GENE EXPRESSION PROFILES IN ESOPHAGEAL TISSUE

Abstract: The present invention results from the examination of tissue from esophageal adenocarcinomas to identify genes differentially expressed between tumor biopsies and normal tissue. The invention includes diagnostic and screening methods using these genes as well as solid supports comprising oligonucleotide arrays that are complementary to or hybridize to the differentially expressed genes.

Description: GENE EXPRESSION PROFILES IN ESOPHAGEAL TISSUE INVENTORS: Amanda WILLIAMS, Joseph F. BOLAND, Reginald V. LORD, Chris ALVARES, Jon C. WETZEL, Uwe SCHERF, Joseph G. VOCKLEY BACKGROUND OF THE INVENTION There are two main types of esophageal cancer; squamous cell carcinoma (SCC) and adenocarcinoma. The worldwide incidence of esophageal SCC is higher than that of adenocarcinoma; however, in the last few decades, the incidence of adenocarcinoma in Western countries has been increasing at a dramatic rate. As a result, esophageal adenocarcinoma is the most common cancer type among Caucasian patients in some populations (Blot & McLaughlin, Semin. Oncol. (1999) 26, 2-8). The main risk factor for development of esophageal adenocarcinoma is the presence of Barrett's esophagus, a disease in which the normal squamous epithelium ofthe lower esophagus is replaced by columnar mucosa in response to injury caused by chronic gastroesophageal reflux (Lagergren et α/., N. Engl. J. Med. (1999) 340, 825-831; Barrett et a/., Nat. Genet. (1999) 22, 106-109; Reid & Weinstein, Annu. Rev. Med. (1987) 38, 477-492). Barrett's esophagus is a disorder in which the lining ofthe esophagus undergoes cellular changes in response to chronic irritation and inflammation of reflux esophagitis. This condition is more common in men than women. The patient with Barrett's esophagus is at an increased risk of developing cancer ofthe esophagus. Symptoms are similar to those of reflux esophagitis and include heartburn, difficulty swallowing and pain relief with antiacid use or eating. The diagnosis of Barrett's is made by a biopsy ofthe esophageal mucosa through an endoscope. Treatment includes control of reflux disease, weight reduction and avoidance of alcohol, tobacco, fatty foods and lying flat after eating. Close follow-up is recommended to be certain the individual does not develop cancer ofthe esophagus. The precursor cell for Barrett's epithelium has not been identified, leaving the origin of Barrett's esophagus open to speculation. One theory suggests that denudation ofthe squamous epithelium layer by reflux acid allows gastric columnar cells to move into the site and take over (Bremner et al, Surgery (1970) 68, 209-16). More recently, cytokeratin expression data has been used to suggest that Barrett's epithelium evolves from a basal cell in the esophageal squamous epithelium (Boch et al, Gastroenterology (1997) 112, 760-765; Salo et α/., Ann. Med. (1996) 28, 305-309). The advent of cDNA and oligonucleotide arrays has enabled researchers to map tissue-specific expression levels for thousands of genes (Alon et β/., Proc. Natl. Acad. Sci. USA (1999) 96, 6745-6750; Iyer et al, (1999) Science 283, 83-87; Khan et al, Cancer Res. (1998) 58, 5009-13; Lee et al, Science (1999) 285, 1390-1393; Wang et al. Gene (1999) 229, 101-108; Whitney et al, Ann. Neural. (1999) 46, 425-428). Instead of assigning individual genes to a disease phenotype, expression profiles can be created which identify changes in total gene expression in the diseased tissue in relationship to normal adjacent tissue. Present day cancer research, particularly research in the field of adenocarcinoma, has focused on the determining the expression levels of individual genes with little effort expended on determining the global changes in gene expression that are correlated with the development and progression of adenocarcinoma. There remains a need in the art for materials and methods that permit a more accurate diagnosis of esophageal cancer and, in particular, esophageal adenocarcinoma. In addition, there remains a need in the art for methods to treat and methods to identify agent that can effectively treat esophageal cancer. The present invention meets these and other needs. SUMMARY OF THE INVENTION The present invention is based in part on the global changes in gene expression associated with esophageal cancer identified by examining gene expression in tissue from normal and diseased esophagus. The present invention also mcludes expression profiles which serve as useful diagnostic markers as well as markers that can be used to monitor disease states, disease progression, drug toxicity, drug efficacy and drug metabolism. The invention includes methods of diagnosing esophageal cancer in a patient comprising the step of detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal cancer. In some preferred embodiments, the method may include detecting the expression level of one or more genes selected from a group consisting of apolipoprotein C-l, galectin 4, keratin 18, annexin A10, cathepsin E, homeobox CIO, MPP1, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. The invention also includes methods of detecting the progression of esophageal cancer. For instance, methods ofthe invention include detecting the progression of esophageal cancer in a patient comprising the step of detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal cancer progression. In some preferred embodiments, the progression may be the progression of Barrett's esophagus to esophageal cancer. In some preferred embodiments, the method may include detecting the expression level of one or more genes selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin A10, cathepsin E, homeobox CIO, MPP1, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. In some aspects, the present invention provides a method of monitoring the treatment of a patient with esophageal cancer, comprising administering a pharmaceutical composition to the patient and preparing a gene expression profile from a cell or tissue sample from the patient and comparing the patient gene expression profile to a gene expression from a cell population comprising normal esophageal cells or to a gene expression profile from a cell population comprising esophageal cancer cells or to both. In some preferred embodiments, the gene profile will include the expression level of one or more genes in Tables 2-8. In other preferred embodiments, one or more genes may be selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin A10, cathepsin E, homeobox CIO, MPP1, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. In another aspect, the present invention provides a method of treating a patient with esophageal cancer, comprising administering to the patient a pharmaceutical composition, wherein the composition alters the expression of at least one gene in Tables 2-8, preparing a gene expression profile from a cell or tissue sample from the patient comprising tumor cells and comparing the patient expression profile to a gene expression profile from an untreated cell population comprising esophageal cancer cells. In one aspect, the present invention provides a method of diagnosing esophageal adenocarcinoma in a patient, comprising detecting the level of expression in a tissue sample of two or more genes from Tables 2-8, wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal adenocarcinoma. In another aspect, the present invention provides a method of detecting the progression of esophageal adenocarcinoma in a patient, comprising detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal adenocarcinoma progression. The present invention also provides materials and methods for monitoring the treatment of a patient with a esophageal adenocarcinoma. The present invention provides a method of monitoring the treatment of a patient with esophageal adenocarcinoma, comprising administering a pharmaceutical composition to the patient, preparing a gene expression profile from a cell or tissue sample from the patient and comparing the patient gene expression profile to a gene expression from a. cell population comprising normal esophageal cells or to a gene expression profile from a cell population comprising esophageal adenocarcinoma cells or to both. In some preferred embodiments, the method may include detecting the level of expression of one or more genes selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin A10, cathepsin E, homeobox CIO, MPPl, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. In a related aspect, the present invention provides a method of treating a patient with esophageal adenocarcinoma, comprising administering to the patient a pharmaceutical composition, wherein the composition alters the expression of at least one gene in Tables 2-8, preparing a gene expression profile from a cell or tissue sample from the patient comprising esophageal adenocarcinoma cells and comparing the patient expression profile to a gene expression profile from an untreated cell population comprising esophageal adenocarcinoma cells. In some preferred embodiments, one or more genes may be selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin A10, cathepsin E, homeobox CIO, MPPl, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. The invention further includes methods of screening for an agent capable of modulating the onset or progression of esophageal cancer, comprising the steps of exposing a cell to the agent; and detecting the expression level of two or more genes from Tables 2-8. In some embodiments, the esophageal cancer may be an esophageal adenocarcinoma. In some preferred embodiments, one or more genes may be selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin A10, cathepsin E, homeobox CIO, MPPl, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. Preferred methods may detect all or nearly all ofthe genes in the tables. The invention further includes compositions comprising at least two ohgonucleotides, wherein each ofthe ohgonucleotides comprises a sequence that specifically hybridizes to a gene in Tables 2-8 as well as solid supports comprising at least two probes, wherein each of the probes comprises a sequence that specifically hybridizes to a gene in Tables 2-8. In some preferred embodiments, one or more genes may be selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin AlO, cathepsin E, homeobox CIO, MPPl, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. The invention further includes computer systems comprising a database containing information identifying the expression level in esophageal tissue of a set of genes comprising at least two genes in Tables 2-8 and a user interface to view the information. In some preferred embodiments, one or more genes may be selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin AlO, cathepsin E, homeobox CIO, MPPl, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. The database may further include sequence information for the genes, information identifying the expression level for the set of genes in normal esophageal tissue and cancerous tissue and may contain links to external databases such as GenBank. Lastly, the invention includes methods of using the databases, such as methods of using the disclosed computer systems to present information identifying the expression level in a tissue or cell of at least one gene in Tables 2-8, comprising the step of comparing the expression level of at least one gene in Tables 2-8 in the tissue or cell to the level of expression ofthe gene in the database. In some preferred embodiments, one or more genes may be selected from a group consisting of apolipoprotein C-1, galectin 4, keratin 18, annexin AlO, cathepsin E, homeobox CIO, MPPl, transglutaminase 1, aquaporin 3, trefoil peptidel, trefoil peptide 2 or mucin 5B. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows the results ofa cluster analysis. Figure la shows genes under expressed in BA while Figures lb, lc and Id show genes overexpressed in BA. Figure 2 shows the results of a cluster analysis. Figure 2a shows genes identified as markers for squamous epithelial cells. Figure 2b shows genes involved in extracellular matrix (ECM) modification. Figure 2c shows genes involved in cell adhesion, migration, proliferation and differentiation. DETAILED DESCRIPTION Many biological functions are accomplished by altering the expression of various genes through transcriptional {e.g., through control of initiation, provision of RNA precursors, RNA processing, etc.) and/or translational control. For example, fundamental biological processes such as cell cycle, cell differentiation and cell death, are often characterized by the variations in the expression levels of groups of genes. Changes in gene expression also are associated with pathogenesis. For example, the lack of sufficient expression of functional tumor suppressor genes and/or the over expression of oncogene/protooncogenes could lead to tumorgenesis or hyperplastic growth of cells (Marshall, (1991) Cell, 64, 313-326; Weinberg, (1991) Science, 254, 1138-1146). Thus, changes in the expression levels of particular genes {e.g., oncogenes or tumor suppressors) serve as signposts for the presence and progression of various diseases. Monitoring changes in gene expression may also provide certain advantages during drug screening development. Often drugs are screened and prescreened for the ability to interact with a major target without regard to other effects the drugs have on cells. Often such other effects cause toxicity in the whole animal, which prevent the development and use ofthe potential drug. Applicants have examined tissue from normal esophageal tissue and tissue from esophageal tumors to identify global changes in gene expression between tumor biopsies and normal tissue. These global changes in gene expression, also referred to as expression profiles, provide useful markers for diagnostic uses as well as markers that can be used to monitor disease states, disease progression, drug toxicity, drug efficacy and drug metabolism. Expression profiles of genes in particular tissues, disease states or disease progression stages provide molecular tools for evaluating toxicity, drug efficacy, drug metabolism, development, and disease monitoring. Changes in the expression profile from a baseline profile can be used as an indication of such effects. Those skilled in the art can use any of a variety of known techniques to evaluate the expression of one or more ofthe genes and/or ESTs identified in the instant application in order to observe changes in the expression profile. The present application has identified differences in gene expression between normal esophageal tissue and esophageal adenocarcinoma. Barrett's epithelium was identified adjacent to many ofthe cancers. In some cases, the tumor involved an extensive area of esophageal mucosa suggesting that it had overgrown the Barrett's epithelium from which it derived. Genes and ESTs have been found whose expression significantly varies (>3 fold change up or down) between normal and malignant tissue. In preferred embodiments, the expression level of one or more of these genes and/or ESTs can be determined using as interrogators probes specific to one or more of these genes and/or ESTs. This permits the determination ofthe expression pattern in unknown cells or samples and their identification as benign or malignant. The expression patterns ofthe genes and ESTs which were examined are listed in Tables 2-8. The complete sequences ofthe genes and ESTs are available from GenBank using the Accession numbers shown in each table. Definitions In the description that follows, numerous terms and phrases known to those skilled in the art are used. In the interest of clarity and consistency of interpretation, the definitions of certain terms and phrases are provided. The present invention provides compositions and methods to detect the level of expression of genes that may be differentially expressed dependent upon the state ofthe cell, i.e., normal versus cancerous. As used herein, the phrase "detecting the level expression" includes methods that quantify expression levels as well as methods that determine whether a gene of interest is expressed at all. Thus, an assay which provides a yes or no result without necessarily providing quantification of an amount of expression is an assay that requires "detecting the level of expression" as that phrase is used herein. As used herein, oligonucleotide sequences that are complementary to one or more of the genes described herein, refers to ohgonucleotides that are capable of hybridizing under stringent conditions to at least part ofthe nucleotide sequence of said genes. Such hybridizable ohgonucleotides will typically exhibit at least about 75% sequence identity at the nucleotide level to said genes, preferably about 80% or 85% sequence identity or more preferably about 90% or 95% or more sequence identity to said genes. "Bind(s) substantially" refers to complementary hybridization between a probe nucleic acid and a target nucleic acid and embraces minor mismatches that can be accommodated by reducing the stringency ofthe hybridization media to achieve the desired detection ofthe target polynucleotide sequence. The terms "background" or "background signal intensity" refer to hybridization signals resulting from non-specific binding, or other interactions, between the labeled target nucleic acids and components ofthe oligonucleotide array {e.g., the oligonucleotide probes, control probes, the array substrate, etc.). Background signals may also be produced by intrinsic fluorescence ofthe array components themselves. A single background signal can be calculated for the entire array, or a different background signal may be calculated for each target nucleic acid. In a preferred embodiment, background is calculated as the average hybridization signal intensity for the lowest 5% to 10% ofthe probes in the array, or, where a different background signal is calculated for each target gene, for the lowest 5% to 10% ofthe probes for each gene. Of course, one of skill in the art will appreciate that where the probes to a particular gene hybridize well and thus appear to be specifically binding to a target sequence, they should not be used in a background signal calculation. Alternatively, background may be calculated as the average hybridization signal intensity produced by hybridization to probes that are not complementary to any sequence found in the sample (e.g., probes directed to nucleic acids ofthe opposite sense or to genes not found in the sample such as bacterial genes where the sample is mammalian nucleic acids). Background can also be calculated as the average signal intensity produced by regions ofthe array that lack any probes at all. The phrase "hybridizing specifically to" refers to the binding, duplexing or hybridizing of a molecule substantially to or only to a particular nucleotide sequence or sequences under stringent conditions when that sequence is present in a complex mixture (e.g., total cellular) DNA or RNA. Assays and methods ofthe invention may utilize available formats to simultaneously screen at least about 100, preferably about 1000, more preferably about 10,000 and most preferably about 1,000,000 or more different nucleic acid hybridizations. The terms "mismatch control" or "mismatch probe" refer to a probe whose sequence is deliberately selected not to be perfectly complementary to a particular target sequence. For each mismatch (MM) control in a high-density array there typically exists a corresponding perfect match (PM) probe that is perfectly complementary to the same particular target sequence. The mismatch may comprise one or more bases. While the mismatch(s) may be located anywhere in the mismatch probe, terminal mismatches are less desirable as a terminal mismatch is less likely to prevent hybridization of the target sequence. In a particularly preferred embodiment, the mismatch is located at or near the center ofthe probe such that the mismatch is most likely to destabilize the duplex with the target sequence under the test hybridization conditions. The term "perfect match probe" refers to a probe that has a sequence that is perfectly complementary to a particular target sequence. The test probe is typically perfectly complementary to a portion (subsequence) ofthe target sequence. The perfect match (PM) probe can be a "test probe", a "normalization control" probe, an expression level control probe and the like. A perfect match control or perfect match probe is, however, distinguished from a "mismatch control" or "mismatch probe." As used herein a "probe" is defined as a nucleic acid, preferably an oligonucleotide, capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (i.e., A, G, U, C or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in probes may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages. The term "stringent conditions" refers to conditions under which a probe will hybridize to its target subsequence, but with only insubstantial hybridization to other sequences or to other sequences such that the difference may be identified. Stringent conditions are sequence-dependent and will be different in different circumstances. Longer sequences hybridize specifically at higher temperatures. Generally, stringent conditions are selected to be about 5°C lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. Typically, stringent conditions will be those in which the salt concentration is at least about 0.01 to 1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30°C for short probes {e.g., 10 to 50 nucleotide). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. The "percentage of sequence identity" or "sequence identity" is determined by comparing two optimally aligned sequences or subsequences over a comparison window or span, wherein the portion ofthe polynucleotide sequence in the comparison window may optionally comprise additions or deletions (i.e., gaps) as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment ofthe two sequences. The percentage is calculated by determining the number of positions at which the identical subunit {e.g., nucleic acid base or amino acid residue) occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison and multiplying the result by 100 to yield the percentage of sequence identity. Percentage sequence identity when calculated using the programs GAP or BESTFIT (see below) is calculated using default gap weights. Homology or identity may be determined by BLAST (Basic Local Alignment Search Tool) analysis using the algorithm employed by the programs blastp, blastn, blastx, tblastn and tblastx (Karhn et α/., (1990) Proc. Natl. Acad. Sci. USA 87, 2264-2268 and Altschul, (1993) J. Mol. Evol. 36, 290-300, fully incorporated by reference) which are tailored for sequence similarity searching. The approach used by the BLAST program is to first consider similar segments between a query sequence and a database sequence, then to evaluate the statistical significance of all matches that are identified and finally to summarize only those matches which satisfy a preselected threshold of significance. For a discussion of basic issues in similarity searching of sequence databases, see Altschul et al, ((1994) Nature Genet. 6, 119-129) which is fully incorporated by reference. The search parameters for histogram, descriptions, alignments, expect (i.e., the statistical significance threshold for reporting matches against database sequences), cutoff, matrix and filter are at the default settings. The default scoring matrix used by blastp, blastx, tblastn, and tblastx is the BLOSUM62 matrix (Henikoffet α/., (1992) Proc. Natl. Acad. Sci. US A 89, 10915-10919, fully incorporated by reference). Four blastn parameters were adjusted as follows: Q=10 (gap creation penalty); R=10 (gap extension penalty); wink=l (generates word hits at every wink position along the query); and gapw=16 (sets the window width within which gapped alignments are generated). The equivalent Blastp parameter settings were Q-9; R=2; wink=l; and gapw=32. A Bestfit comparison between sequences, available in the GCG package version 10.0, uses DNA parameters GAP=50 (gap creation penalty) and LEN=3 (gap extension penalty) and the equivalent settings in protein comparisons are GAP=8 and LEN=2. Uses of Differentially Expressed Genes The present invention identifies those genes differentially expressed between normal esophageal tissue and cancerous esophageal tissue. One of skill in the art can select one or more ofthe genes identified as being differentially expressed and use the information and methods provided herein to interrogate or test a particular sample. For a particular interrogation of two conditions or sources, it is desirable to select those genes that display a great difference in the expression pattern between the two conditions or sources. At least a two-fold difference is desirable, but a three, five-fold or ten-fold difference may be preferred. Interrogations ofthe genes or proteins can be performed to yield information on gene expression as well as on the levels ofthe encoded proteins. Diagnostic Uses for the Esophageal Cancer Markers As described herein, the genes and gene expression information provided in Tables 2- 8 may be used as diagnostic markers for the prediction or identification ofthe malignant state ofthe esophageal tissue. For instance, an esophageal tissue sample or other sample from a patient may be assayed by any ofthe methods known to those skilled in the art, and the expression levels from one or more genes from Tables 2-8, may be compared to the expression levels found in normal esophageal tissue, tissue from esophageal adenocarinoma or both. Expression profiles generated from the tissue or other sample that substantially resemble an expression profile from normal or diseased esophageal tissue may be used, for instance, to aid in disease diagnosis. Comparison ofthe expression data, as well as available sequence or other information may be done by researcher or diagnostician or may be done with the aid of a computer and databases as described herein. Use ofthe Esophageal Cancer Markers for Monitoring Disease Progression Molecular expression markers for esophageal cancer can be used to confirm the type and progression ofthe cancer made on the basis of morphological criteria. For example, squamous cell carcinoma could be distinguished from adenocarcinoma based on the level and type of genes expressed in a tissue sample. In some situations, identifications of cell type or source is ambiguous based on classical criteria. In these situations the molecular expression markers ofthe present invention are useful. In addition, progression of esophageal squamous cell carcinoma to adenocarcinoma can be monitored by following the expression patterns ofthe involved genes using the molecular expression markers ofthe present invention. Perturbed expression can be observed in the diseased state. Monitoring ofthe efficacy of certain drug regimens can also be accomplished by following the expression patterns ofthe molecular expression markers. Although only a few different disease progression time points have been observed, as shown in the examples below, other developmental stages can be studied using these same molecular expression markers. The importance of these markers in development has been shown here, however, variations in their expression may occur at other times. For example, one could study the expression of these markers at benign stages for comparison to expression at malignant states. As described above, the genes and gene expression information provided in Tables 2- 8 may also be used as markers for the monitoring of disease progression, for instance, the development of esophageal cancer. For instance, an esophageal tissue sample or other sample from a patient may be assayed by any ofthe methods known to those of skill in the art, and the expression levels in the sample from a gene or genes from Tables 2-8 may be compared to the expression levels found in normal esophageal tissue, tissue from esophageal cancer, in particular, Barrett's-associated esophageal adenocarcinoma (BA), or both. Comparison ofthe expression data, as well as available sequence or other information may be done by researcher or diagnostician or may be done with the aid of a computer and databases as described herein. Use ofthe Esophageal Cancer Markers for Drug Screening Potential drugs can be screened to determine if application ofthe drug alters the expression of one or more ofthe genes identified herein. This may be useful, for example, in determining whether a particular drug is effective in treating a particular patient or patient population with esophageal cancer. In the case where the expression of a gene is affected by the potential drug such that its level of expression returns to normal, the drug is indicated in the treatment of esophageal cancer. Similarly, a drug that causes expression of a gene which is not normally expressed by epithelial cells in the esophagus, may be contraindicated in the treatment of esophageal cancer. According to the present invention, the genes identified in Tables 2-8 may be used as markers to evaluate the effects of a candidate drug or agent on a cell, particularly a cell undergoing malignant transformation, for instance, an esophageal cancer cell or tissue sample. A candidate drug or agent can be screened for the ability to stimulate the transcription or expression of a given marker or markers (drug targets) or to down-regulate or inhibit the transcription or expression of a marker or markers. According to the present invention, one can also compare the specificity ofthe effects of a drug by looking at the number of markers affected by the drug and comparing them to the number of markers affected by a different drug. A more specific drug will affect fewer transcriptional targets. Similar sets of markers identified for two drugs indicates a similarity of effects. Assays to monitor the expression of a marker or markers as defined in Tables 2-8 may utilize any available means of monitoring for changes in the expression level ofthe nucleic acids ofthe invention. As used herein, an agent is said to modulate the expression ofa nucleic acid ofthe invention if it is capable of up- or down-regulating expression ofthe nucleic acid in a cell. Agents that are assayed in the above methods can be randomly selected or rationally selected or designed. As used herein, an agent is said to be randomly selected when the agent is chosen randomly without considering the specific sequences involved in the association of the a protein ofthe invention alone or with its associated substrates, binding partners, etc. An example of randomly selected agents is the use a chemical library or a peptide combinatorial library, or a growth broth of an organism. . As used herein, an agent is said to be rationally selected or designed when the agent is chosen on a nonrandom basis which takes into account the sequence ofthe target site and/or its conformation in connection with the agent's action. Agents can be rationally selected or rationally designed by utilizing the peptide sequences that make up these sites. For example, a rationally selected peptide agent can be a peptide whose amino acid sequence is identical to or a derivative of any functional consensus site. The agents ofthe present invention can be, as examples, peptides, small molecules, vitamin derivatives, as well as carbohydrates, lipids, ohgonucleotides and covalent and non- covalent combinations thereof. Dominant negative proteins, DNA encoding these proteins, antibodies to these proteins, peptide fragments of these proteins or mimics of these proteins may be introduced into cells to affect function. "Mimic" as used herein refers to the modification of a region or several regions of a peptide molecule to provide a structure chemically different from the parent peptide but topographically and functionally similar to the parent peptide (see Grant, (1995) in Molecular Biology and Biotechnology Meyers (editor) NCH Publishers). A skilled artisan can readily recognize that there is no limit as to the structural nature ofthe agents ofthe present invention. Assay Formats The genes identified as being differentially expressed in esophageal cancer may be used in a variety of nucleic acid detection assays to detect or quantify the expression level of a gene or multiple genes in a given sample. For example, traditional Northern blotting, nuclease protection, RT-PCR and differential display methods may be used for detecting gene expression levels. The protein products ofthe genes identified herein can also be assayed to determine the amount of expression. Methods for assaying for a protein include Western blot, immunoprecipitation, radioimmunoassay. It is preferred, however, that the mRNA be assayed as an indication of expression. Methods for assaying for mRNA include Northern blots, slot blots, dot blots, and hybridization to an ordered array of ohgonucleotides. Any method for specifically and quantitatively measuring a specific protein or mRNA or DNA product can be used. However, methods and assays ofthe invention are most efficiently designed with array or chip hybridization-based methods for detecting the expression ofa large number of genes. Any hybridization assay format may be used, including solution-based and solid support-based assay formats. A preferred solid support is a high density array also known as a DNA chip or a gene chip. In one assay format, gene chips containing probes to at least two genes from Tables 2-8 may be used to directly monitor or detect changes in gene expression in the treated or exposed cell as described herein. Additional assay formats may be used to monitor the ability ofthe agent to modulate the expression of a gene identified in Tables 2-8. For instance, as described above, mRNA expression may be monitored directly by hybridization of probes to the nucleic acids ofthe invention. Cell lines are exposed to an agent to be tested under appropriate conditions and time and total RNA or mRNA is isolated bv standard orocedures such those disclosed in In another format, cell lines that contain reporter gene fusions between the open reading frame and/or the 3' or 5' regulatory regions ofa gene in Tables 2-8 and any assayable fusion partner may be prepared. Numerous assayable fusion partners are known and readily available including the firefly luciferase gene and the gene encoding chloramphenicol acetyltransferase (Alam et α/., (1990) Anal. Biochem. 188, 245-254). Cell lines containing the reporter gene fusions are then exposed to the agent to be tested under appropriate conditions and time. Differential expression of he reporter gene between samples exposed to the agent and control samples identifies agents which modulate the expression ofthe nucleic acid. In another assay format, cells or cell lines are first identified which express one or more ofthe gene products ofthe invention physiologically. Cells and/or cell lines so identified would preferably comprise the necessary cellular machinery to ensure that the transcriptional and/or translational apparatus ofthe cells would faithfully mimic the response of normal or cancerous esophageal tissue to an exogenous agent. Such machinery would likely include appropriate surface transduction mechanisms and/or cytosolic factors. Such cell lines may be, but are not required to be, derived from esophageal tissue. The cells and/or cell lines may then be contacted with an agent and the expression of one or more ofthe genes of interest may then be assayed. The genes may be assayed at the mRNA level and/or at the protein level. In some embodiments, such cells or cell lines may be transduced or transfected with an expression vehicle {e.g., a plasmid or viral vector) containing an expression construct comprising an operable 5 '-promoter containing end ofa gene of interest identified in Tables 2-8 fused to one or more nucleic acid sequences encoding one or more antigenic fragments. The construct may comprise all or a portion ofthe coding sequence ofthe gene of interest which may be positioned 5'- or 3'- to a sequence encoding an antigenic fragment. The coding sequence ofthe gene of interest may be translated or un-translated after transcription ofthe gene fusion. At least one antigenic fragment may be translated. The antigenic fragments are selected so that the fragments are under the transcriptional control ofthe promoter ofthe gene of interest and are expressed in a fashion substantially similar to the expression pattern ofthe gene of interest. The antigenic fragments may be expressed as polypeptides whose molecular weight can be distinguished from the naturally occurring polypeptides. In some embodiments, gene products ofthe invention may further comprise an immunologically distinct tag. Such a process is well known in the art (see Sambrook et al, (1989) Molecular Cloning - A Laboratory Manual, Cold Spring Harbor Laboratory Press). Cells or cell lines transduced or transfected as outlined above are then contacted with agents under appropriate conditions; for example, the agent comprises a pharmaceutically acceptable excipient and is contacted with cells comprised in an aqueous physiological buffer such as phosphate buffered saline (PBS) at physiological pH, Eagles balanced salt solution (BSS) at physiological pH, PBS or BSS comprising serum or conditioned media comprising PBS or BSS and serum incubated at 37°C. Said conditions may be modulated as deemed necessary by one of skill in the art. Subsequent to contacting the cells with the agent, said cells will be disrupted and the polypeptides ofthe lysate are fractionated such that a polypeptide fraction is pooled and contacted with an antibody to be further processed by immunological assay {e.g., ELISA, immunoprecipitation or Western blot). The pool of proteins isolated from the "agent-contacted" sample will be compared with a control sample where only the excipient is contacted with the cells and an increase or decrease in the immunologically generated signal from the "agent-contacted" sample compared to the control will be used to distinguish the effectiveness ofthe agent. Another embodiment ofthe present invention provides methods for identifying agents that modulate the levels, concentration or at least one activity of a protein(s) encoded by the genes in Tables 2-8. Such methods or assays may utilize any means of monitoring or detecting the desired activity. In one format, the relative amounts of a protein ofthe invention produced in a cell population that has been exposed to the agent to be tested may be compared to the amount produced in an un-exposed control cell population. In this format, probes such as specific antibodies are used to monitor the differential expression ofthe protein in the different cell populations. Cell lines or populations are exposed to the agent to be tested under appropriate conditions and time. Cellular lysates may be prepared from the exposed cell line or population and a control, unexposed cell line or population. The cellular lysates are then analyzed with the probe, such as a specific antibody. The genes and ESTs ofthe present invention may be assayed in any convenient form. For example, they may be assayed in the form mRNA or reverse transcribed mRNA. The genes may be cloned or not and the genes may be amplified or not. The cloning itself does not appear to bias the representation of genes within a population. However, it may be preferable to use polyA+ RNA as a source, as it can be used with less processing steps. In some embodiments, it may be preferable to assay the protein or peptide encoded by the gene. The sequences ofthe expression marker genes are in the public databases. Tables 2-8 provide the Accession numbers and name for each ofthe sequences. In Tables 2-6, the number following the notation gb= is the GenBank accession number. The sequences ofthe genes in GenBank are expressly incorporated by reference and are publicly available at, for example, www.ncbi.nih.gov. IMAGE gives the clone number from the IMAGE consortium. Probe design Probes based on the sequences ofthe genes described herein may be prepared by any commonly available method. Oligonucleotide probes for assaying the tissue or cell sample are preferably of sufficient length to specifically hybridize only to appropriate, complementary genes or transcripts. Typically the oligonucleotide probes will be at least 10, 12, 14, 16, 18, 20 or 25 nucleotides in length. In some cases longer probes of at least 30, 40, or 50 nucleotides will be desirable. One of skill in the art will appreciate that an enormous number of array designs are suitable for the practice of this invention. The high density array will typically include a number of probes that specifically hybridize to the sequences of interest. See WO 99/32660 for methods of producing probes for a given gene or genes. In addition, in a preferred embodiment, the array will include one or more control probes. High density array chips ofthe invention include "test probes." Test probes may be ohgonucleotides that range from about 5 to about 500 or about 5 to about 50 nucleotides, more preferably from about 10 to about 40 nucleotides and most preferably from about 15 to about 40 nucleotides in length. In other particularly preferred embodiments, the probes are about 20 or 25 nucleotides in length. In another preferred embodiment, test probes are double or single strand DNA sequences. DNA sequences may be isolated or cloned from natural sources or amplified from natural sources using natural nucleic acid as templates. These probes have sequences complementary to particular subsequences ofthe genes whose expression they are designed to detect. Thus, the test probes are capable of specifically hybridizing to the target nucleic acid they are to detect. In addition to test probes that bind the target nucleic acid(s) of interest, the high density array can contain a number of control probes. The control probes fall into three categories referred to herein as (1) normalization controls; (2) expression level controls; and (3) mismatch controls. Normalization controls are oligonucleotide or other nucleic acid probes that are complementary to labeled reference ohgonucleotides or other nucleic acid sequences that are added to the nucleic acid sample. The signals obtained from the normalization controls after hybridization provide a control for variations in hybridization conditions, label intensity, "reading" efficiency and other factors that may cause the signal of a perfect hybridization to vary between arrays. In a preferred embodiment, signals {e.g., fluorescence intensity) read from all other probes in the array are divided by the signal (, fluorescence intensity) from the control probes thereby normalizing the measurements. Virtually any probe may serve as a normalization control. However, it is recognized that hybridization efficiency varies with base composition and probe length. Preferred normalization probes are selected to reflect the average length ofthe other probes present in the array, however, they can be selected to cover a range of lengths. The normalization control(s) can also be selected to reflect the (average) base composition ofthe other probes in the array, however in a preferred embodiment, only one or a few probes are used and they are selected such that they hybridize well (i.e., no secondary structure) and do not match any target-specific probes. Expression level controls are probes that hybridize specifically with constitutively expressed genes in the biological sample. Virtually any constitutively expressed gene provides a suitable target for expression level controls. Typical expression level control probes have sequences complementary to subsequences of constitutively expressed "housekeeping genes" including, but not limited to the β-actin gene, the transferrin receptor gene, the GAPDH gene, and the like. Mismatch controls may also be provided for the probes to the target genes, for expression level controls or for normalization controls. Mismatch controls are oligonucleotide probes or other nucleic acid probes identical to their corresponding test or control probes except for the presence of one or more mismatched bases. A mismatched base is a base selected so that it is not complementary to the corresponding base in the target sequence to which the probe would otherwise specifically hybridize. One or more mismatches are selected such that under appropriate hybridization conditions {e.g., stringent conditions) the test or control probe would be expected to hybridize with its target sequence, but the mismatch probe would not hybridize (or would hybridize to a significantly lesser extent). Preferred mismatch probes contain a central mismatch. Thus, for example, where a probe is a twenty-mer, a corresponding mismatch probe may have the identical sequence except for a single base mismatch {e.g., substituting a G, a C or a T for an A) at any of positions 6 through 14 (the central mismatch). Mismatch probes thus provide a control for non-specific binding or cross hybridization to a nucleic acid in the sample other than the target to which the probe is directed. Mismatch probes also indicate whether a hybridization is specific or not. For example, if the target is present the perfect match probes should be consistently brighter than the mismatch probes. In addition, if all central mismatches are present, the mismatch probes can be used to detect a mutation. The difference in intensity between the perfect match and the mismatch probe (I(PM) - I(MM)) provides a good measure ofthe concentration ofthe hybridized material. Nucleic Acid Samples As is apparent to one of ordinary skill in the art, nucleic acid samples used in the methods and assays ofthe invention may be prepared by any available method or process. Methods of isolating total mRNA are also well known to those of skill in the art. For example, methods of isolation and purification of nucleic acids are described in detail in Chapter 3 of Laboratory Techniques in Biochemistry and Molecular Biology: Hybridization With Nucleic Acid Probes, Part I Theory and Nucleic Acid Preparation, Tijssen, (1993) (editor) Elsevier Press. Such samples include RNA samples, but also include cDNA synthesized from a mRNA sample isolated from a cell or tissue of interest. Such samples also include DNA amplified from the cDNA, and an RNA transcribed from the amplified DNA. One of skill in the art would appreciate that it may be desirable to inhibit or destroy RNase present in homogenates before homogenates can be used. Biological samples may be of any biological tissue or fluid or cells from any organism as well as cells raised in vitro, such as cell lines and tissue culture cells. Frequently the sample will be a "clinical sample" which is a sample derived from a patient. Typical clinical samples include, but are not limited to, sputum, blood, blood-cells {e.g., white cells), tissue or fine needle biopsy samples, urine, peritoneal fluid, and pleural fluid, or cells therefrom. Biological samples may also include sections of tissues, such as frozen sections or formalin fixed sections taken for histological purposes. Solid Supports Solid supports containing oligonucleotide probes for differentially expressed genes can be any solid or semisolid support material known to those skilled in the art. Suitable examples include, but are not limited to, membranes, filters, tissue culture dishes, polyvinyl chloride dishes, beads, test strips, silicon or glass based chips and the like. Suitable glass wafers and hybridization methods are widely available, for example, those disclosed by Beattie (WO 95/11755). Any solid surface to which ohgonucleotides can be bound, either directly or indirectly, either covalently or non-covalently, can be used. In some embodiments, it may be desirable to attach some ohgonucleotides covalently and others non- covalently to the same solid support. A preferred solid support is a high density array or DNA chip. These contain a particular oligonucleotide probe in a predetermined location on the array. Each predetermined location may contain more than one molecule ofthe probe, but each molecule within the predetermined location has an identical sequence. Such predetermined locations are termed features. There may be, for example, from 2, 10, 100, 1000 to 10,000, 100,000 or 400,000 of such features on a single solid support. The solid support, or the area within which the probes are attached may be on the order of a square centimeter. Oligonucleotide probe arrays for expression monitoring can be made and used according to any techniques known in the art (see for example, Lockhart et al, Nat. Biotechnol. (1996) 14, 1675-1680; McGall et α/., Proc. Nat. Acad. Sci. USA (1996) 93, 13555-13460). Such probe arrays may contain at least two or more ohgonucleotides that are complementary to or hybridize to two or more ofthe genes described herein. Such arrays my also contain ohgonucleotides that are complementary or hybridize to at least 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 50, 70 or more the genes described herein. Oligonucleotide arrays are particularly useful for creating gene expression profiles comparing cancer tissue to adjacent normal tissue. The use of available oligonucleotide arrays enabled the determination ofthe expression levels of numerous genes and ESTs simultaneously. From this mass of expression data, differentially expressed genes were identified using Fold Change and Gene Signature Differential analysis. Gene Signature Differential analysis is a method designed to detect genes present in one sample set, and absent in another. Genes with differential expression in cancer tissue versus normal tissue are better diagnostic and therapeutic targets than genes that do not change in expression. . Methods of forming high density arrays of ohgonucleotides with a minimal number of synthetic steps are known. The oligonucleotide analogue array can be synthesized on a solid substrate by a variety of methods, including, but not limited to, light-directed chemical coupling, and mechanically directed coupling (see Pirrung et al, (1992) U.S. Patent No. 5,143, 854; Fodor et al, (1998) U.S. Patent No. 5,800,992; Chee et al, (1998) 5,837,832 In brief, the light-directed combinatorial synthesis of oligonucleotide arrays on a glass surface proceeds using automated phosphoramidite chemistry and chip masking techniques. In one specific implementation, a glass surface is derivatized with a silane reagent containing a functional group, e.g., a. hydroxyl or amine group blocked by a photolabile protecting group. Photolysis through a photolithogaphic mask is used selectively to expose functional groups which are then ready to react with incoming 5' photoprotected nucleoside phosphoramidites. The phosphoramidites react only with those sites which are illuminated (and thus exposed by removal ofthe photolabile blocking group). Thus, the phosphoramidites only add to those areas selectively exposed from the preceding step. These steps are repeated until the desired array of sequences have been synthesized on the solid surface. Combinatorial synthesis of different oligonucleotide analogues at different locations on the array is determined by the pattern of illumination during synthesis and the order of addition of coupling reagents. In addition to the foregoing, additional methods which can be used to generate an array of ohgonucleotides on a single substrate are described in Fodor et αl, (1993). WO 93/09668. High density nucleic acid arrays can also be fabricated by depositing premade or natural nucleic acids in predetermined positions. Synthesized or natural nucleic acids are deposited on specific locations of a substrate by light directed targeting and oligonucleotide directed targeting. Another embodiment uses a dispenser that moves from region to region to ι deposit nucleic acids in specific spots. Hybridization Nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing (see Lockhart et al, (1999) WO 99/32660). The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration ofthe buffer containing the nucleic acids. Under low stringency conditions {e.g., low temperature and/or high salt) hybrid duplexes {e.g., DNA-DNA, RNA-RNA or RNA-DNA) will form even where the annealed sequences are not perfectly complementary. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency {e.g., higher temperature or lower salt) successful hybridization requires fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency. In a preferred embodiment, hybridization is performed at low stringency, in this case in 6x SSPE- T at 37°C (0.005% Triton x-100) to ensure hybridization and then subsequent washes are performed at higher stringency {e.g., lx SSPE-T at 37°C) to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency {e.g., down to as low as 0.25x SSPET at 37°C to 50°C until a desired level of hybridization specificity is obtained. Stringency can also be increased by addition of agents such as formamide. Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present {e.g., expression level control, normalization control, mismatch controls, etc.). In general, there is a tradeoff between hybridization specificity (stringency) and signal intensity. Thus, in a preferred embodiment, the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than approximately 10%) ofthe background intensity. Thus, in a preferred embodiment, the hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis ofthe data sets thus produced will reveal a wash stringency above which the hybridization pattern is not appreciably altered and which provides adequate signal for the particular oligonucleotide probes of interest. Signal Detection The hybridized nucleic acids are typically detected by detecting one or more labels attached to the sample nucleic acids. The labels may be incorporated by any of a number of means well known to those of skill in the art (see Lockhart et al, (1999) WO 99/32660). Databases The present invention includes relational databases containing sequence information, for instance for the genes of Tables 2-8, as well as gene expression information in various esophageal tissue samples. Databases may also contain information associated with a given sequence or tissue sample such as descriptive information about the gene associated with the sequence information, or descriptive information concerning the clinical status ofthe tissue sample, or the patient from which the sample was derived. The database may be designed to include different parts, for instance a sequences database and a gene expression database. Methods for the configuration and construction of such databases are widely available, for instance, see Akerblom et al, (1999) U.S. Patent 5,953,727, which is specifically incorporated herein by reference in its entirety. The databases ofthe invention may be linked to an outside or external database. In a preferred embodiment, as described in Tables 2-8, the external database is GenBank and the associated databases maintained by the National Center for Biotechnology Information (NCBI). Any appropriate computer platform may be used to perform the necessary comparisons between sequence information, gene expression information and any other information in the database or provided as an input. For example, a large number of computer workstations are available from a variety of manufacturers, such has those available from Silicon Graphics. Client-server environments, database servers and networks are also widely available and appropriate platforms for the databases ofthe invention. The databases ofthe invention may be used to produce, among other things, electronic Northerns to allow the user to determine the cell type or tissue in which a given gene is expressed and to allow determination ofthe abundance or expression level of a given gene in a particular tissue or cell. The databases ofthe invention may also be used to present information identifying the expression level in a tissue or cell of a set of genes comprising at least one gene in Tables 2-8 comprising the step of comparing the expression level of at least one gene in Tables 2-8 in the tissue to the level of expression ofthe gene in the database. Such methods may be used to predict the physiological state of a given tissue by comparing the level of expression ofa gene or genes in Tables 2-8 from a sample to the expression levels found in tissue from normal esophageal tissue, tissue from esophageal adenocarcinoma or both. Such methods may also be used in the drug or agent screening assays as described herein. Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the compounds ofthe present invention and practice the claimed methods. The following working examples therefore, specifically point out the preferred embodiments ofthe present invention, and are not to be construed as limiting in any way the remainder ofthe disclosure. EXAMPLES Example 1: Tissue Sample Acquisition and Preparation For tissue specimens, nine normal esophagus samples and eight B A tissue samples, which included seven matched tumor-normal sets, were used. Six of he eight BA samples were lymph node invasive. With minor modifications, the sample preparation protocol followed the Affymetrix GeneChip Expression Analysis Manual. Frozen tissue was first ground to powder using the Spex Certiprep 6800 Freezer Mill. Total RNA was then extracted using Trizol (Life Technologies). The total RNA yield for each sample (average tissue weight of 300 mg) was 200-500 μg. Next, mRNA was isolated using the Oligotex mRNA Midi kit (Qiagen). Since the mRNA was eluted in a final volume of 400 μl, an ethanol precipitation step was required to bring the concentration to 1 μg/μl. Using 1-5 μg of mRNA, double stranded cDNA was created using the Superscript Choice system (Gibco-BRL). First strand cDNA synthesis was primed with a T7-(dT 24 ) oligonucleotide. The cDNA was then phenol-chloroform extracted and ethanol precipitated to a final concentration of 1 μg/μl. From 2 μg of cDNA, cRNA was synthesized according to standard procedures. To biotin label the cRNA, nucleotides Bio-11-CTP and Bio-16-UTP (Enzo Diagnostics) were added to the reaction. After a 37°C incubation for six hours, the labeled cRNA was cleaned up according to the RNeasy Mini kit protocol (Qiagen). The cRNA was then fragmented (5χ fragmentation buffer: 200 mM Tris-Acetate (pH 8.1), 500 mM KOAc, 150 mM MgOAc) for thirty-five minutes at 94°C. 55 μg of fragmented cRNA was hybridized on the human and the Human Genome U95 set of arrays for twenty-four hours at 60 rpm in a 45°C hybridization oven. The chips were washed and stained with Streptavidin Phycoerythrin (SAPE) (Molecular Probes) in Affymetrix fluidics stations. To amplify staining, SAPE solution was added twice with an anti-streptavidin biotinylated antibody (Vector Laboratories) staining step in between. Hybridization to the probe arrays was detected by fluorometric scanning (Hewlett Packard Gene Array Scanner). Following hybridization and scanning, the microarray images were analyzed for quality control, looking for major chip defects or abnormalities in hybridization signal. After all chips passed QC, the data was analyzed using Affymetrix GeneChip software (v3.0), and Experimental Data Mining Tool (EDMT) software (vl.0). Example 2: Gene Expression Analysis All samples were prepared as described and hybridized onto the Affymetrix Human Genome U95 array set. Each chip contains 16-20 oligonucleotide probe pairs per gene or cDNA clone. These probe pairs include perfectly matched sets and mismatched sets, both of which are necessary for the calculation ofthe average difference. The average difference is a measure ofthe intensity difference for each probe pair, calculated by subtracting the intensity ofthe mismatch from, the intensity ofthe perfect match. This takes into consideration variability in hybridization among probe pairs and other hybridization artifacts that could affect the fluorescence intensities. Using the average difference value that has been calculated, the GeneChip software then makes an absolute call for each gene or EST. The absolute call of present, absent or marginal is used to generate a Gene Signature, a tool used to identify those genes that are commonly present or commonly absent in a given sample set, according to the absolute call. For each set of samples, a median average difference was figured using the average differences of each individual sample within the set. The Gene Signature for one set of samples is compared to the Gene Signature of another set of samples to determine the Gene Signature Differential. This comparison identifies the genes that are consistently present in one set of samples and consistently absent in the second set of samples. The Gene Signature Curve is a graphic view ofthe number of genes consistently present in a given set of samples as the sample size increases, taking into account the genes commonly expressed among a particular set of samples, and discounting those genes whose expression is variable among those samples. The curve is also indicative ofthe number of samples necessary to generate an accurate Gene Signature. As the sample number increases, the number of genes common to the sample set decreases. The curve is generated using the positive Gene Signatures ofthe samples in question, determined by adding one sample at a time to the Gene Signature, beginning with the sample with the smallest number of present genes and adding samples in ascending order. The curve displays the sample size required for the most consistency and the least amount of expression variability from sample to sample. The point where this curve begins to level off represents the minimum number of samples required for the Gene Signature. Graphed on the x-axis is the number of samples in the set, and on the y-axis is the number of genes in the positive Gene Signature. As a general rule, the acceptable percent of variability in the number of positive genes between two sample sets should be less than 5%. Example 3: Expression Profiles Using the above described methods, genes that were predominantly over-expressed in B A, or predominantly under-expressed in B A were identified. The revealed genes were used to identify gene clusters generated by hierarchical clustering that exhibited a consistent fold change and/or dominant expression pattern between the normal and diseased sample sets. Genes with consistent differential expression patterns provide potential targets for broad range diagnostics and therapeutics. First, the expression profiles ofthe nine normal esophagus samples were pooled and used to determine the genes that are commonly expressed or commonly not expressed. To find the expression pattern consistent to disease, the same procedure was followed with the eight samples from patients with B A. Table 1 lists, by array type, the number of genes with expression patterns common to the majority of normal or diseased samples. Next, the unique pattern of genes over-expressed in the disease was identified by determining those genes that were commonly expressed in BA, but commonly NOT expressed in normal esophagus. Similarly, the unique pattern of genes under-expressed in disease was found by identifying genes that were expressed in the majority of normal esophagus samples, but NOT expressed in the majority of BA samples. Table 1 lists the number of genes uniquely under-expressed and over-expressed in B A by array type. With this method 423 genes were identified to be unique for BA. Example 4: Fold Change analysis The data was first filtered to exclude all genes that showed no expression in any ofthe samples. The ratio (tumor/normal) was calculated by comparing the mean expression value for each gene in the BA sample set against the mean expression value of that gene in the normal esophagus sample set. Genes were included in the analysis if they had a fold change > 3 in either direction, and a P value < 0.05 as determined by a two-tail unequal variance t- test. Out ofthe -60,000 genes surveyed by the Human Genome U95 set, 1584 genes were present in the overall fold change analysis, 701 were over-expressed in BA and 883 were under-expressed in BA. Out ofthe 423 unique genes for BA (244 under-expressed and 179 over-expressed) previously identified, 170 were also present in the fold change analysis. Determining these 170 genes independently by both methods overcomes the limitations of accuracy inherent in either method. These 170 key disease-related genes have both significant overall fold changes, and 87 are not detectable in BA while the remaining 83 are not detectable in normal esophagus. The genes identified in the fold change analysis are listed in Tables 2-6. Table 2 lists those genes identified using the Human Genome U95A chip, Table 3 lists those genes identified using the Human Genome U95B chip, Table 4 lists those genes identified using the Human Genome U95C chip, Table 5 lists those genes identified using the Human Genome U95D chip and Table 6 lists those genes identified using the Human Genome U95E chip. Example 5: Cluster Analysis The data was first filtered to exclude all genes that showed no expression in any ofthe samples. To normalize the data, fold change values for the samples were calculated by dividing each gene expression value by the mean ofthe expression values for all samples, both normal esophagus and B A, for that gene. Genes were included in the cluster analysis if they had at least one instance of a fold change > 3 in either direction, and a P value of <0.05 as assessed by a two-tail unequal variance t-test. Using a hierarchical clustering algorithm, genes were grouped according to their expression pattern similarities across all samples (Eisen, et al, Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863-14868 (1998)). For the Human Genome U95A array, 1100 full-length known genes or ESTs (8.7% of the genes present on the array) were included in the cluster analysis. The resulting dendrogram (Fig. 1) grouped all nine normal esophagus samples and seven ofthe eight BA samples into separate trees. BA sample 316 clustered in a branch with its matched normal esophagus sample (315) rather than with the other tumors. A number of genes on the Human Genome U95A array are present in duplicate. In most cases the duplicate genes cluster next to each other or in close proximity of each other, verifying internal microarray reproducibihty. Four clusters were chosen for in-depth analyses, based on the presence of a portion ofthe 170 key disease-related genes previously identified by our fingeφrinting and fold change analysis methods. Figure 1 shows the results obtained using a hierarchical clustering to measure expression variation for 1100 full-length genes present on the Affymetrix Human Genome U95 A oligonucleotide array. Four clusters (a-d) are presented that include genes from the 170 gene list identified by both our analysis methods. Those genes are labeled in red. Cluster (a) contains genes under-expressed in Barrett's-associated esophageal adenocarcinoma (BA), while clusters (b-d) contain genes over-expressed in BA. The dendrogram summarizes the expression similarities between samples. Each gene is represented by a single row, and each sample by a single column. Relative to the mean expression level of all samples, red squares represent an over-expression, green squares represent an under-expression, black squares represent no expression change, and grey squares denote a missing sample. The overall fold change (FC), the fold change calculated between the two groups of samples, for each gene is also listed. Figure 2 shows the results obtained from a clustering analysis performed for 4,521 genes from the Human Genome U95 array set. A representative cluster was chosen that contained a number of genes from the U95A (Figure 1 cluster d). Genes in common between clusters are labeled in green. Based on expression similarities to known genes, the biological function of ESTs can be determined. The genes thus identified are listed in Table 8. The cluster analysis also identified genes not identified in the fold change analysis. Table 7 provides a list of those genes identified as present in the U95A chip cluster analysis but not identified as present in the fold change analysis. The clusters of genes thus identified contain genes that exhibit a consistent fold change between the normal and diseased sample sets, providing targets for broad range diagnostics and therapeutics. Example 6; Tissue markers As the progression from normal esophagus to BA occurs, squamous epithelial cells are replaced with a heterogeneous population of columnar cells that exhibit both intestinal and gastric-like characteristics. The methods ofthe present invention were used to identify clusters containing genes differentially expressed in all normal or diseased samples. The genes thus identified were screened for the presence of marker genes corresponding to gross morphological changes. The stratified squamous epithelial terminal differentiation markers, transglutaminase 1, transglutaminase 3, involucrin, envoplakin, periplakin and sciellin were all present in the cluster of genes under-expressed in BA (Figure la). A distinct cluster (Figure lc) was also identified that included over-expressed genes associated with the Barrett's esophagus phenotype (see Labouvie, et ah, Differential expression of mucins and trefoil peptides in native epithelium, Barrett's metaplasia and squamous cell carcinoma ofthe oesophagus. J. Cancer Res. Clin. Oncol 125, 71-6 (1999) and Westerveld, et ah, Gastric proteases in Barrett's esophagus. Gastroenterology 93, 774-8 (1987)). The genes trefoil peptide 1(TFF- 1), trefoil peptide 2 (TFF-2), mucin 5B, and pepsinogen C were present in this cluster. Example 7: Metastasis-related genes The majority of BA tumors in this study (6 out of 8) displayed regional lymph node invasion. Genes with expression changes that correlate highly with the metastatic phenotype are very valuable diagnostic markers. The first step in metastasis is the loss of cell adhesion at the primary site. Desmosomes are multi-component structures involved in epithelial cell to cell adhesion and intracellular anchoring of intermediate filaments. The desmosomal components, desmoglein 3, desmocollin 2, and desmoplakin, are all present in the cluster of genes under-expressed in BA (Figure la). Once cell to cell adhesion is broken, the extracellular matrix (ECM) must be breached to enable movement into metastatic sites. A number of proteases, including metalloproteinase 1 (MMP-1), metalloproteinase 11 (MMP-11), cathepsin E, cathepsin K, and urokinase plasminogen activator (u-PA), that are involved in basement membrane and ECM degradation are spread throughout the clusters containing genes over-expressed in B A (Figures lb-Id). MMP-1, MMP-11, and u-PA expression has previously been correlated with metastasis and /or poor prognosis in esophageal carcinoma (see Murray, et al., Matrix metalloproteinase- 1 is associated with poor prognosis in oesophageal cancer. J. Pathol. 185, 256-61 (1998), Porte, et al, Overexpression of stromelysin-3, BM-40/SPARC, and MET genes in human esophageal carcinoma: implications for prognosis. Clin. Cancer Res. 4, 1375- 82 (1998) and Hewin, et al., Plasminogen activators in oesophageal carcinoma. Br. J. Surg. 83, 1152-5 (1996)). In parallel with the expression increase in ECM proteinases, an expression decrease was seen in a number of proteinase inhibitors, including squamous cell carcinoma antigen 1 (SCCA1), squamous cell carcinoma antigen 2 (SCCA2), cystatin 6, and ELANH2 (Fig. 3A, i). The loss of inhibitory proteinases may allow metastatic tumor progression to occur more rapidly. As the tumor moves through the stromal compartment into secondary sites, a balance must be reached between ECM degradation and renewal. The tumor requires the break down of ECM components to enable invasion, but the stromal environment must also be altered to create an environment with which the tumor can adhere and migrate. SPARC/osteonectin, SPPl/osteopontin, and thrombospondin-2 are secreted proteins involved in mediating cell to matrix interactions. These genes cluster together (Figure Id), and are over-expressed in BA. SPARC, SPP-1, and thrombospondin-1 have previously been linked to oesophageal carcinoma (see Porte, et al, supra, Casson, et al, Ras mutation and expression ofthe ras- regulated genes osteopontin and cathepsin L in human esophageal cancer. Int. J. Cancer 72, 739-45 (1997) and Oshiba,et al, Stromal thrombosρondin-1 expression is correlated with progression of esophageal squamous cell carcinoma. Anticancer Res. 19, 4375-8 (1999)). Further denoting the changes in the stromal environment, the ECM components, chondroitin sulfate proteoglycan 2, collagen type XI alpha 1, and collagen type X alpha 1, are also present in this cluster (Figure Id). Example 8: Other Gene Clusters A number of additional clusters, besides those directly related to the metastatic process, have been identified. Reflecting a change in the tumor's transcriptional program, one distinct cluster under-expressed in BA contained the homeobox genes, PITX1, PAX9 and BARX2 (Figure la). The homeobox gene, HOXB7, was over-expressed in BA (Figure lb). Homeobox genes are nuclear transcription factors that regulate development. Another cluster contained genes elicited by the body's anti-tumoral immune response (Figure lb). Two genes induced by interferon alpha and beta, IFI35 and IFI30, two genes induced by interferon gamma, ISG15 and GIP3, and interferon-induced complement component C2 were present in a cluster over-expressed in BA (Figure lb). Natural killer transcript 4 (NK4) also clusters with these genes. = " = Example 9: EST Clustering Clustering was performed for the full Human Genome U95 set. After filtering, 4521 genes (7.5% ofthe genes present on all 5 arrays) were analyzed via hierarchical clustering and the results are shown in Figure 2. A list ofthe genes thus identified is provided in Table 8. A hierarchical clustering was used to measure expression variation for 4,521 known genes or ESTs from the Affymetrix HG-U95 array set. Three clusters are shown that include genes from the HG-U95A analysis (see Figure 1). Genes in common between clusters are labeled in green. The dendrogram summarizes expression similarities between samples. Each gene and sample presentation is the same as in Figure 1. The overall fold change (FC), fold change between the groups of tissue samples, are also listed for each gene. Based on expression similarities to known genes, the biological function of some EST's can be assigned. Cluster A represents a number of marker genes for squamous epithelial cells. ESTs grouped around these genes are novel diagnostic markers whose expression loss follows BA progression. Cluster B represents a number of genes involved in ECM modification. Cluster C represents genes involved in cell adhesion, migration, proliferation and differentiation. Interestingly, EST AA877900 clusters around the cell surface protein encoded by tetraspanins and shows homology to the mouse cell surface antigen 114/A10 precursor. The resulting dendrogram grouped all nine normal oesophagus and all eight BA samples into separate trees. Figure 2 shows the incorporation of these additional genes, consisting primarily of ESTs, into the Human Genome U95A cluster (Figure 1). The U95A cluster contained a number of proteins involved in extracellular matrix modification and structure. Based on expression similarities to known genes, the biological function of surrounding ESTs can be postulated. Supporting this theory, the extracellular matrix proteins, collagen type V alpha 2, biglycan, and SPP1 (osteopontin) are represented in the new cluster (Figure 2). The present invention provides methods to identify genes and ESTs that are differentially expressed in normal and cancerous esophageal tissue. The method entails using several tissues ofthe same disease type to identify the gene expression patterns that are unique to normal and diseased tissues, comparing these patterns to determine the expression patterns that uniquely identify the disease, and performing fold change analysis to discover which genes are the most important determinants of disease. Applying the method, Applicants have identified key disease-related genes, and furthermore demonstrate that these weighted genes, can be used to identify significant clusters generated by hierarchical clustering algorithms. This overall approach, can potentially determine novel targets for diagnostic and therapeutic intervention in a wide variety of tissues, as demonstrated here with BA. Although the present invention has been described in detail with reference to examples above, it is understood that various modifications can be made without departing from the spirit ofthe invention. Accordingly, the invention is limited only by the following claims. All cited patents and publications referred to in this application are herein incorporated by reference in their entirety. Table 1. A) Affymetrix probe array data was used to determine the number of genes expressed in normal esophagus and the number of genes NOT expressed in Barrett's -associated esophageal adenocarcinoma (BA). Relative to gene expression in normal esophagus, 244 genes were uniquely under-expressed in BA. B) The number of genes expressed in BA was compared against the number of normal esophagus genes whose expression was NOT detectable. 179 genes were uniquely over-expressed in BA. HG-U95A HG-U95B HG-U95C HG-U95D HG-U95E Totals A) No. of genes whose expression was detected in normai esophagus 5652 4394 2743 699 1266 14754 No. of genes whose expression was not detected in BA 5123 6158 8162 10397 8017 37857 No. of genes uniquely under-expressed in BA 101 62 46 20 15 244 B) No. of genes whose expression was detected in BA 5433 4659 2772 934 1667 15465 No. of genes whose expression was not detected in normal esophagus 4170 5347 7184 9621 8163 34485 No. of genes uniquely over-expressed in BA 47 51 19 25 37 179 Sum of genes unique for BA 423 Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95_A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95_A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 2. U95_A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95 A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 2. U95_A Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95_B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3„ U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 3. U95 B Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Affy ID Gene Name Fold Change P-value Cluster Incl. AI692575:wd73h12.x1 Homo sapiens cDNA, 3 end /clone=IMAGE-2337287 /clone_end=3' 46163 at /gb=AI692575 /gi=4969915 /ug=Hs.59761 /len=493' 0.021738726 0.000026 Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4- U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95_C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's - associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. U95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 4. TJ95 C Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95JD Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0\33 under-expressed in BA) Table 5- U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95JD Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95JD Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95JD Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change. Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 5. U95 D Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95_E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95JE Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95JE Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95_E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 6. U95 E Fold Change Genes (>3 over-expressed in Barrett's associated esophageal adenocarcinoma (BA), <0.33 under-expressed in BA) Table 7. Genes present in the U95 A cluster which are not in the Fold Change list (>1 over-expressed in Barrett's-asssociated esophageal denocarcinoma (BA), <1 under-expressed in BA) Table 7. Genes present in the U95 A cluster which are not in the Fold Change list (>1 over-expressed in Barrett's-asssociated esophageal denocarcinoma (BA), <1 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 1. Genes present in the U95 A cluster which are not in the Fold Change list (>1 over-expressed in Barrett's-asssociated esophageal denocarcinoma (BA), <1 under-expressed in BA) Affy ID Gene Name Fold Change P-value Table 8. Genes identified by heirarchical clustering of the full Human Genome U95 set showing fold change between normal and diseased sample sets. Table 8. Genes identified by heirarchical clustering of the full Human Genome U95 set showing fold change between normal and diseased sample sets. Table 8. Genes identified by heirarchical clustering of the full Human Genome U95 set showing fold change between normal and diseased sample sets.

Claims: What is claimed is: 1. A method of diagnosing esophageal cancer in a patient, comprising: (a) detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal cancer. 2. A method of detecting the progression of esophageal cancer in a patient, comprising: (a) detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal cancer progression. 3. A method according to claim 2, wherein the progression is the progression of Barrett's esophagus to adenocarcinoma. 4. A method of monitoring the treatment of a patient with esophageal cancer, comprising: (a) administering a pharmaceutical composition to the patient; (b) preparing a gene expression profile from a cell or tissue sample from the patient; and (c) comparing the patient gene expression profile to a gene expression from a cell population selected from the group consisting of normal esophageal cells, cells from Barrett's esophagus and esophageal adenocarcinoma cells. 5. A method of treating a patient with esophageal cancer, comprising: (a) administering to the patient a pharmaceutical composition, wherein the composition alters the expression of at least one gene in Tables 2-8; (b) preparing a gene expression profile from a cell or tissue sample from the patient comprising esophageal cancer cells; and (c) comparing the patient expression profile to a gene expression profile selected from the group consisting of normal esophageal cells, cells from Barrett's esophagus and esophageal adenocarcinoma cells. 6. A method of diagnosing esophageal adenocarcinoma in a patient, comprising: (a) detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal adenocarcinoma. 7. A method of detecting the progression of esophageal adenocarcinoma in a patient, comprising: (a) detecting the level of expression in a tissue sample of two or more genes from Tables 2-8; wherein differential expression ofthe genes in Tables 2-8 is indicative of esophageal adenocarcinoma progression. 8. A method of monitoring the treatment of a patient with esophageal adenocarcinoma, comprising: (a) administering a pharmaceutical composition to the patient; (b) preparing a gene expression profile from a cell or tissue sample from the patient; and (c) comparing the patient gene expression profile to a gene expression from a cell population comprising normal esophagealo cells or to a gene expression profile from a cell population comprising esophageal adenocarcinoma cells or to both. 9. A method of treating a patient with esophageal adenocarcinoma, comprising: (a) administering to the patient a pharmaceutical composition, wherein the composition alters the expression of at least one gene in Tables 2-8; (b) preparing a gene expression profile from a cell or tissue sample from the patient comprising esophageal adenocarcinoma cells; and (c) comparing the patient expression profile to a gene expression profile from an ' untreated cell population comprising esophageal adenocarcinoma cells. 10. A method of screening for an agent capable of modulating the onset or progression of esophageal cancer, comprising: (a) preparing a first gene expression profile of a cell population comprising esophageal cancer cells, wherein the expression profile determines the expression level of one or more genes from Tables 2-8; (b) exposing the cell population to the agent; (c) preparing second gene expression profile ofthe agent-exposed cell population; and (d) comparing the first and second gene expression profiles. 11. The method of claim 14, wherein the esophageal cancer is a esophageal adenocarcinoma. 12. A composition comprising at least two ohgonucleotides, wherein each ofthe ohgonucleotides comprises a sequence that specifically hybridizes to a gene in Tables 2-8. 13. A composition according to claim 12, wherein the composition comprises at least 3 ohgonucleotides. 14. A composition according to claim 12, wherein the composition comprises at least 5 ohgonucleotides. 15. A composition according to claim 12, wherein the composition comprises at least 7 ohgonucleotides. 16. A composition according to claim 12, wherein the composition comprises at least 10 ohgonucleotides. 17. A composition according to any one of claims 12-16, wherein the ohgonucleotides are attached to a solid support. 18. A composition according to claim 17, wherein the solid support is selected from a group consisting of a membrane, a glass support, a filter, a tissue culture dish, a polymeric material, a bead and a silica support. 19. A solid support comprising at least two ohgonucleotides, wherein each ofthe ohgonucleotides comprises a sequence that specifically hybridizes to a gene in Tables 2-8. 20. A solid support according to claim 19, wherein the ohgonucleotides are covalently attached to the solid support. 21. A solid support according to claim 20, wherein the ohgonucleotides are non- covalently attached to the solid support. 22. A solid support according to claim 19, wherein the support comprises at least about 10 different ohgonucleotides in discrete locations per square centimeter. 23. A solid support according to claim 19, wherein the support comprises at least about 100 different ohgonucleotides in discrete locations per square centimeter. 24. A solid support according to claim 19, wherein the support comprises at least about 1000 different ohgonucleotides in discrete locations per square centimeter. 25. A solid support according to claim 19, wherein the support comprises at least about 10,000 different ohgonucleotides in discrete locations per square centimeter. 26. A computer system comprising: (a) a database containing information identifying the expression level in esophageal tissue of a set of genes comprising at least two genes in Tables 2-8; and (b) a user interface to view the information. 26. A computer system of claim 25, wherein the database further comprises sequence information for the genes. 27. A computer system of claim 25, wherein the database further comprises information identifying the expression level for the set of genes in normal esophageal tissue. 28. A computer system of claim 25, wherein the database further comprises information identifying the expression level ofthe set of genes in esophageal cancer tissue. 29. A computer system of claim 28, wherein the esophageal cancer tissue comprises esophageal adenocarcinoma cells. 30. A computer system of claim 31-36, further comprising records including descriptive information from an external database, which information correlates said genes to records in the external database. 31. A computer system of claim 30, wherein the external database is GenBank. 32. A method of using a computer system of any one of claims 26-29 to present information identifying the expression level in a tissue or cell of at least one gene in Tables 2- 8, comprising: (a) comparing the expression level of at least one gene in Tables 2-8 in the tissue or cell to the level of expression ofthe gene in the database. 33. A method of claim 32, wherein the expression level of at least two genes are compared. 34. A method of claim 32, wherein the expression level of at least five genes are compared. 35. A method of claim 32, wherein the expression level of at least ten genes are compared. 36. A method of claim 32, further comprising displaying the level of expression of at least one gene in the tissue or cell sample compared to the expression level in esophageal cancer.

Inventors: ALVARES CHRISTOPHER, BOLAND JOSEPH F, LORD REGINALD V, SCHERF UWE, VOCKLEY JOSEPH G, WETZEL JON C, WILLIAMS AMANDA, ALVARES, CHRISTOPHER, BOLAND, JOSEPH, F., LORD, REGINALD, V., SCHERF, UWE, VOCKLEY, JOSEPH, G., WETZEL, JON, C., WILLIAMS, AMANDA
Applicants: ALVARES CHRISTOPHER, BOLAND JOSEPH F, GENE LOGIC INC, LORD REGINALD V, SCHERF UWE, VOCKLEY JOSEPH G, WETZEL JON C, WILLIAMS AMANDA, ALVARES, CHRISTOPHER, BOLAND, JOSEPH, F., GENE LOGIC, INC., LORD, REGINALD, V., SCHERF, UWE, VOCKLEY, JOSEPH, G., WETZEL, JON, C., WILLIAMS, AMANDA
Application year: 2001
Main classification: A61K48
IPC codes: C12Q1, G06F19