, 4.Weinstein I.B. Joe A. Oncogene addiction.Cancer Res. 2008; 68 (discussion 3080): 3077-3080Crossref PubMed Scopus (713) Google Scholar, 5.Weinstein I.B. Joe A.K. Mechanisms of disease: Oncogene addiction–a rationale for molecular targeting in cancer therapy.Nat. Clin. Pract. Oncol. 2006; 3: 448-457Crossref PubMed Scopus (552) Google Scholar). Receptor tyrosine kinases are the most extensively studied oncogenic targets and receptor tyrosine kinase inhibitors have proven anticancer therapeutic efficacy. A receptor tyrosine kinase, MET, whose ligand is hepatocyte growth factor (HGF), is frequently amplified and overexpressed (6.Kuniyasu H. Yasui W. Kitadai Y. Yokozaki H. Ito H. Tahara E. Frequent amplification of the c-met gene in scirrhous type stomach cancer.Biochem. Biophys. Res. Commun. 1992; 189: 227-232Crossref PubMed Scopus (320) Google Scholar, 7.Heideman D.A. Snijders P.J. Bloemena E. Meijer C.J. Offerhaus G.J. Meuwissen S.G. Gerritsen W.R. Craanen M.E. Absence of tpr-met and expression of c-met in human gastric mucosa and carcinoma.J. Pathol. 2001; 194: 428-435Crossref PubMed Scopus (32) Google Scholar) in gastric cancer, the second highest cause of cancer mortality globally (8.Catalano V. Labianca R. Beretta G.D. Gatta G. de Braud F. Van Cutsem E. Gastric cancer.Crit. Rev. Oncol. Hematol. 2005; 54: 209-241Crossref PubMed Scopus (76) Google Scholar, 9.Peek Jr., R.M. Blaser M.J. Helicobacter pylori and gastrointestinal tract adenocarcinomas.Nat. Rev. Cancer. 2002; 2: 28-37Crossref PubMed Scopus (1470) Google Scholar). Human gastric cancer cell lines harboring MET amplicons and overexpressing MET are readily induced to apoptosis by selective inhibitors of MET (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2316-2321Crossref PubMed Scopus (442) Google Scholar, 11.Zou H.Y. Li Q. Lee J.H. Arango M.E. McDonnell S.R. Yamazaki S. Koudriakova T.B. Alton G. Cui J.J. Kung P.P. Nambu M.D. Los G. Bender S.L. Mroczkowski B. Christensen J.G. An orally available small-molecule inhibitor of c-Met, PF-2341066, exhibits cytoreductive antitumor efficacy through antiproliferative and antiangiogenic mechanisms.Cancer Res. 2007; 67: 4408-4417Crossref PubMed Scopus (614) Google Scholar), several of which are under active development for clinical use (12.Comoglio P.M. Giordano S. Trusolino L. Drug development of MET inhibitors: targeting oncogene addiction and expedience.Nat. Rev. Drug. Discov. 2008; 7: 504-516Crossref PubMed Scopus (702) Google Scholar). One of the selective small molecular inhibitors, PHA-665752, designed chemically as (3Z)-5-[(2,6-dichlorobenzyl)sulfonyl]-3-[(3,5-dimethyl-4-{[(2R)-2-(pyrrolidin-1-ylmethyl)pyrrolidin-1-yl]carbonyl}-1H-pyrrol-2-yl)methylene]-1,3-dihydro-2H-indol-2-one (molecule weight of 641.61), specifically suppresses tyrosine phosphorylation of MET. PHA-665752 has >50-fold higher selectivity for MET than for other tyrosine and serine/threonine kinases (13.Christensen J.G. Schreck R. Burrows J. Kuruganti P. Chan E. Le P. Chen J. Wang X. Ruslim L. Blake R. Lipson K.E. Ramphal J. Do S. Cui J.J. Cherrington J.M. Mendel D.B. A selective small molecule inhibitor of c-Met kinase inhibits c-Met-dependent phenotypes in vitro and exhibits cytoreductive antitumor activity in vivo.Cancer Res. 2003; 63: 7345-7355PubMed Google Scholar). The inhibition of MET kinase function by PHA-665752 on cancer cells had been confirmed with siRNA knockdown of MET, and a number of downstream effectors of MET signaling pathways were confirmed to be effectively abrogated by this compound (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2316-2321Crossref PubMed Scopus (442) Google Scholar, 13.Christensen J.G. Schreck R. Burrows J. Kuruganti P. Chan E. Le P. Chen J. Wang X. Ruslim L. Blake R. Lipson K.E. Ramphal J. Do S. Cui J.J. Cherrington J.M. Mendel D.B. A selective small molecule inhibitor of c-Met kinase inhibits c-Met-dependent phenotypes in vitro and exhibits cytoreductive antitumor activity in vivo.Cancer Res. 2003; 63: 7345-7355PubMed Google Scholar). PHA-665752 has been widely used as a potent and selective tool for the evaluation of MET-dependent cellular functions and signal transduction (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. 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Giordano S. Trusolino L. Drug development of MET inhibitors: targeting oncogene addiction and expedience.Nat. Rev. Drug. Discov. 2008; 7: 504-516Crossref PubMed Scopus (702) Google Scholar), raises an unexplained paradox. MET-overexpressing cancer cells could reasonably be expected to be more tolerant of MET kinase inhibition compared with cancer cells that do not overexpress MET. In reality, the opposite occurs. The underlying molecular mechanisms are incompletely understood. To investigate this paradox we undertook a systematic exploration of responses of a MET-overexpressing gastric cancer cell line, SNU5, to sublethal MET inhibition using the iTRAQ-based quantitative proteomics approach. Our results unexpectedly showed a predominant perturbation of mitochondrial proteins in response to MET inhibition. Next, we found that MET inhibition was rapidly associated with altered mitochondrial functions. These observations raised the possibility that mitochondria might be a direct target of MET inhibition. Both protein immunoblotting and confocal microscopy showed the presence of highly activated MET in the mitochondria of sensitive cancer cells. Furthermore, we observed that activating phosphorylation of tyrosine residues of mitochondrial MET was critically modulated by sublethal PHA-665752 treatment. All chemicals were purchased from Sigma-Aldrich unless otherwise stated. A selective MET inhibitor PHA-665752 (13.Christensen J.G. Schreck R. Burrows J. Kuruganti P. Chan E. Le P. Chen J. Wang X. Ruslim L. Blake R. Lipson K.E. Ramphal J. Do S. Cui J.J. Cherrington J.M. Mendel D.B. A selective small molecule inhibitor of c-Met kinase inhibits c-Met-dependent phenotypes in vitro and exhibits cytoreductive antitumor activity in vivo.Cancer Res. 2003; 63: 7345-7355PubMed Google Scholar) was from Pfizer Global Research and Development (La Jolla Laboratories, San Diego, CA). Stock solutions of this compound were prepared in DMSO, stored in −80 °C and diluted with fresh medium before use. In all experiments, the final concentration of DMSO was <0.1%. Gastric cancer cell lines AGS, Kato III, SNU1, SNU5, SNU16, NCIN87, and Hs746T, and a human fibroblast cell line, Hs68, were obtained from American Type Culture Collection (ATCC, Manassas, VA) and cultured as recommended. MKN7, and IM95 cells were from Japan Health Science Research Resource Bank and were cultured as recommended. YCC cells were a gift from Dr. Sun Young Rha (Yonsei Cancer Center, Seoul, Korea) and were grown in MEM supplemented with 10% fetal bovine serum (Hyclone, Thermo Fisher Scientific, Waltham, MA), 100 U penicillin, and 100 μg streptomycin per ml (Invitrogen). Total RNA was extracted from cell lines using the RNeasy Mini kit (Qiagen, Valencia, CA) and profiled using Affymetrix HG-U133 and HG-U133 Plus 2.0 GeneChip® (Affymetrix, Santa Clara, CA). Each RNA sample was amplified, labeled, and hybridized according to the manufacturer's protocols. Normal gastric tissue RNA samples from two commercial sources were employed as controls. FirstChoice Human Stomach Total RNA (Ambion, Austin, TX) was RNA from a single individual. MVP Total RNA, Human Stomach (Stratagene, La Jolla, CA) was pooled RNA from two individuals. Four probe sets (203510_at, 211599_x_at, 213807_x_at and 213816_s_at) for MET were printed on the arrays. Cell viability based on redox enzyme activity was quantified using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, the MTT assay as described (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2316-2321Crossref PubMed Scopus (442) Google Scholar). Four experimental groups of SNU5 cells were prepared in the absence or presence of PHA-665752. Three groups were exposed to 50 nm PHA-665752 for three time periods i.e. 4 h, 24 h, and 72 h. A parallel group without treatment served as the control. After treatment, proteins were extracted and three independent biological replicate flasks for each experimental condition were pooled and quantified by bicinchoninic acid protein assay kit as described previously (24.Guo T. Gan C.S. Zhang H. Zhu Y. Kon O.L. Sze S.K. Hybridization of pulsed-Q dissociation and collision-activated dissociation in linear ion trap mass spectrometer for iTRAQ quantitation.J. Proteome Res. 2008; 7: 4831-4840Crossref PubMed Scopus (56) Google Scholar). Two-hundred micrograms of protein from each experimental condition were tryptically digested and labeled with 4-plex iTRAQ reagents (Applied Biosystems, Foster City, CA) as follows: control, 114; 4 h, 115; 24 h, 116; 72 h, 117. The labeled samples were pooled and resolved into 20 fractions using strong cation exchange (SCX)(24.Guo T. Gan C.S. Zhang H. Zhu Y. Kon O.L. Sze S.K. Hybridization of pulsed-Q dissociation and collision-activated dissociation in linear ion trap mass spectrometer for iTRAQ quantitation.J. Proteome Res. 2008; 7: 4831-4840Crossref PubMed Scopus (56) Google Scholar). Eluted fractions were vacuum dried and desalted using SEP-PAK C18 cartridges (Waters, Milford, MA). Dried peptides were stored at −80 °C before MS analysis. liquid chromatography International Protein Index false discovery rate electron transfer chain mitochondrial permeability transition pore. The LC-MS/MS analysis was performed as previously described (24.Guo T. Gan C.S. Zhang H. Zhu Y. Kon O.L. Sze S.K. Hybridization of pulsed-Q dissociation and collision-activated dissociation in linear ion trap mass spectrometer for iTRAQ quantitation.J. Proteome Res. 2008; 7: 4831-4840Crossref PubMed Scopus (56) Google Scholar, 25.Datta, A., Park, J. E., Li, X., Zhang, H., Ho, Z. S., Heese, K., Lim, S. K., Tam, J. P., Sze, S. K., Phenotyping of an in vitro model of ischemic penumbra by iTRAQ-based shotgun quantitative proteomics. J. Proteome Res. 9, 472–484,Google Scholar) with some modifications. Briefly, dried iTRAQ-labeled peptide samples were dissolved in HPLC grade water (Mallinckrodt Baker) acidified with 0.1% formic acid, and sequentially injected and separated in a home-packed nanobored C18 column with a picofrit nanospray tip (75 μm ID × 15 cm, 5-μm particles) (New Objectives, Woburn, MA) on a TempoTM nano-MDLC system coupled with a QSTAR® Elite Hybrid LC-MS/MS system (Applied Biosystems). Each sample was divided into two equal aliquots and independently analyzed by the LC-MS/MS over a gradient of 120 min. The flow rate of LC system was set constantly at 300 nl/min. Data acquisition in QSTAR Elite was set to positive ion mode using Analyst® QS 2.0 software (Applied Biosystems). Precursors with a mass range of 300–2000 m/z and a calculated charge of +2 to +4 were selected for fragmentation. For each MS spectrum, a maximum of three most abundant peptides above a five-count threshold were selected for MS/MS. Each selected precursor ion was dynamically excluded for 30 s with a mass tolerance of 0.03 Da. Smart information-dependent acquisition was activated with automatic collision energy and automatic MS/MS accumulation. The fragment intensity multiplier was set to 20 and maximum accumulation time was 2 s. Spectra acquired in LC-MS/MS system from the two independent runs were submitted in a batch to ProteinPilot (v2.0.1, Applied Biosystems) for peak-list generation, as well as protein identification and quantification against the International Protein Index (IPI) human database (version 3.34; 67758 sequences) supplemented with porcine trypsin. The Paragon algorithm in ProteinPilot software was configured as previously described (25.Datta, A., Park, J. E., Li, X., Zhang, H., Ho, Z. S., Heese, K., Lim, S. K., Tam, J. P., Sze, S. K., Phenotyping of an in vitro model of ischemic penumbra by iTRAQ-based shotgun quantitative proteomics. J. Proteome Res. 9, 472–484,Google Scholar) with some modifications. Briefly, default parameters including fixed and variable modifications for tryptically digested samples labeled with 4-plex iTRAQ reagents (peptide labeled) were employed. The search was done thoroughly where all cleavage variants were considered. The confidence threshold for both peptide and protein identification was set to 70%. Default precursors and the fragments mass tolerance for QSTAR ESI MS instrument were adopted by the software. A concatenated target-decoy database search strategy was also employed to estimate the false discovery rate (FDR) (26.Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2827) Google Scholar). FDR was calculated as twofold of the percentage of decoy matches divided by the total matches. After stringent filtering as described in Results, FDR of the reported iTRAQ data set was <1%. ProteinPilot software employed the peak area of iTRAQ reporters for quantification. Details of the quantification algorithm can be found in the supplier's manual. Isoform-specific strategy was adopted to deal with quantification of isoforms. Quality control of the data set is addressed in Results. Gene IDs of the proteins of interest were searched in a batch using PANTHER classification system (27.Thomas P.D. Campbell M.J. Kejariwal A. Mi H. Karlak B. Daverman R. Diemer K. Muruganujan A. Narechania A. PANTHER: a library of protein families and subfamilies indexed by function.Genome Res. 2003; 13: 2129-2141Crossref PubMed Scopus (2185) Google Scholar) against NCBI (H. sapiens) dataset and the results were presented as genes. Most protein groups had more than one molecular function hit. Cellular localization information of the 50 proteins of interest was checked manually in Gene Ontology (28.Ashburner M. Ball C.A. Blake J.A. Botstein D. Butler H. Cherry J.M. Davis A.P. Dolinski K. Dwight S.S. Eppig J.T. Harris M.A. Hill D.P. Issel-Tarver L. Kasarskis A. Lewis S. Matese J.C. Richardson J.E. Ringwald M. Rubin G.M. Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat. Genet. 2000; 25: 25-29Crossref PubMed Scopus (26995) Google Scholar). Western blotting was performed using primary antibodies at the dilutions indicated: 1:500 SDHB (clone 21A11), 1:500 NDUFS3 (clone 17D95), 1:1000 VDAC1 (clone 20B12), 1:1000 MET (clone C-12), 1:1000 phospho-MET (Y1234/1235), 1:1000 phospho-MET (Y1349), 1:1000 E-cadherin (G-10), 1:2500 actin (Clone C4), 1:2000 α-tubulin (clone B-7). Phospho-MET antibodies were from Cell Signaling (Danvers, MA), actin antibody was from Millipore (Billerica, MA), whereas the other primary antibodies were from Santa Cruz Biotechnology (Santa Cruz, CA). Antibody against integrin αL (1:500), MHM23, was kindly supplied by Dr Alex Law (School of Biological Sciences, Nanyang Technological University, Singapore). Cells with or without PHA-665752 treatment were washed with ice-cold PBS and incubated with 5 μg/ml rhodamine 123 for 1 h, followed by flow cytometric analysis on FACS Calibur and CellQuest Pro software (Becton Dickinson, Franklin Lakes, NJ). The activity of mitochondrial transition pore was evaluated by the MitoProbe™ Transition Pore Assay Kit (Becton Dickinson) following the manufacturer's instruction. Briefly, cells were washed twice with ice-cold Hanks' balanced salt solution containing 1.3 mm calcium (Invitrogen) before incubation in the presence or absence of cobalt chloride at 37 °C for 15 min, followed by flow cytometry analysis as described earlier. SNU5 cells were washed with HEPES twice, before incubating with 500 nm Mito Tracker Red CMXRos (Invitrogen) for 15 min. Cells were then fixed in 3% paraformaldehyde for 20 min and permeabilized with 0.1% Triton X-100 for 2 min. After blocking nonspecific antibody binding sites with 1% BSA for 1 h at 37 °C, cells were probed with primary antibodies (1:500) overnight at 37 °C and Alexa 488- conjugated goat-anti-rabbit secondary antibodies (Invitrogen) for 1 h at 37 °C. Finally the cells were washed with PBS and counterstained with Vectashield mounting medium with DAPI (Vector Laboratories, Burlingame, CA). Images were captured with a Zeiss LSM 710 confocal microscope. Mitochondria isolation kit (Miltenyi Biotec, Bergisch Gladbach, Germany) was employed to isolate mitochondria following the manufacturer's protocol. Briefly, 5 × 107 SNU5 cells with or without treatment were washed twice with PBS, and lysed in 2 ml of the provided lysis buffer supplemented with Complete Protease Inhibitor Mixture Tablets and phosSTOP (Roche, Basel, Switzerland). The crude cell lysate was incubated with anti-TOM22 MicroBeads for 1 h at 4 °C with gentle shaking. Subsequently, the suspension was loaded onto a pre-equilibrated MACS column, washed three times with separation buffer before removing the column from the magnetic field and eluting the mitochondria. As PHA-665752 is differentially cytotoxic in cancer cells depending on MET expression levels (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2316-2321Crossref PubMed Scopus (442) Google Scholar), we first evaluated MET expression data of a panel of 16 gastric cancer cell lines, (AGS, Kato III, SNU1, SNU5, SNU16, NCIN87, Hs746T, MKN7, IM95, YCC1, YCC2, YCC3, YCC6, YCC9, YCC11, and YCC16) in order to focus on a model cell line for systematic proteomics exploration. Our transcriptome data showed that SNU5 cells had markedly elevated levels of MET transcription (>40-fold compared with normal human stomach tissues), while MET expression of SNU1 cells was comparable to the controls (supplemental Fig. 1). MET protein expression levels of these two cell lines were compared by immunoblotting (supplemental Fig. 2). SNU5 and SNU1 cells showed high and low expression of MET, respectively, in agreement with our transcriptome data as well as a previous study (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2316-2321Crossref PubMed Scopus (442) Google Scholar). We determined cytotoxic responses of the two gastric cancer cell lines to PHA-665752 using MTT assay (supplemental Fig. 3). The mean IC50 of PHA-665752 in SNU5 cells was ∼77 nm, whereas SNU1 cells were relatively resistant to the compound (IC50 > 500 nm). SNU5 was selected as the model cell line in subsequent temporal quantitative proteomics analyses because it was highly sensitive to PHA-665752. Conversely, SNU1 was chosen as being representative of gastric cancer cells resistant to MET inhibition in functional studies. We treated SNU5 cells with PHA-665752 and analyzed the temporal dynamics of the proteome. First, we sought to determine an appropriate concentration of PHA-665752 for quantitative proteomic investigation of SNU5 cells, in order to trace early cellular responses of the cells to MET inhibition. The ideal treatment conditions with PHA-665752 should suppress phosphorylation of MET without causing substantial cell death. SNU5 cells were exposed for varying durations to two concentrations of PHA-665752 around its IC50 (determined at 72 h) and tested for viability using MTT assays. Our results showed that 50 nm PHA-665752 did not significantly impair cell viability, whereas 150 nm was rapidly cytotoxic (supplemental Fig. 4). As such, we regarded 50 nm as a sublethal concentration for SNU5 cells, and adopted these conditions for the subsequent proteomics study. It is worth noting that it remains a daunting challenge that some small-molecule kinase inhibitors exhibit off-target effects that cannot be ignored (29.Zhang J. Yang P.L. Gray N.S. Targeting cancer with small molecule kinase inhibitors.Nat. Rev. Cancer. 2009; 9: 28-39Crossref PubMed Scopus (2058) Google Scholar). To minimize off-target effects in this study, we selected one of the most potent and specific MET inhibitors PHA-665752, which is >50 times more selective for MET than other protein kinases (13.Christensen J.G. Schreck R. Burrows J. Kuruganti P. Chan E. Le P. Chen J. Wang X. Ruslim L. Blake R. Lipson K.E. Ramphal J. Do S. Cui J.J. Cherrington J.M. Mendel D.B. A selective small molecule inhibitor of c-Met kinase inhibits c-Met-dependent phenotypes in vitro and exhibits cytoreductive antitumor activity in vivo.Cancer Res. 2003; 63: 7345-7355PubMed Google Scholar). Moreover, we applied it to a cell line SNU5 that overexpresses MET at unusually high levels i.e. >40 times higher than normal stomach tissue. In addition, we intentionally employed a low concentration of PHA-665752, i.e. 50 nm, which is sublethal to SNU5 cells but sufficient to inhibit MET activity. This further refined the data as arising from specific inhibition of MET because most off-target effects happen when inhibitors are used at high concentrations, such as >1 μm. We believe, in this scenario, the probability of inhibiting other proteins with even comparable or higher affinity than MET is low or negligible. Furthermore, previous work has documented that the differential effects of PHA-665752 are truly attributed to its effect on MET using small interfering (si) RNA targeting the MET receptor transcript in SNU5 cells (10.Smolen G.A. Sordella R. Muir B. Mohapatra G. Barmettler A. Archibald H. Kim W.J. Okimoto R.A. Bell D.W. Sgroi D.C. Christensen J.G. Settleman J. Haber D.A. Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2316-2321Crossref PubMed Scopu
4203 Aneuploidy is common in gastric cancer while little is known of structurally rearranged chromosomes that may drive carcinogenesis. Pan-genomic copy number profiles of gastric cancer currently lack utility. We studied (a) copy number aberrations as genomic classifiers and prognostic markers; (b) highly expressed genes of gastric cancer amplicons; and (c) gene expression changes in recurrent translocation breakpoint loci. Analysis of 203 primary gastric adenocarcinomas (Progenetix CGH database, July 2004) using a neural network (Self Organising Tree Algorithm, EBI) segregated 2 genomic subclasses of 89 and 114 tumors. Supervised class prediction (GeneCluster 2.0) was performed on training (203 gastric cancers) and test (44 gastric cancers and 18 human gastric cancer cell lines) datasets. Models were built from the training set using a S2N similarity metric, median values for class estimate and 10 4 random permutations. Genomic classifiers (17q and 20q) were evaluated on the test set by weighted voting and leave-one-out cross-validation with 73% accuracy (45/62 correctly assigned). Genomic subclasses correlated significantly with clinical stage (p=0.011, Fisher’s exact test). The cell lines were almost equally distributed between the genomic subclasses, indicating that they were true biological representations of primary gastric cancers. 16p copy number gains were more prevalent in stage 3 and 4 gastric cancers (p=0.047, Fisher’s test) than in stages 1 and 2. 16p gains were associated with shorter survival (p=0.01, log rank rest). Overexpression of a 16p gene, SLC9A3R2, in 56% of gastric cancers was however associated with longer survival (p=0.038, log rank test). Forty-eight recurrent translocation breakpoints e.g. 2p22 and 8q22, were identified from parallel CGH and spectral karyotyping of 18 gastric cancer cell lines (AGS, NCIN87, Hs746T, Kato III, SNU1, SNU5, SNU16, IM95, MKN7, FU97, YCC-1, YCC-2, YCC-3, YCC-6, YCC-9, YCC-10, YCC-11 and YCC-16). Copy number gains among 247 primary gastric cancers were more frequent in recurrent breakpoint regions than in cytobands unaffected by translocations (p≤0.001, Mann-Whitney test). We ascertained the expression status of breakpoint genes in 55 primary gastric cancers. Eighty-two breakpoint genes were overexpressed (> 2-fold) in 20-96% of gastric cancers, while 83 were underexpressed (
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