Identification of Novel Cellular Targets in Biliary Tract Cancers Using Global Gene Expression Technology
2003; Elsevier BV; Volume: 163; Issue: 1 Linguagem: Inglês
10.1016/s0002-9440(10)63645-0
ISSN1525-2191
AutoresDonna E. Hansel, Ayman Rahman, Manuel Hidalgo, Paul J. Thuluvath, Keith D. Lillemoe, Richard Shulick, Ja‐Lok Ku, Jae‐Gahb Park, Kohje Miyazaki, Raheela Ashfaq, Ignacio I. Wistuba, Ram Varma, Lesleyann Hawthorne, Joseph Geradts, Pedram Argani, Anirban Maitra,
Tópico(s)Cancer-related gene regulation
ResumoBiliary tract carcinoma carries a poor prognosis, and difficulties with clinical management in patients with advanced disease are often due to frequent late-stage diagnosis, lack of serum markers, and limited information regarding biliary tumor pathogenesis. RNA-based global analyses of gene expression have led to the identification of a large number of up-regulated genes in several cancer types. We have used the recently developed Affymetrix U133A gene expression microarrays containing nearly 22,000 unique transcripts to obtain global gene expression profiles from normal biliary epithelial scrapings (n = 5), surgically resected biliary carcinomas (n = 11), and biliary cancer cell lines (n = 9). Microarray hybridization data were normalized using dCHIP (http://www.dCHIP.org) to identify differentially up-regulated genes in primary biliary cancers and biliary cancer cell lines and their expression profiles was compared to that of normal epithelial scrapings using the dCHIP software as well as Significance Analysis of Microarrays or SAM (http://www-stat.stanford.edu/∼tibs/SAM/). Comparison of the dCHIP and SAM datasets revealed an overlapping list of 282 genes expressed at greater than threefold levels in the cancers compared to normal epithelium (t-test P <0.1 in dCHIP, and median false discovery rate <10 in SAM). Several pathways integral to tumorigenesis were up-regulated in the biliary cancers, including proliferation and cell cycle antigens (eg, cyclins D2 and E2, cdc2/p34, and geminin), transcription factors (eg, homeobox B7 and islet-1), growth factors and growth factor receptors (eg, hepatocyte growth factor, amphiregulin, and insulin-like growth factor 1 receptor), and enzymes modulating sensitivity to chemotherapeutic agents (eg, cystathionine β synthase, dCMP deaminase, and CTP synthase). In addition, we identified several "pathway" genes that are rapidly emerging as novel therapeutic targets in cancer (eg, cytosolic phospholipase A2, an upstream target of the cyclooxygenase pathway, and ribosomal protein S6 kinase and eukaryotic translation initiation factor 4E, two important downstream mediators of the mitogenic Akt/mTOR signaling pathway). Overexpression of selected up-regulated genes was confirmed in tissue microarrays of biliary cancers by immunohistochemical analysis (n = 4) or in situ hybridization (n = 1), and in biliary cancer cell lines by reverse transcriptase PCR (n = 2). The majority of genes identified in the present study has not been previously reported in biliary cancers, and represent novel potential screening and therapeutic targets of this cancer type. Biliary tract carcinoma carries a poor prognosis, and difficulties with clinical management in patients with advanced disease are often due to frequent late-stage diagnosis, lack of serum markers, and limited information regarding biliary tumor pathogenesis. RNA-based global analyses of gene expression have led to the identification of a large number of up-regulated genes in several cancer types. We have used the recently developed Affymetrix U133A gene expression microarrays containing nearly 22,000 unique transcripts to obtain global gene expression profiles from normal biliary epithelial scrapings (n = 5), surgically resected biliary carcinomas (n = 11), and biliary cancer cell lines (n = 9). Microarray hybridization data were normalized using dCHIP (http://www.dCHIP.org) to identify differentially up-regulated genes in primary biliary cancers and biliary cancer cell lines and their expression profiles was compared to that of normal epithelial scrapings using the dCHIP software as well as Significance Analysis of Microarrays or SAM (http://www-stat.stanford.edu/∼tibs/SAM/). Comparison of the dCHIP and SAM datasets revealed an overlapping list of 282 genes expressed at greater than threefold levels in the cancers compared to normal epithelium (t-test P <0.1 in dCHIP, and median false discovery rate 50% neoplastic cells on frozen section examination. The nine human biliary cancer cell lines used for this study included EGI-1 and TFK-19Saijyo S Kudo T Suzuki M Katayose Y Shinoda M Muto T Fukuhara K Suzuki T Matsuno S Establishment of a new extrahepatic bile duct carcinoma cell line, TFK-1.Tohoku J Exp Med. 1995; 177: 61-71Crossref PubMed Scopus (128) Google Scholar, 10Steffen M Zuehlke I Scherdin U Motility factors identified in supernatants of human cholangiocarcinoma cell lines.Int J Oncol. 2001; 18: 1107-1112PubMed Google Scholar (obtained from the German Collection of Microorganisms and Cell Cultures Department, Braunschweig, Germany), HUH2811Sugimoto H Nishino H Effect of recombinant human basic fibroblast growth factor (bFGF) on the growth of human tumor cell lines.Hum Cell. 1996; 9: 129-140PubMed Google Scholar and HUCCT-112Miyazaki M Ohashi R Tsuji T Mihara K Gohda E Namba M Transforming growth factor-β 1 stimulates or inhibits cell growth via down- or up-regulation of p21/Waf1.Biochem Biophys Res Commun. 1998; 246: 873-880Crossref PubMed Scopus (59) Google Scholar (obtained from the Health Science Research Resources Bank, Osaka, Japan), SNU 245, SNU 308, and SNU 107913Ku JL Yoon KA Kim IJ Kim WH Jang JY Suh KS Kim SW Park YH Hwang JH Yoon YB Park JG Establishment and characterisation of six human biliary tract cancer cell lines.Br J Cancer. 2002; 87: 187-193Crossref PubMed Scopus (78) Google Scholar (obtained from the Korean Cell Line Bank, Seoul, Korea), GB-H3, and GB-D1.14Li H Shimura H Aoki Y Date K Matsumoto K Nakamura T Tanaka M Hepatocyte growth factor stimulates the invasion of gallbladder carcinoma cell lines in vitro.Clin Exp Metastasis. 1998; 16: 74-82Crossref PubMed Scopus (29) Google Scholar Of these, SNU308, GB-H3, and GB-D1 cell lines were derived from gallbladder carcinomas, HuH28, HuCCT-1, and SNU 1079 were derived from intrahepatic cholangiocarcinomas, and EGI-1, TFK-1, and SNU 245 were derived from extrahepatic biliary cancers. All cell lines except EGI-1 were grown in RPMI (Life Technologies Inc., Gaithersburg, MD) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Life Technologies Inc.); EGI-I was grown in Dulbecco's MEM (Life Technologies Inc.) supplemented with 10% FBS. Cells were incubated at 37°C in a humidified atmosphere of 5% CO2 in air. Sample preparation and processing procedure was performed at the Roswell Park Cancer Institute Microarray Core Facility, as described in the Affymetrix GeneChip Expression Analysis Manual (Affymetrix Inc., Santa Clara, CA). Briefly, frozen tumor tissues were crushed in TRIzol (Invitrogen Inc., Carlsbad, CA) by using a Polytron homogenizer (Brinkman Instruments, Westbury, NY). Total RNA was then extracted from the crushed tissue and cleaned using RNeasy columns according to manufacturer's protocol (Qiagen Inc., Valencia, CA). For biliary cancer cell lines and biliary epithelial scrapings, the RNeasy protocol for human cell lines was directly used for extraction of total RNA. The integrity of total RNA was confirmed in each case using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Using 5 to 40 μg of total RNA, double-stranded cDNA was synthesized following SuperScript Choice system (Invitrogen Inc.). T7-(dT24) oligomer was used for priming the first-strand cDNA synthesis. The resultant cDNA was purified using Phase Lock Gel, phenol/chloroform extraction, and precipitated with ethanol. The cDNA pellet was collected and dissolved in appropriate volume. Using cDNA as template, cRNA was synthesized using a T7 MegaScript In-Vitro Transcription (IVT) kit (Ambion, Austin, TX). Biotinylated-11-CTP and 16-UTP ribonucleotides (Enzo Diagnostics Inc., Farmingdale, NY) were added to the reaction as labeling reagents. IVT reactions were carried out at 37°C for 6 hours and, the labeled cRNA obtained was purified using RNeasy columns (Qiagen Inc.). The cRNA was fragmented in a fragmentation buffer (40 mmol/L Tris-acetate, pH 8.1, 100 mmol/L KOAc, 30 mmol/L MgOAc) for 35 minutes at 94°C. Fragmented cRNA (10 to 11 μg/probe array) was used to hybridize to human U133A GeneChip array at 45°C for 24 hours in a hybridization oven with constant rotation (60 rpm). The chips were washed and stained using Affymetrix fluidics stations. Staining was performed using streptavidin phycoerythrin conjugate (SAPE; Molecular Probes, Eugene, OR), followed by the addition of biotinylated antibody to streptavidin (Vector Laboratories, Burlingame, CA), and finally with streptavidin phycoerythrin conjugate. Probe arrays were scanned using fluorometric scanners (Hewlett Packard Gene Array Scanner; Hewlett Packard Corporation, Palo Alto, CA). The scanned images were inspected and analyzed using established quality control measures. The 25 .CEL files generated by the Affymetrix Microarray Suite (MAS) version 5.0 were converted into .DCP files using dCHIP (www.dCHIP.org), as described previously by Li and Wong.15Li C Hung Wong W Model-based analysis of oligonucleotide arrays: model validation, design issues, and standard error application.Genome Biol. 2001; 2 (RESEARCH0032)Google Scholar The .DCP files were normalized, and raw gene expression data generated using the dCHIP system of model-based analysis. To evaluate how the 25 samples grouped together according to the similarity of their gene expression profiles, we used hierarchical clustering with the average linkage method, with a subset of 2308 genes demonstrating the largest variation across samples (SD/mean ≥1). For hierarchical cluster analysis, data were log-transformed, median-centered, and visualized using the CLUSTER and TREEVIEW programs.16Eisen MB Spellman PT Brown PO Botstein D Cluster analysis and display of genome-wide expression patterns.Proc Natl Acad Sci USA. 1998; 95: 14863-14868Crossref PubMed Scopus (13311) Google Scholar For comparison of global gene expression profiles between normal and cancer samples, a two-pronged strategy was used. The first comparison was performed using the dCHIP software itself, wherein the five biliary epithelial scrapings were designated as "baseline" (B), and the 20 biliary cancer specimens designated as "experiment" (E). Genes expressed threefold or higher in the cancers versus normal samples were then identified by defining the appropriate filtering criteria in the dCHIP software (mean E/mean B >3; mean E − mean B = 100, P < 0.1, t-test). The second comparison was performed using significance analysis of microarrays or SAM v1.13 (http://www-stat.stanford.edu/∼tibs/SAM/),17Tusher VG Tibshirani R Chu G Significance analysis of microarrays applied to the ionizing radiation response.Proc Natl Acad Sci USA. 2001; 98: 5116-5121Crossref PubMed Scopus (9825) Google Scholar, 18Xu Y Selaru FM Yin J Zou TT Shustova V Mori Y Sato F Liu TC Olaru A Wang S Kimos MC Perry K Desai K Greenwald BD Krasna MJ Shibata D Abraham JM Meltzer SJ Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer.Cancer Res. 2002; 62: 3493-3497PubMed Google Scholar which contains a sliding scale for false discovery rate (FDR) of significantly up-regulated genes. The output criteria selected for SAM included threefold or greater expression in the biliary cancers as compared to normal tissues, and a significance threshold expected to produce a median FDR of less than 10 genes. A biliary cancer tissue microarray was generated from 40 biliary tract cancers (15 gallbladder carcinomas, 10 intrahepatic cholangiocarcinomas, and 15 extrahepatic cholangiocarcinomas), as previously described.19van Heek NT Meeker AK Kern SE Yeo CJ Lillemoe KD Cameron JL Offerhaus GJ Hicks JL Wilentz RE Goggins MG De Marzo AM Hruban RH Maitra A Telomere shortening is nearly universal in pancreatic intraepithelial neoplasia.Am J Pathol. 2002; 161: 1541-1547Abstract Full Text Full Text PDF PubMed Scopus (286) Google Scholar Each cancer specimen was represented by four 1.4-mm cores on the tissue microarrays, to obtain adequate representation of neoplastic cells. In addition, non-neoplastic biliary tissues, and additional control tissues from extra-biliary sites were also included on the tissue array. Slides were deparaffinized in fresh xylenes and rehydrated through sequential graded ethanol steps. Antigen retrieval was performed by citrate buffer incubation (18 mmol/L citric acid, 8.2 mmol/L sodium citrate, pH 6.0) using a household vegetable steamer (Black and Decker) for 60 minutes. Slides were incubated for 5 minutes with 3% hydrogen peroxide, washed in TBS/T (20 mmol/L Tris, 140 mmol/L NaCl, 0.1% Tween-20, pH 7.6), and incubated in appropriate antibody dilutions for cdc2(p34) (Zymed, South San Francisco, CA, 1:100), topoisomerase II α [topoIIα] (Neomarkers, Fremont, CA, 1:3200), bone morphogenetic protein receptor 1A [BMPR1A/Activin A receptor/ALK3] (Santa Cruz Biotechnology, Inc., Santa Cruz, CA, 1:500), and geminin (1:500, a generous gift from Dr. Anindya Dutta, Brigham and Woman's Hospital, Boston, MA) for 60 minutes at room temperature. Normal saline was substituted for the primary antibody in control sections. The avidin-biotin-peroxidase complex method from DAKO (Glostrup, Denmark) was used and slides were subsequently counterstained with hematoxylin. Assessment of immunohistochemical labeling in the tissue microarrays was performed by two of the authors (D.E.H. and A.M.). For the three nuclear markers (cdc2, geminin, and topo IIα), scoring was performed as follows: negative, 25% nuclear labeling; for BMPR1A, labeling was scored as negative ( 25% cytoplasmic and membranous staining). Non-radioisotopic in situ hybridization was performed for the transcription factor islet-1 (ISL-1). Sense and antisense riboprobes were prepared from the corresponding sequence-verified I.M.A.G.E. clone (Invitrogen, Inc.), as previously described,20Iacobuzio-Donahue CA Ryu B Hruban RH Kern SE Exploring the host desmoplastic response to pancreatic carcinoma: gene expression of stromal and neoplastic cells at the site of primary invasion.Am J Pathol. 2002; 160: 91-99Abstract Full Text Full Text PDF PubMed Scopus (155) Google Scholar and labeled using Digoxin RNA Labeling mix (Boehringer Mannheim, Germany). Biliary cancer tissue microarrays were rehydrated through ethanol gradients (as described above), incubated for 10 minutes in 1% hydrogen peroxide (Super G Brand), washed in TBS, treated with proteinase K for 30 minutes at 37°C, and incubated overnight in 300 μl of diluted sense or antisense probe at 50°C. The next day, slides were washed in 2X SSC, treated with 250 μl Rnase A/T1 cocktail (Ambion, Austin, TX) for 30 minutes at 37°C, washed in 2X SSC with 50% formamide (American Bioanalytical, Natick, MA), washed in 0.8X SSC, blocked, incubated with rabbit HRP-anti-DIG (DAKO), and developed in the dark with biotinyl-tyramide (DAKO). Slides were then counterstained as previously described.20Iacobuzio-Donahue CA Ryu B Hruban RH Kern SE Exploring the host desmoplastic response to pancreatic carcinoma: gene expression of stromal and neoplastic cells at the site of primary invasion.Am J Pathol. 2002; 160: 91-99Abstract Full Text Full Text PDF PubMed Scopus (155) Google Scholar The specificity of hybridization was assessed by absence of signal in the sense riboprobe slide; the antisense riboprobe slide was then used for analysis of transcript expression pattern in neoplastic versus non-neoplastic biliary cells. The differential expression of two genes, homeobox B7 (HoxB7) and dickkopf 1 homolog (Dkk-1), was validated by reverse transcriptase PCR. Total RNA was prepared from five biliary cancer cell lines (GB-H3, SNU1079, SNU308, SNU245, and HuCCT1), and three normal biliary epithelial scrapings using the RNeasy Mini kit (Qiagen, Inc.). For cDNA preparation, a mix of 4 μg total RNA and Oligo (dT) primers (Invitrogen, Inc.) was incubated at 70°C for 10 minutes to denature RNA and briefly placed on ice. A final buffer mixture of 1X First Strand Buffer, 20 mmol/L DTT, and 2 mmol/L dNTP (Invitrogen) was added, samples were incubated at 42°C for 2 minutes, 200 units of Super Script II RNase H-reverse transcriptase (Invitrogen, Inc.) were added, and samples were again incubated at 42°C for 50 minutes. A final incubation at 70°C for 15 minutes was performed. Polymerase chain reaction was performed in a final mixture of 1X Platinum PCR Supermix (Invitrogen, Inc.), 400 nmol/L forward and reverse primer, and 2 μl cDNA. PCR reactions were performed in a Thermo Hybaid MBS 0.2S cycler as follows: 96°C for 5 minutes, 35 cycles of 94°C for 45 seconds, 55°C for 45 seconds, and 72°C for 3 minutes, followed by 72°C for 5 minutes, then 4°C. PCR products were visualized by 1% agarose gel electrophoresis, followed by eithidium bromide staining. The primer sequences were as follows: HoxB7 foward primer 5′-AGCCTCAAGTTCGGTTTTCG-3′, Hox B7 reverse primer 5′-GCGTCAGGTA-GCGATTGTAG-3′, Dkk-1 forward primer 5′-ACCAGACCATTGACAACTAC-3′, Dkk-1 reverse primer 5′-GTGTCTAGCACAACACAATC-3′. Standardization was performed using GAPDH; forward primer and reverse primers were used as previously described.4Iacobuzio-Donahue CA Maitra A Shen-Ong GL van Heek T Ashfaq R Meyer R Walter K Berg K Hollingsworth MA Cameron JL Yeo CJ Kern SE Goggins M Hruban RH Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.Am J Pathol. 2002; 160: 1239-1249Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar RNA samples extracted from neoplastic and non-neoplastic biliary tissues and cell lines were hybridized to the Affymetrix U133A expression microarray containing ∼22,000 unique transcripts, and normalized gene expression data were generated using the dCHIP software. Hierarchical clustering was performed using a subset of 2308 genes with the greatest variation between 25 samples. Based on gene expression profiling, two major clusters were identified, the first containing 9 of 9 cell lines and 4 of 11 resected tumors, and the second containing 5 of 5 normal biliary epithelial scrapings and 7 of 11 primary tumors (Figure 1A). As expected, all five normal biliary scrapings clustered with remarkable identity and separately from the nine biliary cancer cell lines on the dendrogram; the clustering of a subset of primary biliary cancers near the biliary epithelial samples was not unexpected given the presence of residual stroma and non-neoplastic epithelium in many of the resected tumor samples. Although there were examples of site-of-origin-specific clustering (for example, three gallbladder carcinoma lines on the same branch of the dendrogram), in general, we found intermingling on the dendrogram between cancers from different sites in the biliary tree (Figure 1A). Thus, while most cancer specimens, especially cell lines, clustered separately from normal epithelium, there was no unequivocal site-of-origin clustering. A pictorial representation of the principal component analysis of biliary cancers and normal biliary epithelium is illustrated in Figure 1B, with red representing relative overexpression and green representing relative underexpression (TREEVIEW software). To achieve a high level of stringency for identification of differentially up-regulated genes in biliary cancers, a two-pronged strategy was used. First, the dCHIP normalized hybridization data from biliary cancers and normal epithelium was compared using the dCHIP software itself, which yielded 512 Affymetrix fragments expressed at threefold or greater intensity in the cancers versus normal samples (P <0.1, t-test). Second, the dCHIP normalized hybrdidization data were exported into, and analyzed by SAM, which yielded 373 Affymetrix fragments expressed at threefold or greater intensity in the cancers, using a threshold median discovery rate (FDR) of <10 genes. The merging of the two datasets from dCHIP and SAM analysis yielded 347 overlapping Affymetrix fragments. After purging this set of 347 Affymetrix fragments for duplicate fragments from the same gene, unnamed hypothetical proteins, and ESTs, we identified 282 unique known genes up-regulated at least threefold or higher in primary resected cancers and biliary cancer cell lines versus normal epithelial scrapings. Table 1 lists a representative subset of 50 annotated genes identified in our analysis; the complete list of 282 up-regulated genes is available publicly on the Johns Hopkins gallbladder and bile duct cancer website (http://pathology2.jhu.edu/gbbd/microarray). We then performed a similar series of analyses comparing only the nine biliary cancer cell lines with the five biliary epithelial scrapings. This direct comparison of neoplastic versus non-neoplastic epithelium yielded 700 Affymetrix fragments by dCHIP analysis, 717 fragments by SAM analysis, and 638 fragments that were common to both analyses. The 638 fragments were then parsed for duplicates, unnamed hypothetical proteins, and ESTs, identifying 514 known genes that were overexpressed threefold or greater in the cell lines versus normal epithelium (data available publicly on our website). The larger number of differentially expressed genes when only cell lines were compared with normal epithelium possibly reflects either the "dilution" effect caused by non-neoplastic epithelial and stromal elements within primary cancers, or the effects of in vitro culture. In passing, it should be mentioned that we also identified 513 genes that were down-regulated threefold or greater in the cancers versus normals; however, since the objective of the current study was to identify novel tumor markers, the ensuing discussion will focus on up-regulated genes only.Table 1Representative Subset of Differentially Overexpressed Genes in Biliary CancersAffymetrix tag numberGene nameFold changep ValueChromosomeFunction205239_atAmphiregulin (schwanomma-derived growth factor)5.647930.0113874q13-q21Autocrine growth factor, mitogen204832_s_atBone morphogenetic protein receptor, type IA3.924100.00031410q22.3Integral membrane protein, TGF-β mediator209642_atBUB1 budding uninhibited by benzimidazoles 1 homolog (yeast)3.379730.0002752q14Spindle assembly checkpoint regulator216602_s_atCalreticulin3.084730.00031519p13.3-p13.2Protein folding, calcium storage206075_s_atCasein kinase 2, alpha 1 polypeptide3.416820.00007420p13Phosphorylation of proteins (eg, p53)203968_s_atCDC6 cell division cycle 6 homolog5.168980.00000417q21.3DNA replication checkpoint control203213_atCell division cycle 2, G1 to S and G2 to M3.510150.00013910q21.1Cell cycle regulator205394_atCHK1 checkpoint homolog (S. pombe)3.644720.00026111q24Cell cycle regulator202613_atCTP synthase4.204090.0000191p34.1Phospholipid and nucleic acid biosynthesis; multidrug resistance associated203418_atCyclin A25.026500.0000764q27Cell cycle regulator200953_s_atCyclin D23.611390.00511412p13Cell cycle regulator205034_atCyclin E24.013280.0001968q21.3Cell cycle regulator212816_s_atCystathione beta synthase7.391670.0000921q22.3
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