Abstract:
The present invention provides methods for the diagnosis of a disease or condition in an individual. The methods employ a primary self-organizing map trained with biological marker profiles from tissues having known diseases or conditions, in combination with a secondary self-organizing map which displays a representation of a subset of the primary self-organizing map with sample data obtained from an individual in need of diagnosis. A result is prepared from the secondary SOM(s) that reveals the extent of similarity between the known diseases or conditions with the sample data set of the individual. The result can be provided to a practitioner to aid in the diagnosis or prognosis of the individual. The result can additionally be used to select an individual for a clinical trial to evaluate a treatment.

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS 
       [0001]    This application is a continuation-in-part of U.S. patent application Ser. No. 11/617,303, filed Dec. 28, 2006, entitled “Self-Organizing Maps in Clinical Diagnostics” which is incorporated herein by reference in its entirety and for all purposes. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates to computational methods of presentation and interpretation of clinical data. 
       BACKGROUND OF THE INVENTION 
       [0003]    The following description is provided solely to assist the understanding of the present invention. None of the references cited or information provided is admitted to be prior art to the present invention. 
         [0004]    The use of biochemical assay data such as gene expression data (i.e., gene expression profiling) is rapidly expanding the diagnosis and treatment of disease. However, large quantities of data can be difficult for a human to comprehend en masse. Thus, techniques have been developed to present complex data to individuals for evaluation. For example, statistical methodologies directed at classification of disease have been described, based on gene expression data. See Tothill et al. ( Cancer Res.  2005, 65:4031-4040); Ma et al. ( Arch. Pathol. Lab. Med.,  2006, 130:465-473); Ramaswamy et al. ( Proc. Natl. Acad. Sci. USA,  2001, 98:15149-15154); Eils (U.S. Pub. Pat. Appl. No. 2004/0076984); Botstein et al. (U.S. Pub. Appl. No. 2006/0040302); Tamayo et al. (EP 1 037 158, U.S. Pub. Appl. No. 2002/0115070); Bloom et al. ( Amer. J. Pathology,  2004, 164:9-16); Giordano et al. ( Amer. J Pathology,  2001, 159:1231-1238). Neural network methods also have been described in the context of expansive data, including gene expression data. See Covell et al. ( Molecular Cancer Therapeutics,  2003, 2:317-332); Golub et al. (U.S. Pat. No. 6,647,341); Ingber et al. (U.S. Pat. No. 6,888,543); Buckhaults et al. ( Cancer Research,  2003, 63:4144-4149); Petricoin et al. ( Lancet,  2002, 359:572-577); Mavroudi et al. ( Bioinformatics,  2002, 18:1446-1453); Otte et al. (U.S. Pat. No. 6,321,216); Tamayo et al. U.S. Pub. Pat. Appl. No. 2002/0115070); Mori (U.S. Pub. Pat. Appl. No. 2006/0184461); Zhang (U.S. Pat. No. 6,897,875); Hsu et al. ( Bioinformatics,  2003, 19:2131-2140). 
       SUMMARY OF THE INVENTION 
       [0005]    The present invention provides methods for the diagnosis of a disease or condition in an individual. These methods include assessing the level of selected biological markers within a biological sample obtained from the individual, comparing the levels of these markers in the sample with the levels of these markers in tissue or body fluid from an individual having a known disease, disorder or condition, and presenting the comparison in a form suitable for medical diagnosis or prognosis. 
         [0006]    As used herein, “biological marker” refers to a biomolecule, for example nucleic acid or protein. As a non-limiting example, the present invention provides methods for determining the primary source of a metastatic carcinoma; i.e., cancer of unknown primary. The terms “cancer of unknown primary,” “CUP,” and terms of like important refer to cancers that present in one or more metastatic sites and in which the primary site is not known. The terms “primary,” “primary site,” “primary tissue type,” “primary cancer type” and terms of like import refer in the context of cancer to the original site (i.e., tissue) in which the cancer formed. The terms “metastatic site,” “secondary site,” and terms of like import refers to other parts of the body in which cancer presents but which are not the primary site. As well understood by those of ordinary skill in the art, cancers can spread from a primary site to one or more metastatic sites. Cancers are named according to origin (i.e., primary site) regardless of where in the body the cancers spread. Because knowledge of a primary site is an important factor in determining diagnosis, treatment, and prognosis (Buckhaults et al., supra), attempts (e.g., clinical tests) are often made to determine the primary site giving rise to the metastatic site. When a primary site is determined, a cancer is no longer considered a cancer of unknown primary and is renamed according to the newly discovered primary site. For example, a lung cancer that spreads to the lymph nodes, adrenal glands, and the liver is still classified as lung cancer and not as a lymphoma (i.e., cancer of the lymph nodes), adenocarcinoma (i.e., cancer of the adrenal glands), or hepatoma (i.e., cancer of the liver). In the case of CUP, a subject may present with a metastatic cancer for which the primary cancer is occult or even no longer extant. As described herein, in some embodiments the invention contemplates gene expression level data of tissues from histologically certified primary cancer types, which data have been analyzed and transformed into a representation wherein similar types of cancer appear close to one another. The term “histologically certified primary cancer types” refers to primary cancers which have been diagnosed by an oncologist, pathologist, or other specialist using methods well known in the art of cancer diagnostics. An assay (e.g., biopsy) of a metastatic cancer can be conducted, and the levels of gene expression within the metastatic cancer can be determined by methods well known in the art. The gene expression profile of the metastatic cancer can then be compared by methods provided herein with the gene expression profiles of the histologically certified primary cancer types. The comparison is presented to a medical practitioner in a form which is understandable, and which provides assistance of diagnosis and prognosis. 
         [0007]    In a first aspect, the invention provides a method for diagnosis of a disease or condition in an individual, the method comprising: a) providing a primary self organizing map (SOM) constructed using a plurality of data sets of measurements obtained from a plurality of individuals each having a disease or condition; b) preparing a secondary SOM using a distinct labeling set, said distinct labeling set encompassing data sets of measurements of a particular disease or condition, said secondary SOM including a sample data set obtained from a sample of said individual; and c) preparing a result from the secondary SOM that reveals the extent of similarity between the data sets of measurements of the distinct labeling set and the sample data set of the individual; whereby a medical practitioner can use the result to diagnose said disease or condition. In some embodiments, the plurality of individuals providing the data sets of measurements used to construct the primary SOM represent a plurality of diseases or conditions. In some embodiments, step b) is repeated to prepare multiple secondary SOMs for different diseases or conditions 
         [0008]    As used herein, “self-organizing map,” “SOM,” and terms of like import refer to a clustering technique, and the representation of the result thereof, which technique groups data such that similar data are generally clustered closer than are dissimilar data. The terms “nearer” “closer,” “proximate” and terms of like import in this context refers to literal proximity in a SOM. Minor variations in the positioning of data comprising a SOM can be tolerated without departing from the underlying description of the SOM as provided herein and in references cited herein and known to one of ordinary skill in the art. The SOM, first enunciated by Kohonen (see e.g., Kohonen, T. “Self-Organized Formation of Topologically Correct Feature Maps”,  Biological Cybernetics,  1982, 43:59-69; Kohonen, T., “The Self-Organizing Map”  Proc. of the IEEE,  1985, 73:1551-1558; Kohonen, T. “The Self-Organizing Map”,  Proc. of the IEEE,  1990, 78:1464-1480; Kohonen, T.,  Self - Organizing Maps, Springer,  1995), is a neural network model that is capable of projecting high-dimensional input data (i.e., multivariate data vectors) onto a lower-dimensional array, typically 2-dimensional. This projection produces a lower-dimensional representation that is useful in detecting and analyzing features from the higher-dimensional input space. The term “dimension” in the context of a multivariate data vector refers to the length of the data vector, such that each of the multiple variables thereof describes a unique dimension. For example, a dimension can refer to the gene expression level, optionally normalized, of a specific gene. The term “dimension” in the context of a representation (e.g., visual representation) refers to the 1-, 2-, or 3-dimensional presentations generally used to provide information to a human. Provision of such information can be interactive as for example on a computer screen, printed, or otherwise displayed. In general, a SOM includes a set of map cells represented in a 1-, 2-, or 3-dimensional space, wherein the map cells are located in an ordered array. As used herein, the term “SOM” is understood to refer to a self-organizing map data structure and/or the display thereof showing clustering of the similar data. 
         [0009]    In some embodiments of the methods provided herein, the sets of measurements representing a plurality of different diseases or conditions. In some embodiments, the data sets of measurements are obtained from a plurality of individuals, each having a known disease or condition. In some embodiments, the sample data sets obtained from a sample from an individual in need of diagnosis are gene expression levels from a test sample. In some embodiments, the data sets are protein levels. As used herein, “sample” or “test sample” refers to any liquid or solid material that can assayed for gene expression or protein concentration. In preferred embodiments, a test sample is obtained from a biological source (i.e., a “biological sample”), a tissue sample or bodily fluid from an animal, most preferably from a human. Preferred sample tissues include, but are not limited to, lesions of specific organs including skin, colon, rectum, lung, breast, ovary, prostate, stomach, or kidney. 
         [0010]    In some embodiments the different diseases or conditions are tumors including the following types: adrenal, brain, breast, carcinoid-intestine, cervix-adeno, cervix-squamous, endometrium, gallbladder, germ-cell-ovary, gastrointestinal stromal, kidney, leiomyosarcoma, liver, lung-adeno-large cell, lung-small cell, lung-squamous, lymphoma-B cell, lymphoma-Hodgkin, lymphoma-T cell, memigioma, mesothelioma, osteosarcoma, ovary-clear, ovary-serous, pancreas, skin-basal cell, skin-melanoma, skin-squamous, small bowel, large bowel, soft tissue-liposarcoma, soft tissue-malignant fibrous histiocytoma, soft tissue-sarcoma-synovial, stomach-adeno, testis-other, testis-seminoma, thyroid-follicular-papillary, thyroid-medullary, and urinary bladder. 
         [0011]    In some embodiments, the sets of measurements representing a plurality of different diseases or conditions include CD (i.e., cluster of differentiation) or IHC (i.e., immunohistochemistry) markers. Representative IHC markers includes without limitation carcinoembryonic antigen (CEA), CD15, CD30, alpha fetoprotein, CD117, prostate specific antigen (PSA), and the like. 
         [0012]    Methods of assaying gene expression levels are well known in the art, and include protein and nucleic acid determination. As used herein, “nucleic acid” refers broadly to segments of a chromosome, segments or portions of DNA, cDNA, and/or RNA. Nucleic acid may be derived or obtained from an originally isolated nucleic acid containing sample from any source (e.g., isolated from, purified from, amplified from, cloned from, reverse transcribed from sample DNA or RNA). 
         [0013]    As used herein, “target nucleic acid” or “target sequence” refers to a sequence to be amplified and/or detected. These include the original nucleic acid sequence to be amplified, its complementary second strand of the original nucleic acid sequence to be amplified, and either strand of a copy of the original sequence which is produced by the amplification reaction. Target sequences may be composed of segments of a chromosome, a complete gene with or without intergenic sequence, segments or portions a gene with or without intergenic sequence, or sequence of nucleic acids to which probes or primers are designed. Target nucleic acids may include wild type sequences, nucleic acid sequences containing mutations, deletions or duplications, tandem repeat regions, a gene of interest, a region of a gene of interest or any upstream or downstream region thereof. Target nucleic acids may represent alternative sequences or alleles of a particular gene. Target nucleic acids may be derived from genomic DNA, cDNA, or RNA, preferably cDNA. Target nucleic acid may be native DNA or a copy of native DNA such as by PCR (i.e., polymerase chain reaction) amplification. 
         [0014]    As used herein, “amplification” or “amplify” as used herein means one or more methods known in the art for copying a target nucleic acid, thereby increasing the number of copies of a selected nucleic acid sequence. Amplification may be exponential or linear. A target nucleic acid may be either DNA or RNA. The sequences amplified in this manner form an “amplicon.” While the exemplary methods described hereinafter relate to amplification using PCR, numerous other methods are known in the art for amplification of nucleic acids (e.g., isothermal methods, rolling circle methods, etc.). The skilled artisan will understand that these other methods may be used either in place of, or together with, PCR methods. See, e.g., Saiki, “Amplification of Genomic DNA” in  PCR Protocols , Innis et al., Eds., Academic Press, San Diego, Calif. 1990, pp 13-20; Wharam et al.,  Nucleic Acids Res.  2001 Jun. 1; 29(11):E54-E54; Hafner et al.,  Biotechniques  2001 April; 30(4):852-6, 858, 860 passim; Zhong et al.,  Biotechniques  2001 April; 30(4):852-6, 858, 860 passim. 
         [0015]    As used herein, a “primer” for amplification is an oligonucleotide that specifically anneals to a target or marker nucleotide sequence. The 3′ nucleotide of the primer should be identical to the target or marker sequence at a corresponding nucleotide position for optimal amplification. 
         [0016]    As used herein, “sense strand” means the strand of double-stranded DNA (dsDNA) that includes at least a portion of a coding sequence of a functional protein. “Anti-sense strand” means the strand of dsDNA that is the reverse complement of the sense strand. 
         [0017]    As used herein, a “forward primer” is a primer that anneals to the anti-sense strand of dsDNA. A “reverse primer” anneals to the sense-strand of dsDNA. 
         [0018]    As used herein, “normalized” in the context of gene expression data refers to arithmetic manipulation of observed gene expression data. Such manipulation can include the subtraction of the gene expression levels of genes which do not change in the disease or condition relative to the non-diseased state (i.e., “housekeeping” gene as known in the art.) Such manipulation can further include other arithmetic operations including multiplication by a factor, addition of an offset, negation, and the like. Further normalization procedures include subtraction of the average expression level of a specific gene from each individual sample. Exemplary housekeeping genes include without limitation those listed in Table 1. As used herein, the term “locus” in the context of the identity of a biomolecule refers to the LOCUS field in an entry of the GenBank® database. GenBank® is the NIH (National Institutes of Health) genetic sequence database which includes an annotated collection of all publicly available DNA sequences ( Nucleic Acids Research,  2004 32:23-6). 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Exemplary housekeeping genes for gene expression level determination. 
               
             
          
           
               
                 Locus 
                 Description 
               
               
                   
               
               
                 NM_001101 
                   Homo sapiens  actin, beta (ACTB), mRNA 
               
               
                 NM_000034 
                   Homo sapiens  aldolase A, fructose-bisphosphate (ALDOA), mRNA 
               
               
                 NM_002046 
                   Homo sapiens  glyceraldehyde-3-phosphate dehydrogenase (GAPD), 
               
               
                   
                 mRNA 
               
               
                 NM_000291 
                   Homo sapiens  phosphoglycerate kinase 1 (PGK1), mRNA 
               
               
                 NM_005566 
                   Homo sapiens  lactate dehydrogenase A (LDHA), mRNA 
               
               
                 NM_002954 
                   Homo sapiens  ribosomal protein S27a (RPS27A), mRNA 
               
               
                 NM_000981 
                   Homo sapiens  ribosomal protein L19 (RPL19), mRNA 
               
               
                 NM_000975 
                   Homo sapiens  ribosomal protein L11 (RPL11), mRNA 
               
               
                 NM_007363 
                   Homo sapiens  non-POU domain containing, octamer-binding (NONO), 
               
               
                   
                 mRNA 
               
               
                 NM_004309 
                   Homo sapiens  Rho GDP dissociation inhibitor (GDI) alpha (ARHGDIA), 
               
               
                   
                 mRNA 
               
               
                 NM_000994 
                   Homo sapiens  ribosomal protein L32 (RPL32), mRNA 
               
               
                 NM_022551 
                   Homo sapiens  ribosomal protein S18 (RPS18), mRNA 
               
               
                 NM_007355 
                   Homo sapiens  heat shock 90 kDa protein 1, beta (HSPCB), mRNA 
               
               
                 BC006091 
                 TSSC4, tumor suppressing subtransferable candidate 4 
               
               
                 AL137727 
                 TMEM55B, transmembrane protein 55B 
               
               
                 BC016680 
                 SP2, Sp2 transcription factor 
               
               
                 BC003043 
                 ARF5, ADP-ribosylation factor 5 
               
               
                 AF308803 
                 VPS33B, vacuolar protein sorting 33B 
               
               
                   
               
             
          
         
       
     
         [0019]    The plurality of data sets of measurements representing a plurality of different diseases or conditions may be narrowed in number by methods well known in the art. Standard, well-known regression techniques and other mathematical modeling may be employed to identify the most appropriate set of genes for the construction of the primary SOM, and to determine the values of the coefficients of these variables. The precise set of genes that are identified and the predictive ability of the resulting model (i.e., SOM) generally may depend upon the quality of the underlying data that is used to develop the model. Such factors as the size and completeness of the data set may be significant. The selection of the relevant variables and the computation of the appropriate coefficients are well within the skill of an ordinary person skilled in the art. In some embodiments, the plurality of data sets of measurements representing a plurality of different diseases or conditions may be narrowed in number by forward or backward stepwise logistic regression, linear regression, logistic regression, or non-stepwise logistic regression, all known to one of skill in the art. 
         [0020]    As used herein, “map cell,” “cell,” and terms of like import refer to the individual weight vectors, and the spatial representation thereof, which form a SOM in the sense that each map cell is uniquely associated with a weight vector. 
         [0021]    As used herein, “weight vector” refers to a multivariate data vector associated with a unique map cell (i.e., each map cell is characterized by a weight vector) which represents the results of training the SOM. 
         [0022]    As used herein, “training vector,” “training sample” and terms of like import refer to a multivariate data vector that represents a set of characteristics used for training the SOM. As used herein, “set of characteristics used for training the SOM” refers to measurable properties of tissue having a disease or condition including, without limitation, levels of gene expression or protein levels as described herein. Weight vectors and training vectors of necessity must overlap with respect to some dimensions; however, both weight vectors and training vectors may contain additional dimensions not included in the other. For example, a training vector may include (i.e., be associated with) additional entries (e.g., name, location, and the like) which are not used in training a SOM. Conversely, a weight vector may contain additional entries (e.g., display properties of the associated map cell) which have no counterpart in a training vector. In certain embodiments, map cells can be designated (i.e., highlighted by color, shaded, annotated, or otherwise distinguished) to focus attention on an individual map cell. 
         [0023]    As used herein, “multivariate data vector” refers to a plurality of ordered data elements. Examples of multivariate data vectors include, without limitation, the expression levels of nucleic acids and proteins in a biological sample. Weight vectors and training vectors are examples of multivariate data vectors. 
         [0024]    As used herein, “data sets of measurements representing a plurality of different diseases or conditions” and terms of like import refer to quantified levels of biological markers obtained from samples having known disease or condition. Examples of such biological markers include, without limitation, gene expression and protein levels. Examples of biological markers suitable for use with the invention include the proteins provided in Table 2 herein. “Sample data set obtained from a sample from an individual in need of diagnosis” and terms of like import refer to quantified levels of biological markers obtained from a sample from an individual in need of diagnosis, which in this context includes diseased tissue, for example a metastatic cancer site. Assessment of such biological marker data is routinely conducted by those skilled in the art employing methods including without limitation determination of levels of nucleic acid and protein. In some embodiments, gene expression data from samples having known pathology, and from an individual in need of diagnosis, form the individual dimensions of training and weight vectors. 
         [0025]    As used herein, “ordered array of map cells” and like terms refer to the spatial arrangement of map cells forming a SOM. For example, in a 1-dimensional context, map cells can assume e.g. a regular spacing on a line. In a 2- or 3-dimension context, map cells can assume a variety of regularly spaced arrangements, for example, square or hexagonal lattices. 
         [0026]    As used herein, “training the SOM,” “training phase,” “SOM calculation” and like terms refer to a process wherein the weight vectors of map cells of the SOM, after initialization, are changed in response to repeated input of training vectors. As used herein, “initializing a SOM” refers to the process whereby a SOM is initially populated with weight vectors prior to training the SOM with training vectors. Methods of training the SOM are well known in the art. During the training phase, the weight vectors of the map cells gradually change so as to align according to the distribution of the training vectors. 
         [0027]    As used herein, “primary SOM” means a self-organizing map which has been trained with a set of training vectors. 
         [0028]    As used herein, “secondary SOM” means all or part of a primary SOM which may optionally include a sample data set obtained from a sample from an individual in need of diagnosis. The term “display of all or part of a primary SOM” refers to a selective display of individual map cells in a SOM. The term “selective display,” “distinct labeling set,” and like terms refer to indicia within the SOM data structure (e.g., subject information including diagnosis, therapeutic regimens, results of therapy, age, sex, case history reference numbers, and the like) or presented with a display of the SOM (e.g. coloring or other highlighting, flashing, annotation, and the like) to distinguish individual map cells. The selection of individual map cells in a SOM can follow any of numerous types of information associated with training vectors, including without limitation, the tissue source of the training vector most similar to the weight vector characterizing a map cell, the number of training vectors which are most similar to a specific weight vector characterizing a map cell, age, sex, prognosis, the response of the disease or condition to an agent or therapeutic regimen, and other criteria well known in the art. Preferably, a secondary SOM selectively displays map cells associated with weight vectors which are most similar to training vectors derived from a single tissue type or cancer type. For example, a secondary SOM directed at colorectal cancer selectively displays map cells which are associated with training vectors derived from tissues characterized by colorectal cancer. Accordingly, in the case of colorectal cancer the distinct labeling set contemplates training vectors derived from tissues characterized as having colorectal cancer. Additionally, a secondary SOM is optionally augmented by a sample data set obtained from a sample from an individual in need of diagnosis, which means that the map cell of the secondary SOM having a weight vector which most closely matches the sample data set is distinguished by any of the indicia described above. The terms “extent of similarity,” “most similar,” “most closely matches,” and terms of like import refer to the comparison of multivariate data vectors by methods well known in the art and as described herein. Preferably, similarity is calculated as the Euclidean distance between two multivariate data vectors, as described herein. In some embodiments, similarity is calculated as the Mahalanobis, Hamming, or Chebychev distance between two multivariate data vectors, as described herein. As understood of one of skill in the art, lower distance between multivariate data vectors indicates higher similarity of the multivariate data vectors. 
         [0029]    As used herein, “preparing a result” and terms of like import in the context of a secondary SOM refer to preparation of a measure of the extent of similarity between the data sets of measurements resulting from a disease or condition and the sample data set of an individual. In preferred embodiments, the data sets of measurements result from known (e.g., histologically certified, or otherwise diagnosed) diseases or conditions. In some embodiments, the result is a display of one or more secondary SOMs showing at least a distinct labeling set and a map cell representing the sample data set of the individual. In some embodiments, the result is a numeric representation of the extent of similarity between the multivariate data vectors contemplated by a distinct labeling set and the sample data set of the individual. For example without limitation, the result may represent the average Euclidean distance (Eqn. 1) between the multivariate data vectors contemplated by a distinct labeling set and the sample data set of the individual. In other embodiments, the result may represent the average distance as calculated by any of the methods of Mahalanobis, Hamming, or Chebychev. In the context of multiple secondary SOMs, the result may represent the average distances as described herein over a plurality of distinct labeling sets. Other representations of the extent of similarity between the multivariate data vectors contemplated by a distinct labeling set and the sample data set of the individual are possible as known in the art, including for example without limitation descriptions of qualitative differences. As used herein, “qualitative differences,” “qualitative features” and like terms in the context of the similarity between multivariate data vectors refer to descriptions of the comparison of multivariate data vectors as known to one skilled in the art. Examples of such description include without limitation, rank ordering of distances, mapping of distances to a simple scale (e.g., 1-10, wherein 1 indicates high similarity between data vectors and 10 indicates low similarity), simple trivariate description (i.e., “less than,” “equal to”, or “greater than”), and the like. In some embodiments, the result is a numeric probability that the unknown disease or condition is one of the known diseases or conditions represented in the data sets of measurements used to construct the primary and secondary SOMs. 
         [0030]    Well known techniques of computer imagery can be employed to project a 3-dimensional SOM onto a 2-dimensional display (e.g., computer screen) allowing interactive manipulation (e.g., rotation, translation, and scaling) of the 2-dimension display. In certain embodiments, the SOM can be adapted to provide a variety of functionalities. For example, the display of a SOM can be adapted such that each map cell thereof is independently pickable. 
         [0031]    As used herein, “pickable” refers to the ability of a computer displayed object to be picked (i.e., chosen, identified, highlighted, or otherwise designated) in response to the action of a computer user. In some embodiments, the user action is the positioning of a cursor by, for example, the movement of a computer pointing device (e.g., computer mouse and the like) which is optionally clicked after positioning. In some embodiments, annotation associated with a picked map cell is displayed to a computer user in response to a picking action by the user. Annotation so displayed can provide a variety of information, including without limitation selected case history data including previous therapeutic regimens and responses thereto, age, sex, and other factors known to one skilled in the art. In some embodiments of methods provided herein, information associated with a map cell of a primary or secondary SOM is displayed. In some embodiments, the information associated with a map cell is displayed after the map cell is picked. In some embodiments, the displayed information comprises annotation associated with the training vectors which correspond to the picked map cell. In some embodiments, the display further comprises annotation associated with map cells near the picked map cell. As used herein “near the picked map cell” and like terms refer to map cells in proximity (e.g., nearest neighbor, next-nearest neighbor, and the like) to a picked map cell. 
         [0032]    As used herein, “data element,” “scalar,” and like terms refer to the individual components of a multivariate data vector, each occupying a different dimension of the multivariate data vector. Such data elements can be continuous (e.g., a real number) or discrete (e.g., on/off, yes/no, male/female, and the like). 
         [0033]    As used herein, “clustering technique,” “method of clustering,” and like terms refer to a variety of techniques whereby data are grouped (i.e., segregated based on similarity). In some embodiments, clustering is achieved by K-means clustering, hierarchical clustering, or expectation maximization clustering. The term “representation of clustering technique” refers to a printed or otherwise displayed (e.g., computer image) representation of the result of a clustering technique. A SOM is a clustering technique and a representation of a clustering technique. Representations of clustering techniques can be 1-, 2-, or 3-dimensional, preferably 2-dimensional (e.g., printed or displayed as a computer image). 
         [0034]    As used herein, “Euclidean distance” is used in the conventional sense to refer to the distance d AB  in an N-dimension space between multivariate data vectors A and B having N components a i  and b i , respectively, according to the generalized Pythagorean Theorem, Eqn. (1): 
         [0000]    
       
         
           
             
               
                 
                   
                     d 
                     AB 
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         N 
                       
                        
                       
                         
                           ( 
                           
                             
                               a 
                               i 
                             
                             - 
                             
                               b 
                               i 
                             
                           
                           ) 
                         
                         2 
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Thus, Euclidian distance is calculated pairwise with respect to individual ordered data elements of a pair of multivariate data vectors. 
         [0035]    In another aspect, the invention provides a method for diagnosis of a disease or condition in an individual comprising: a) providing a primary self organizing map (SOM) constructed using a plurality of data sets of measurements representing a plurality of different diseases or conditions, wherein the primary SOM includes at least one distinct labeling set, which distinct labeling set represents a disease or condition; b) forming at least one secondary SOM using the primary SOM with a sample data set obtained from a sample from an individual, thereby providing a display of the sample data set with respect to at least one distinct labeling set, whereby a medical practitioner can diagnose a disease or condition from the display. 
         [0036]    In another aspect, the invention provides a method for diagnosis of a disease or condition in an individual, which method includes the following steps: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements representing a plurality of different diseases or conditions; b) forming at least one secondary SOM by augmenting a primary SOM with a sample data set obtained from a sample from an individual in need of diagnosis, wherein such secondary SOM displays the sample data set with respect to a distinct labeling set which represents a disease or condition; and c) providing at least one secondary SOM to a medical practitioner for diagnosing a disease or condition. 
         [0037]    In another aspect, the invention provides a method for constructing a self-organizing map useful in the diagnosis of an individual suffering from a disease or condition, the method comprising: a) constructing a primary self organizing map by using a plurality of data sets of measurements, the data sets representing a plurality of different diseases or conditions, with the data sets obtained from a plurality of individuals each having a disease or condition; and b) forming at least one secondary SOM using at least one distinct labeling set, each distinct labeling set encompassing data sets of measurements of a particular disease or condition, with the secondary SOM including a sample data set obtained from a sample of the individual suffering from a disease or condition, thereby providing a SOM suitable for diagnosis of a disease or condition in the individual. 
         [0038]    In another aspect, the invention provides methods for constructing a SOM useful in the diagnosis of an individual suffering from a disease or condition, which include the following steps: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements representing a plurality of different diseases or conditions, wherein the primary SOM comprises at least one distinct labeling set, the distinct labeling set representing a disease or condition; and b) forming at least one secondary SOM using the primary SOM with a sample data set obtained from a sample from the individual, thereby providing a display of the sample data set with respect to the at least one distinct labeling set, thereby providing a SOM suitable for diagnosis of a disease or condition in said individual. 
         [0039]    In another aspect, the invention provides methods for constructing a SOM useful in the diagnosis of an individual suffering from a disease or condition, which include the following steps: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements representing a plurality of different diseases or conditions; and b) forming at least one secondary SOM by augmenting the primary SOM with a sample data set obtained from a sample from the individual suffering from a disease or condition, wherein the at least one secondary SOM displays the sample data set with respect to a distinct labeling set, and wherein the distinct labeling set represents a disease or condition; thereby providing a SOM suitable for diagnosis of a disease or condition in an individual. 
         [0040]    In another aspect, the invention provides a method of displaying a self organizing map useful in the diagnosis of an individual suffering from a disease or condition, the method comprising: a) constructing a primary self organizing map by using a plurality of data sets of measurements, the data sets representing a plurality of different diseases or conditions, with the data sets obtained from a plurality of individuals each having a disease or condition; b) forming at least one secondary SOM using at least one distinct labeling set, the distinct labeling set encompassing data sets of measurements of a particular disease or condition, and the secondary SOM including a sample data set obtained from a sample of said individual; and c) displaying said primary SOM or said at least one secondary SOM. 
         [0041]    In another aspect, the invention provides a method for displaying a SOM useful in the diagnosis of an individual suffering from a disease or condition, which method includes the following steps: a) providing a primary self organizing map (SOM) constructed using a plurality of data sets of measurements representing a plurality of different diseases or conditions, wherein the primary SOM comprises at least one distinct labeling set, the distinct labeling set representing a disease or condition; b) forming at least one secondary SOM by using the primary SOM with a sample data set obtained from a sample from the individual, thereby providing a display of the sample data set with respect to the at least one distinct labeling set, and c) displaying the primary SOM or the at least one secondary SOM. 
         [0042]    In another aspect, the invention provides methods for displaying a SOM useful in the diagnosis of an individual suffering from a disease or condition, wherein include the following steps: a) constructing a primary SOM by using a plurality of data sets of measurements representing a plurality of different diseases or conditions; b) forming at least one secondary SOM by augmenting the primary SOM with a sample data set obtained from a sample from the individual suffering from a disease or condition, wherein the at least one secondary SOM displays the sample data set with respect to a distinct labeling set, and wherein the distinct labeling set represents a disease or condition; and c) displaying at least one of said primary SOM or said at least one secondary SOM. 
         [0043]    In another aspect, the invention provides a program product comprising machine-readable program code for causing a machine to perform the following method steps: a) constructing a primary self organizing map using a plurality of data sets of measurements obtained from a plurality of individuals each having a disease or condition; and b) preparing a secondary SOM using at least one distinct labeling set, the distinct labeling set encompassing data sets of measurements of a particular disease or condition, with the secondary SOM including a sample data set obtained from a sample of said individual. In some embodiments, the invention provides a program product further comprising machine-readable program code for causing a machine to perform the following method steps: c) preparing a result from the secondary SOM that reveals the extent of similarity between the data sets of measurements of the distinct labeling set and the sample data set of the individual suffering from a disease or condition. In some embodiments of methods related to program products provided herein, there is provided machine-readable code for causing a machine to display information associated with a map cell of a primary or secondary SOM. In some embodiments, the information associated with a map cell is displayed after the map cell is picked. In some embodiments, the displayed information comprises annotation associated with the training vectors which correspond to the picked map cell. In some embodiments, the display further comprises annotation associated with map cells near the picked map cell. 
         [0044]    In another aspect, the invention provides program products which include machine-readable program code for causing a machine to perform the following method steps: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements representing a plurality of different diseases or conditions, wherein the primary SOM comprises at least one distinct labeling set, the distinct labeling set representing a disease or condition; and b) forming at least one secondary SOM using the primary SOM with a sample data set obtained from a sample from an individual suffering from a disease or condition, wherein said at least one secondary SOM displays said sample data set with respect to a distinct labeling set. 
         [0045]    In another aspect, the invention provides program products which include machine-readable program code for causing a machine to construct a primary self organizing map (SOM) by using a plurality of data sets of measurements representing a plurality of different diseases or conditions, wherein the primary SOM comprises at least one distinct labeling set, the distinct labeling set representing a disease or condition. 
         [0046]    In another aspect, the invention provides program products which include machine-readable program code for causing a machine to form at least one secondary SOM using a primary SOM with a sample data set obtained from a sample from an individual suffering from a disease or condition, wherein the at least one secondary SOM displays the sample data set with respect to a distinct labeling set. 
         [0047]    In another aspect, the invention provides program products which include machine-readable program code for causing a machine to perform the following method steps: a) constructing a primary SOM by using a plurality of data sets of measurements representing a plurality of different diseases or conditions; and b) forming at least one secondary SOM by augmenting the primary SOM with a sample data set obtained from a sample from an individual suffering from a disease or condition, wherein the at least one secondary SOM displays the sample data set with respect to a distinct labeling set, which distinct labeling set represents a disease or condition. 
         [0048]    In another aspect, the invention provides a method for providing therapy response information associated with at least one pickable map cell of a primary or secondary SOM, the method comprising: a) providing annotation of therapy response information for at least one pickable map cell of a primary or secondary SOM; and b) displaying the therapy response information after the map cell is picked. In some embodiments, the method further comprises displaying therapy response information of map cells near the picked map cell. 
         [0049]    In another aspect, the invention provides a method for reducing the number of biological markers required to construct a primary SOM useful for the diagnosis of an individual having a disease or condition, the method comprising using a reduction method to find the minimum set of biological markers that contribute a model to predict the possible diseases or conditions, wherein the reduction method is selected from the group consisting of forward stepwise logistic regression, backward stepwise logistic regression, linear regression, logistic regression, and non-stepwise logistic regression, As used herein “reduction method” refers to a mathematical method of eliminating data while retaining most of the underlying information. In some embodiments, the biological markers are particular genes. In some embodiments, the biological markers are levels of particular proteins. In some embodiments, the disease or condition is cancer of unknown primary. 
         [0050]    In another aspect, the invention provides a method for diagnosis of cancer of unknown primary in an individual, said method comprising: a) providing a primary self organizing map (SOM) constructed using a plurality of data sets of measurements obtained from a plurality of individuals representing a plurality of particular cancers; b) preparing a plurality of secondary SOMs each using a distinct labeling set, with each of the distinct labeling sets encompassing data sets of measurements obtained from individuals having a particular cancer, and with the secondary SOM including a sample data set obtained from a sample of said individual; c) preparing a result from the plurality of secondary SOMs that reveals the extent of similarity between the data sets of measurements of the distinct labeling set and the sample data set of the individual; and d) providing the result to a medical practitioner for use to diagnosis cancer of unknown primary, wherein the result is selected from the group consisting of a primary SOM, one or more secondary SOMs, a display of a primary SOM, a display of one or more secondary SOMs, and a probability that the sample data set is one or more of the particular cancers. 
         [0051]    In another aspect, the invention provides a method for evaluating the likelihood of a clinical response for an individual to a treatment for a disease or condition, which method includes: a) providing a primary self organizing map (SOM) constructed using a plurality of data sets of measurements obtained from a plurality of individuals, the plurality of individuals each having undergone a treatment for a disease or condition, the individuals each having a clinical response to the treatment; b) preparing a secondary SOM using a distinct labeling set, which distinct labeling set encompasses one or more of the clinical responses of the plurality of individuals to the treatment, the secondary SOM including a sample data set obtained from a sample of an individual in need of evaluation; and c) preparing a result from the secondary SOM that reveals the extent of similarity between the data sets of measurements of the distinct labeling set and the sample data set of the individual in need of evaluation, whereby a medical practitioner can use the result to evaluate the likelihood of a clinical response for the individual in need of evaluation to the treatment. 
         [0052]    As used herein, “evaluating the likelihood of a clinical response” and like terms refer to prognosis with respect to a specific treatment, as understood by a medical practitioner, for an individual undergoing a treatment or contemplated to undergo a treatment. “Clinical response” and like terms refer to possible outcomes for treatment. In some cases, the clinical response is positive; i.e., the treatment successfully treats or otherwise ameliorates a disease or condition consistent with the medical goals identified by the medical practitioner. In some cases, the clinical response is negative; i.e., the treatment does not successfully treat or otherwise ameliorate a disease or condition, such that a reasonably prudent medical practitioner would not subject the individual to the treatment. In some cases, the clinical response may be a graded response representing a spectrum of possible responses, e.g., highly positive, positive, neutral, negative, highly negative. “Highly positive” in this context refers to a treatment which offers better treatment and/or amelioration of the disease or condition than observed in a positive response. Conversely, “highly negative” refers to a treatment which a reasonably prudent medical practitioner would avoid. As used herein, “neutral” in the context of a clinical response refers to a treatment which is not deleterious and which is not successful in treating the disease or condition. In some cases, the clinical response may have an associated numerical representation, for example without limitation, 1: highly positive; 2: positive; 3: neutral; 4: negative; 5: highly negative, or like numerical scales. 
         [0053]    As used herein, “undergone a treatment for a disease or condition” and like terms refer to the status of an individual as having already undergone a treatment for the disease or condition and as having a clinical response to the treatment as described herein. 
         [0054]    In another aspect, the invention provides a method for constructing a self-organizing map (SOM) useful for evaluating the likelihood of a positive clinical response for an individual to a treatment for a disease or condition, which method includes: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements, wherein the data sets are obtained from a plurality of individuals each having a disease or condition; the individuals each having undergone a treatment for the disease or condition, the individuals each having a clinical response to said treatment; and b) forming at least one secondary SOM using at least one distinct labeling set, the distinct labeling set encompassing clinical responses of the plurality of individuals to the treatment, the secondary SOM including a sample data set obtained from a sample of an individual in need of evaluation, thereby providing a SOM suitable for evaluating the likelihood of a clinical response for the individual to the treatment. 
         [0055]    In another aspect, the invention provides a method for selecting an individual in need of treatment for a treatment for a disease or condition, which method includes: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements, the data sets obtained from a plurality of individuals each having a disease or condition; the individuals each having undergone a treatment for the disease or condition, the individuals each having a clinical response to the treatment; b) forming at least one secondary SOM using at least one distinct labeling set, the distinct labeling set encompassing clinical responses of the plurality of individuals to the treatment, the secondary SOM including a sample data set obtained from a sample of an individual in need of treatment; and c) selecting for the treatment the individual in need of treatment based on a result showing the proximity of the sample data set of the individual within the secondary SOM to the data sets obtained from the plurality of individuals having clinical responses to the treatment, thereby providing selection of the individual in need of treatment for the treatment for the disease or condition. 
         [0056]    In another aspect, the invention provides a method for selecting an individual in need of treatment for a clinical trial evaluating a treatment for a disease or condition, which method includes: a) constructing a primary self organizing map (SOM) by using a plurality of data sets of measurements, the data sets obtained from a plurality of individuals each having a disease or condition; the individuals each having undergone a treatment for the disease or condition, the individuals each having a clinical response to the treatment; b) forming at least one secondary SOM using at least one distinct labeling set, the distinct labeling set encompassing clinical responses of the plurality of individuals to the treatment, the secondary SOM including a sample data set obtained from a sample of an individual in need of treatment; and c) selecting the individual in need of treatment based on a result showing the proximity of the sample data set of the individual within the secondary SOM to the data sets obtained from the plurality of individuals having clinical responses to the treatment, thereby providing selection of the individual in need of treatment for a clinical trial evaluating the treatment for the disease or condition 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0057]      FIG. 1  provides an exemplary schematic flow of steps in the construction of a primary SOM. 
           [0058]      FIG. 2  provides an exemplary secondary SOM. Legend: solid filled black: map cell representing sample data set from individual in need of diagnosis; clusters obtained from a clustering of training samples: diagonal stripes, horizontal stripes, and solid gray highlighting in order of Euclidean distance from the map cell representing the sample data set. 
           [0059]      FIG. 3  is an exemplary set of secondary SOMs, suitable for presentation to a practitioner for diagnosing cancer of unknown primary. Legend: solid filled black: map cell representing sample data set from individual in need of diagnosis; other clusters obtained from a clustering of distinct labeling sets: solid filled gray, crosshatched, diagonal stripes, respectively in order of proximity to sample data set from individual in need of diagnosis. 
           [0060]      FIG. 4  provides an exemplary secondary SOM suitable for presentation to a practitioner for evaluating the likelihood of a clinical response for an individual to a treatment for a disease or condition. Legend: solid filled black: map cell representing sample data set from individual; horizontal stripes, map cells representing a plurality of individuals each having undergone a treatment for a disease or condition, wherein the treatment resulted in a negative response; solid filled gray, map cells representing a plurality of individuals each having undergone a treatment for a disease or condition, wherein the treatment resulted in a positive response. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0061]    The construction of primary SOMs as described herein employs methodologies and software tools well known to the skilled artisan. Descriptions of suitable methods of construction are provided herein and by references described herein. Software packages which provide computational support for the construction of SOMs are available as commercial and public domain software packages including, without limitation, MATLAB® (The Mathworks, Inc., Natick, Mass.) and the SOM Toolbox for MATLAB® (Laboratory of Computer and Information Science, Helsinki University of Technology, Finland). 
         [0062]    Briefly, construction of 2-dimensional SOMs may generally follow the steps as diagrammed in  FIG. 1 . Initially, each map cell (e.g., rectangular or hexagonal lattice point in a 2-dimension SOM) is assigned an initial weight vector (Step 0101). Many methods for the initial assignment of weight vectors are known to the skilled artisan including, without limitation, random assignment of a number to each scalar forming the weight vectors. The term “random” refers to equal probability for any of a set of possible outcomes. The numeric value of such randomly assigned scalar values may be approximately bounded at the lower and upper extrema by the corresponding extrema observed in the training vectors. Another method of initiation of weight vectors include a systematic (e.g., linear) variation in the range of each dimension of each weight vector to approximately overlap the corresponding range observed in the training vectors. In yet another method of initialization, the weights are initialized by values of the vectors ordered along a two-dimension subspace spanned by the two principal eigenvectors of the training vectors obtaining by methods of orthogonalization well known in the art (e.g., Gram-Schmidt orthogonalization). In yet a further initialization procedure, initial values are set to randomly chosen patterns of the training sample. 
         [0063]    In step  0102 , a training vector is selected. The selection may be random or systematic, preferably random. When a training vector is selected, the Euclidean distance between the selected training vector and each weight vector of the SOM is calculated. 
         [0064]    In step  0103 , the weight vector having the smallest Euclidean distance is declared the “best matching unit” (BMU). Once a BMU is identified, the neighborhood about this BMU is optionally scaled (step  0104 ) by methods well known in the art. 
         [0065]    At step  0105  a decision is made whether to re-iterate processes  0102 - 0104 , or to terminate construction of the SOM. This decision is based on whether a predefined convergence criterion has been met. The term “convergence criterion” in the context of SOM construction refers to any of a variety of metrics available to the skilled artisan. Such criteria include an absolute iteration limit (e.g., 100, 200, 500, 1000, 2000, 5000, or even more), an absolute largest change in Euclidean distance between the selected training vector and each weight vector of the SOM (e.g., 100, 10, 1, 0.1, 0.01, 0.001, and even less), a relative largest change in Euclidean distance between the selected training vector and each weight vector of the SOM (e.g., 10%, 1%, 0.1%, 0.01%, and even less), or any of these criteria additionally coupled with a requirement that all training vectors be selected a minimum number of times (e.g, 1, 2, 3, 4, 5, 10, 20, 50, 100, or even more). After convergence is reached, the procedure terminates (step  0106 ). 
         [0066]    In some embodiments of methods provided herein for the diagnosis of a disease or condition in an individual, each of the plurality of diseases or conditions which are represented in data sets of measurements contemplated in the construction of a primary SOM is a cancer. As used herein “specific cancers,” “particular cancers” and terms of like import contemplated in this context include without limitation melanoma, pancreatic cancer, colorectal cancer, non-small cell lung cancer, breast cancer, small cell lung cancer, ovarian cancer, prostate cancer, stomach cancer, or kidney cancer. 
         [0067]    In certain embodiments of methods provided herein, the sample data set obtained from a sample from an individual in need of diagnosis, and the data sets of measurements which represent a plurality of different diseases or conditions, comprise data vectors of scalars (i.e., multivariate data vectors). The scalars may be continuous or discrete, as understood by one of skill in the art. In preferred embodiments, the sample data set is isomorphic with the data sets of measurements representing a plurality of different diseases or conditions used to construct the primary and secondary SOMs. As used herein, “isomorphic” refers to correspondence of each element, on an element by element basis, of multivariate data vectors used to construct a SOM. For example without limitation, two multivariate data vectors are isomorphic if each dimension thereof used in construction of a SOM represents the same biological marker. In some embodiments, the dimensionality of the data vectors of scalars described herein is greater than 2. In some embodiments, the dimensionality of the data vectors of scalars described herein is greater than or equal to 2, 3, 4, 5, 10, 15, 20, 25, 29, 40, 50, 75, 87, 100, or even more. In some embodiments, the dimensionality of the data vectors of scalars described herein is at least 20. In some embodiments, the dimensionality of the data vectors of scalars described herein is at least 29. In some embodiments, the dimensionality of the data vectors of scalars described herein is 29. 
         [0068]    In certain embodiments, a plurality of secondary SOMs, each employing a different distinct labeling set, are formed by methods described herein. Exemplary distinct labeling sets include without limitation distinct labeling sets directed at melanoma, pancreatic cancer, colorectal cancer, non-small cell lung cancer, breast cancer, small cell lung cancer, ovarian cancer, prostate cancer, stomach cancer, or kidney cancer. 
         [0069]    In certain embodiments, the medical practitioner to whom the at least one secondary SOM is provided is a non-veterinary medical practitioner. 
         [0070]    In certain embodiments, the individual in need of diagnosis presents with cancer of unknown primary. In some embodiments, diagnosis of the individual is the determination of the primary source of a metastatic cancer. 
         [0071]    In certain embodiments, a method of diagnosis of a disease or condition in an individual further includes a step of providing to a medical practitioner a probability P related   i  that the sample data set is related to one of the different diseases or conditions represented by the plurality of data sets of measurements. 
         [0072]    In certain embodiments, the calculation of P related   i  includes the following steps: i) determining a plurality of nearest neighbors of the sample data set with respect to the data sets of measurements representing a plurality of different diseases or conditions; and ii) determining if the plurality of nearest neighbors so calculated all represent the same disease or conditions. As used herein, “nearest neighbor” and terms of like import refer to the data sets of measurements representing a plurality of diseases or conditions which are most similar to the sample data set obtained from an individual in need of diagnosis. In this context, similarity may be assessed by calculation of the Euclidean distance as described herein. In some embodiments, similarity may be assessed by calculation of the Mahalanobis distance, Hamming distance, or Chebychev distance. Thus, if a rank ordering of data set of measurements were constructed using the Euclidean distance, for example without limitation, with respect to the sample data set obtained from an individual in need of diagnosis as a metric for ranking, the nearest neighbors would contiguously occupy the rank ordering with the lowest Euclidean distances. The number of nearest neighbors can be any positive integer less than or equal to the number of data sets of measurements representing a plurality of diseases or conditions, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or even more. Preferably, the number of nearest neighbors is 2, 3 or 4, more preferably 3. 
         [0073]    In certain embodiments, when each of the plurality of nearest neighbors represents the same disease or condition, P related   i  is assigned a value of 1, corresponding to 100% probability that the sample data set obtained from the individual in need of diagnosis is similar in gene expression profile to data sets obtained from tissue having the disease or condition of the nearest neighbors. 
         [0074]    In certain embodiments, when the plurality of nearest neighbors do not each represent the same disease or condition, P related   i  is calculated by evaluating a probability P cluster   i  and equating P related   i  with P cluster   i . 
         [0075]    In certain embodiments, P cluster   i  is calculated by evaluating the expression 
         [0000]    
       
         
           
             
               
                 
                   
                     P 
                     cluster 
                     i 
                   
                   = 
                   
                     
                       1 
                       
                         d 
                         j 
                         2 
                       
                     
                     
                       
                         ∑ 
                         
                           p 
                           = 
                           1 
                         
                         T 
                       
                        
                       
                         1 
                         
                           d 
                           p 
                           2 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
         [0000]    for one or more of the diseases or conditions represented in the plurality of nearest neighbors calculated as described herein, wherein in Eqn. (2) d j  is the Euclidian distance between the sample data set obtained from a sample from the individual in need of diagnosis and the closest cluster center of T clusters obtaining from a clustering of the distinct labeling sets representing the disease or condition represented in the plurality of nearest neighbors, and d p  is the Euclidean distance between the sample data set and any of the T cluster centers. 
         [0076]    As used herein, “clustering of the distinct labeling sets” refers to a clustering procedure wherein data sets representing the same disease or condition are clustered. For example without limitation, if the disease or condition were melanoma, then the clustering of the distinct labeling set would be over all data sets representing melanoma. Using methodology well known in the art, clustering of the distinct labeling set can be initiated for example by a hierarchical clustering, wherein the similarity, as measured by for example Euclidean distance between each pair of training samples is calculated. All samples representing a specific disease or condition are then grouped into a binary hierarchical tree using the method of simple linkage, well known in the art. The resulting hierarchical tree is then cut into clusters using an inconsistency coefficient, which as known in the art characterizes each link in a cluster tree by comparing its length with the average length of other links at the same level of hierarchy. The higher the value of the inconsistency coefficient, the less similar the objects connected by the link. The inconsistency coefficient criterion can assume any real value, preferably 1.0. After the cutting of clusters using an inconsistency coefficient, all single-sample clusters are removed. A cluster center is then defined for each remaining cluster, which cluster center has in each dimension the arithmetic mean of the corresponding dimensions of the training samples included within the cluster. Accordingly, the sum in Eqn. (2) is over all training sample clusters except single-sample clusters, with the exception that for diseases or conditions (e.g., tissues having a histologically certified cancer) which have multiple clusters, only the closest such cluster center is used in the sum of Eqn. (2). 
         [0077]    In embodiments of the invention provided herein, at least one secondary SOM displays the sample data set with respect to a distinct labeling set, wherein the distinct labeling set represents a disease or condition. An idealized secondary SOM is shown in  FIG. 2 . In  FIG. 2 , the map cell representing the sample data set obtained from a sample from an individual in need of diagnosis is displayed as a solid hexagon in the upper left corner. In this idealized figure, 17 additional map cells are highlighted which correspond to 17 different data sets of measurement arising from 17 unique training samples. These 17 training samples have been classified into 3 clusters, having diagonal stripes, horizontal stripes, and solid gray highlighting in order of Euclidean distance from the map cell representing the sample data set. 
         [0078]    In certain embodiments, when the plurality of nearest neighbors do not each represent the same disease or condition, P related   i  is calculated by evaluating a probability P tissue   i  and equated P related   i  with P tissue   i . 
         [0079]    In certain embodiments, P tissue   i  is calculated by evaluating the expression 
         [0000]    
       
         
           
             
               
                 
                   
                     P 
                     tissue 
                     i 
                   
                   = 
                   
                     
                       1 
                       
                         d 
                         k 
                         2 
                       
                     
                     
                       
                         ∑ 
                         
                           q 
                           = 
                           1 
                         
                         U 
                       
                        
                       
                         1 
                         
                           d 
                           q 
                           2 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
         [0000]    for one or more of the diseases or conditions represented in the plurality of nearest neighbors calculated as described herein, wherein in Eqn. (3) d k  is the Euclidian distance between the sample data set obtained from a sample from the individual in need of diagnosis and the center of a distinct labeling set representing a disease or condition, and d q  is the Euclidean distance between the sample data set and any of the U centers of the distinct labeling set representing the disease or condition. For example without limitation, if a specific disease or condition is associated with a specific tissue, and if a particular secondary SOM displays one of the nearest neighbors found in the procedure described above (i.e., one of the nearest neighbors is found in the tissue type of the specific disease or condition), then d q  is the Euclidean distance between the sample data set and the center of each cluster found within the particular secondary SOM. 
         [0080]    In certain embodiments, when the plurality of nearest neighbors do not each represent the same disease or condition, P related   i  is calculated by evaluating probabilities P cluster   i  and P tissue   i  as described above, and further calculating the probability 
         [0000]        P   related   i   =αP   cluster   +βP   tissue   (4) 
         [0000]    wherein α+β=1. The proportionality factors α and β can be optimized, for example without limitation, by evaluating the prediction of histologically certified test samples. In certain embodiments, the histologically certified test samples do not form any of the samples used for training the primary SOM. In certain embodiments, α=0.3 and β=0.7. 
         [0081]    In certain embodiments, the method for constructing a SOM useful in the diagnosis of an individual suffering from a disease or condition employs the method described herein for construction of a primary SOM, and the formation of at least one secondary SOM employs methods described herein. 
         [0082]    In certain embodiments, in the method for constructing a SOM useful in the diagnosis of an individual suffering from a disease or condition, the sample data and data sets of measurements representing a plurality of different diseases or conditions are data vectors of scalars, wherein the scalars are continuous or discrete. In some embodiments, the dimensionality of these data vectors is greater than 2. In some embodiments, the dimensionality of these data vectors is greater than 20. In some embodiments, the dimensionality of these data vectors is at least 29. In some embodiments, the dimensionality of these data vectors is 29. In some embodiments, a plurality of secondary SOMs, each using a different distinct labeling set, are formed. 
         [0083]    In certain embodiments, in the method for evaluating the likelihood of a clinical response for an individual to a treatment for a disease or condition, the plurality of individuals from which the plurality of data sets of measurements are obtained and used to construct the primary SOM, represents a plurality of clinical responses. In some embodiments, the clinical response is negative; in some embodiments, the clinical response is positive. In some embodiments, the clinical responses are both negative and positive. In some embodiments, the clinical responses are negative, positive and/or neutral. 
         [0084]    Further this aspect, in certain embodiments, the step of preparing a secondary SOM is repeated for different clinical responses, each forming a distinct labeling set, thereby preparing multiple secondary SOMs. In some embodiments, the secondary SOM represents negative clinical responses, and the distinct labeling set contemplates negative clinical responses. In some embodiments, the secondary SOM represents positive clinical responses, and the distinct labeling set contemplates positive clinical responses. In some embodiments, the multiple secondary SOMs represent negative and positive clinical responses. In some embodiments, the result of the method is a display of one or more of the multiple secondary SOMs. In some embodiments, the result is a display of the sample data set of the individual with respect to the data sets of measurements of the plurality of individuals. In some embodiments, the result is a display of the sample data set of the individual with respect to the one or more distinct labeling sets. In some embodiments, the result of the method includes a numeric representation of the extent of similarity between the map cell of the individual and the map cells contemplated by the distinct labeling sets, as described herein. In some embodiments, the method contemplates gene expression levels or proteins levels in the construction of the primary SOM. In some embodiments, the method contemplates gene expression levels in the construction of the primary SOM. 
         [0085]    In certain embodiments, in the method for constructing a SOM useful for evaluating the likelihood of a positive clinical response for an individual to a treatment for a disease or condition, the plurality of individuals contemplated in the construction of the primary SOM represents a plurality of clinical responses. In some embodiments, the clinical responses are negative. In some embodiments, the clinical responses are positive. In some embodiments, the clinical responses are negative and positive. In some embodiments, the clinical responses are negative, positive and/or neutral. In some embodiments, the method is repeated thereby providing multiple secondary SOMs for different clinical responses. In some embodiments, one or more of the multiple secondary SOMs have distinct labeling sets which contemplate negative clinical responses. In some embodiments, one or more of the multiple secondary SOMs have distinct labeling sets which contemplate positive clinical responses. 
         [0086]    In certain embodiments, in the method for selecting an individual in need of treatment for a treatment for a disease or condition, the plurality of individuals, data sets of measurements of which are used in the construction of the primary SOM, represent a plurality of clinical responses. In some embodiments, the clinical response is negative. In some embodiments, the clinical response is positive. In some embodiments, the clinical responses are negative and positive. In some embodiments, the clinical responses are negative, positive and/or neutral. In some embodiments, the step of forming at least one secondary SOM is repeated to provide multiple secondary SOMs for different clinical responses. In some embodiments of this aspect, the result is a display of one or more of the multiple secondary SOMs. In some embodiments, the result is a display of the sample data set of the individual with respect to the data sets of measurements of the distinct labeling set contemplated in the formation of the secondary SOM. In some embodiments, the result is a numeric representation of the extent of similarity between the sample data set of the individual and the data sets of measurements of the plurality of individuals used in constructing the secondary SOM, as described herein. In some embodiments, the method contemplates gene expression levels or proteins levels. In some embodiments, the method contemplates gene expression levels. In some embodiments, when the sample data set of the individual is proximate to data sets obtained from a plurality of individuals having a positive clinical response to the treatment, the individual is selected for treatment. In some embodiments, when the sample data set of the individual is not proximate to data sets obtained from a plurality of individuals having a positive clinical response to the treatment, or when the sample data set of the individual is proximate to data sets obtained from a plurality of individuals having a negative clinical response to the treatment, the individual is not selected for treatment. 
         [0087]    In certain embodiments, in the method for selecting an individual in need of treatment for a clinical trial evaluating a treatment for a disease or condition, the plurality of individuals, data sets of measurements of which are used in the construction of the primary SOM, represents a plurality of clinical responses. In some embodiments, the clinical response is negative. In some embodiments, the clinical response is positive. In some embodiments, the clinical response is negative and positive. In some embodiments, the clinical response is negative, positive and/or neutral. In some embodiments, the step of forming at least one secondary SOM is repeated using data sets of measurements of a plurality of individuals having a plurality of clinical responses, thereby providing multiple secondary SOMs for different clinical responses. In some embodiments, the result is a display of one or more of the multiple secondary SOMs. In some embodiments, the result is a display of the sample data set with respect one or more distinct labeling sets. In some embodiments, the result is a numeric representation of the extent of similarity between the sample data set of the individual and the data sets of measurements of the plurality of individuals used in constructing the secondary SOM, as described herein. In some embodiments, the sample data set and data sets of measurements include gene expression levels or protein levels. In some embodiments, the sample data set and data sets of measurements include gene expression levels. 
         [0088]    Further this method, in certain embodiments the sample data set of the individual is proximate to data sets of measurements of a plurality of individuals having positive clinical response to the treatment, and the individual is selected for the clinical trial. In some embodiments, the sample data set of the individual is not proximate to data sets of measurements of a plurality of individuals having positive clinical response to the treatment, and the individual is not selected for the clinical trial. In some embodiments, the sample data set of the individual is proximate to data sets of measurements of a plurality of individuals having negative clinical response to the treatment, and the individual is not selected for the clinical trial. 
         [0089]    Further any of the methods contemplating clinical responses of an individual as described herein, the clinical response of the individual may be positive. 
       EXAMPLES 
     Diagnostic for Cancer of Unknown Primary 
       [0090]    The expression levels of 87 target genes (Table 2) and 5 housekeeping genes (Table 3) were collected for 221 histologically certified tumor tissue samples, including 36 breast cancer, 32 colorectal cancer, 11 kinase cancer, 14 melanoma cancer, 30 non-small cell lung cancer, 33 ovary cancer, 24 pancreas cancer, 20 prostate cancer, 12 stomach cancer, and 9 small cell lung cancer tissue samples. Gene expression levels were determined by PCR as described herein, which employed the forward and reverse primers and probes tabulated in Table 4. 
         [0091]    The expression levels of 87 target genes from all samples were each normalized by subtracting from each of these values the average expression levels of the 5 housekeeping genes for each sample, and further subtracting the average gene expression level for each gene representing all samples. The “average gene expression level” is the average expression level across all 221 samples for one gene. After normalization, a step-wise logistic regression was conducted to find the minimum set of genes that contribute a model to predict each tumor tissue type. The minimum set of genes for the 10 tumor tissue types were then combined, which resulted in 29 unique genes to be used in the diagnostic procedure, listed as follows by GenBank® locus: AA782845, AB038160, AF133587, AF301598, AI309080, AI804745, AI985118, AK027147, AK054605, AW291189, AW473119, AY033998, BC001293, BC001639, BC002551, BC004331, BC006537, BC009084, BC010626, BC012926, BC013117, BC015754, M95585, NM — 004062, NM — 004063, NM — 019894, NM — 033229, R45389, and X69699. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Target genes for CUP diagnosis. 
               
             
          
           
               
                 Locus 
                 Description 
               
               
                   
               
               
                 AA456140 
                 zx65f08.s1 Soares_total_fetus_Nb2HF8_9w 
               
               
                   
                 ( Homo sapien ) 
               
               
                 AA745593 
                 NCI_CGAP_GCB1 ( Homo sapien ) 
               
               
                 AA765597 
                 NCI_CGAP_GCB1 ( Homo sapien ) 
               
               
                 AA782845 
                 Soares_parathyroid_tumor_NbHPA ( Homo sapien ) 
               
               
                 AA865917 
                 NCI_CGAP_GC4 ( Homo sapien ) 
               
               
                 AA946776 
                 NCI_CGAP_Kid5 ( Homo sapien ) 
               
               
                 AA993639 
                 Soares_total_fetus_Nb2HF8_9w ( Homo sapien ) 
               
               
                 AB038160 
                 TMPRSS3d mRNA for serine protease ( Homo sapien ) 
               
               
                 AF104032 
                 L-type amino acid transporter subunit LAT1 
               
               
                   
                 ( Homo sapien ) 
               
               
                 AF133587 
                 rhabdoid tumor deletion region protein 1 ( Homo sapien ) 
               
               
                 AF301598 
                 empty spiracles-like protein (EMX2) ( Homo sapien ) 
               
               
                 AF332224 
                 testis protein ( Homo sapien ) 
               
               
                 AI041545 
                 Soares_testis_NHT ( Homo sapien ) 
               
               
                 AI147926 
                 Soares_pregnant_uterus_NbHPU ( Homo sapien ) 
               
               
                 AI309080 
                 NCI_CGAP_Br15 ( Homo sapien ) 
               
               
                 AI341378 
                 NCI_CGAP_GC6 ( Homo sapien ) 
               
               
                 AI457360 
                 NCI_CGAP_Co14 ( Homo sapien ) 
               
               
                 AI620495 
                 NCI_CGAP_Pr28 ( Homo sapien ) 
               
               
                 AI632869 
                 NCI_CGAP_Ut1 ( Homo sapien ) 
               
               
                 AI683181 
                 NCI_CGAP_Ut1 ( Homo sapien ) 
               
               
                 AI685931 
                 NCI_CGAP_Pr28 ( Homo sapien ) 
               
               
                 AI802118 
                 NCI_CGAP_Lu24 ( Homo sapien ) 
               
               
                 AI804745 
                 NCI_CGAP_Pr28 ( Homo sapien ) 
               
               
                 AI952953 
                 NCI_CGAP_GC6 ( Homo sapien ) 
               
               
                 AI985118 
                 NCI_CGAP_Kid11 ( Homo sapien ) 
               
               
                 AJ000388 
                 HSCANPX calpain-like protease( Homo sapien ) 
               
               
                 AK025181 
                 FLJ21528 fis, clone COL05977 ( Homo sapien ) 
               
               
                 AK027147 
                 FLJ23494 fis, clone LNG01885 ( Homo sapien ) 
               
               
                 AK054605 
                 FLJ30043 fis, clone 3NB692001548 ( Homo sapien ) 
               
               
                 AL023657 
                 HSDSHP ( Homo sapien ) SH2D1A cDNA, ( Homo sapien ) 
               
               
                 AL039118 
                 DKFZp566J244_s1 566 (synonym: hfkd2) ( Homo sapien ) 
               
               
                 AL110274 
                 DKFZp564I0272 ( Homo sapien ) 
               
               
                 AL157475 
                 DKFZp761G151 ( Homo sapien ) 
               
               
                 AW118445 
                 NCI_CGAP_Brn35 ( Homo sapien ) 
               
               
                 AW194680 
                 NCI_CGAP_Kid13 ( Homo sapien ) 
               
               
                 AW291189 
                 NCI_CGAP_Sub4 ( Homo sapien ) 
               
               
                 AW298545 
                 NCI_CGAP_Sub6 ( Homo sapien ) 
               
               
                 AW445220 
                 NCI_CGAP_Sub5 ( Homo sapien ) 
               
               
                 AW473119 
                 NCI_CGAP_Ut1 ( Homo sapien ) 
               
               
                 AY033998 
                 HUDPRO1 ( Homo sapien ) 
               
               
                 BC000045 
                 vestigial like 1 Drosophila ( Homo sapien ) 
               
               
                 BC001293 
                 homeobox C10 ( Homo sapien ) 
               
               
                 BC001504 
                 pyrroline-5-carboxylate reductase 1 ( Homo sapien ) 
               
               
                 BC001639 
                 solute carrier family 43, member 1 ( Homo sapien ) 
               
               
                 BC002551 
                 cell division cycle associated 3 ( Homo sapien ) 
               
               
                 BC004331 
                 hydroxysteroid dehydrogenase like 2 ( Homo sapien ) 
               
               
                 BC004453 
                 5-hydroxytryptamine (serotonin) receptor 3A 
               
               
                   
                 ( Homo sapien ) 
               
               
                 BC005364 
                 chromosome 10 open reading frame 59 ( Homo sapien ) 
               
               
                 BC006537 
                 homeobox A9 ( Homo sapien ) 
               
               
                 BC006811 
                 peroxisome proliferative activated receptor ( Homo sapien ) 
               
               
                 BC006819 
                 S100 calcium binding protein P ( Homo sapien ) 
               
               
                 BC008764 
                 kinesin family member 2C ( Homo sapien ) 
               
               
                 BC008765 
                 syndecan 1 ( Homo sapien ) 
               
               
                 BC009084 
                 selenium binding protein 1 ( Homo sapien ) 
               
               
                 BC009237 
                 thyroid stimulating hormone receptor ( Homo sapien ) 
               
               
                 BC010626 
                 kinesin family member 12 ( Homo sapien ) 
               
               
                 BC011949 
                 carbonic anhydrase II ( Homo sapien ) 
               
               
                 BC012926 
                 EPS8-like 3 ( Homo sapien ) 
               
               
                 BC013117 
                 regulator of G-protein signalling 17 ( Homo sapien ) 
               
               
                 BC015754 
                 Ca2+dependent secretion activator ( Homo sapien ) 
               
               
                 BC017586 
                 calcyphosine-like ( Homo sapien ) 
               
               
                 BE552004 
                 NCI_CGAP_GC6 ( Homo sapien ) 
               
               
                 BE962007 
                 NIH_MGC_65 ( Homo sapien ) 
               
               
                 BF224381 
                 NCI_CGAP_Lu24 ( Homo sapien ) 
               
               
                 BF437393 
                 NCI_CGAP_Pr28 ( Homo sapien ) 
               
               
                 BF446419 
                 NCI_CGAP_Lu24 ( Homo sapien ) 
               
               
                 BF592799 
                 NCI_CGAP_GC6 ( Homo sapien ) 
               
               
                 BI493248 
                 Morton Fetal Cochlea ( Homo sapien ) 
               
               
                 H05388 
                 Soares infant brain 1NIB ( Homo sapien ) 
               
               
                 H07885 
                 Soares infant brain 1NIB ( Homo sapien ) 
               
               
                 H09748 
                 Soares infant brain 1NIB ( Homo sapien ) 
               
               
                 M95585.1 
                 Human hepatic leukemia factor ( Homo sapien ) 
               
               
                 N64339 
                 Morton Fetal Cochlea ( Homo sapien ) 
               
               
                 NM_000065 
                 complement component 6 ( Homo sapien ) 
               
               
                 NM_001337 
                 chemokine (C—X3—C motif) receptor 1 ( Homo sapien ) 
               
               
                 NM_003914 
                 cyclin A1 ( Homo sapien ) 
               
               
                 NM_004062 
                 cadherin 16 ( Homo sapien ) 
               
               
                 NM_004063 
                 cadherin 17 ( Homo sapien ) 
               
               
                 NM_004496 
                 forkhead box A1 ( Homo sapien ) 
               
               
                 NM_006115 
                 preferentially expressed antigen in melanoma (PRAME), 
               
               
                   
                 transcript variant 1 ( Homo sapien ) 
               
               
                 NM_019894 
                 transmembrane protease, serine 4 (TMPRSS4), transcript 
               
               
                   
                 variant 1 ( Homo sapien ) 
               
               
                 NM_033229 
                 tripartite motif-containing 15 (TRIM15), transcript 
               
               
                   
                 variant 1( Homo sapien ) 
               
               
                 R15881 
                 Soares infant brain 1NIB ( Homo sapien ) 
               
               
                 R45389 
                 Soares infant brain 1NIB ( Homo sapien ) 
               
               
                 R61469 
                 Soares infant brain 1NIB ( Homo sapien ) 
               
               
                 X69699 
                 Pax8 ( Homo sapien ) 
               
               
                 X96757 
                 MAP kinase kinase ( Homo sapien ) 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Housekeeping genes for CUP diagnosis 
               
             
          
           
               
                   
                 Locus 
                 Description 
               
               
                   
                   
               
               
                   
                 BC006091 
                 TSSC4, tumor suppressing subtransferable candidate 4 
               
               
                   
                 AL137727 
                 TMEM55B, transmembrane protein 55B 
               
               
                   
                 BC016680 
                 SP2, Sp2 transcription factor 
               
               
                   
                 BC003043 
                 ARF5, ADP-ribosylation factor 5 
               
               
                   
                 AF308803 
                 VPS33B, vacuolar protein sorting 33B 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Genes, forward primers, reverse primers, and probes 
                   
               
               
                 for CUP diagnosis. 
               
             
          
           
               
                   
                 Forward 
                 Reverse 
                   
                   
               
               
                   
                 Primer 
                 Primer 
                 Probe 
               
               
                 Locus 
                 (5′-3′) 
                 (5′-3′) 
                 (5′-3′) 
               
               
                   
               
               
                 AA456140 
                 CAGTCTAGACATGCT 
                 TGTGCGTTCAAGAAA 
                 AACGGACTTTAGAAT 
                   
               
               
                   
                 GCAAGGAA 
                 GGATATGGAA 
                 CTTCT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AA745593 
                 CCTGGAGACCCGGAG 
                 AGTCGTGACAGTTCC 
                 AGGCCTGGACAAGGA 
               
               
                   
                 ACA 
                 CGTGTT 
                 (SEQ ID NO:_) 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AA765597 
                 TTGTACTGAGCTGTG 
                 GCCACCATCCAAACC 
                 AGTTTATTCATGGAG 
               
               
                   
                 AAGTCAGTGTT 
                 TCAAT 
                 CATGC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AA782845 
                 CCGCGGTGTACAATA 
                 GGAAGTAAAAGCAGC 
                 ACATTGTGCAGGA 
               
               
                   
                 CCCATA 
                 CAGCAAT 
                 GGG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AA865917 
                 CCCTTACATTCTGCA 
                 CCCTTTCCAAGTCCC 
                 CTGAGCTTAGGAT 
               
               
                   
                 CTTCATAGTTG 
                 TCCAT 
                 CATC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AA946776 
                 GGCGGAGCGAGAGCA 
                 CTGATCAGAAATGAA 
                 CATCAGGCCGCAG 
               
               
                   
                 AA 
                 AAGCGTGTCTT 
                 TCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AA993639 
                 TGTGCCTCCTCTTAG 
                 GGCAGGCATTTTATT 
                 CTGACTCCCAGTT 
               
               
                   
                 CATCTGTT 
                 CATCATTT 
                 ATTT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AB038160 
                 GAGAAGATTGTCTAC 
                 CAGCTTCATAAGGGC 
                 TTGCCCAGCCTCT 
               
               
                   
                 CACAGCAAGT 
                 GATGTCA 
                 TTG 
               
               
                   
                 SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AF104032 
                 CCAGCGGTTTCCACT 
                 CACAACGACTGAAAA 
                 TTTTCAAGCACAA 
               
               
                   
                 TGTG TGCACTTG 
                 CCC 
               
               
                   
                 SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AF133587 
                 TCAAGTGGCCGAAGC 
                 GGCTCAGGGTTTGAA 
                 CCGGATCGCCATC 
               
               
                   
                 CTTAC 
                 CTCGAT 
                 AG 
               
               
                   
                 (SEQ ID NO:_) 
                 SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AF301598 
                 GGCAAGTTTTCAAGC 
                 ACATTAAGGAAGCAT 
                 TTCCAAGATCATA 
               
               
                   
                 ACTGAGTT 
                 TTGTCACTCTCT 
                 GACTTAC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AF332224 
                 CATTCTCAACAGGGA 
                 TCCCATGATTCTTCA 
                 ACTTTGTAAAGCA 
               
               
                   
                 AACCCTACT 
                 AAAAGTTCTGTAT 
                 AATAATG 
               
               
                   
                   
                 CTT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI041545 
                 AGACCATCGCCAGCA 
                 TGCCTTTGCTGTGGT 
                 CCTTCAGGGTGTT 
               
               
                   
                 TCTG 
                 AAGAATTC 
                 CGG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI147926 
                 TGAACAAGATGAACC 
                 CCTTTAACAATGTCT 
                 AAAGAAGTCCGAG 
               
               
                   
                 AATGTGGATT 
                 GGATATTTTGGA 
                 ATATT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI309080 
                 GACCCTTGGAGCAGT 
                 GAGGCTTTATTGACA 
                 AACTTGCCTAGAA 
               
               
                   
                 GTTGTG 
                 ACGGAGAAG 
                 CTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI341378 
                 GCCAAAACACTACAA 
                 ATCACAAAAATTAGT 
                 TTTCACCAAAA 
               
               
                   
                 GCCTCTTG 
                 AAGCCTGAGATGT 
                 CCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI457360 
                 AGACACTGTCACCCC 
                 CAGCGAACATCTCTG 
                 CCACAAGACTGGC 
               
               
                   
                 CTTTCC 
                 CTTCATC 
                 AGAG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI620495 
                 GCACACTGAGTCTTA 
                 CAACTGGGCTTGGCG 
                 TGGAAACAGTTTG 
               
               
                   
                 GCGTTTCTG 
                 TTATT 
                 GATTGTA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI632869 
                 CTGGAACCAGCTCTC 
                 TGACTTGGCAATGTA 
                 TTGTGCCCCACAC 
               
               
                   
                 TCCTAATATTC 
                 AGACACACA 
                 TAAC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI683181 
                 CCTGTCAAGATTGCA 
                 GCTGCTTCGGAACAA 
                 AAATGTACGGAGC 
               
               
                   
                 AGAACATGT 
                 TATAACGT 
                 TTCAT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI685931 
                 CAAATCCTCCTGCCT 
                 CTGGTTCTCCCCACA 
                 TCAGCATCACTTC 
               
               
                   
                 GAAGAAG 
                 AATGC 
                 AGC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI802118 
                 CCGCTCCTGCAAATT 
                 CACACATTGTCTCTA 
                 ATGCCTGCCTTT 
               
               
                   
                 GAGAT 
                 ATCCTTACAATGAC 
                 CAA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI804745 
                 GGCACCCCGCATTCG 
                 TCCACCCCCCAAAAT 
                 TGTGAGGTTTGTT 
               
               
                   
                 (SEQ ID NO:_) 
                 CAAC 
                 TGTCC 
               
               
                   
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI952953 
                 TCACGATGATCCTGA 
                 CAAAGTGCCCTTCTG 
                 CATGAGAGCCCAG 
               
               
                   
                 CAATGC 
                 CTCCTT 
                 AACA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AI985118 
                 TTTCTAGTGAGCTAA 
                 CACAACGATCTTCTA 
                 CCTACAGGATACA 
               
               
                   
                 CCGTAACAGAGA 
                 CACGTGACA 
                 CGTGAGA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AL000388 
                 GCCTACCTAGACCAG 
                 AGTTAAACAGACTGG 
                 CATTTTTAGCTCG 
               
               
                   
                 CAAGCAT 
                 AAAACATGGTAAA 
                 CTCATT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AK025181 
                 GCACCGCTGGATGAA 
                 CCTTTGTTTGTTAAC 
                 AGGCTAGAGGCTG 
               
               
                   
                 AGG 
                 TGCTCTTTCC 
                 AGGG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AK027147 
                 GAGAGGAAGAATTGC 
                 CCAAAGAACAGACAT 
                 ATCATGCCAAT 
               
               
                   
                 AGAGTAGTTTGT 
                 GCAGTTATTG 
                 TCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AK054605 
                 CAAGGATTTTTCCAG 
                 ACCTTGGCCTCTCCA 
                 CATACCTGTAATC 
               
               
                   
                 GCACAGT 
                 AGCA 
                 CC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AL023657 
                 CCATGTACTGGCAAG 
                 CAGGCCACACTCCAC 
                 TATGGATGCCGTG 
               
               
                   
                 ACCTGATT 
                 TTTTGT 
                 GGAG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AL039118 
                 GCGCAAATGCCGCAT 
                 GCATATGACCACAGT 
                 TTGAGTGATTGTT 
               
               
                   
                 AA 
                 ATCACAATCAA 
                 AATGTTGTCT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AL110274 
                 CCTCCTCTAGCATGT 
                 TCACATTTTTTGTTG 
                 AGCCACTAACCAA 
               
               
                   
                 GTCCAAGT 
                 CAGTCCAA 
                 CTAG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AL157475 
                 GTGCTGTTTGCATTG 
                 GTTTTACACCCAGCG 
                 CTCTCTGCCATCC 
               
               
                   
                 TACTCATT 
                 ATGCTT 
                 CC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AW118445 
                 TTCCAGACTTGTCAC 
                 CTGCCCACAGCCTCT 
                 CTGGAGCAGGTG 
               
               
                   
                 TGACTTTCCT 
                 TTTTC 
                 GC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AW194680 
                 AAGGCGCTGGTGTTT 
                 AATAACCTGCATTCA 
                 TGAGTTTTAAGA 
               
               
                   
                 TGCT 
                 CCGAAGAG 
                 GATCCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AW291189 
                 GCCCGGATGAAGCAT 
                 CCGCTACACGTTGGT 
                 TTCACGCACTGT 
               
               
                   
                 GAGAT 
                 GCTA 
                 CCCTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AW298545 
                 CCCTTCCCTCAATTT 
                 AGGAATCTCCGAGTT 
                 AAACTGAATGGC 
               
               
                   
                 CCTGTTT 
                 GAGGAAAA 
                 ACGAAA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AW445220 
                 CACGGGACTGCCAC 
                 ACAAGTTTAATGCAA 
                 ATGCTCCGGAAG 
               
               
                   
                 AGA 
                 CAGGTGACAAC 
                 GCTCA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AW473119 
                 CAATGCTTTTTGTGC 
                 ACAATTTGGCATTTG 
                 CAGTGTAGAGCT 
               
               
                   
                 ACTACATACTCT 
                 AGCCTTTTCC 
                 CTTGTTTTA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 AY033998 
                 CACACATACACGAAA 
                 AACACTGGCTTATAA 
                 ACTTTTCAAGGC 
               
               
                   
                 GAGAGAGAAACA 
                 AGTCCATGGT 
                 TTATATTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC000045 
                 AAGACACGGCAGCAA 
                 CAAGTGGGTGTGAGC 
                 CTGCATATTGT 
               
               
                   
                 GACATC 
                 AGCTTT 
                 TCCAGATAA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC001293 
                 CATAGCAAAGCAAAG 
                 AATATCTTTAAATAA 
                 CCCCCCAAATA 
               
               
                   
                 ACAGAATGC 
                 CACAACTCCCAGACA 
                 TT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC001504 
                 GTGGAATAGTGGAGG 
                 GCAGATGCCCTCCAA 
                 TGATTAGACAA 
               
               
                   
                 CCTTCAA 
                 GATGT 
                 GGCCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC001639 
                 GCATGTGTCTGTGTA 
                 AGGCCCCTTTCCTTC 
                 AGAGACACAGC 
               
               
                   
                 TGTGTGAATGT 
                 TGAAA 
                 CCTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC002551 
                 CCAGGACCATGACAA 
                 GCCATGCAGGGCCTA 
                 AGCACTTTCCC 
               
               
                   
                 GGAAAAT 
                 GCT 
                 TTGGTG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC004331 
                 TGGCGGGGCTTCTGT 
                 TGGCTTTTATTAGCG 
                 TAGGCTGGATG 
               
               
                   
                 TTTATTT 
                 ATTCATGAA 
                 CTACCCA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC004453 
                 GATAACTCTGTACGA 
                 AGGGAAGCTGCCACA 
                 CTAGTGTCTTT 
               
               
                   
                 GGCTTCTCTAACC 
                 AGTGA 
                 TTTTTCTTCAC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC005364 
                 AATTCCTCACACCTT 
                 TTTTAAGTACCACTT 
                 ACTTTTCTGAA 
               
               
                   
                 GCACCTT 
                 TTCCTCCAACAA 
                 TTGCTATGACT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC006537 
                 AAACCGCCATTGGGC 
                 AGTGTAAGTTCAGTC 
                 CATCAAGGATA 
               
               
                   
                 TACT 
                 TGATGGAAACC 
                 CAAATCTAC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC006811 
                 AGAAGACGGAGACAG 
                 CTCAGGACTCTCTGC 
                 CCCGCTCCTGC 
               
               
                   
                 ACATGAGT 
                 TAGTACAAGT 
                 AGGAG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC006819 
                 TGCAGAGTGGAAAAG 
                 TGGCGTCCAGGTCCT 
                 CCGTGGATAAA 
               
               
                   
                 ACAAGGAT 
                 TGA 
                 TTG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC008764 
                 GGGAGAGAGACGGAG 
                 GCCCAAAGGCGTAGA 
                 ACAGCTATCTG 
               
               
                   
                 CCTTTA 
                 AGGTT 
                 CTGGCT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC008765 
                 CTGGGCTGGAATCAG 
                 GGATAAGTAGAGTTT 
                 CCAAAGAGTGA 
               
               
                   
                 GAATATTT 
                 TGCCAAAAGC 
                 TAGTCTTT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC009084 
                 CGATTGTAGCTCTGA 
                 GGGCCCAAAATAGGG 
                 TCCACCCTCAT 
               
               
                   
                 CATCTGGATT 
                 AGTGT 
                 CACCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC009237 
                 TGCCTGGCACAAAGA 
                 CCCCATGATTGTAAG 
                 AAATGATAGTT 
               
               
                   
                 AGGA 
                 TTCTTCCA 
                 CGACTCGTCT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC010626 
                 ACCCAGGAGACTGCT 
                 CATTCAGCAGATGGG 
                 CTCCACACTCT 
               
               
                   
                 GTGTGA 
                 CAGACT 
                 TGGGC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC011949 
                 AAATGCTGCTTTTAA 
                 TGCCTTAACTAGCTC 
                 TAGAATGGTTG 
               
               
                   
                 AACATAGGAAA 
                 AATTTATCTTGTG 
                 AGTGCAAAT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC012926 
                 GGCCCCGCTGATGCA 
                 TGCTGCAAACTGGGA 
                 ATGGCAGATCT 
               
               
                   
                 (SEQ ID NO:_) 
                 TCCA 
                 GATACCC 
               
               
                   
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC013117 
                 GAGCTATTTATCTCT 
                 CCACAGTTTTGGCAG 
                 CCAGAGGAATC 
               
               
                   
                 GTTTGTTGGAAAA 
                 TGAACAA 
                 CCC 
               
               
                   
                 TCC 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC015754 
                 CATTTTGATCTGTAA 
                 CAAGATGGATCCACT 
                 CTGCAGCAAAC 
               
               
                   
                 CTGCACAACCC 
                 ACTTTACATGGA 
                 CCCA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BC017586 
                 CCATGTGGCTCCAAA 
                 TTAGGATGAGTGTGA 
                 TGTCAGCTCAA 
               
               
                   
                 TGACTAA 
                 AATCAAATACGA 
                 AAACCAGA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BE552004 
                 AGGCCCAGGTTTCGA 
                 GGCTCCGAAATGGCA 
                 AGGGAGAGAAA 
               
               
                   
                 CAGA 
                 TCTC 
                 ACC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BE962007 
                 GTGAGAAACTGAATG 
                 GTGCAAATTGACTTT 
                 ACTGAGTGCCT 
               
               
                   
                 TATTATTCAAGGA 
                 TACATTC 
                 TCATTT 
               
               
                   
                 AGA 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BF224381 
                 ACGCCACAGGAGGAC 
                 TCACACCCCCATACT 
                 CTGCAGATGTA 
               
               
                   
                 ATGTT 
                 CTTCTGTT 
                 GTTGCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BF437393 
                 CGCTGTGGGCAATTG 
                 CCCATAAAGCAATTC 
                 TTCACAGTAAA 
               
               
                   
                 TTACA 
                 ACGGATACAG 
                 CCTAAGAACACT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BF446419 
                 AGCTCCACAACCCTG 
                 GCTTGGGAAACCGCA 
                 ACTGCAGGACC 
               
               
                   
                 TTTGG 
                 CTTT 
                 AGAAG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BF592799 
                 GCCATGACTGGTGAT 
                 ATGCATGGGCCATTG 
                 CCTCCGTAGGC 
               
               
                   
                 TTCATGA 
                 ATCTT 
                 ATCA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 BI493248 
                 AAATGTGTAGTTTCT 
                 GGTCACATAAAAATA 
                 TGCAACACTGT 
               
               
                   
                 TAATCGCACTACCT 
                 CATGAGGATGATAA 
                 GTATTAG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 H05388 
                 ACAGGTTCTTATCTG 
                 TGACTGGCCCTGCAG 
                 TTGCTTAGACA 
               
               
                   
                 CAAGGTTCAA 
                 AATACT 
                 TTGTTTTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 H07885 
                 GTCACTGTCATAGCA 
                 CCCACTCCCCATCAA 
                 CAAGGAAGGGT 
               
               
                   
                 GCTGTGATTT 
                 CCA 
                 GCTGCA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 H09748 
                 TGTACAAGATTTTGG 
                 AAATGGACAGACACA 
                 TCCTTAATGTC 
               
               
                   
                 GCCTCTTTT 
                 TGCTGAACT 
                 ACAATGTT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 M95585 
                 TTGTAAGATGGACCA 
                 CCAAGAGAGACCAGT 
                 CAAATGGTAGC 
               
               
                   
                 TCCAAATTTAT 
                 GCTCAAATA 
                 TGAAAAA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 N64339 
                 GCTTTCTGAATGTAG 
                 TTGGCAAACGGATGA 
                 TGGAAGCAGAA 
               
               
                   
                 ACGGAACAGT 
                 GTTAAAAA 
                 GGC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_000065 
                 TCTTCAATGAGTTA 
                 TGAATGAAGATATGA 
                 CCTCTGAAACA 
               
               
                   
                 ATAAACAGAAATCTC 
                 AAGCTGGGCTT 
                 CATTCTTG 
               
               
                   
                 CAGAA 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_001337 
                 GTTAGACCACAAATA 
                 ATGAATACACAGTCT 
                 TTCTATGTAGTTT 
               
               
                   
                 GTGCTCGCT 
                 GGTAGAGTCTTCT 
                 GGTAATTATCA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_003914 
                 TTCCAGAACTTCACC 
                 GATCCAACGTGCAGA 
                 AGTGCCAATAA 
               
               
                   
                 TCCATATCA 
                 AGCCTAT 
                 TCG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_004062 
                 GCCTGGACACCAACT 
                 GGGCTTTATTATTGG 
                 AGTGCTCCAAA 
               
               
                   
                 TTATGG 
                 GCAAACA 
                 TGTC 
                   
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_004063 
                 CAAACACAACCTACT 
                 GCATGGCAGGTAGTG 
                 AAAGGAACCAG 
               
               
                   
                 CTGCAAAC 
                 AGGAAA 
                 TCAGCTG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_004496 
                 CATTGCCATCGTGTG 
                 ACCCTCTGGCTATAC 
                 CAGTGTTATGC 
               
               
                   
                 CTTGT 
                 TAACACC 
                 ACTTTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_006115 
                 GATTCTGGCTTGGGA 
                 GCTTCTCTTTATTTT 
                 AATCCCTGTGT 
               
               
                   
                 AGTACATG 
                 CAACAGTTTCTTTAC AGACTGT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_019894 
                 CCCACACTACTGAAT 
                 CCTCTCCAGCCCACA 
                 CTGTCTTGTAA 
               
               
                   
                 GGAAGCA 
                 GTGAT 
                 AAGCC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 NM_033229 
                 GCGTGAGGCGAGAGA 
                 GAGCTGAGGGCCTAA 
                 AGTCTCGAACA 
               
               
                   
                 ACAG 
                 GATAAATAAAGT 
                 GCGGTT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 R15881 
                 TCAGAACCCACTTTC 
                 GCTGCTTGCGCCTCT 
                 TGCTGTGCCAG 
               
               
                   
                 AAGATGCT 
                 TTTT 
                 TGTGA 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 R45389 
                 AGTGGATCAGACAGT 
                 TCCAAAGCAGCTTAG 
                 CTGGTGAATGT 
               
               
                   
                 ACGACTTTGA 
                 GTGAAAAA 
                 AAACAAT 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 R61469 
                 TTCCCCGGGCATTTG 
                 CATGTCGCAGGGTTA 
                 TTCAAACAGAC 
               
               
                   
                 TT 
                 AGTATGA 
                 TTTAACCTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 X69699 
                 TGTTTGGGTCAAGCT 
                 GGCAAAGAGAGACAT 
                 CCCCCAGACTT 
               
               
                   
                 TCCTTCT 
                 TTCACTC 
                 TGG 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
               
                 X96757 
                 CCCTGCCTCTCAGAG 
                 ATTCCAAGGCCCCCT 
                 CTCTCCCAATT 
               
               
                   
                 GGTTT 
                 TAAGA 
                 TTC 
               
               
                   
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
                 (SEQ ID NO:_) 
               
               
                   
               
             
          
         
       
     
         [0092]    A primary SOM was constructed by the methods described herein using the 29 gene set normalized gene expression data described above. Additionally, a metastatic site of an individual in need of diagnosis was biopsied, and the gene expression data obtained therefrom (i.e., sample data set) was used with the primary SOM to form various secondary SOMs as shown in  FIG. 3 . In  FIG. 3 , the map cell in each secondary SOM most similar to the gene expression of the individual needing diagnosis is indicated (i.e., solid black filled hexagon). In this case, the 3 nearest neighbors (i.e., individual tissue samples with lowest Euclidean distance) of the sample data set belong to two different tissue types, colorectal and stomach. Accordingly, the probability of origin of the cancer of the metastatic site was calculated using Eqn. (4). In this example, the sample is predicted to be colorectal cancer with a probability of 81%, and stomach with a probability of 8%, using α=0.3 and β=0.7. 
       Therapy Response Profiling 
       [0093]    The invention provides methods of therapy response profiling using the methods of SOM construction and display as described herein. As used herein, “therapy response profile” refers to the pattern of expression of a group of genes of a particular tissue type in a particular disease or condition, which pattern is labeled with a distinct labeling set according to the response of the disease or condition to a particular agent or therapeutic regimen. Therapy response profiling can be used to determine if a particular disease or condition will be susceptible to a particular agent or therapeutic regimen. 
         [0094]    Thus, gene expression levels of a plurality of samples of tissues having a known disease or condition can be collected and used to construct a primary SOM by the methods described herein. The results of subsequent therapeutic intervention (e.g., administration of a particular drug) in each case can then be used to construct a distinct labeling set which characterizes the efficacy of such therapeutic interventions. For example, if a particular disease or condition does not respond to a particular agent or therapeutic regimen, the distinct label for the disease or condition to the agent or therapeutic regimen would be for example “non-responsive.” Alternatively, if a particular disease or condition responds very well to a particular agent or therapeutic regimen, the distinct label for the disease or condition would be labeled “highly responsive.” Intermediate states of response (e.g., “low response,” “intermediate response” and the like) may be employed in the construction of the distinct labeling sets. 
         [0095]    When a sample from a subject suffering from the disease or condition used to train the primary SOM is analyzed for gene expression levels, the gene expression pattern so obtained can be used to form a plurality of secondary SOMs, each having a different distinct labeling set, wherein each distinct labeling set characterizes a particular therapeutic regimen. Then, by inspection of the distinct labeling set of each secondary SOM, a prediction can be drawn on the susceptibility of the underlying disease or condition to a particular therapeutic regimen. For example, if the unknown sample mapped near a known sample having a favorable response to a particular drug, then that drug would be indicated for therapeutic intervention for the underlying disease or condition. In one embodiment, the therapy response profile may be applied to cancer as the disease or condition. 
       Therapy Response Information 
       [0096]    The invention provides methods of providing therapy response information using the methods of SOM construction and display as described herein. As used herein, “therapy response information” refers to annotation describing the historic result of therapeutic intervention in a disease or condition of one or more samples used to provide the plurality of data sets of measurements used to construct a primary SOM. Examples of therapy response information include previous therapeutic regimens (e.g., drugs administered and the like) and responses thereto. In some embodiments, after a map cell in a primary or second SOM is picked, therapy response information associated with the picked map cell, and optionally associated with nearby map cells, is displayed. Thus, by picking the map cell in a primary or secondary SOM representing the individual in need of diagnosis, the clinician is provided with information on the efficacy of various drugs and other therapeutic regimens with respect to the underlying disease or condition. 
       Autoimmune Disorder Diagnosis 
       [0097]    The invention provides methods for diagnosis of autoimmune disorders using the methods of SOM construction and display as described herein. Autoimmune disorders occur when the normal control processes for differentiating self from non-self are disrupted. Such disorders result in a variety of conditions, including destruction of one or more types of body tissues, abnormal growth of an organ, or changes in organ function. Examples of autoimmune disorders include without limitation Hashimoto&#39;s thyroiditis, pernicious anemia, Addison&#39;s disease, type I diabetes, rheumatoid arthritis, systemic lupus erythematosus, dermatomyositis, Sjorgren&#39;s syndrome, lupus erythematosus, multiple sclerosis, myasthenia gravis, Reiter&#39;s syndrome, Grave&#39;s disease, and celiac disease. 
         [0098]    In one embodiment, the expression levels of genes associated with a plurality of autoimmune disorders could be obtained by methods described herein, which gene expression levels could then be used to construct a primary SOM. Such genes may include, for example, genes encoding MHC (i.e., major histocompatibility complex) antigen (Shirai,  Tohoku J. Exp. Med.,  1994, 173:133-40). In this case, the distinct labeling sets as described herein corresponds to each specific autoimmune disease. One or more secondary SOMs could be formed using the gene expression levels of an individual suspected of suffering from an autoimmune disorder. Visualization of one or more of the secondary SOMs then provides assistance in the diagnosis of a specific autoimmune disease by methods described herein. 
       Evaluating the Likelihood of a Clinical Response 
       [0099]    The invention provides methods for evaluating the likelihood of a specific clinical response for an individual to a treatment for a disease or condition, using the methods of SOM construction and display as described herein. If an individual presents to a medical practitioner with a specific disease or condition, the medical practitioner could use the methods of the present invention to determine whether a specific treatment might be effective in treating the individual. For example, the clinical results for a plurality of individuals who have undergone a specific treatment for a specific disease may be known, In some cases, the clinical response may be negative. In some cases, the clinical response may be positive. Accordingly, data sets of measurements of individuals who have already undergone a specific treatment could be provided, and a primary SOM could be generated therefrom. Then, secondary SOMs could be formed using distinct labeling sets which identify the responses, and additionally provide the sample data set of an individual. The resulting secondary SOMs can then be provided to a medical practitioner to evaluate the likelihood of a specific clinical response for the individual. 
         [0100]    With reference to  FIG. 4 , in this hypothetical example data sets of measurements from a plurality of individuals, each having undergone a specific treatment, can be used to construct a primary SOM. Then, multiple secondary SOMs can be formed therefrom which identify different clinical responses. In  FIG. 4 , the map cell representing the individual in need of evaluation (solid black) is proximate map cells representing a group of individuals (solid gray) having positive clinical response. Accordingly, the specific treatment may be indicated for the individual. The result provided for example in  FIG. 4  could additionally be accorded a numerical value to represent the extent of similarity between the map cell of the individual and the map cells of the distinct labeling sets. For example without limitation, a value representing the average distance as described herein between the map cell of the individual and the individual map cells comprising the distinct labeling sets in the multiple secondary SOMs could be calculated and then provided to the medical practitioner. The numeric value provided to the medical practitioner may additionally represent a qualitative feature of the distances, as described herein. 
         [0101]    All patents and other references cited in the specification are indicative of the level of skill of those skilled in the art to which the invention pertains, and are incorporated by reference in their entireties, including any tables and figures, to the same extent as if each reference had been incorporated by reference in its entirety individually. 
         [0102]    One skilled in the art would readily appreciate that the present invention is well adapted to obtain the ends and advantages mentioned, as well as those inherent therein. The methods, variances, and compositions described herein as presently representative of preferred embodiments are exemplary and are not intended as limitations on the scope of the invention. Changes therein and other uses which will occur to those skilled in the art, which are encompassed within the spirit of the invention, are defined by the scope of the claims. 
         [0103]    It will be readily apparent to one skilled in the art that varying substitutions and modifications may be made to the invention disclosed herein without departing from the scope and spirit of the invention. Thus, such additional embodiments are within the scope of the present invention and the following claims. 
         [0104]    The invention illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein. Thus, for example, in each instance herein any of the terms “comprising”, “consisting essentially of” and “consisting of” may be replaced with either of the other two terms. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention that in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims. 
         [0105]    In addition, where features or aspects of the invention are described in terms of Markush groups or other grouping of alternatives, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group or other group. 
         [0106]    Also, unless indicated to the contrary, where various numerical values are provided for embodiments, additional embodiments are described by taking any two different values as the endpoints of a range. Such ranges are also within the scope of the described invention. 
         [0107]    Thus, additional embodiments are within the scope of the invention and within the following claims.