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Ann Thorac Surg 2004;77:1008-1015
© 2004 The Society of Thoracic Surgeons

Original article: general thoracic

Gene expression profiles in esophageal adenocarcinoma

Peter S. Dahlberg, MD, PhDa*, Lance F. Ferrin, MD, PhDb, Suzanne M. Grindle, BSd, Curtis M. Nelson, BSa, Chuong D. Hoang, MDa, Blake Jacobson, PhDc

a Department of Surgery–Division of Cardiovascular and Thoracic Surgery, University of Minnesota, Minneapolis, Minnesota USA
b Department of Medicine–Division of Gastroenterology, University of Minnesota, Minneapolis, Minnesota USA
c Department of Medicine–Division of Hematology and Oncology, University of Minnesota, Minneapolis, Minnesota USA
d University of Minnesota Cancer Center Bioinformatics Core Facility, University of Minnesota, Minneapolis, Minnesota, USA

* Address reprint requests to Dr Dahlberg, Department of Surgery, Division of Cardiovascular and Thoracic Surgery, MMC 207, 420 Delaware St SE, Minneapolis, MN 55455, USA e-mail: dahlb002@umn.edu.

Presented at the Poster Session of the Thirty-ninth Annual Meeting of The Society of Thoracic Surgeons, San Diego, CA, Jan 31–Feb 2, 2003.


   Abstract
Top
 Abstract
Introduction
 Material and methods
 Results
 Comment
 References
 
BACKGROUND: The incidence of esophageal adenocarcinoma (EAC) has risen dramatically in the last two decades. As with other malignancies, changes in gene expression play a key role in the development and progression of these tumors.

METHODS: Microarray analysis was used to study gene expression of 12,000 genes in EAC specimens. Adenocarcinoma tissue samples (n = 10) and controls of normal stomach (n = 6) and esophageal (n = 7) mucosa were collected fresh, then rapidly frozen in liquid nitrogen. The messenger ribonucleic acid (mRNA) from the samples was isolated, reverse transcribed, and used to generate biotin-labeled mRNA fragments, which were hybridized to Affymetrix U95 gene chips (AME Bioscience, Norway) for analysis. Additional samples analyzed included tissue containing dysplastic Barrett's epithelium from three patients, metastatic lymph nodes from two patients with EAC, one squamous carcinoma, and two esophageal cancer cell lines. Samples were segregated into groups with similar patterns of gene expression using clustering algorithms and gene sets that differentiated tumors from normal tissue were generated.

RESULTS: There were 150 genes that were fourfold up regulated and 183 genes that were fourfold down regulated in the esophageal adenocarcinoma specimens, as compared to normal esophageal mucosa tissue controls. Using paired specimens (n = 5) and the paired t-test (p Value of 0.05) as a filter, only 64 genes were fourfold up regulated and 110 were fourfold down regulated. These groups included cytoskeletal, cell adhesion, tumor suppressor, and signal transduction genes.

Hierarchical clustering segregated the samples into the expected divisions. The esophageal cancer cell lines, OE19 and OE33, clustered separately from the EAC specimens. Extremely high gene expression levels of the ERBB2 gene, seen in the microarray analysis of the 2 cell lines, correlated with amplification of the gene determined by Southern blotting.

CONCLUSIONS: Gene expression patterns from a small subset of genes distinguish EAC specimens from normal controls. This technique can rapidly identify genes for targeted chemotherapeutic approaches to cancer treatment.


   Introduction
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 Abstract
 Introduction
Material and methods
 Results
 Comment
 References
 
Esophageal cancer is the ninth most common malignancy in the world. Since 1970, the incidence of esophageal adenocarcinoma (EAC) has risen fourfold, the most rapid increase of any type of cancer. Esophageal adenocarcinoma now accounts for >50% of all cases of esophageal cancer in the United States (US), causing about 10,000 deaths per year [1]. As with other malignancies, changes in gene expression play a key role in the development and progression of these tumors. Data from most studies of gene expression in EAC patients are derived from a focused, but limited, gene-by-gene analysis. These experiments have identified several genetic changes that are associated with the progression from normal squamous epithelium to invasive EAC.

A broader analysis of gene expression in EAC patients has several advantages. First, expressed genes with unknown roles in carcinogenesis may be identified. Second, relationships among multiple genes in a single cellular pathway may be characterized. Third, complex but highly specific patterns of gene expression may characterize a stage in the progression of EAC or may confer a unique prognosis or response to therapy. Fourth, depending on the results of expression analysis, tumor-specific chemotherapy or radiotherapy may be rationally designed and tested. The purpose of these experiments was to determine: (1) whether EAC tumors can be distinguished from benign esophageal and gastric tissue by hierarchical clustering analysis of microarray data, and (2) to determine a gene subset that specifies the clustering patterns.


   Material and methods
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 Abstract
 Introduction
 Material and methods
Results
 Comment
 References
 
Cell lines
OE19 [2] is an EAC cell line obtained from a moderately differentiated, stage III, esophagogastric junction tumor. OE33 [3] is a poorly differentiated, Barrett's-associated EAC cell line generated from a stage II lower esophageal tumor. Both cell lines were grown to confluence in 5% CO2 at 37°C in RPMI 1640 supplemented with 2 mM glutamine and 10% fetal bovine serum.

Tumor and normal tissues
The institutional review board at the University of Minnesota approved the protocols by which tissue was obtained from endoscopic procedures or surgical resections. Samples weighing approximately 100 mg were dissected from esophageal tumors, normal gastric or esophageal mucosa, and areas of Barrett's metaplasia. They were flash frozen in liquid nitrogen and stored at -80°C. Tissue samples from adjacent areas of tumor were prepared and examined by a pathologist to confirm histologic type and a predominance of tumor cells (>50%).

RNA isolation
Sample total ribonucleic acid (RNA) was prepared by a Tri Reagent (Sigma, St. Louis, MO) technique. Frozen tissue was ground to a cornmeal texture using mortar and pestle cooled with liquid nitrogen, and then it was homogenized in 3 mL of Tri Reagent. The RNA was isolated by phenol-chloroform extraction followed by isopropanol precipitation. It was washed in 75% ethanol and resuspended in RNAse-free water. The sample was further purified by a cleanup procedure utilizing RNeasy (Qiagen, The Netherlands) kits and following the manufacturer's directions. The RNA concentration was measured by spectrophotometry, and its integrity was assessed by formaldehyde-agarose gel electrophoresis.

Microarrays
Double-stranded cDNA was synthesized from the purified RNA using the SuperScript Choice kit (Invitrogen, San Diego, CA), a polydeoxythymidine primer, and instructions provided by Affymetrix (AME Bioscience, Norway). Amounts of total cellular RNA, ranging from 5 to 40 µg, were used for the synthesis. The DNA was extracted in phenol-chloroform and precipitated in ethanol. Finally, biotin-labeled cRNA was generated from the cDNA using the Enzo BioArray High Yield RNA Transcript Labeling Kit (Enzo Diagnostics, Farmingdale, NY). The RNA was purified using an Rneasy mini column (Qiagen), concentrated by ethanol precipitation, and fragmented. Samples were hybridized to Affymetrix U95 gene chips using the manufacturer's protocols. About 12,000 transcripts are analyzed in each experiment, including most all of the genes that have been described as being important in cancer.

Genomic analysis
The DNA copy number of selected genes in EAC cell lines and tumors were determined by Southern blotting of genomic DNA with probes specific for ERBB2.

Data analysis
Microarray analysis was performed on GeneData Expressionist Suite v4.0 (GeneData, Basil, Switzerland). Normalization of all chips was based on a reference experiment, which was computed as a featurewise average experiment from all of the chips included in the study. Unsupervised, agglomerative two-dimensional hierarchical clustering was performed using the entire gene data set, and it confirmed the pathological experiment groups. Data were refined by application of statistical analysis and filtering. Analysis of variance (ANOVA) and Kruskal Wallis tests identified a subset of genes differentially expressed between tumor and benign-normal samples. In a separate but complementary method, the paired t-test (p < 0.05), combined with a gene ranking, was used to identify the subset of genes with significant differential expression between the tumors and matched normal samples. Changing gene ontologies and pathways were identified by applying statistically significant changes in gene expression (p = 0.05) to GenMAPP [4] pathways.


   Results
Top
 Abstract
 Introduction
 Material and methods
 Results
Comment
 References
 
There were 150 genes that were fourfold up regulated and 183 genes that were fourfold down regulated in the esophageal adenocarcinoma specimens (n = 10), as compared to normal esophageal mucosa tissue control (n = 7). Using paired specimens (n = 5) and the paired t test (p value of 0.05) as a filter, only 64 genes were fourfold up regulated and 110 were fourfold down regulated. In the EAC specimens, as compared to normal stomach tissue, 171 genes were fourfold up regulated and 267 were fourfold down regulated. Using the paired t test as a filter (p value 0.05) for the paired samples (n = 3), 68 genes were fourfold up regulated, and 113 genes were fourfold down regulated. Unsupervised hierarchical clustering separated samples, for the most part into the expected divisions (Fig 1). One EAC specimen collected from a patient that had a near complete response to induction therapy clustered with the normal esophageal mucosa samples. A second EAC specimen clustered with the two high-grade dysplasia samples.



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  		Fig 1. Unsupervised hierarchical clustering of samples. The cluster analysis was performed as a visualization tool to show that samples will cluster on the basis of their gene expression profiles into their expected pathological groups. The dendogram in the figure was generated using a set of 200 genes chosen by their ranking as defined by K group analysis. The K group analysis uses ANOVA and Kruskall Wallis tests to identify genes that are differentially expressed among samples. (ANOVA = analysis of variance; EAC = esophageal adenocarcinoma; SCCA = esophageal squamous cell carcinoma.) (Yellow box = esophageal adenocarcinoma; purple box = metastatic esophageal adenocarcinoma; light green box = esophageal adenocarcinoma cell line; dark green box = normal esophagus; maroon box = high-grade dysplasia; blue box = normal stomach; pink box = esophageal muscle and inflamed mucosa.)

 
Paired analysis of matched samples (n = 5 EAC vs normal esophagus) was done for all 12,000 gene fragments. Using a p value of 0.05, 1891 genes were identified that were differentially expressed between tumor samples and their matched normal esophageal mucosa control samples. The same analysis (n = 3 EAC vs normal gastric mucosa), applied to tumor and matched normal gastric mucosa, identified 1861 differentially expressed genes. A Venn diagram containing the two gene sets was constructed and gene lists of the union (3,361 genes) and intersection (369 genes) were generated (Fig 2). The genes in these two sets were ranked for their ability to discriminate sample groups by the support vector machine algorithm. Using the intersection analysis set of 369 genes, 64 genes were identified by gene ranking and with a discrimination error rate of less than 14%. The identity and biologic function of the genes are listed in Table 1. The two-dimensional cluster analysis is shown in Fig 3.



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  		Fig 2. Paired analysis of gene expression in adenocarcinomas versus normal esophageal and normal gastric mucosal controls. At the p = 0.05 level, 1,891 genes showed differential levels of expression between esophageal adenocarcinoma samples and normal esophageal mucosa controls; 1,861 genes were differentially expressed between esophageal adenocarcinomas and normal gastric mucosa controls. The overlap of these two sets contained 369 genes. (EAC = esophageal adenocarcinoma; ESO = normal esophagus; STO = normal stomach.)

 

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  		Table 1. Paired Analysis of Gene Expression in Adenocarcinomas Versus Normal Esophageal and Normal Gastric Mucosal Controlsa

 


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  		Fig 3. Two-dimensional hierarchical cluster analysis. All tumors with matched normal tissue controls were used. Figure 2 describes how the subset of 369 genes used for the analysis was chosen. Gene ranking analysis was used to find the smallest group of genes that differentiates the experimental groups with the lowest error rate. With a 14% error rate, 64 genes were found to discriminate between tumor and normal tissue controls; this final set is displayed in the figure. (EAC = esophageal adenocarcinoma; ESO = normal esophagus; STO = normal stomach.)

 
In order to validate some of the results of the microarray analysis, we looked for genes that were highly overexpressed. One of the most dramatic examples was the expression level of ERBB2 in the two EAC cell lines, OE19 and OE33, and two of the tumors (Table 2). Gene amplification is one mechanism that can cause genes to be highly expressed. Therefore, we determined DNA copy number of the ERBB2 gene in the two cell lines by Southern blotting. OE19 had 100 copies of the ERBB2 gene and OE33 had 14. We also determined ERBB2 DNA copy number in a panel of 25 EAC specimens—not all had corresponding microarray data. Three tumors (12%) were found to contain amplified ERBB2 sequences; DNA copy numbers in those tumors were 40, 10, and 13.


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  		Table 2. Expression levels of ERBB2/Normalized Expression Levels of ERBB2 in Selected Samplesa

 
We mapped gene expression changes onto various biological pathways using GenMapp (Gladstone Institute, San Francisco, CA). We looked at gene expression data from several of the pathways thought to be involved in EAC carcinogenesis. Figure 4 shows the results of this analysis for the apoptosis pathway. Of the genes listed in the diagram, tumors significantly underexpressed JNNK, APAF-1, and the nuclear transcription factor c-Jun. IAP1 and FADD were overexpressed in tumors compared to normal tissue controls.



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  		Fig 4. Induction of apoptosis pathway. GenMAPP is a computer application designed to visualize gene expression data on maps representing biological pathways. MappFinder is an accessory program to GenMAPP that analyzes pathways for statistically significant changes in gene expression using the percentages of genes changing, and then ranking the pathways from most to least changed. The induction of apoptosis pathway is shown as a representative example. The direction of gene expression changes in tumor samples would be expected to lead to less induction of apoptosis occurring in these cells. Genes underexpressed in esophageal adenocarcinoma specimens versus tissue from normal esophageal and gastric mucosa are highlighted in red; those overexpressed in tumors are highlighted in blue. (APAF-1 = apoptotic protease activating factor; BAX = BCL2-associated X protein; BCL2 = B-cell chronic lymphocytic leukemia/lymphoma 2; BCL-X = BCL2-like 1; BID = BH3 interacting domain death agonist; EAC = esophageal adenocarcinoma; FADD = FAS [TNFRSF6]-associated via death domain; FAS = tumor necrosis factor receptor superfamily, member 6 [TNFRSF6]; IKK = conserved helix-loop-helix ubiquitous kinase; JNKK = mitogen-activated protein kinase 4; MCL-1 = myeloid cell leukemia sequence 1; MDM-2 = transformed 3T3 cell double minute 2, p53 binding protein [mouse]; MEKK1 = mitogen-activated protein kinase 1; NF-kB = nuclear factor kB; NIK = mitogen-activated protein kinase 14; PARP = adenosine diphosthate-ribosyltransferase [NAD+; poly {ADP-ribose}] polymerase; RIP = human immunodeficiency virus-1 Rev binding protein; TNF = tumor necrosis factor; TNFR1 = tumor necrosis factor receptor 1; TNFR2 = tumor necrosis factor receptor 2; TNFRSF = tumor necrosis factor receptor superfamily; TRADD = TNFRSF1A-associated via death domain; TRAF1 = TNF receptor-associated factor 1; TRAF2 = TNF receptor-associated factor 2; TRAIL = tumor necrosis factor [ligand] superfamily, member 10 [TNFSF10].)

 

   Comment
Top
 Abstract
 Introduction
 Material and methods
 Results
 Comment
References
 
Changes in expression levels of many genes have been correlated to steps in the transition from normal esophageal squamous mucosa to invasive EAC. For example, levels of mRNA of epidermal growth factor (EGF) and its receptor (EGFR) are increased in nearly 70% of Barrett’s esophagus and EAC samples [5]. Loss of tumor suppressor gene (TSG) product contributes to malignant transformation. The TSG most commonly mutated in human cancer patients is p53, which is located at 17p, a common area of loss in EAC. A second example of a TSG important in EAC is the p16 (CDKN2A) gene. It codes for a cyclin-dependent kinase inhibitor of CDK4 and CDK6, both of which phosphorylate the retinoblastoma (RB) protein. Because unphosphorylated RB prevents the cell from entering the S phase of the cell cycle, loss of p16 promotes cell division. There are examples of abnormal TSGs that are inactivated by aberrant methylation of CpG islands located in their promoters. P16 is hypermethylated in tumors from patients with esophageal, as well as breast, lung, and colon cancer [6]. The loss of E-cadhedrin, a cell-surface adhesion molecule, is associated with a metastatic phenotype in EAC patients. Normally, its promoter is completely unmethylated. In contrast, up to 85% of tumors show increased E-cadhedrin CpG island promoter methylation [7].

Dysregulation of programmed cell death (apoptosis) has emerged as an important mechanism in carcinogenesis. Internal signals triggering the cell death pathway are mediated by Bcl-2, which, under normal circumstances, binds to Apaf-1. Intracellular damage results in release of Apaf-1 by Bcl-2. Apaf-1 then activates caspase 9, which triggers apoptosis. Bcl-2 behaves as a proto-oncogene with increased expression presumably resulting in a lower probability of a cell undergoing apoptosis. Bcl-2 expression increases in response to gastroesophageal reflux disease (GERD) and BE. However, as BE becomes more dysplastic, Bcl-2 expression decreases and is rarely seen at high levels in EAC patients [8, 9]. Therefore, it is likely that Bcl-2 acts at an early stage in EAC development.

Mapping of the genetic changes observed in our samples onto the apoptosis pathway demonstrates several interesting features. Apaf-1 was down regulated in tumors versus the normal control tissue. Less Apaf-1 protein in the cancer cells would lead to those cells being less likely to undergo apoptosis because of less procaspase 9 activation. Down regulation of the c-Jun nuclear transcription factor, which increases transcription of apoptotic genes, in cancer cells would have the same type of antiapoptotic effect.

Telomerase is a ribonucleoprotein responsible for synthesizing DNA at the ends of the chromosomes. In normal cells, telomerase is not expressed, and teleomers shorten with cell division. In contrast, telomerase expression increases during EAC development. In fact, many specimens containing high-grade dysplasia or EAC overexpress telomerase [10]. However, telomerase gene expression was not detected in any of the tumor or normal tissue samples that we examined.

Reports from gene profiling studies have begun to add to the list of important genes that are altered in cancers. Gene profiling has also been successfully used to differentiate a number of cancers from their normal tissue counterparts. Hierarchical clustering of a broad variety of adenocarcinomas identified 61 genes whose expression levels predicted the site of origin of the tumor [11]. In breast cancer a gene expression signature based on 70 genes was highly predictive of recurrence and survival in both node-negative and node-positive patients [12]. In gastric cancer of the intestinal subtype, 124 genes were commonly up or down regulated, and profiles of 12 of these were associated with lymph node metastasis [13]. Several genes that are up regulated in squamous cell cancers of the esophagus have been identified and include matrix metalloproteinases, cadherins, and growth factors [14]. Adenocarcinomas of the esophagus have been profiled [15], and profiling has also been used to distinguish between Barrett's esophagus and esophageal adenocarcinoma [16, 17].

Our data confirm the feasibility of using microarray analysis to distinguish adenocarcinomas from normal stomach and normal esophageal mucosa. Although the number of high-grade dysplasia samples was small, these also clustered separately from the cancers (with one exception) in our analysis. Smaller gene sets were identified that reliably distinguishes the groups of malignant samples from normal tissues. The EAC specific genes, as well as groups expressed in EAC and normal tissues, were easily identified by visualization of the sets. Many of the individual genes had functions in DNA repair, apoptosis, cell cycle control, and metabolism. The list is intriguing but targeted experiments on the primary tumors will be necessary before conclusions can be made about their role in tumor promotion, metastasis, and other steps in oncogenesis. A change in the expression of many, if not most, of these genes probably reflects the increased cell turnover in tumor samples compared to normal controls.

The contribution of nontumor cells to changes in overall sample gene expression is not addressed by these experiments. Laser microdissection techniques have the sensitivity to resolve different cell populations; however, stromal cell gene expression is probably important in tumor growth, and clinical applications of microarray technology are simplified by their inclusion. Our study also lacked clinical outcomes data so we could not correlate specific gene signatures with prognosis. This approach has been successful in analyzing and predicting outcomes from other tumors, and will likely provide prognostic information about EAC that is better than what is available from current staging procedures. Microarray analysis is also likely to provide useful information about the risk of premalignant lesions containing foci of cancer or progressing to invasive cancers. Modifications in the screening protocol or even esophagectomy could be recommended on the basis of microarray analysis of biopsy specimens. Chemotherapeutic approaches could also be tailored to the specific genetic changes apparent in the microarray analysis.


   References
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 Abstract
 Introduction
 Material and methods
 Results
 Comment
 References
 

  1. Chen X., Yand C.S. Esophageal adenocarcinoma: a review and perspectives on the mechanisms of carcinogenesis and chemoprevention. Carcinogenesis 2001;22:1119-1129.[Abstract/Free Full Text]
  2. http://www.biotech.ist.unige.it/cldb/cl3761.html
  3. http://www.biotech.ist.unige.it/cldb/cl3762.html
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  5. Wijnhoven B.P.L., Tilanus H.W., Dinjens W.N.M. Molecular biology of Barrett's adenocarcinoma. Ann Surg 2001;233:322-337.[Medline]
  6. Eads C.A., Danenberg K.P., Kawahami K., et al. MethylLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 2000;28:e32.
  7. Corn P.G., Heath E.l, Heitmiller R., et al. Frequent hypermethylation of the 5' CpG island of E-cadhedrin in esophageal adenocarcinoma. Clin Cancer Res 2001;7:2765-2769.[Abstract/Free Full Text]
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  9. Rioux-Leclercq N., Turlin B., Sutherland F., et al. Analysis of Ki-67, p53, and Bcl-2 expression in the dysplasia-carcinoma sequence of Barrett's esophagus. Oncol Rep 1999;6:877-882.[Medline]
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