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Originally published as JCO Early Release 10.1200/JCO.2005.03.3688 on December 12 2005 © 2006 American Society of Clinical Oncology. Gene Expression Profiling of Localized Esophageal Carcinomas: Association With Pathologic Response to Preoperative ChemoradiationFrom the Departments of Hematopathology, Pathology, Experimental Therapeutics, Biostatistics and Applied Math, GI Medicine & Nutrition, Thoracic & Cardiovascular Surgery, and GI Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX Address reprint requests to Rajyalakshmi Luthra, PhD, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, 8515 Fannin St, NAO1.091, Houston, TX 77054; e-mail: rluthra{at}mdanderson.org
PURPOSE: Patients with localized esophageal carcinoma have a 5-year survival rate of less than 20%. Patients are often treated similarly (ie, with preoperative chemoradiotherapy) but the outcomes vary greatly. Chemoradiotherapy and surgery can result in significant undesirable consequences. Currently, however, there are no tools to help select optimum therapy. We hypothesized that gene expression profiling could provide clues and biomarkers for selection of therapy. METHODS: Pretreatment endoscopic cancer biopsies from 19 patients (16 with adenocarcinoma, two with squamous cell carcinoma, and one with adenosquamous carcinoma) enrolled onto a preoperative chemoradiotherapy protocol were profiled using oligonucleotide microarrays. Surgical specimens following therapy were assessed for the degree of pathologic response. On the basis of array data, selected genes were analyzed by polymerase chain reaction. RESULTS: Unsupervised hierarchical cluster analysis segregated the cancers into two molecular subtypes, each consisting 10 and nine specimens, respectively. Most cancers (five of six) that had pathologic complete response (pathCR) clustered in molecular subtype I. Subtype II, with one exception, consisted cancers that had less than pathCR (< pathCR). Using a combination marker approach, levels of PERP, S100A2, and SPRR3 allowed discrimination of pathCR from < pathCR with high sensitivity and specificity (85%). Pathway analysis identified apoptotic pathway as one of the key functions downregulated in molecular type II in comparison with type I. CONCLUSION: These encouraging, albeit preliminary, data suggest that expression profiling may distinguish cancers with different pathologic outcome. This is the first report to show subtypes of esophageal cancers with distinct molecular signatures. The potential of PERP, S100A2, and SPRR3 as biomarkers of pathCR warrants further validation.
Esophageal cancer (ECA) is the ninth most common malignancy in the world and is estimated to be responsible for approximately 13,000 deaths and 14,000 new diagnoses in the United States in 2004.1,2 Even when localized, the 5-year survival rate of less than 20% has not changed significantly in several decades.2,3 The incidence of adenocarcinomas (ACAs) of the esophagus has risen faster than any other malignancy, especially in white males with an estimated increase in incidence by more than 70% in 20 years, thus making ACA the most common histologic type in the West.4,5 Progression of Barrett's metaplasia appears to be one of the major contributors to the observed increase in incidence of ACA.6-8 The most common approach to treating patients with localized carcinoma of the esophagus, irrespective of the histologic type, is preoperative chemoradiotherapy. This approach provides hypothetical advantages including, higher rate of curative surgery, reduced local relapse, and early therapy of micrometastases. Because of empiric nature, current approaches lead to considerable uncertainty in patient outcome and result in administration of toxic therapies. Pretreatment clinical parameters such as TNM classification, primary location, sex and histologic type are unable to predict differences in the biologic behavior of these cancers in patients receiving preoperative chemoradiotherapy.9 One can, however, predict outcome after surgery by reviewing the American Joint Committee on Cancer (AJCC) stage. The most favorable survival is noted in patients who do not have any residual cancer in the resected specimen (pathologic complete response [pathCR]).10-12 The fraction of pathCR patients is approximately 25%. However, biomarkers are not available to identify patients who respond to chemoradiotherapy and thus may be spared from potentially harmful interventions, and patients who benefit from more aggressive treatments. Many expression profiling studies have been conducted over the last few years to understand the biology of ECA and to identify bio-markers that can be targeted.13-23 However, these studies lacked treatment and pathologic outcome data to correlate with specific transcriptional signatures. Identification of molecular signatures that predict outcome would be of value in individualizing management of these patients. With this ultimate goal, we profiled pretreatment endoscopic cancer biopsies from patients with ECA using Affymetrix U133A Chip (Santa Clara, CA) and correlated their molecular profiles with pathologic response. The expression levels of a few genes selected on the basis of array data were assessed by polymerase chain reaction (PCR) as biomarkers of pathologic response. In addition, we used Ingenuity Pathways Analysis Software (Ingenuity Systems, Mountain View, CA) to identify, from the microarray data, key biologic pathways, and functions associated with chemoradiotherapy resistance.
Patient Selection and Evaluation All patients in this report participated in a clinical trial approved by The University of Texas M.D. Anderson Cancer Center (Houston, TX) institutional review board. Patients with localized histologically confirmed squamous cell carcinoma (SCCA) or ACA of the thoracic esophagus were considered eligible. Patients were evaluated by chest radiograph, computed tomography of the chest and abdomen, upper GI double-contrast barium radiographs, an esophagogastroduodenoscopy with endoscopic ultrasonography (EUS), ECG, SMA-12, electrolytes, CBC including platelet count, and serum baseline carcinoembryonic antigen (CEA) level. Positron emission tomography (PET) was performed when available. Patients with T2-3 with any N, patients with M1a cancer (celiac nodes associated with a gastroesophageal junction carcinoma), and patients with T1N1 carcinoma were considered eligible. All patients were evaluated before registration by a multidisciplinary team that included thoracic oncology surgeons, radiation oncologists, gastroenterologists, and medical oncologists. Eligible patients had to have cancer that was considered technically resectable and medically operable on the basis of the clinical staging and evaluation. All patients signed a written informed consent, which was approved by the institutional review board. Patients with T4 cancer and patients with T1N0 lesions were excluded. Patients with any evidence of metastatic cancer were also not enrolled. Patients with uncontrolled medical conditions (such as diabetes, hypertension, heart condition classified as New York Heart Association class III or IV, or psychiatric illness) were not eligible. Patients who could not comprehend the purpose of this clinical trial or comply with its requirements were not enrolled.
Treatment
Step 1: Induction Chemotherapy
Step 2: Preoperative Chemoradiotherapy
Step 3: Surgery
Tissue Collection
Histologic Evaluation Postchemoradiation resected surgical specimens with no residual cancer were classified as achieving pathCR, whereas others with the presence of any cancer cell in the specimen were classified as less than pathCR (< pathCR).
Synthesis of Biotin-Labeled cRNA and Hybridization
Oligonucleotide Microarray Analysis
Microarray Suite (MAS) 5.0 software and custom tools developed by the M.D. Anderson Cancer Center Bioinformatics Department were used to analyze the data. Briefly, the microarray data were processed using the positional dependent nearest neighbor model to normalize and to extract gene expression values.24 Then, a hierarchical clustering algorithm was used to cluster genes and samples.25 The absent genes and the invariant genes were filtered out before clustering. The genes with below-median expression value were regarded as absent genes. The invariant genes were selected according to the standard deviation of expression values across all samples ( Differentially expressed genes were identified using standard t test. The false discovery rate of the list of the differentially expressed genes was estimated using the beta-uniform mixture (BUM) distribution model.26
Ingenuity Pathways Analysis The average log2 expression values were used to calculate the fold change (log2 FC) between cancer subtypes I and II. The data set containing gene identifiers and their corresponding expression values (log2 FC values) were then uploaded into the INGP as a tab-delimited text file to perform the analysis. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. A fold-change cutoff of 2 was set to identify genes whose expressions were differentially regulated. These genes, called focus genes, were then used as the starting point for generating biologic networks. To start building networks, the application queries the Ingenuity Pathways Knowledge Base for interactions between focus genes and all other gene objects stored in the knowledge base, and generates a set of networks with a network size of 20 genes/proteins. INGP Analysis then computes a score for each network according to the fit of the user's set of significant genes. The score is derived from a P value and indicates the likelihood of the focus genes in a network being found together as a result of random chance. A score of 2 indicates that there is a 1-in-100 chance that the focus genes are together in a network as a result of random chance. Therefore, scores of 2 or higher have at least a 99% confidence of not being generated by random chance alone. Biologic functions are then calculated and assigned to each network. Biologic functions were assigned to each gene network by using the findings that have been extracted from the scientific literature and stored in the Ingenuity Pathways Knowledge Base. The biologic functions assigned to each network are ranked according to the significance of that biologic function to the network. Fisher's exact test is used to calculate a P value determining the probability that the biologic function assigned to that network is explained by chance alone.
Real-Time Quantitative PCR
Comparative CT Method for Relative Quantification of Gene Expression
Discrimination Analysis
Patient characteristics are described in Table 1. PathCR was observed in 32% of cancers (six of 19). Unsupervised hierarchical cluster analysis segregated the cancers into two major categories, each consisting of 10 and nine cancers respectively (Fig 1). Approximately 400 genes were differentially expressed between the two subtypes, with an estimated false-discovery rate of 5%. The molecular subtype I comprised seven ACAs and two SCCAs and one ASCCA, whereas subtype II contained only ACAs. Thus, ACAs segregated into two categories. It is worth noting that the segregation of ACAs into two subtypes remained same when two of the SCCAs were excluded from the clustering analysis (data not shown). Five of the cancers with pathCR (four of five ACAs and one of one SCCA) clustered together in type I. Subtype II, with one exception, consisted of cancers with < pathCR. The clustering pattern was robust against the gene filtering process and clustering algorithm used in the study. For instance, the partitioning of the subtypes remained unchanged when complete linkage algorithm was used instead of average linkage algorithm. Additionally, the partitioning of the two main sub-branches (ie, the two subtypes) and the partitioning of the pathCR samples in to the two sub-branches remained the same when the number of variant genes included in the cluster analysis changed from 50 to 800 by altering the 3 x  boundary.
The median time to locoregional and metastatic progression has not yet been reached by either of the molecular subtypes. Nevertheless, the molecular subtype II appears to portend shorter disease-free survival (DFS) time, with a mean time to DFS of 22.42 months (95% CI, 15 to 29) compared to 28.55 months (95% CI, 21 to 36) for the molecular subtype I. At 14 months 54% of subtype II was free of disease compared with 75% of subtype I. Similarly, the median time of overall survival (OS) has not yet been reached by either of the molecular subtypes. Again molecular subtype II portends a worse OS, with a median OS time of 23 months (95% CI, 16 to 30) compared with 27.3 months (95% CI, 20 to 35) for subtype I. At 14 months, 57.4% of subtype II survived compared with 77.7% of subtype 1.
Greater than two-fold differences in the expression levels were observed in 80 genes using the t test (P < .0001). Genes associated with apoptosis, calcium homeostasis, stress response, and proliferation were downregulated in molecular subtype II in comparison with subtype I (Table 2). They include genes encoding annexin 1, chromosome 1 open reading frame 10 (C1orf10), cystatin A and B (stefin A and B), S100 calcium binding proteins, (S100A2, S100A7-9 and S100A14), small proline-rich proteins (SPRR1A, SPRR1B, SPRR2A, SPRR2C, SPRR3), heat shock protein 27 (Hsp27), TACSTD2, and transglutaminase 3 (TGM3). Several of these proteins are Ca2+-binding or -regulating proteins and are components of the cornified cell envelope, which is a specialized structure that forms in terminally differentiated epithelial cells and provides a barrier against mechanical and chemical stress. For instance, TGM3, a Ca2+-dependent enzyme that catalyzes covalent cross-linking reactions between proteins or peptides by
The top four functions identified by IGNP to be differentially regulated between the two molecular subtypes of ECA were embryonic development, tissue development, cell-to-cell signaling and interactions, and cell death. The network profile shown in Figure 2 generated by INGP highlights the inter-relationship between various genes and the apoptotic pathway downregulated in subtype II.
The relative expression levels of genes PERP, S100A2, and SPRR3 evaluated by real-time qPCR are shown in Figure 3. Because of insufficient quantities of RNA, specimens 24 and 20 were not included in the real-time PCR analysis. PERP (TP53 effector related to peripheral myelin protein 22 [PMP22]) is a novel type of effector involved in p53-dependent apoptosis.28 This protein is a member of expanding family of tetraspan membrane proteins, including PMP22 and the epithelial membrane proteins 1, 2 and 3 (EMP1-3).29 Overexpression of EMP proteins has been shown to induce cell death through a mechanism that involves association with the P2X7 cation channel and the consequent induction of membrane blebbing. Because of significant sequence homology to both PMP22 and EMPs, it is postulated that PERP, too, can induce membrane blebbing that contributes to activation of the apoptotic pathway. The S100A2 gene encoding a calcium binding protein is considered as candidate tumor suppressor gene because of its underexpression in several cancers, including esophageal SCCA, in comparison with healthy epithelia.30-32 In addition, S100A2 recently has been shown to be a novel downstream mediator of  Np63.33 SPRR3, a member of small proline-rich proteins, is a component of the cell envelope and is expressed in stratified squamous epithelia during differentiation. This gene has been identified as a marker of esophageal cancer progression.34-37
The relative expression values of all the three genes were lower in tumors belonging to subtype II in comparison with tumors in type I (Fig 3), confirming our microarray data. For example, the expression values of PERP were below 75 (range, 1.4 to 75) in subtype II; they were higher than 100 (range, 100 to 394) with one exception in cancers belonging to subtype I. Levels of S100A2 ranged between 0.3 and 38 and were below 10 in subtype II cancers except for cancer 11, and ranged between 5 and 50,000 with values above 10 in subtype I except for cancers 6 and 16. The expression of SPRR3, though overall lower in type II tumors, varied similarly among tumors, ranging from 0.01 to 6 in subtype II and from 0.13 to 23,522 in subtype I. Thus, no single marker was able to segregate the two molecular subtypes without an overlap. We used a statistical method (LDA) to see if the combination of genes examined by PCR has the potential to separate subtype I and subtype II into two distinct groups. Using SPRR3 and S100A2, the separation is statistically significant (P = .014; Hotelling T squared for differences in means between subtype I and subtype II). The P value was .0006 when sample 6, which appears to be an outlier, is omitted from the analysis. Thus, combining S100A2 and SPPR3 produces a classifier that separated subtype I and subtype II samples with only one outlier (sample 6). We noticed that expression values of the three marker genes were substantially higher in cancers that achieved pathCR compared with cancer with < pathCR. When we used an arbitrary cutoff value of 100 for relative expression, seven of 17 cancers showed expression values > 100 in at least two of the three markers. These seven included five cancers that achieved pathCR (cancers 1, 3, 16, 56, and 23). Thus, only two of 17 cancers, 2 and 19, with < pathCR showed expression values > 100 in at least two of the three markers. The specificity (true negatives/true negatives plus false positives) and the sensitivity (true positives/true positives plus false negatives) of the combination marker approach for identifying pathCR were 85% (11 of 13) and 86% (six of seven), respectively.
The clinical course of patients with ECA is heterogeneous. Thus, patients with the same disease stage have variable outcomes from uniform therapy. Patients with chemoradiotherapy-resistant cancer have a high likelihood of developing metastases.38 Currently, an empiric approach is utilized for patients with locoregional esophageal cancer, because one is not able to predict the degree of chemoradiotherapy resistance before surgery. The early identification of nonresponders would allow physicians to discontinue ineffective treatment regimens and institute alternative treatments, thereby avoiding both overtreatment and undertreatment of patients. Therefore, the need for markers that predict response early during the course of therapy is widely acknowledged. In an attempt to identify a panel of biomarkers that allow us to predict response to chemoradiotherapy, we profiled pretreatment cancer biopsies from 19 patients enrolled in a clinical protocol. Six of these patients (32%) had a pathCR. Unsupervised cluster analysis separated the cancers into two categories. Interestingly, five (83%) of the six cancers that achieved pathCR clustered in one molecular subtype (type I). Only one cancer with pathCR fell in subtype II. There was no clear segregation, however, of pathCR from < pathCR in subtype I, because 30% (five of 13) of cancers with < pathCR also clustered in this subtype. Nevertheless, our PCR data point out that expression analysis of a limited set of biomarkers selected from the list of genes that were regulated differentially between the two subtypes increases the predictive power. Thus, simply using three markers, PERP, S100A2, and SPRR3, and choosing an arbitrary expression cutoff value of 100, we were able to assign cancers to pathCR and < pathCR categories in 15 of 17 cancers tested by PCR. Median time to locoregional and metastatic progression was not reached by either of the molecular subtypes. Similarly, the median time of OS was not yet reached by either of the molecular subtypes. However, the molecular subtype II appears to portend shorter DFS time, and a worse OS compared with subtype I. Many of the genes with differential expression between the two types of ECAs have previously been reported to show altered expression in esophageal cancers by other investigators, confirming that they were cancer related.19,21,30,36,39-43 It is interesting to note that Luo et al,21 using high-density cDNA microarray platform, also observed that several genes including annexin 1, SPRRS, S100A8 and A9, TGM3, CK4, CK13, and CK15, were downregulated in SCCA in comparison with healthy squamous epithelium.
Collective down regulation of several members of apoptotic pathway such Bcl-2/EIB 19 kDa interacting protein 3 (BNIP3), PERP, epithelial membrane protein (EMP1), p63, stratifin (SFN)/14-3-3 To our knowledge, this is the first report showing two types of esophageal ACA with distinct molecular signatures. It is clear from published studies that the genes expressed differentially in the two molecular subtypes in our study are cancer-related genes. Because many of these genes are highly and uniformly expressed in healthy squamous epithelium, earlier profiling studies comparing tumors with healthy squamous epithelium may have clustered tumors with varying degree of loss of expression in to one category. Excluding healthy esophageal mucosa in microarray analysis in our strategy may, in fact, have accentuated the separation of the molecular subtypes based on differences in the relative expression levels among the tumors and not between tumors and healthy mucosa. Thus, it appears that it is not the loss or gain of expression of these genes in comparison with healthy squamous epithelium, but it is the relative levels in different tumors that distinguish responders from nonresponders. Because our tumor specimens were unselected with regard to percentage of stromal infiltration or inflammation, we realize that the clustering results might reflect contributions from non-neoplastic cellular elements to the expression signatures. However, both tumor and its surrounding microenvironment are important in tumor growth and response, inclusion of these components may be more beneficial than detrimental in studies such as ours that are designed to associate molecular signatures with pathologic response. Our data indicate that analysis of combination of biomarkers that are analyzed easily by quantitative assays such as PCR may be sufficient for distinguishing cancers that respond to therapy from those resistant to therapy. However, our study included only a small number of specimens; hence, vigorous validation with a larger set of samples is warranted to assess the predictive power of these potential markers.
The authors indicated no potential conflicts of interest.
Supported by a Multidisciplinary Research Program Grant from The University of Texas M.D. Anderson Cancer Center, the Cantu, Smith, Dallas, and Park families, and the Rivercreek Foundation. Terms in blue are defined in the glossary, found at the end of this article and online at www.jco.org. Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.
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Copyright © 2006 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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TOP
ABSTRACT
INTRODUCTION
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glutamyl lysine isopeptide bonds is important for effective epithelial barrier formation and the assembly of the cell envelope. 
isoform and mediate nuclear export of 
