MX2008003933A - Methods for diagnosing pancreatic cancer - Google Patents

Abstract

The present invention provides a method of identifying origin of a metastasis of unknown origin by obtaining a sample containing metastatic cells;measuring Biomarkers associated with at least two different carcinomas;combining the data from the Biomarkers into an algorithm where the algorithm normalizes the Biomarkers against a reference;and imposes a cut-off which optimizes sensitivity and specificity of each Biomarker, weights the prevalence of the carcinomas and selects a tissue of origin determining origin based on highest probability determined by the algorithm or determining that the carcinoma is not derived from a particular set of carcinomas;and optionally measuring Biomarkers specific for one or more additional different carcinoma, and repeating the steps for additional Biomarkers.

Description

METHODS FOR DIAGNOSING PANCREATIC CANCER FIELD OF THE INVENTION The present invention provides methods for diagnosing pancreatic cancer.

BACKGROUND OF THE INVENTION Pancreatic cancer is a deadly disease that has a mortality rate in the United States of more than 27,000 people per year. Lillemoe et al (2000). About 85% of those diagnosed with the disease have metastasis or spread of the disease beyond the pancreas and it is practically impossible to cure them with surgical resection. If growth is found sooner, it can be resected with a much better hope of cure. Only 20% of tumors are resectable and the survival benefit of approved chemotherapy regimens is quite poor and the chances of cure are usually 25% or less. Kroep et al. (1999); Wiesenauer et al. (2003); Ros et al. (2001); Ryu et al. (2002); and Ito et al. (2001). Early diagnosis is necessary for successful early treatment. Despite advances in diagnostic imaging methods such as ultrasonography (US), endoscopic ultrasonography (EUS), dual-phase spiral computer tomography (CT), magnetic resonance imaging (MRT), endoscopic retrograde cholangiopancreatography (ERCP) and transcutaneous or fine needle aspiration guided by EUS, distinguishing pancreatic carcinoma from benign pancreatic diseases, especially chronic pancreatitis, is difficult because of the similarities in radiological and imaging characteristics and the lack of specific clinical symptoms for pancreatic carcinoma. Substantial efforts have been directed towards the development of useful tools for the early diagnosis of carcinomas. However, a definitive diagnosis often depends on exploratory surgery that is performed inevitably after the disease has advanced beyond the point in time. that the treatment can be done early. 20060029987. The neoplasms and the exocrine pancreas can arise from the ductal, acinic and stromal cells. Eighty percent of pancreatic carcinomas are derived from the ductal epithelium. 60% of these tumors are located in the head of the pancreas. 10% in the tail and 30% are located in the body of the pancreas, or are diffuse. Warshau et al. (1992). Histologically, these tumors are categorized as well as differentiated, moderately differentiated and poorly differentiated. Some tumors are classified as adenosquamous, mucinous, undifferentiated or undifferentiated with giant cells similar to osteoblasts. Gibson et al. (1978). Several profiles of genetic expression and genetic markers related to pancreatic cancer have been previously raised. 20050009067; 20040219572; and 20030212264.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1A and Figure B describe the microarray data showing the intensities of two genes in a tissue panel. (Figure 1A) Prostatic stem cell antigen (PSCA). (Figure 1 B) coagulation factor V (F5). The bar graphs show intensity on the Y axis and tissue on the X axis. Pane Ca, pancreatic cancer; Pnc N, normal pancreas. Figure 2A and Figure 2B show the electropherograms obtained from an Agilen Bioanalyzer. RNA was isolated from FFPE tissue using a three-hour (Figure 2A) or sixteen-hour digestion (Figure 2B) of proteinase K. Sample C22 (red) was a one-year-old block, whereas sample C23 ( blue) was a block of five years of age. A side scale is shown in green. Figure 3A-Figure 3C describe a comparison between the values obtained from three different qRTPCR methods: random hexamer primerization in reverse transcription followed by qPCR with the resulting cDNA (2-step RH), gene-specific primer (reverse primer) in the reverse transcription followed by qPCR with the resulting cDNA (2 step GSP), or specific primerization of the gene and qRTPCR in a one step reaction (1 step GSP). RNA from eleven samples was divided into the three methods and RNA levels were measured for three genes: β-actin (Figure 3A), HUMSPB (Figure 3B), and TTF (Figure 3C). The average Ct value obtained with each method is indicated by the solid line. Figure 4A-Figure 4D show the optimization of the assay. (A and B) Electropherograms obtained from an Agilent Bioanalyzer. RNA was isolated from FFPE tissue using a three-hour (Figure 4A) or sixteen-hour digestion (Figure 4B) of proteinase K. Sample C22 (red) was a one-year-old block, whereas sample C23 ( blue) was a block of five years of age. A side scale is shown in green. (Figures 4C and 4D) Comparison of the Ct values obtained from three different methods qRTPCR: random primerization of hexamer in the reverse transcription followed by qPCR with the resulting cDNA (2-step RH), specific primerization of the gene (reverse bait) in the reverse transcription followed by qPCR with the resulting cDNA (2 step GSP), or gene specific primer and qRTPCR in a one step reaction (1 step GSP). RNA from eleven samples was divided into three methods and RNA levels were measured for two genes: β-actin (Figure 4C), HUMSPB (Figure 4D). The average value of Ct obtained with each method is indicated with the solid line. Figure 5 is a heat map showing the relative expression levels of the Marker 10 panel through 239 samples. Red indicates the highest expression.

DETAILED DESCRIPTION OF THE INVENTION A Biomarker is any indices of the expression level of a indicated marker gene. The indices can be direct or indirect and measure the low expression or overexpression of the gene given the physiological parameters and the comparison with an internal control, the normal tissue or another carcinoma. Biomarkers include, without limitation, nucleic acids (both overexpression and downregulation and direct and indirect). The use of nucleic acids as biomarkers can include any method known in the art, including, without limitation, measurement of DNA amplification, RNA, microRNA, loss of heterozygosity (LOH), single nucleotide polymorphisms (SNP's, Brookes (1999) ), Microsatellite DNA, hypo or hypermethylation of DNA. The use of proteins as biomarkers can include any method known in the art, including, without limitation, quantity measurement, activity, modifications such as glycosylation, phosphorylation, ADP-ribosylation, ubiquitination, etc., immune chemistry (IHC). Other biomarkers include imaging, cell counting and apoptosis markers. The indicated genes provided herein are those associated with a particular tumor or tissue type. A marker gene may be associated with numerous cancers but with the proviso that the expression of the gene is sufficiently associated with a tumor or type of tissue to be identified, using the algorithm described herein, as specific to a particular origin. , the gene can be used in the claimed invention to determine the tissue of origin of a carcinoma of unknown primary origin (CUP). Numerous genes associated with one or more cancers are known in the art. The present invention provides preferred marker genes and combinations of even more preferred marker genes. These are described here in detail. "Origin", as mentioned in 'tissue of origin' means any of the tissue type (lung, colon, etc.) or histological type (adenocarcinoma, squamous cell carcinoma, etc.) depending on the particular medical circumstances, and It will be understood by anyone with experience in the art. A marker gene corresponds to the sequence designated by a SEQ ID NO. when it contains that sequence. A segment or gene fragment corresponds to the sequence of that gene when it contains a portion of the reference sequence or its complement sufficient to distinguish it as the sequence of the gene. A gene expression product corresponds to such a sequence when its RNA, mRNA, or cDNA hybridizes with the composition having the sequence (e.g., a probe) or, in the case of a peptide or protein, is encoded by said mRNA. A segment or fragment of a gene expression product corresponds to the sequence of the gene or gene expression product when it contains a portion of the gene expression product of reference or sufficient complement to distinguish it as the gene sequence or gene expression product.

The methods, compositions, articles and kits thereof of the invention, those described and claimed in this specification, include one or more marker genes. "Marker" or "marker gene" is used throughout this specification to refer to genes and gene expression products that correspond to any overexpression or down expression of the gene from which it is associated with a tumor or tissue type. Preferred marker genes are described in greater detail in Tables 1 and 15.

TABLE 1 CUP panel TTF1 1024 21 s at gtgattcaaatgggttttccacgctagggcgggg cacagattggagagggctctgtgctgacatggct ctggactctaaagaccaaacttcactctgggcac actctgccagcaaagaggactcgcttgtaaatac caggatttttttttttttttgaagggaggacgggagc tggggagaggaaagagtcttcaacataaccca cttgtcactgacacaaaggaagtgccccctcccc ggcaccctctggccgcctaggctcagcggcgac cgccctccgcgaaaatagtttgtttaatgtgaactt gtagctgtaaaacgctgtcaaaagttggactaaa tgcctagtttttagtaatctgtacattttgttgtaaaaa gaaaaaccactcccagtccccagcccttcacatt ttttatgggcattgacaaatctgtgtatattatttggc agtttggtatttgcggcgtcagtctttttctgttgtaac DSG3 205,595 t at ccatcccatagaagtccagcagacaggatttgtt aagtgccagactttgtcaggaagtcaaggagctt ctgctttgtccgcctctgggtctgtccagccagctg tttccatccctgaccctctgcagcatggtaactattt agtaacggagacttactcggcttctggttccctcgt gcaaccttccactgcaggctttgatccacttctcac acaaaatgtgatagtgacagaaagggtgatctg tcccatttccagtgttcctggcaacctagctggccc aacgcagctacgagggtcacatactatgctctgt acagaggatccttgctcccgtctaatatgaccag aatgagctggaataccacactgaccaaatctgg atctttggactaaagtattcaaaatagcatagcaa agctcactgtattgggctaataatttggcacttatta gcttctctcataaactgatcacgattataaattaaa tgtttgggttcataccccaaaagcaatatgttgtca ctcctaattctcaagtac 209847 HPT1 at ctgcacccacctacttagatatttcatgtgctatag acattagagagatttttcatttttccatgacatttttcc tctctgcaaatggcttagctacttgtgtttttcccttttg gggcaagacagactcattaaatattctgtacatttt ttctttatcaaggagatatatcagtgttgtctcatag aactgcctggattccatttatgttttttctgatt ccatc ctgtgtccccttcatccttgactcctttggtatttcact gaatttcaaacatttgtc 8 PDEF 220192 x at gagtggggcccttaaactggattcaaaaa atgctctaaacataggaatggttgaagagg tcttgcagtcttcagatgaaactaaatctcta gaagaggcacaagaatggctaaagcaat tcatccaagggccaccggaagtaattaga gctttgaaaaaatctgtttgttcaggcagag agctatatttggaggaagcattacagaacg aaagagatcttttaggaacagtttggggtgg gcctgcaaatttagaggctattgctaagaa aggaaaatttaataaataattggtttttcgtgt ggatgtactccaagtaaagctccagtgact aatatgtataaatgttaaatgatattaaatat gaacatcagttaaaaaaaaaattctttaag gctactattaatatgcagacttacttttaatcat ttgaaatctgaactcatttacctcatttcttgcc aattactcccttgggtatttactgcgta PSA 10 204 582 s at tggtgtaattttgtcctctctgtgtcctggggaa tactggccatgcctggagacatatcactca atttctctgaggacacagataggatggggt gtctgtgttatttgtggggtacagagatgaaa gaggggtgggatccacactgagagagtg gagagtgacatgtgctggacactgtccatg aagcactgagcagaagctggaggcacaa cgcaccagacactcacagcaaggatgga gctgaaaacataacccactctgtcc October 15 WT1 206 067 s at atagatgtacatacctccttgcacaaatgga ggggaattcattttcatcactgggagtgtcctt agtgtataaaaaccatgctggtatatggctt caagttgtaaaaatgaaagtgactttaaaa gaaaataggggatggtccaggatctccact gataagactgtttttaagtaacttaaggacct ttgggtctacaagtatatgtgaaaaaaatga gacttactgggtgaggaaatccattgtttaa agatggtcgtgtgtgtgtgtgtgtgtgtgtgtgt gttgtgttgtgttttgttttttaagggagggaatt tattatttaccgttgcttgaaattactgtgtaaa tatatgtctgataatgatttgctctttgacaact aaaattaggactgtataagtactagatgcat cactgggtgttgatcttacaagat The present invention provides a method for diagnosis of pancreatic cancer. Therefore, the present invention provides methods for determining the direction of therapy, by identifying pancreatic cancers sufficiently early to avoid resection and thus allowing chemotherapeutic regimens. The present invention also provides a composition containing at least one isolated sequence selected from SEQ ID NOS .: 39-41 and 43-45. The present invention also provides kits for conducting an assay in accordance with the methods provided herein and also containing biomarker detection reagents. The present invention also provides methods for measuring gene expression by generating the amplicons of SEQ ID NOS. 42 and 46 to determine the genetic expression and compare the levels of at least one of these amplicons with the genetic expression of normal tissue, to diagnose pancreatic cancer. The present invention also provides microarrays or genetic chips to perform the methods described herein. The present invention also provides diagnostic / prognostic portfolios containing isolated nucleic acid sequences, their complements, or portions thereof, of a combination of genes as described herein, where the combination is sufficient to measure or characterize genetic expression in a biological sample that has metastatic cells relative to cells of different carcinomas or normal tissue. Any method described in the present invention may also include measuring the expression of at least one gene constitutively expressed in the sample. Preferably, markers for pancreatic cancer are coagulation factor V (F5), prostate stem cell antigen (PSCA), integrin, ß6 (ITGB6), kallikrein 10 (KLK10), claudin 18 (CLDN18), trio isoform (TR10), and the hypothetical protein FLJ22041 similar to the binding proteins FK506 (FKBP10). Preferably, the biomarkers for F5 and PSCA are measured together. The biomarkers for ITGB6, KLK10, CLDN18, TR10, and FKBP10 can be measured in addition to or instead of the F5 and / or PSCA. F5 is described, for example, in documents 20040076955; 20040005563; and WO2004031412. PSCA is described, for example, in W01998040403; 20030232350; and WO2004063355. ITGB6 is described, for example, in WO2004018999; and 6339148. KLK10 is described, for example, in WO2004077060; and 20030235820. CLDN18 is described, for example, in WO2004063355; and WO2005005601. TR10 is described, for example, in 20020055627. FKBP10 is described, for example, in WO2000055320. The invention also provides a method for providing a prognosis by determining the presence of pancreatic cancer according to the methods described herein and identifying the corresponding prognosis for this.

The invention also provides a method for finding biomarkers, comprising determining the level of expression of a marker gene in a particular metastasis, measuring a biomarker for the marker gene, to determine the expression thereof, analyzing the expression of the marker gene in accordance with the methods described here and determine if the marker gene is effectively specific for pancreatic cancer. The invention also provides compositions comprising at least one isolated sequence selected from SEQ ID NOS .: 39-46. The invention also provides kits, articles, microarrays or genetic chip, diagnostic / prognostic portfolios to conduct the assays described herein and patient reports to report the results obtained by means of the present methods. The mere presence or absence of the particular nucleic acid sequences in a tissue sample has only rarely been found to have a diagnostic or prognostic value. The information about the expression of several proteins, peptides or mRNA, on the other hand, is increasingly considered more important. The mere presence of nucleic acid sequences that have the potential to express proteins, peptides or mRNA (these sequences called "genes") within the genome itself, is not determinative of whether a protein, peptide, or mRNA is expressed in a cell Dadaist. Whether a gene that is or is not capable of expressing protein, peptides or mRNA, does or does not do so, or to what degree that expression occurs, if it occurs, is determined by a variety of complex factors. Regardless of the difficulties in understanding and evaluating these factors, the genetic expression tested can provide useful information about the occurrence of important events such as tumorigenesis, metastasis, apoptosis, and other relevant clinical phenomena. Indications regarding the degree to which genes are active or inactive can be found in gene expression profiles. The gene expression profiles of this invention are used to provide a diagnosis and treat patients for CUP. In the above methods, the sample may be prepared by any method known in the art, including, but not limited to, volume tissue preparation and laser capture microdissection. The preparation of tissue in volume can be obtained for example from a biopsy or a surgical specimen. In the above methods, the measurement of gene expression may also include measuring the level of expression of at least one gene constitutively expressed in the sample. In the above methods, the specificity is preferably at least about 40% and the sensitivity at least about 80%. In the above methods, the predetermined cut-off levels are at least about 1.5 times the overexpression or down expression in the sample relative to benign cells or normal tissue.

In the methods above, the predetermined cut-off levels have at least one overexpression with statistically significant p-value in the sample having the metastatic cells relative to the benign cells or normal tissue, preferably the p-value is less than 0.05. . In the above methods, gene expression can be measured by any method known in the art including, without being limited to a microarray or genetic chip, nucleic acid amplification driven by polymerase chain reaction (PCR) such as a chain reaction. Reverse transcription polymerase (RT-PCR), which measures or detects a protein encoded by the gene such as an antibody specific for the protein or by measuring a characteristic of the gene such as DNA amplification, methylation, mutation and allelic variation. The microarray can be, for example, a cDNA array or an oligonucleotide array. All of these methods may also contain one or more internal control reagents. The present invention provides a method for generating a patient report of pancreatic cancer prognosis, determining the results of any of the methods described herein and preparing a report that shows the results and the patient reports generated by them. The report may also contain an evaluation of the patient's outcome and / or the probability of risk in relation to the patient's population. The preparation of the sample requires the collection of patient samples. The patient samples used in the methods of the invention are those that are suspected of containing diseased cells such as cells taken from a nodule in a tissue aspiration by means of a fine needle (FNA). Mass tissue preparation obtained from a biopsy or surgical specimen and micro dissection by laser capture are also suitable for use. The technology of micro dissection by laser capture (LCM) is a way to select the cells to be studied, minimizing the variability caused by the heterogeneity of the cell type. Consequently, moderate or small changes in the expression of the marker gene can be easily detected between normal or benign cells and cancer cells. The samples may also comprise circulating epithelial cells extracted from the peripheral blood. These can be obtained according to numerous methods, but the most preferred method is the magnetic separation technique described in 6136182. Once the sample containing the cells of interest has been obtained, a gene expression profile is obtained. using a biomarker, for the genes in the appropriate portfolios. Preferred methods for establishing gene expression profiles include determining the amount of RNA that is produced by a gene that can code for a protein or peptide. This is achieved through reverse transcriptase PCR (RT-PCR), competitive RT-PCR, real-time RT-PCR, differentially spread RT-PCR, Northern blot analysis and other related tests. Although it is possible to conduct these techniques using individual PCR reactions, it is better to amplify the complementary DNA (cDNA) or complementary RNA (cRNA) produced from the mRNA and analyze it via the microarray. Numerous arrangement configurations and methods for their production are known to those skilled in the art, and are described for example in documents 5445934; 5532128 5556752; 5242974; 5384261; 5405783; 5412087; 5424186; 5429807 5436327; 5472672; 5527681; 5529756; 5545531; 5554501; 5561071-5571639; 5593839; 5599695; 5624711; 5658734; and 5700637. The technology of microarrays allows the measurement of the state of stillness of the mRNA level of thousands of genes simultaneously, thus presenting a powerful tool to identify the effects such as the initiation, arrest, or modulation of cell proliferation. not controlled. Currently, two microarray technologies are widely used. The first are cDNA arrays, and the second are oligonucleotide arrays. Although there are differences in the construction of these chips, essentially all analysis and data outputs, downstream, are equal. The product of these analyzes is typically measurements of the intensity of the signal received from a labeled probe used to detect a cDNA sequence in the sample that hybridizes to a nucleic acid sequence at a known location in the microarray. Typically, the intensity of the signal is proportional to the amount of cDNA, and thus of mRNA, expressed in the cells of the sample. A large number of these techniques are available and useful. Preferred methods for determining gene expression can be found in documents 6271002; 6218122; 6218114; and 6004755. The analysis of the expression levels is conducted by means of the comparison of the intensities of the signals. This is best done by generating a relationship matrix of the expression intensities of the genes in a test versus those of a control sample. For example, the intensities of genetic expression of a diseased tissue can be compared with the expression intensities generated from benign or normal tissue of the same type. A relationship of these expression intensities indicates the times of the change in gene expression between the test and control samples. The selection can be based on statistical tests that produce ordered lists related to the evidence of the significance for differential expression of each gene among the factors related to the site of original origin of the tumor. Examples of these tests include ANOVA and Krushal-Wallis. The orders can be used as weights in a model designed to interpret the sum of these weights, up to a cut, according to the preponderance of the evidence in favor of one class over the other. Previous evidence, as described in the literature, can also be used to adjust weights. A preferred embodiment is to normalize each measurement by identifying a stable control set and scaling this set to zero variance across all the samples. This control set is defined as any single endogenous transcript or set of endogenous transcripts affected by the systematic error in the assay, and which is not known to change independently of this error. All the markers are adjusted by the specific factor of the sample that generates the zero variance for any descriptive statistics of the control set, such as an average or a mean, or for a direct measurement. Alternatively, if the premise of variation of the controls related only to the systematic error is not true, although the resulting classification error is lower when normalization is performed, the control set will still be used as established. Non-endogenous peak controls may also be useful, but are not preferred. Genetic expression profiles can be displayed in numerous ways. The most common is to adjust the gross fluorescence intensities or the ratio matrix in a graphical dendogram where the columns indicate the test samples and the rows indicate the genes. The data is arranged so that genes that have similar expression profiles are close to each other. The expression ratio for each gene is displayed as a color. For example, a minus-one relationship (down-regulation) appears in the blue portion of the spectrum, while a ratio greater than one (up-regulation) appears in the red portion of the spectrum. Computer software programs are commercially available to display these data including "Genespring" (Silicon Genetics, Inc.) and "Discovery" and "Infer" (Partek, Inc.).

In the case of measurement of protein levels to determine gene expression, any method known in the art is suitable provided that it results in the appropriate specificity and sensitivity. For example, protein levels can be measured by agglutinating an antibody or antibody fragment specific for the protein, and measuring the amount of protein bound to the antibody. The antibodies can also be labeled by means of radioactive, fluorescent or other detectable reagents, to facilitate detection. Detection methods include, without limitation, the enzyme-linked immunosorbent assay (ELISA) and immune spotting techniques. The modulated genes used in the methods of the invention are described in the Examples. Genes that are differentially expressed are either regulated at discharge or down-regulated in patients with carcinoma of a particular origin in relation to those with carcinomas of different origins. The regulation to the high and the downward regulation are relative terms that mean that a detectable difference is found (beyond the contribution of the noise in the system used to measure it) in the amount of expression of the genes in relation to some line base. In this case, the baseline is determined based on the algorithm. The genes of interest in the diseased cells are then either regulated at high or regulated downward, relative to the level of the baseline using the same measurement method. Sick, in this context, refers to an alteration of the state of a body that interrupts or disturbs, or that has the potential to disrupt, and correct performance of bodily functions, as occurs with the uncontrolled proliferation of cells. Someone is diagnosed with a disease when some aspect of that person's genotype or phenotype is consistent with the presence of the disease. However, the act of conducting a diagnosis or prognosis may include the determination of disease / status elements such as the determination of the possibility of relapse, the type of therapy and monitoring of therapy. In therapy monitoring, clinical judgments are made regarding the effect of a given course of therapy by comparing the expression of genes over time to determine whether gene expression profiles have changed or are changing towards more consistent patterns with the normal tissue The genes can be grouped so that the information obtained about the group of genes in the group provides a basis of sounding to make clinically relevant judgments such as diagnosis, prognosis or choice of treatment. These sets of genes make up the portfolio of the invention. As with diagnostic markers, it is often desirable to use the least sufficient number of markers to make a correct medical judgment. This avoids the delay in the treatment that depends on the subsequent analysis, as well as the unproductive use of time and resources. One method to establish the genetic expression portfolio is through the use of optimization algorithms such as the average variance algorithm widely used in the establishment of material portfolios. This method is described in detail in document 20030194734. Essentially, the method proposes the establishment of a set of inputs (materials in financial applications, expression measured by intensity, here) that will optimize the return (for example, the signal that is generated ) that is received by using it while minimizing the return variability. Many commercial software programs are available to conduct these operations. The "Wagner Associates Variance Optimization Application", known as "Wagner Software", is preferred through this specification. This software uses the functions of the "Variance Optimization Library" of the average of Wagner Associates "to determine an efficient and optimal frontier portfolio in Markowitz's sense of preference. (Markowitz (1952).) The use of this type of software requires that the microarray data be transformed so that they can be treated as an entry in the sense in which the return of material and risk measurements are used when the software is used for its purposes of financial design analysis.The procedure of selecting a portfolio may also include the application of heuristic rules. Preferably, these rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to the output from the optimization method. For example, the average variance method of portfolio selection can be applied to the microarray data for a number of genes differentially expressed in subjects with cancer. The output of the method would be an optimized set of genes that could include some genes that are expressed in peripheral blood as well as in diseased tissue. If the samples used in the test method are obtained from the peripheral blood and certain genes differentially expressed in the case of cancer can also be differentially expressed in the peripheral blood, then a heuristic rule can be applied in which a portfolio is selected from the efficient frontier excluding those that are differentially expressed in the peripheral blood. Of course, the rule can be applied before the formation of the efficient frontier, for example, by applying the rule during the preselection of the data. Other heuristic rules that do not necessarily relate to the biology in question may apply. For example, a rule can be applied that only a prescribed percentage of the portfolio can be represented by a particular gene or group of genes. Commercially available software such as the Wagner Software easily accommodates these types of heuristics. This can be useful, for example, when factors other than accuracy and precision (for example, anticipated license fees) have an impact on the desire to include one or more genes. The gene expression profiles of this invention can also be used in conjunction with other non-genetic diagnostic methods useful in the diagnosis, prognosis and monitoring of cancer treatment.

For example, in some circumstances it is beneficial to combine the diagnostic power of the above methods based on gene expression with data from conventional markers such as serum protein markers (eg, cancer antigen 27.29 ("CA 27.29")) . There is a variety of these markers, including analytes such as CA 27.29. In such a method, blood is periodically taken from a treated patient and then subjected to an enzyme immune assay for one of the serum markers described above. When the concentration of the marker suggests the return of the tumors or the failure of the therapy, a source of samples sensitive to the analysis by genetic expression is taken. Where there is a suspicious mass, a fine needle aspiration (FNA) is taken and the expression profiles of the cells of the mass are taken, then analyzed as described above. Alternatively, tissue samples may be taken from areas adjacent to the tissue from which a tumor was previously removed. This approach can be particularly useful when other tests produce ambiguous results. The kits made in accordance with the invention include assays formatted to determine gene expression profiles. These may include all or some of the materials that are needed to conduct the assays such as reagents and instructions and a medium through which the biomarkers are tested. The articles of this invention include representations of gene expression profiles useful for treating, diagnosing, predicting, and otherwise evaluating diseases. These profile representations are reduced to a means that can be automatically read by means of a machine, such as a computer-readable medium (magnetic, optical and the like). Articles may also include instructions for evaluating gene expression profiles in that medium. For example, the articles may include a CD ROM having computer instructions for comparing the gene expression profiles of the gene portfolios described above. The articles may also have gene expression profiles digitally recorded therein so that they can be compared with the genetic expression data of the patient's samples. Alternatively, the profiles can be recorded in different format representations. A graphic reminder, it's one of those formats. Clustering algorithms such as those incorporated in the software "DISCOVERY" and "INFER" of Partek, Inc., mentioned above, can help in the visualization of the data better. The different types of articles of manufacture according to the invention are the formatted means or assays that are used to reveal the gene expression profiles. These may comprise, for example, microarrays in which the sequence complements or the probes are fixed to a matrix for which the sequences indicative of the genes of interest are combined, creating a readable determinant of their presence. Alternatively, the articles according to the invention can be made in the form of reagent kits for conducting hybridization, amplification, and signal generation indicative of the level of expression of the genes of interest to detect cancer. The following examples are provided to illustrate, but not limit, the claimed invention. All references cited here are incorporated herein by reference.

EXAMPLE 1 Materials and methods Genetic discovery of pancreatic cancer markers. RNA was isolated from pancreatic, normal pancreatic, lung, colon, breast and ovarian tissue using Trizol. The RNA was then used to generate amplified, labeled RNA (Lipshutz et al (1999)) which was then hybridized in Affymetrix U133A arrays. Then the data was analyzed in two ways. In the first method, this data set was filtered to retain only those genes with at least two calls present throughout the entire data set, This filtering left 14,547 genes. It was determined that, 736 genes were overexpressed in pancreatic cancer versus normal pancreas with a p-value of less than 0.05. Forty-five genes of the 736 were also over-expressed by at least two points compared to the maximum intensity found from the lung and colon tissues. Finally, sets of probes were found that were overexpressed by at least two points compared to the maximum intensity found in lung, colon, breast and ovarian tissues. In the second method, this data set was filtered to retain only those genes with no more than two calls present in breast, colon, lung and ovarian tissues. This filtering left 4,654 genes. It was found that 160 genes of the 4,654 genes had at least two calls present in the pancreatic tissues (normal and cancer). Finally, eight sets of probes were selected that showed the highest differential expression between pancreatic cancer and normal tissues.

Tissue samples. A total of 260 FFPE metastases and primary tissues from a variety of commercial vendors were purchased. The samples tested included: 30 breast metastases, 30 colorectal metastases, 56 lung metastases, 49 ovarian metastases, 43 pancreatic metastases, 18 primary prostate and 2 prostate metastases, and 32 from other sources (6 stomach, 6 kidney , 3 of larynx, 2 of liver, 1 of esophagus, 1 of pharynx, 1 of bile duct, 1 of pleura, 3 of bladder, 5 of melanoma, 3 of lymphoma).

RNA extraction. Isolation of RNA from sections of tissue in paraffin was based on the methods and reagents described in the manual High Puré RNA Paraffin Kit (Highly Pure RNA Paraffin Equipment) (Roche) with the following modifications. The tissue samples integrated in paraffin were sectioned according to the size of the integrated metastasis (2-5mm = 9 X 10μm, 6-8mm = 6 X 10μm, 8-> 10mm = 3 X 10μm), and were placed in Eppendorf RNase / DNase 1.5ml tubes. The sections were removed from the paraffin by incubation in 1 ml of xylene for 2-5 min at room temperature followed by 10-0 seconds of vortexing. Then the tubes were centrifuged and the floating was removed and the paraffin removal step was repeated. After removing the float, 1 ml of ethanol was added and the mixture vortexed for 1 minute, centrifuged and the float removed. This procedure was repeated one more time. The residual ethanol was removed and the pill was dried in an oven at 55 ° C for 5-10 minutes and resuspended in 100 μl of tissue lysis buffer, 16 μl of 10% SDS and 80 μl of Proteinase K. Samples they were vortexed and incubated in a thermo mixer at 400 rpm for 2 hours at 55 ° C. Agglutination buffer and 325 μl of ethanol were added to each sample and after mixing, centrifuged, and the float was added to the filter column. The filter column, together with a collection tube, was centrifuged for 1 minute at 8000 rpm and the flow through was discarded. A series of sequential washes (500 μl of wash buffer I? 500 μl of wash buffer II? 300 μl wash buffer II) was performed in which each solution was added to the column, centrifuged and discarded flow through . The column was then centrifuged at maximum speed for 2 minutes, a fresh 1.5 ml tube was placed and 90 μl of elution buffer was added. The RNA was obtained after 1 minute of incubation at room temperature followed by 1 minute of centrifugation at 8000 rpm. The sample was treated with DNase with the addition of 10 μl of DNase incubation buffer, 2 μl of DNase I and incubated for 30 minutes at 37 ° C. The DNase was inactivated following the addition of 20 μl of tissue lysis buffer, 18 μl of 10% SDA and 40 μl of proteinase K. Again, 325 μl of search buffer and 325 μl of protein were added to each sample. ethanol that was then mixed, centrifuged, and the float was added to the filter column. The sequential washings and elution of the RNA proceeded as set forth above, with the exception of 50 μl of elution buffer that was used to elute the RNA. To eliminate the contamination by glass fiber transported from the column, the RNA was centrifuged for 2 minutes at full speed and the float was removed in a fresh 1.5 ml Eppendorf tube. The samples were quantified by OD of 260/280 readings obtained by means of a spectrophotometer and the samples were diluted to 50 ng / μl. The isolated RNA was stored in Rnasa-free water at -80 ° C until use.

TaqMan bait and probe design. The appropriate access numbers of the mRNA reference sequence were used in conjunction with Oligo 6.0, to develop the TaqMan® CUP assays (lung markers: pulmonary-associated human tensoactive protein B (HUMPSPBA), thyroid transcription factor 1 ( TTF1), desmoglein 3 (DSG3), colorectal marker: cadherin 17 (CDH17), breast markers: mammaglobin (MG), prostate-derived ets transcription factor (PDEF), ovarian marker: Wilms tumor 1 (WT1), pancreas markers: prostate stem cell antigen (PSCA), factor V of coagulation (F5), prostate marker kallikrein 3 (KLK3)) and home work assays of beta actin (β-Actin), hydroxymethylbilane synthase (PBGD). Baits and hydrolysis probes for each assay are listed in Table 2. Amplification of genomic DNA was excluded by designing assays around the exon-intron binding sites. The hydrolysis probes were labeled at the 5 'nucleotide with FAM as the reporter dye and at the 3' nucleotide with BHQ1-TT as the internal quench dye.

Real-time quantitative polymerase chain reaction. The quantification of the specific RNA of the gene was carried out in a 384-well tray in the ABI Prism sequence detection system 7900 HT (Applied Biosystems). For each run of the cycler, calibrators and standard curves were amplified. The calibrators for each marker consisted of in vitro transcripts of the target gene that were diluted in the rat kidney carrier RNA at 1X105 copies. The standard curves for home work markers consisted of in vitro transcripts of the target gene that were serially diluted in rat kidney carrier RNA at 1X107, 1X105 and 1X103 copies. Nor were meta controls included in each trial run to ensure the absence of environmental contamination. All samples and controls were run in duplicate. The qRTPCR was performed with reagents for general laboratory use in a 10μl reaction containing: RT-PCR regulator (50nM Bicin / KOH pH 8.2, 115 nM KAc, 8% glycerol, 2.5 mM MgCl2, 3.5 mM MnS04 , 0.5 mM each of dCTP, dATP, dGTP and dTTP), additives (2 mM Tris-CI pH 8, 0.2 mM bovine albumin, 150 mM Trehalose, 0.002% Tween 20), enzyme mixture (2 U Tth ( Roche), 0.4 mg / μl of Ab TP6-25), bait mix and probe (0.2 μM probe, 0.5 μM bait). The following cycling parameters were followed: 1 cycle at 95 ° C for 1 minute; 1 cycle at 55 ° C for 2 minutes; 5% ramp; 1 cycle at 70 ° C for 2 minutes; and 40 cycles of 95 ° C for 15 seconds, 58 ° C for 30 seconds. After the PCR reaction was complete, the baseline and threshold values were established in the ABI 7900HT Prism software and the calculated Ct values were exported to Microsoft Excel.

One-step reaction versus two steps. The first strand synthesis was performed using either 100 ng of random hexamers or specific baits of the gene per reaction. In the first step, 11.5 μl of mixture 1 (baits and 1 μg of total RNA) were heated at 65 ° C for 5 minutes and then cooled on ice. 8.5 μl of mixture 2 (1x regulator, 0.01 mM DTT, 0.5 mM each of dNTP's, 0.25U / μl RNasin®, 10 U / μl of Superscript III), were added to mixture 1 and incubated at 50 ° C for 60 minutes followed by 95 ° C for 5 minutes. The cDNA was stored at -20 ° C until ready for use. The qRTPCR for the second step of the two-step reaction was performed as stated above with the following cycling parameters: 1 cycle at 95 ° C for 1 minute; 40 cycles of 95 ° C for 15 seconds, 58 ° C for 30 seconds. The qRTPCR for the one-step reaction was performed exactly as stated in the previous paragraph. You loved one-step reactions and two steps were performed on 100 ng of template (RNA / cDNA). After the PCR reaction was complete, the baseline and threshold values were established in the ABI 7900HT Prism software and the calculated Ct values were exported to Microsoft Excel.

Generation of a heat map. For each sample, a? Ct was calculated by taking the average Ct of each CUP marker and subtracting the average Ct from an average of the domestic work markers (? Ct = Ct (marker CUP) - Ct (Prom. Marker trab. )). For each sample, the minimum? Ct was determined for each marker tissue of established origin (lung, breast, prostate, colon, ovary and pancreas). The tissue of origin with the general minimum Ct was recorded as one and all other tissues of origin were recorded as zero. The data was stored according to the pathological diagnosis. The Partek Pro was populated with modified viability data and an intensity graph was generated.

RESULTS Discovery of new pancreatic tumor markers of origin and cancer status. First, five candidates for pancreatic markers were analyzed: prostatic stem cell antigen (PSCA), serine proteinase inhibitor, Ciado A member 1 (SERPINA1), cytokeratin 7 (KRT7), matrix metalloprotease 11 (MMP11), and mucin 4 (MUC4) (Varadhachary et al (2004); Fukushima et al. (2004); Argani et al. (2001); Jones et al. (2004); Prasad et al. (2005); and Moniaux et al. (2004)) using DNA microarrays and a panel of 13 pancreatic ductal adenocarcinomas, five normal pancreatic tissues, and 98 samples of breast, colorectal, lung and ovarian tumors. Only the PSCA showed moderate sensitivity (six of thirteen or 46% of pancreatic tumors were detected) at high specificity (91 of 98 or 93% were correctly identified as not being of pancreatic origin) (Figure 1A). In contrast, KRT7, SERPINA1, MMP11, and MUC4 demonstrated sensitivities of 38%, 31%, 85%, and 31%, respectively, at specificities of 66%, 91%, 82%, and 81%, respectively. These data coincided well with the qRTPCR performed in 27 metastases of pancreatic origin and in 39 metastases of non-pancreatic origin for all markers except for the MMP11 that showed poorer sensitivity and specificity with the qRTPCR and metastasis. In conclusion, the microarray data on frozen primary tissues instantly serves as a good indicator of the ability of markers to identify a metastasis of FFPE as pancreatic origin using the qRTPCR, but that additional markers may be useful for optimal performance . Because pancreatic ductal adenocarcinoma develops from ductal epithelial cells that comprise only a small percentage of all pancreatic cells (with acinic cells and islet cells comprising the majority) and because the tissues of pancreatic adenocarcinoma contain a significant amount of adjacent normal tissue (Prasad et al. (2005); and Ishikawa et al. (2005)), it has been difficult to identify pancreatic cancer markers (ie, regulated at discharge in cancer) that would also differentiate This organ of the organs. For use in a CUP panel, this differentiation is necessary. The first method of consultation (see Materials and Methods) returned six sets of probes: coagulation factor V (F5), a hypothetical protein FLJ22041 similar to the binding proteins FK506 (FKBP10), ß6-integrin (ITGB6), transglutaminase 2 ( TGM2), heterogeneous nuclear ribonucleoprotein A0 (HNRP0), and BAX delta (BAX). The second query method (see Materials and methods) returned eight sets of probes: F5, TGM2, pairing-like homeodomain transcription factor 1 (PITX1), trio isoform mRNA (TRIO), mRNA for p73H (p73), a protein unknown for MGC: 10264 (SCD), and two sets of probes for claudin 18. F5 and TGM2 were present in both results of the consultations, of the two, F5 was shown as the most promising (Figure 1 B).

Optimization of prep and qRTPCR of the sample using FFPE tissues. Then, RNA isolation and qRTPCR methods were optimized using fixed tissues before examining the performance of the marker panel. First, the effect of reducing the incubation time of proteinase K from sixteen hours to 3 hours was analyzed. There was no effect on production. However, some samples showed larger fragments of RNA when the shortest step of proteinase K was used (Figure 2A and Figure 2B). For example, when the RNA was isolated from the one-year-old block (C22), no difference was observed in the electropherograms. However, when the RNA was isolated from a five-year-old block (C23), a fraction of higher molecular weight RNAs was observed, evaluated by the hump on the shoulder, when the shortest digestion of proteinase K was used. This trend was generally maintained when other samples were processed, regardless of the organ of origin for FFPE metastasis. In conclusion, shortening the digestion time of proteinase K does not sacrifice RNA yields and may help isolate the larger, less degraded RNA. Next, we compared three different methods of reverse transcription: reverse transcription with random hexamers followed by qPCR (step two), reverse transcription with a specific bait of the gene followed by qPCR (step two), and one-step qRTPCR using specific baits of the gene . The RNA was isolated from eleven metastases and the Ct values were compared through the three methods for β-actin, human tensoactive protein B (HUMSPB), and thyroid transcription factor (TTF) (Figure 3A-Figure 3C). There were statistically significant differences (p <0.001) for all comparisons. For all three genes, reverse transcription with random hexamers followed by qPCR (two-step reaction) gave the highest Ct values, while reverse transcription with a specific bait of the gene followed by qPCR (two-step reaction) gave values slightly (but statistically significant) less than Ct than the corresponding 1-step reaction. However, the 2-step RTPCR with specific baits of the gene had a longer step of reverse transcription. When the Ct values of HUMSPB and TTF were normalized to the corresponding value of β-actin for each sample, there were no differences in the normalized values of Ct across the three methods. In conclusion, the optimization of the reaction conditions of RTPCR can generate lower values of Ct, which can help to analyze the older paraffin blocks (Cronin et al (2004)), and a one-step RTPCR reaction with specific baits of the gene can generate Ct values comparable to those generated in the corresponding two-step reaction.

Performance of the diagnosis of a CUP qRTPCR assay. The next 12 qRTPCR reactions (10 markers and two housekeeping genes) were performed on 239 metastases of FFPE. The markers used for the assay are shown in Table 2. The lung markers were pulmonary-associated human tensoactive protein B (HUMPSPBA), thyroid transcription factor 1 (TTF1) and desmoglein 3 (DSG3). The colorectal marker was cadherin 17 (CDH17). The breast markers were mammaglobin (MG) and transcription factor Ets derived from prostate (PDEF). The ovarian marker was tumor 1 of Wilms (WT1). The pancreas markers were the prostatic stem cell antigen (PSCA) and the coagulation factor V (F5), and the prostate marker was kallikrein 3 (KLK3). For gene descriptions, see Table 15.

TABLE 2 Bait and probe sequences, access numbers and amplicon lengths * The probes are 5'FAM-3'BHQ1-TT The analysis of the normalized values of Ct in a heat map revealed the high specificity of the breast and prostate markers, the moderate specificity of the colon, lung and ovary, and the somewhat lower specificity of pancreatic markers. The combination of standardized qRTPCR data with computational refinement improves the performance of the marker panel. The results were obtained from the combined normalized data of qRTPCR with the algorithm and the precision of the qRTPCR assay was determined.

DISCUSSION In this example, profiling of the expression based on microarrays in primary tumors was used to identify the candidate markers to be used with metastasis. The fact that primary tumors can be used to discover tumor markers of origin for metastasis is consistent with several recent discoveries. For example, Weigelt and colleagues have shown that the genetic expression profiles of primary breast tumors are maintained in distant metastases. Weigelt et al. (2003). Italian and colleagues found that the EGFR status, as assessed by IHC, was similar in 80 primary colorectal tumors and in the 80 related metastases. Italiano et al. (2005). Only five of the 80 showed discordance in the EGFR status. Italiano et al. (2005). Backus and colleagues identified putative markers to detect breast cancer metastasis using genome-wide gene expression analysis of breast and other tissues and demonstrated that mammaglobin and CK19 detected clinically operable metastasis in sentinel lymph nodes of the breast with 90% sensitivity and 94% specificity. Backus et al. (2005). The microarray-based studies with primary tissue confirmed the specificity and sensitivity of the known markers. As a result, with the exception of F5, all the markers used have high specificity for the tissues studied here. Argani et al (2001; Backus et al. (2005); Cunha et al. (2005); Borgono et al. (2004); McCarthy et al. (2003); Hwang et al. (2004); Fleming et al. (2000), Nakamura et al (2002), and Khoor et al. (1997) .A recent study determined that, using IHC, PSCA is overexpressed in prostate cancer metastasis Lam et al. (2005). et al. (2002) also showed that PSCA could be used as a tumor marker of origin for the pancreas and prostate As shown here, strong expression of PSCA is found in some prostate tissues at the RNA level but, due to to the inclusion of PSA in the trial, prostate and pancreatic cancers can now be segregated.A new finding in this study was the use of an F5 as a complementary marker (to the PSCA) for pancreatic tissue of origin. of microarray data with primary tissue and from the qRTPCR data set with the FFPE metastasis, it was found that the F5 complement to the PSCA (Figure 4A-Figure 4D and Table 3).

TABLE 3 Feasibility data Previous investigators have generated CUP assays using IHC microarrays. His et al. (2001); Ramaswamy et al. (2001); and Bloom et al. (2004). More recently, the SAGE has been coupled to a small marker panel qRTPCR. Dennis (2002); and Buckhaults et al. (2003). This study is the first to combine expression profiling based on a microarray with a small panel of qRTPCT assays. Microarray studies with primary tissue identified some, but not all, of the markers of the same tissue of origin as those previously identified through SAGE studies. Some studies have shown that there is a modest overlap between profiling data based on the SAGE microarray and DNA and that the correlation improves for genes with higher expression levels. Van Ruissen et al. (2005); and Kim (2003). For example, Dennis and colleagues identified the PSA, MG, PSCA, and HUMSPB, while Buckhaults and colleagues (Dennis et al. (2002)) identified the PDEF. It is preferred to run the CUP assay using qRTPCR because it is a robust technology and may have performance advantages over the IHC. Al-Mulla et al. (2005); and Haas et al. (2005). As shown here, the qRTPCT protocol improved through the use of specific baits of the gene in a one-step reaction. This is the first demonstration of the use of specific baits of the gene in a one-step qRTPCR reaction with FFPE tissue. Other investigators have done either a two-step qRTPCR (synthesis of cDNA in a reaction followed by qPCR) or have used random hexamers or specific baits of the truncated gene. Abrahamsen et al. (2003); Specht et al. (2001); Godfrey et al. (2000); Cronin et al. (2004); and Mikhitarian et al. (2004).

EXAMPLE 2 Pancreatic ductal adenocarcinoma develops from ductal epithelial cells that comprise only a small percentage of all pancreatic cells (comprising most acinic cells and islet cells) in the normal pancreas. In addition, the tissues of pancreatic adenocarcinoma contain a significant amount of adjacent normal tissue. Prasad et al. (2005); and Ishikawa et al. (2005). Because of this, pancreatic candidate markers were enriched for the high genes in pancreatic adenocarcinoma relative to normal pancreatic cells. The first consultation method returned six sets of probes: coagulation factor V (F5), a hypothetical protein FLJ22041 similar to the binding proteins FK506 (FKBP10), beta 6 integrin (ITGB6), transglutaminase 2 (TGM2), heterogeneous nuclear ribonucleoprotein A0 (HNRPO), and BAX delta (BAX). The second query method (see Materials and Methods section for details) returned eight sets of probes: F5, TGM2, homeodomain-like transcription factor 1 to paired (PITX1), mRNA trio isoform (TRIO), mRNA to p73H ( p73), an unknown protein for MGC: 10264 (SCD), and two sets of probe for claudin 18. A total of 23 tissue-specific marker candidates were selected for another RT-PCR validation in the FFPE tissues of metastatic carcinoma by means of of qRT-PCR. Candidate markers were tested in 205 metastatic carcinomas of FFRP, lung, pancreas, colon, breast, ovarian, prostate and primary prostate carcinomas. Table 4 provides the genetic symbols of the specific tissue markers selected for the RT-PCR validation and also summarizes the results of the tests performed with these markers.

TABLE 4 Of the 23 markers tested, thirteen were rejected based on their cross-reactivity, their low level of expression in the corresponding metastatic tissues, or their redundancy. Ten markers were selected for the final version of the test. The lung markers were pulmonary associated human tensoactive protein B (HUMPSPB), thyroid transcription factor 1 (TTF1) and desmoglein 3 (DSG3). The pancreas markers were the prostatic stem cell antigen (PSCA) and coagulation factor V (F5), and the prostate marker was kallikrein 3 (KLK3). The colorectal marker was cadherin 17 (CDH17). The breast markers were mammaglobin (MG) and prostate-derived transcription factor Ets (PDEF). The ovarian marker was Wilms tumor 1 (WT1).

Optimization of sample preparation and qRT-PCR using FFPE tissues. After RNA and qRTPCR isolation, the methods were optimized using fixed tissues before examining the performance of the marker panel. First, the effect of reducing the incubation time of proteinase K from sixteen hours to 3 hours was analyzed. There was no effect on production. However, some samples showed larger fragments of RNA when the shortest step of proteinase K was used (Figure 4A, 4B). For example, when the RNA was isolated from a one-year-old block (C22), no difference was observed in the electropherograms. However, when the RNA was isolated from a five-year-old block (C23), a fraction of higher molecular weight RNAs was observed, as evaluated by the hump on the shoulder, when the shortest digestion of the proteinase K. This trend was generally maintained when other samples were processed, importing the organ of origin for the metastasis of FFPE. In conclusion, shortening the digestion time of proteinase K does not sacrifice RNA yields and may help isolate longer, less degraded RNA. The following three methods of reverse transcription were compared: reverse transcription with random hexamers followed by qPCR (two steps), reverse transcription with a specific bait of the gene followed by qPCR (two steps), and a one-step qRTPCR using specific baits of the gene . The RNA was isolated from eleven metastases and the Ct values were compared through the three methods for β-actin, HUMSPB (Figures 4C, D) and TTF. The results showed statistically significant differences (p <; 0.001) for all comparisons. For both genes, reverse transcription with random hexamers followed by qPCR (two-step reaction) gave the highest Ct values, while reverse transcription with a specific bait of the gene followed by qPCR (two-step reaction) gave values of Ct slightly lower (but statistically significant) than those corresponding to the 1-step reaction. However, the two-step RTPCT with specific baits of the gene had a major step of reverse transcription. When the Ct values of HUMSPB were normalized to the corresponding β-actin value for each sample, there were no differences in the normalized Ct values across the three methods. In conclusion, the optimization of the RTPCR reaction conditions can generate lower Ct values, which helps in the analysis of older paraffin bl (Cronin et al. (2004)), and a one-step RTPCR reaction with baits Specific to the gene can generate Ct values comparable to those generated in the corresponding two-step reaction.

Diagnostic performance of the optimized qRTPCR assay. We performed 12 qRTPCR reactions (10 markers and 2 housekeeping genes) in a new set of 260 FFPE metastases. Twenty-one samples gave high Ct values for the housekeeping genes, so only 239 n a heat map analysis were used. The analysis of the normalized Ct values in a heat map revealed the high specificity of the breast and prostate markers, the moderate specificity of the colon, lung and ovary, and the somewhat low specificity of the pancreatic markers (Figure 5). By combining the standardized data from qRTPCR with computational refinement, the performance of the marker panel is improved. Using the expression values, normalized to the average of the expression of two genes of domestic work, an algorithm was developed to predict the tissue of origin of the metastasis by combining the qRTPCR data normalized with the algorithm, and the accuracy was determined of the qRTPCR assay by performing a cross-validation test of leave-out (LOOCV). For the six tissue types included in the trial, it was estimated separately that both of the number of false-positive calls, when one sample was erroneously predicted as another type of tumor included in the trial (pancreas as colon, for example), and of the number of times a sample was not predicted as those types included in the trial (other). The results of the LOOCV are presented in Table 5.

TABLE 5 The tissue of origin was correctly predicted for 204 of the 260 samples tested with an overall accuracy of 78%. A significant proportion of the false positive calls were due to the cross-reactivity of the marker in histologically similar tissues. For example, three squamous cell metastatic carcinomas that originated in the pharynx, larynx and esophagus were mistakenly predicted as lung, due to the expression of DSG3 in these tissues. The positive expression of CDH17 in carcinomas other than Gl of colon, including stomach and pancreas, caused the false classification of 4 of 6 stomach cancer metastases tested and 3 of 43 of pancreas, as of colon .. In addition to a test LOOCV, the data were randomly divided into 3 separate pairs of test and training sets. Each division contained approximately 50% of the samples of each class. In the 50/50 divisions in three separate pairs of training and test sets, the general classification accuracies of the trial were 77%, 71% and 75%, confirming the stability of the trial performance. Finally, another 48 FFPE metastatic carcinomas were tested that included metastatic carcinoma of known primary CUP specimens with a tissue diagnosis of origin represented by the pathological evaluation including IHC, and the CUP specimens that remained as CUP after being tested with IHC . The prediction accuracy of the tissue of origin was estimated separately for each category of samples. Table 6 summarizes the results of the trial.

TABLE 6 The prediction of the tissue of origin was, with only a few exceptions, consistent with the diagnosis of primary tissue or of known origin, diagnosed by clinical / pathological evaluation including IHC. Similar to the training set, the trial could not differentiate squamous cell carcinomas originating from different sources and falsely predicted them as lung. The trial also putatively made the diagnosis of the tissue of origin for eight of eleven samples that remained as CUP after standard diagnostic tests. One of the CUP cases was especially interesting. A male patient with a history of prostate cancer was diagnosed with metastatic carcinoma of the lung and pleura. The serum PSA and IHC tests with PSA antibodies in the metastatic tissue were negative, so the diagnosis of the pathologist was CUP with an inclination toward gastrointestinal tumors. The trial strongly predicted (posterior probability of 0.99) the tissue of origin as colon.

DISCUSSION In this study, microarray-based expression that profiles in primary tumors was used to identify candidate markers for use with metastases. The fact that primary tumors can be used to discover the tumor from markers of origin for metastasis is consistent with several recent discoveries. For example, Weigelt and colleagues have shown that genetic expression profiles of primary breast tumors are maintained in distant metastases. Weigelt et al. (2003). Backus and colleagues identified putative markers to detect breast cancer metastasis using gene expression analysis of a broad genome of breast and other tissues and demonstrated that mammaglobin and CK19 clinically detected the actionable metastases in sentinel lymph nodes of the breast with 90% sensitivity and 94% specificity. Backus et al. (2005). During the development of the trial, the selection focused on six types of cancer, including lung, pancreas and colon, which are among the most prevalent in CUP (Ghosh et al. (2005) and Pavlidis et al. (2005)) and breast, ovarian and prostate for which the treatment could potentially be more beneficial for patients. (Ghosh et al (2005).) However, tissue types and additional markers can be added to the panel as long as the overall accuracy of the assay is not compromised and, if applicable, does not interfere with the logistics of the RTPCR reactions. based on microarray with primary tissue confirmed the specificity and sensitivity of the known markers.As a result, most tissue-specific markers have high specificity for the tissues studied here.A recent study found that, using IHC, the PSCA Overexpressed in prostate cancer metastases Lam et al (2005) Dennis et al (2002) also showed that PSCA could be used as a marker of tumor origin for the pancreas and prostate. of PSCA in some prostate tissues at the RNA level but, due to the inclusion of PSA in the assay, pancreatic and prostate cancers can now be secreted. The study in this study was the use of F5 as a complementary marker (a PSCA) for the pancreatic tissue of origin. In both sets of microarray data with primary tissue and the qRTPCR data set with FFPE metastasis, it was found that F5 complemented the PSCA. Previous researchers have generated CUP assays using IHC (Brown et al. (1997), De Young et al. (2000) and Dennis et al. (2005a)) or microarrays. His et al. (2001); Ramaswamy et al. (2001); and Bloom et al. (2004). More recently, the SAGE has been coupled to a small marker panel of qRTPCR. Dennis et al. (2002); and Buckhaults et al. (2003). This study is the first to combine expression profiling based on a microarray with a small panel of qRTPCR assays. The microarray studies with primary tissue identified some, but not all, of the same tissue markers of origin as those previously identified by the SAGE studies. This finding is not surprising given the studies that have shown that there is a modest overlap between SAGE data and profiling based on DNA microarray, and that the correlation improves for genes with higher expression levels. Van Ruissen et al. (2005); and Kim et al. (2003). For example, Dennis and colleagues identified the PSA, MG, PSCA, and HUMSPB, while Buckhaults and colleagues (Buckhaults et al. (2003)) identified the PDEF. Execution of the CUP assay is preferably by qRTPCR because it is a robust technology and may have performance advantages over the IHC. Al-Mulla et al. (2005); and Haas et al. (2005). In addition, as shown here, the qRTPCR protocol has improved through the use of gene-specific baits in a one-step reaction. This is the first demonstration of the use of specific baits of the gene in a one-step qRTPCR reaction with FFPE tissue. Other investigators have done either a two-step qRTPCR (synthesis of cDNA in a reaction followed by qPCR) or have used random hexamers or specific baits of the truncated gene. Abrahamsen et al. (2003); Specht et al. (2001); Godfrey et al. (2000); Cronin et al. (2004); and Mikhitarian et al. (2004). In sum, the overall 78% accuracy of the assay for six tissue types compares favorably with other studies. Brown et al. (1997); DeYoung et al. (2000); Dennis et al. (2005a); His et al. (2001); Ramaswamy et al. (2001); and Bloom et al. (2004).

EXAMPLE 3 In this study, the classifiers using the genetic marker portfolios were constructed by choosing them from MVO and using this classifier to predict tissue origin and cancer status for five major types of cancer, including breast, colon, lung, ovarian and prostate. Three hundred and seventy-eight primary cancers, 23 benign proliferative epithelial lesions and 103 specimens of frozen human tissue were instantly analyzed using the human Affymetrix U133A GeneChip. Leukocyte samples were also analyzed to subtract potentially masked gene expression by means of coexpression in the cells of the leukocyte environment. A new bioinformatics method based on MVO was developed to select the genetic marker portfolio for the tissue of origin and cancer status. The data showed that a panel of 26 genes could be used to accurately predict tissue of origin and cancer status among 5 types of cancer. Therefore, a multichannel classification method is possible to obtain by determining the gene expression profiles of a reasonably small number of genetic markers. Table 7 shows the markers identified for the indicated tissue origins. For genetic descriptions, see Table 15.

TABLE 7 The sample set included a total of 299 metastatic carcinomas of the colon, breast, pancreas, ovary, prostate, lung, and other carcinomas and samples of primary cancer of the prostate cancer. QC based on histological evaluation, RNA production and beta-actin gene expression control were implemented. Another category of samples included metastasis originating from carcinomas of the stomach (5), kidney (6), cholangio / gallbladder (2), liver (2), head and neck (4), ileum (1), and mesothelioma. Table 8 summarizes the results.

TABLE 8 The above test samples resulted in the narrowing of the marker set for those in Table 9 with the results shown in Table 10.

TABLE 9 TABLE 10 The results showed that of 205 metastatic tumors embedded in paraffin, 166 samples (81%) had inconclusive test results, Table 11.

TABLE 11 Of the false positive results, many false ones were derived from histologically and embryologically similar tissues, Table 12.

TABLE 12 The following parameters were considered for the development of the model: Separate markers in the feminine and masculine sets and CUP probability calculation separately for male and female patients. The male set included: SP_B, TTF1, DSG3, PSCA, F5, PSA, HPT1; the female set included SP_B, TTF1, DSG3, PSCA, F5, HPT1, MGB, PDEF, WT1. The expression of the environment was excluded from the results of the trial; Lung: SP_B, TTF1, DSG3; Ovary: WT1; and Colon: HPT1. The CUP model was adjusted to the prevalence of CUP (%): lung 23, pancreas 16, colorectal 9, breast 3, ovarian 4, prostate 2, other 43. Prevalence for breast and ovarian adjusted to 0% for male patients, and prostate adjusted to 0% for female patients. The following steps were taken: Place markers on a similar scale. Reduce the number of variables from 12 to 8, selecting the minimum value of each specific set of tissue. Leave out a sample. Build the model from the remaining samples. The test left out the sample. Repetition: until 100% of the samples are tested. Let out randomly ~ 50% of the samples (~ 50% per tissue). Build the model from the remaining samples. Test ~ 50% of the samples. Repeat for 3 different random divisions. The accuracy of the classification was adjusted to the prevalence of cancers. To produce the results summarized in Table 13 with the raw data shown in Table 14. Although the above invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, the descriptions and examples do not they should be constructed as limitations on the competence of the invention.

TABLE 13 TABLE 14 ID of the Géne¬ Source BK Prediction BACTIN PBGD Ave CDH17 DSG3 F5 HUMP KLK3 MG PDEF PSCA TTF1 WT1 sample ro 128 sine lung other 23.37 30.04 26.71 40.00 37.78 35.74 22.19 40.00 40.00 30.36 29.96 29.39 34.85 134 sine uk sine 19.60 27.00 23.30 40.00 31.27 30.83 40.00 40.00 29.51 25.07 24.67 40.00 34.13 166 breast sine sine 25.12 31.40 28.26 40.00 40.00 40.00 40.00 40.00 22.26 26.01 40.00 40.00 40.00 356 sine uk sine 28.59 33.89 31.24 40.00 34.01 40.00 40.00 40.00 35.73 33.19 30.72 40.00 40.00 163 colon uk colon 24.69 30.34 27.52 29.39 40.00 26.52 40.00 40.00 40.00 37.72 40.00 40.00 36.17 184 m colon uk colon 22.47 28.63 25.55 26.22 33.26 28.76 40.00 40.00 40.00 34.07 33.44 40.00 31.64 CD C 339 colon colon colon 28.35 34.29 31.32 33.76 40.00 40.00 40.00 40.00 40.00 40.00 35.99 40.00 40.00 40.00 346 m colon lung colon 23.15 28.77 25.96 26.36 40.00 32.64 20.89 40.00 40.00 32.47 40.00 26.75 30.58 363 m colon uk colon 24.46 30.62 27.54 26.20 31.84 29.98 34.44 40.00 40.00 30.45 35.00 40.00 30.35 101.35 m Lung Lung 22.05 27.50 24.78 40.00 40.00 32.24 23.68 40.00 40.00 25.79 25.02 26.42 37.27 110 m Lung Lung Lung 29.19 32.32 30.76 40.00 40.00 40.00 21.21 40.00 40.00 32.77 32.43 30.70 36.13 112 lung lung uk other 22.48 27.79 25.14 40.00 37.05 37.38 36.08 40.00 40.00 37.12 36.04 40.00 37.45 199 f lung uk lung 21.21 27.07 24.14 35.65 25.56 31.23 40.00 40.00 28.94 32.19 27.95 32.14 31.60 200 m lung Lung 22.16 26.94 24.55 40.00 24.53 33.69 40.00 40.00 40.00 36.67 38.34 38.61 33.55 313 m lung uk other 24.76 30.05 27.41 38.40 40.00 40.00 40.00 40.00 40.00 40.00 40.00 40.00 35.11 323 m lung uk pancreas 23.82 30.24 27.03 32.43 31.82 33.81 40.00 40.00 40.00 33.60 28.12 40.00 31.87 325 m lung Lung Lung 22.09 27.97 25.03 40.00 26.84 34.88 38.61 40.00 38.04 34.29 27.31 39.21 31.23 335 m lung uk another 24.89 29.73 27.31 40.00 29.62 38.00 40.00 40.00 40.00 39.23 40.00 31.12 32.12 347 m lung uk lung 23.40 29.08 26.24 40.00 26.72 37.21 40.00 40.00 40.00 36.10 30.76 40.00 39.44 374 m lung lungs uk 22.50 28.23 25.37 40.00 40.00 38.76 21.38 40.00 37.26 26.56 38.26 24.86 36.60 385 f lung uk lung 21.65 26.44 24.05 37.05 40.00 34.51 19.89 40.00 40.00 27.36 40.00 23.72 37.09 114 f other lung another 24.80 30.56 27.68 40.00 40.00 28.16 21.51 40.00 40.00 35.76 37.85 28.19 37.21 129 m another lung another 21.49 28.25 24.87 39.47 40.00 28.86 20.65 40.00 40.00 32.98 40.00 28.14 31.11 179 f other uk another 23.97 30.45 27.21 40.00 40.00 29.79 40.00 40.00 40.00 40.00 40.00 40.00 32.64 194 m another uk another 25.28 32.47 28.88 40.00 40.00 28.90 40.00 40.00 40.00 40.00 40.00 34.75 35.41 302 f other colon sine 25.67 31.47 28.57 34.17 40.00 40.00 40.00 40.00 40.00 30.55 32.47 40.00 38.20 305 m another uk another 23.80 29.74 26.77 29.64 40.00 34.06 40.00 40.00 40.00 31.82 40.00 40.00 40.00 317 m another uk pancreas 25.90 30.62 28.26 40.00 40.00 27.75 40.00 40.00 40.00 31.89 33.06 40.00 35.12 333 f another uk another 22.45 28.82 25.64 30.54 40.00 37.01 40.00 40.00 40.00 37.85 40.00 40.00 40.00 334 m another uk another 22.14 29.20 25.67 31.79 40.00 36.27 40.00 40.00 40.00 34.69 40.00 40.00 40.00 342 f other uk pancreas 27.32 31.37 29.35 32.36 40.00 29.24 40.00 40.00 40.00 32.89 40.00 40.00 38.18 382 m another uk another 25.04 30.22 27.63 40.00 40.00 36.13 40.00 40.00 40.00 38.30 40.00 40.00 34.91 404 m another uk another 23.27 30.16 26.72 40.00 39.36 34.75 40.00 40.00 40.00 39.02 40.00 40.00 34.24 354 ovary uk ovary 24.62 31.54 28.08 40.00 40.00 34.90 40.00 40.00 40.00 36.62 40.00 40.00 29.71 148 f ovary uk pancreas 23.55 29.88 26.72 26.72 40.00 40.00 30.60 38.84 40.00 40.00 32.12 31.76 40.00 38.59 417 f pancreas uk pancreas 23.42 29.46 26.44 28.28 38.96 29.05 37.01 40.00 40.00 30.15 30.23 40.00 30.69 136 m prostate lung prostate 22.37 26.95 24.66 40.00 40.00 29.47 23.69 21.38 40.00 24.70 24.28 30.89 31.16 407 m Prostate prostate lung 28.20 31.87 30.04 40.00 40.00 40.00 27.70 25.98 40.00 27.65 40.00 39.33 39.33 39.33 39.76 116 f CUP uk lungSCC 21.66 27.31 24.49 28.95 27.86 31.06 40.00 40.00 30.28 33.49 29.31 40.00 38.11 123 m CUP lung colon 27.09 30.59 28.84 27.92 36.01 40.00 40.00 40.00 40.00 40.00 40.00 40.00 36.65 03 OO 157 m CUP uk pancreas 26.81 31.94 29.38 40.00 40.00 26.82 40.00 40.00 40.00 36.68 40.00 40.00 40.00 177.00 CUP uk pancreas 25.44 31.52 28.48 40.00 40.00 27.15 40.00 40.00 40.00 39.67 40.00 40.00 34.71 306 m CUP uk lung 23.15 28.38 25.77 37.30 40.00 34.94 19.71 40.00 40.00 30.81 40.00 25.45 39.28 360 m CUP uk ovary 23.16 29.12 26.14 40.00 40.00 34.07 40.00 40.00 40.00 32.93 40.00 40.00 25.28 187 f CUP uk colon 24.44 29.80 27.12 26.83 35.91 26.32 30.55 40.00 40.00 40.00 40.00 29.75 40.00 TABLE 15 REFERENCES US Patent and Patent Application Publications 5242974 5545531 6218122 5384261 5554501 6339148 5405783 5556752 5412087 5561071 20020055627 20030194733 20030212264 5424186 5571639 5429807 5593839 5436327 5599695 20030232350 20030235820 20040005563 5445934 5624711 5472672 5658734 5527681 5700637 20040076955 20040219572 20050009067 5529756 6004755 5532128 6218114 20060029987 Patent Publications and Foreign Patents W01998040403 WO2004063355 WO2000055320 WO2004077060 WO2004018999 WO2005005601 WO2004031412 Journal articles Abrahamsen et al. (2003) Towards quantitative mRNA analysis in paraffin-embedded tissues using real-time reverse transcriptase-polymerase chain reaction J Mol Diag 5: 34-41 Al-Mulla et al. (2005) BRCA1 gene expression in breast cancer: a correlative study between real-time RT-PCR and immunohistochemistry J Histochem Cytochem 53: 621-629 Argani et al. (2001) Discovery of new markers of cancer through serial analysis of gene expression: prostate stem cell antigen is overexpressed in pancreatic adenocarcinoma Cancer Res 61: 4320-4324 Backus et al. (2005) Identification and characterization of optimal gene expression Markers for breast cancer detection metastasis J Mol Diagn 7: 327-336 Bloom et al. (2004) Multi-platform, multi-site, microarray-based human tumor classification Am J Pathol 164: 9-16 Borgono et al. (2004) Human tissue kallikreins: physiologic roles and applications in cancer Mol Cancer Res 2: 257-280 Brookes (1999) The essence of SNPs Gene 23: 177-186 Brown et al. (1997) Immunohistochemical identification of tumor Markers in metastatic adenocarcinoma. A diagnostic adjunct in the determination of primary site Am J Clin Pathol 107: 12-19 Buckhaults et al. (2003) Identifying tumor origin using a gene expression-based classification map Cancer Res 63: 4144-4149 Cronin et al. (2004) Measurement of gene expression in archival paraffin-embedded tissue Am J Pathol 164: 35-42 Cunha et al. (2006) Tissue-specificity of prostate specific antigens: Comparative analysis of transcript levéis in prostate and non-prostatic tissues Cancer Lett 236: 229-238 Dennis et al. (2002) Identification from public data of molecular Markers of adenocarcinoma characteristic of the site of origin Can Res 62: 5999-6005 Dennis et al. (2005a) Hunting the primary: novel strategies for defining the origin of tumors J Pathol 205: 236-247 DeYoung et al. (2000) Immunohistologic evaluation of metastatic carcinomas of unknown origin: an algorithmic approach Semin Diagn Pathol 17: 184-193 Fleming et al. (2000) Mammaglobin, a breast-specific gene, and its utility as a Marker for breast cancer Ann NY Acad Sci 923: 78-89 Ghosh et al (2005) Management of patients with metastatic cancer of unknown primary Curr Probl Surg 42:12 -66 Godfrey et al. (2000) Quantitative mRNA expression analysis from formaün-fixed, paraffin-embedded tissues using 5 'nuclease quantitative reverse transcription-polymerase chain reaction J Mol Diag 2: 84-91 Haas et al. (2005) Combined application of RT-PCR and immunohistochemistry on paraffin embedded sentinel lymph nodes of prostate cancer patients Pathol Res Pract 200: 763-770 Hwang et al. (2004) Wilms tumor gene product: sensitive and contextually specific Marker of serous carcinomas of ovarian surface epithelial origin Appl Immunohistochem Mol Morphol 12: 122-126 Ishikawa et to the. (2005) Experimental trial for diagnosis of pancreatic ductal carcinoma based on gene expression profiles of pancreatic ductal cells Cancer Sci 96: 387-393 Italiano et al. (2005) Epidermal growth factor receptor (EGFR) status in primary colorectal tumors correlates with EGFR expression in related metastatic sites: biological and clinical implications Ann Oncol 16: 1503-1507 Jones et al. (2004) Comprehensive analysis of matrix metalloproteinase and tissue inhibitor expression in pancreatic cancer: increased expression of matrix metalloproteinase-7 predicts poor survival Clin Cancer Res 10: 2832-2845 Khoor et al. (1997) Expression of surfactant protein B precursor and surfactant protein B mRNA in adenocarcinoma of the lung Mod Pathol 10: 62-67 Kim (2003) Comparison of oligonucleotide-microarray and serial analysis of gene expression (SAGE) in transcript profiling analysis of megakaryocytes derived from CD34 + cells Exp Mol Med 35: 460-466 Lam et al. (2005) Prostate stem cell antigen is overexpressed in prostate cancer metastases Clin Can Res 11: 2591-2596 Lipshutz et al. (1999) High density synthetic oligonucleotide arrays Nature Genetics 21: S20-24 Markowitz (1952) Portfolio Selection J Finance 7: 77-91 McCarthy et al. (2003) Novel Markers of pancreatic adenocarcinoma in fine-needle aspiration: mesothelin and prostate stem cell antigen labeling increases accuracy cytologically borderline cases Appl Immunohistochem Mol Morphol 11: 238-243 Mikhitarian et al. (2004) Enhanced detection of RNA from paraffin-embedded tissue using a panel of truncated gene-specific primers for reverse transcription BioTechniques 36: 1-4 Moniaux et al. (2004) Multiple roles of mucins in pancreatic cancer, a lethal and challenging malignancy Br J Cancer 91: 1633-1638 Nakamura et al. (2002) Expression of thyroid transcription factor-1 in normal and neoplastic lung tissues Mod Pathol 15: 1058-1067 Prasad et al. (2005) Gene expression profiles in pancreatic intraepithelial neoplasia reflecting the effects of Hedgehog signaling on pancreatic ductal epithelial cells Cancer Res 65: 1619-1626 Ramaswamy et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures Proc Nati Acad Sci USA 98: 15149-15154 Specht et al. (2001) Quantitative gene expression analysis in microdissected archival formalin-fixed and paraffin-embedded tumor tissue Amer J Pathol 158: 419-429 Su et al. (2001) Molecular classification of human carcinomas by use of gene expression signatures Cancer Res 61: 7388-7393 van Ruissen et al. (2005) Evaluation of the similarity of gene expression data estimated with SAGE and Affymetrix GeneChips BMC Genomics 6:91 Weigelt et al. (2003) Gene expression profiles of primary breast tumors maintained in distant metastases Proc Nati Acad Sci USA 100: 15901-15905 Lillemoe et al (2000) Pancreatic cancer: state-of-the-art care CA Cancer J Clin 50: 241-68 Warshau et al. (1992) N Engl J Med 326: 4555-4565 Kroep et al. (1999) Ann Oncol 10 (Suppl 4): 234-238 Wiesenauer et al. (2003) Preoperative Predictors of Malignancy in Pancreatic Intraductal Papillary Mucinous Neoplasms Arch Surg 138: 610-618 Ros et al. (2001) Imaging features of pancreatic neoplasms JBR-BTR 84: 239-49 Ryu et al. (2002) Relationships and differentially expressed genes among pancreatic cancers examined by large-scale serial analysis of gene expression Cancer Res 62: 819-26 Ito et al. (2001) Molecular basis of T cell-mediated recognition of pancreatic cancer cells Cancer Res 61: 2038-46 Gibson et al. (1978) Histological typing of tumors of the liver, biliary tract and pancreas WHO Geneva

Claims (9)

NOVELTY OF THE INVENTION CLAIMS

1. - An in vitro method to identify pancreatic carcinoma, comprising the step of: measuring the biomarkers associated with the expression of the F5, PSCA, ITGB6, KLK10, CLDN18, TR10 or FKBP10 marker genes, where the expression levels of the genes markers above or below the predetermined cut-off levels are indicative of the presence of pancreatic cancer in the sample.

2. The method according to claim 1, further characterized in that the marker genes are F5 and PSCA.

3. The method according to claim 2, further characterized in that the marker genes also comprise, or are replaced by, ITGB6, KLK10, CLDN18, TR10 and / or FKBP10.

4. The method according to any of claims 1-3, further characterized in that the gene expression is measured using at least one of SEQ ID NOs: 39-41 and 43-45.

5. A composition comprising at least one isolated sequence selected from SEQ ID NOs: 39-41 and 43-45.

6. A kit for carrying out an assay according to one of claims 1-3, comprising: biomarker detection reagents.

7. - A microarray or genetic chip to perform the method of one of claims 1-3.

8. A diagnostic / prognostic portfolio comprising isolated sequences of nucleic acid, its complements, or portions thereof, or a combination of genes according to one of claims 1-3, wherein the combination is sufficient to measure or characterize genetic expression in a biological sample that has metastatic cells relative to cells of different carcinomas or to normal tissue.

9. The method according to any of claims 1-3, further characterized in that it also comprises measuring the expression of at least one gene constitutively expressed in the sample.