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Classification and Subtype Prediction of Adult Soft Tissue Sarcoma by Functional Genomics

2003; Elsevier BV; Volume: 163; Issue: 2 Linguagem: Inglês

10.1016/s0002-9440(10)63696-6

1525-2191

Neil H. Segal, Paul Pavlidis, Cristina R. Antonescu, Robert G. Maki, William Stafford Noble, Diann DeSantis, James M. Woodruff, Jonathan J. Lewis, Murray F. Brennan, Alan N. Houghton, Carlos Cordón-Cardo,

Cancer-related molecular mechanisms research

Adult soft tissue sarcomas are a heterogeneous group of tumors, including well-described subtypes by histological and genotypic criteria, and pleomorphic tumors typically characterized by non-recurrent genetic aberrations and karyotypic heterogeneity. The latter pose a diagnostic challenge, even to experienced pathologists. We proposed that gene expression profiling in soft tissue sarcoma would identify a genomic-based classification scheme that is useful in diagnosis. RNA samples from 51 pathologically confirmed cases, representing nine different histological subtypes of adult soft tissue sarcoma, were examined using the Affymetrix U95A GeneChip. Statistical tests were performed on experimental groups identified by cluster analysis, to find discriminating genes that could subsequently be applied in a support vector machine algorithm. Synovial sarcomas, round-cell/myxoid liposarcomas, clear-cell sarcomas and gastrointestinal stromal tumors displayed remarkably distinct and homogenous gene expression profiles. Pleomorphic tumors were heterogeneous. Notably, a subset of malignant fibrous histiocytomas, a controversialhistological subtype, was identified as a distinct genomic group. The support vector machine algorithm supported a genomic basis for diagnosis, with both high sensitivity and specificity. In conclusion, we showed gene expression profiling to be useful in classification and diagnosis, providing insights into pathogenesis and pointing to potential new therapeutic targets of soft tissue sarcoma. Adult soft tissue sarcomas are a heterogeneous group of tumors, including well-described subtypes by histological and genotypic criteria, and pleomorphic tumors typically characterized by non-recurrent genetic aberrations and karyotypic heterogeneity. The latter pose a diagnostic challenge, even to experienced pathologists. We proposed that gene expression profiling in soft tissue sarcoma would identify a genomic-based classification scheme that is useful in diagnosis. RNA samples from 51 pathologically confirmed cases, representing nine different histological subtypes of adult soft tissue sarcoma, were examined using the Affymetrix U95A GeneChip. Statistical tests were performed on experimental groups identified by cluster analysis, to find discriminating genes that could subsequently be applied in a support vector machine algorithm. Synovial sarcomas, round-cell/myxoid liposarcomas, clear-cell sarcomas and gastrointestinal stromal tumors displayed remarkably distinct and homogenous gene expression profiles. Pleomorphic tumors were heterogeneous. Notably, a subset of malignant fibrous histiocytomas, a controversialhistological subtype, was identified as a distinct genomic group. The support vector machine algorithm supported a genomic basis for diagnosis, with both high sensitivity and specificity. In conclusion, we showed gene expression profiling to be useful in classification and diagnosis, providing insights into pathogenesis and pointing to potential new therapeutic targets of soft tissue sarcoma. Soft tissue sarcomas (STS) define a group of histologically and genetically diverse cancers that account for approximately 1% of all adult malignancies with an annual incidence in the United States of approximately 8000 cases.1Jemal A Thomas A Murray T Thun M Cancer statistics, 2002.CA Cancer J Clin. 2002; 52: 23-47Crossref PubMed Scopus (2945) Google Scholar There are over 50 subtypes of this disease, which are currently diagnosed by genetic and morphological criteria.2Brennan M Alektiar K Maki R Sarcomas of soft tissue and bone: soft tissue sarcoma.in: Cancer: Principles and Practice of Oncology. 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Diagnosis and Management of Soft Tissue Sarcoma. 2002Google Scholar The molecular classification of STS includes two major categories on the basis of 1) a single recurrent genetic alteration, such as chromosomal translocations (synovial sarcoma, myxoid/round-cell liposarcoma, clear-cell sarcoma) or activating mutation (KIT), or 2) non-recurrent genetic aberrations, which form part of a complex abnormal karyotype.5Mertens F Fletcher CD Dal Cin P De Wever I Mandahl N Mitelman F Rosai J Rydholm A Sciot R Tallini G Van den Berghe H Vanni R Willen H Cytogenetic analysis of 46 pleomorphic soft tissue sarcomas and correlation with morphologic and clinical features: a report of the CHAMP study group: chromosomes and morphology.Genes Chromosomes Cancer. 1998; 22: 16-25Crossref PubMed Scopus (159) Google Scholar It is possible to classify some STS by their recurrent chromosomal translocations or somatic mutation,6Tomescu O Barr FG Chromosomal translocations in sarcomas: prospects for therapy.Trends Mol Med. 2001; 7: 554-559Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar such as the presence of SYT-SSX fusion transcript in synovial sarcoma,7Clark J Rocques PJ Crew AJ Gill S Shipley J Chan AM Gusterson BA Cooper CS Identification of novel genes, SYT and SSX, involved in the t(X;18)(p11.2;q11.2) translocation found in human synovial sarcoma.Nat Genet. 1994; 7: 502-508Crossref PubMed Scopus (674) Google Scholar, 8Fligman I Lonardo F Jhanwar SC Gerald WL Woodruff J Ladanyi M Molecular diagnosis of synovial sarcoma and characterization of a variant SYT-SSX2 fusion transcript.Am J Pathol. 1995; 147: 1592-1599PubMed Google Scholar EWS-ATF1 in clear-cell sarcoma,9Zucman J Delattre O Desmaze C Epstein AL Stenman G Speleman F Fletchers CD Aurias A Thomas G EWS and ATF-1 gene fusion induced by t(12;22) translocation in malignant melanoma of soft parts.Nat Genet. 1993; 4: 341-345Crossref PubMed Scopus (452) Google Scholar, 10Antonescu CR Tschernyavsky SJ Woodruff JM Jungbluth AA Brennan MF Ladanyi M Molecular diagnosis of clear cell sarcoma: detection of EWS-ATF1 and MITF-M transcripts and histopathological and ultrastructural analysis of 12 cases.J Mol Diagn. 2002; 4: 44-52Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar TLS-CHOP in myxoid/round-cell liposarcoma11Crozat A Aman P Mandahl N Ron D Fusion of CHOP to a novel RNA-binding protein in human myxoid liposarcoma.Nature. 1993; 363: 640-644Crossref PubMed Scopus (776) Google Scholar, 12Antonescu CR Tschernyavsky SJ Decuseara R Leung DH Woodruff JM Brennan MF Bridge JA Neff JR Goldblum JR Ladanyi M Prognostic impact of P53 status, TLS-CHOP fusion transcript structure, and histological grade in myxoid liposarcoma: a molecular and clinicopathologic study of 82 cases.Clin Cancer Res. 2001; 7: 3977-3987PubMed Google Scholar and ASPL-TFE3 in alveolar soft-part sarcoma.13Ladanyi M Lui MY Antonescu CR Krause-Boehm A Meindl A Argani P Healey JH Ueda T Yoshikawa H Meloni-Ehrig A Sorensen PH Mertens F Mandahl N van den Berghe H Sciot R Cin PD Bridge J The der(17)t(X;17)(p11;q25) of human alveolar soft part sarcoma fuses the TFE3 transcription factor gene to ASPL, a novel gene at 17q25.Oncogene. 2001; 20: 48-57Crossref PubMed Scopus (516) Google Scholar Most of these translocations produce chimeric transcription factors, which presumably deregulate the expression of several target genes.14May WA Lessnick SL Braun BS Klemsz M Lewis BC Lunsford LB Hromas R Denny CT The Ewing's sarcoma EWS/FLI-1 fusion gene encodes a more potent transcriptional activator and is a more powerful transforming gene than FLI-1.Mol Cell Biol. 1993; 13: 7393-7398Crossref PubMed Scopus (451) Google Scholar In the case of gastrointestinal stromal tumors (GIST), a distinct somatic mutation has been described in KIT,15Longley BJ Reguera MJ Ma Y Classes of c-KIT activating mutations: proposed mechanisms of action and implications for disease classification and therapy.Leuk Res. 2001; 25: 571-576Abstract Full Text Full Text PDF PubMed Scopus (290) Google Scholar, 16Miettinen M Lasota J Gastrointestinal stromal tumors–definition, clinical, histological, immunohistochemical, and molecular genetic features and differential diagnosis.Virchows Arch. 2001; 438: 1-12Crossref PubMed Scopus (1578) Google Scholar, 17Berman J O'Leary TJ Gastrointestinal stromal tumor workshop.Hum Pathol. 2001; 32: 578-582Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar which leads to ligand-independent constitutive activation of its encoded receptor tyrosine kinase. This in turn results in altered cell proliferation and tumorigenesis. The group of tumors characterized by numerous, non-recurrent chromosomal alterations includes MFH, conventional fibrosarcoma, leiomyosarcoma, de-differentiated liposarcoma and pleomorphic liposarcoma. In particular, the diagnosis of MFH has been long controversial. Originally described in the 1960s as a fibrous xanthoma,18Kauffman SL Stout AP Histiocytic tumors (fibrous xanthoma and histiocytoma) in children.Cancer. 1961; 14: 469-482Crossref PubMed Scopus (239) Google Scholar, 19Ozzello L Stout AP Murray MR Cultural characteristics of malignant histiocytomas and fibrous xanthomas.Cancer. 1963; 16: 331-344Crossref PubMed Scopus (421) Google Scholar, 20O'Brien JE Stout AP Malignant fibrous xanthomas.Cancer. 1964; 17: 1445-1455Crossref PubMed Scopus (742) Google Scholar MFH was considered a true histiocytic tumor displaying facultative fibroblastic properties. Subsequent ultrastructural evaluation found the predominant cell type to be in fact a fibroblast or one of its variants, leading to the conclusion that MFH should be reclassified as pleomorphic fibrosarcoma.21Antonescu CR Erlandson RA Huvos AG Primary fibrosarcoma and malignant fibrous histiocytoma of bone: a comparative ultrastructural study: evidence of a spectrum of fibroblastic differentiation.Ultrastruct Pathol. 2000; 24: 83-91Crossref PubMed Scopus (35) Google Scholar, 22Suh CH Ordonez NG Mackay B Malignant fibrous histiocytoma: an ultrastructural perspective.Ultrastruct Pathol. 2000; 24: 243-250Crossref PubMed Scopus (29) Google Scholar Others consider MFH to be a final common pathway for certain types of STS and represent tumor progression or de-differentiation.23Brooks JJ The significance of double phenotypic patterns and markers in human sarcomas: a new model of mesenchymal differentiation.Am J Pathol. 1986; 125: 113-123PubMed Google Scholar, 24Hashimoto H Daimaru Y Tsuneyoshi M Enjoji M Soft tissue sarcoma with additional anaplastic components. A clinicopathologic and immunohistochemical study of 27 cases.Cancer. 1990; 66: 1578-1589Crossref PubMed Scopus (65) Google Scholar, 25Fletcher CD Pleomorphic malignant fibrous histiocytoma: fact or fiction? A critical reappraisal based on 159 tumors diagnosed as pleomorphic sarcoma.Am J Surg Pathol. 1992; 16: 213-228Crossref PubMed Scopus (436) Google Scholar The molecular classification of cancer has recently been prompted by the sequencing and annotation of the human genome and technical advancement in gene transcription profiling.26Lockhart DJ Dong H Byrne MC Follettie MT Gallo MV Chee MS Mittmann M Wang C Kobayashi M Horton H Brown EL Expression monitoring by hybridization to high-density oligonucleotide arrays.Nature Biotechnol. 1996; 14: 1675-1680Crossref PubMed Scopus (2831) Google Scholar, 27Golub TR Slonim DK Tamayo P Huard C Gaasenbeek M Mesirov JP Coller H Loh ML Downing JR Caligiuri MA Bloomfield CD Lander ES Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.Science. 1999; 286: 531-537Crossref PubMed Scopus (9355) Google Scholar, 28Tamayo P Slonim D Mesirov J Zhu Q Kitareewan S Dmitrovsky E Lander ES Golub TR Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.Proc Natl Acad Sci USA. 1999; 96: 2907-2912Crossref PubMed Scopus (2421) Google Scholar These profound scientific advancements have permitted high-throughput analysis and molecular correlation between tumors that provides insight into molecular pathways and mechanisms. The support vector machine (SVM) model has, in particular, been shown to be useful in classification tasks using gene expression data.29Brown MP Grundy WN Lin D Cristianini N Sugnet CW Furey TS Ares Jr, M Haussler D Knowledge-based analysis of microarray gene expression data by using support vector machines.Proc Natl Acad Sci USA. 2000; 97: 262-267Crossref PubMed Scopus (1845) Google Scholar, 30Furey TS Cristianini N Duffy N Bednarski DW Schummer M Haussler D Support vector machine classification and validation of cancer tissue samples using microarray expression data.Bioinformatics. 2000; 16: 906-914Crossref PubMed Scopus (1985) Google Scholar, 31Ramaswamy S Tamayo P Rifkin R Mukherjee S Yeang CH Angelo M Ladd C Reich M Latulippe E Mesirov JP Poggio T Gerald W Loda M Lander ES Golub TR Multiclass cancer diagnosis using tumor gene expression signatures.Proc Natl Acad Sci USA. 2001; 98: 15149-15154Crossref PubMed Scopus (1674) Google Scholar In this study, we investigated the gene expression profiles of 51 high-grade STS, representing nine different histological subtypes. We focused on high-grade lesions, as these often pose a diagnostic challenge and would potentially benefit from molecular-based classification and a diagnostic algorithm. Using hierarchical cluster analysis, multidimensional scaling and SVM analysis, we determined the molecular relationship of STS and compared this to the current histological classification, for the purpose of a novel biology-based model of STS. Tumor specimens, obtained from 51 patients undergoing surgery at Memorial Sloan-Kettering Cancer Center, included MFH (n = 11), conventional fibrosarcoma (n = 8), leiomyosarcoma (n = 6), round-cell liposarcoma (n = 4), pleomorphic liposarcoma (n = 3), de-differentiated liposarcoma (n = 5), clear-cell sarcoma (n = 4), synovial sarcoma (n = 5), and GIST (n = 5). Specimens were collected under an IRB-approved tissue procurement protocol. Representative tumor tissue was embedded in OCT compound and frozen as tissue blocks using liquid nitrogen. Tumor specimens were selected for analysis according to validation of histological diagnosis. Round-cell liposarcoma, de-differentiated liposarcoma and pleomorphic liposarcoma were dissected from microscopically identified regions within the frozen tumor block, to ensure selection of high-grade areas only. Prior therapy was not considered an exclusion criterion, as we showed in a pilot study that tumors did not cluster differently by prior treatment. For additional details on genotype, subtype, prior therapy, site and stage, see Supplemental Data at http://www.amjpathol.org, or http://www.mskcc.org/genomic.sts.32Memorial Sloan Kettering Cancer Center 2003.http://www.mskcc.org/genomic.stsGoogle Scholar Tumor specimens have been used in a similar study in the classification of clear-cell sarcoma.33Segal N Pavlidis P Noble W Antonescu C Viale A Wesley U Busam K Gallardo H DeSantis D Brennan M Cordon-Cardo C Wolchok J Houghton A Classification of clear cell sarcoma as a subtype of melanoma by genomic profiling.J Clin Oncol. 2003; 21: 1775-1781Crossref PubMed Scopus (167) Google Scholar In all cases histological slides were available from the primary resection specimen and were reviewed independently by two soft-tissue pathologists (C.R.A., J.M.W.). Histological diagnosis was supported in every case by an appropriate immunohistochemical panel and/or molecular genetic evaluation. RT-PCR using total RNA extracted from frozen tissue was performed for detection of specific fusion transcripts such as SYT-SSX, TLS-CHOP, and EWS-ATF1, used in the molecular diagnosis of synovial sarcoma,34Kawai A Woodruff J Healey JH Brennan MF Antonescu CR Ladanyi M SYT-SSX gene fusion as a determinant of morphology and prognosis in synovial sarcoma.N Engl J Med. 1998; 338: 153-160Crossref PubMed Scopus (560) Google Scholar myxoid/round-cell liposarcoma,12Antonescu CR Tschernyavsky SJ Decuseara R Leung DH Woodruff JM Brennan MF Bridge JA Neff JR Goldblum JR Ladanyi M Prognostic impact of P53 status, TLS-CHOP fusion transcript structure, and histological grade in myxoid liposarcoma: a molecular and clinicopathologic study of 82 cases.Clin Cancer Res. 2001; 7: 3977-3987PubMed Google Scholar and clear-cell sarcoma,10Antonescu CR Tschernyavsky SJ Woodruff JM Jungbluth AA Brennan MF Ladanyi M Molecular diagnosis of clear cell sarcoma: detection of EWS-ATF1 and MITF-M transcripts and histopathological and ultrastructural analysis of 12 cases.J Mol Diagn. 2002; 4: 44-52Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar respectively. All GIST tumors were tested for the presence of KIT mutations, using PCR amplification of genomic DNA, followed by direct sequencing.35Lasota J Wozniak A Sarlomo-Rikala M Rys J Kordek R Nassar A Sobin LH Miettinen M Mutations in exons 9 and 13 of KIT gene are rare events in gastrointestinal stromal tumors: a study of 200 cases.Am J Pathol. 2000; 157: 1091-1095Abstract Full Text Full Text PDF PubMed Scopus (305) Google Scholar These studies were performed in the laboratories of the Division of Molecular Pathology, Memorial Sloan-Kettering Cancer Center. Cryopreserved tumor sections were homogenized under liquid nitrogen by mortar and pestle. Total RNA was extracted in Trizol reagent and purified using the Qiagen Rneasy kit. RNA quality was assessed on ethidium bromide agarose gel electrophoresis. cDNA was then synthesized in the presence of oligo(dT)24-T7 from Genset Corp. (La Jolla, CA). cRNA was prepared using biotinylated UTP and CTP and hybridized to HG U95A oligonucleotide arrays (Affymetrix Inc., Santa Clara, CA). Fluorescence was measured by laser confocal scanner (Agilent, Palo Alto, CA) and converted to signal intensity by means of Affymetrix Microarray Suite v4.0 software. For complete expression data, see Supplemental Data at http://www.amjpathol.org, or http://www.mskcc.org/genomic.sts.32Memorial Sloan Kettering Cancer Center 2003.http://www.mskcc.org/genomic.stsGoogle Scholar Hierarchical cluster analysis was performed using XCluster (http://genome-www.stanford.edu/∼sherlock/cluster.html), using a centered Pearson correlation coefficient distance metric and average linkage to measure cluster distances during partitioning.36Eisen MB Spellman PT Brown PO Botstein D Cluster analysis and display of genome-wide expression patterns.Proc Natl Acad Sci USA. 1998; 95: 14863-14868Crossref PubMed Scopus (13352) Google Scholar A nonparametric bootstrap was used to estimate confidence of the cluster structure.37Felsenstein J Confidence limits on phylogenies: an approach using the bootstrap.Evolution. 1985; 39: 783-791Crossref PubMed Google Scholar For each bootstrap sample, the clustering obtained was compared to the clustering obtained with the original data set. Two clusters (branches of the hierarchy) were considered identical if they contained the same members. As an alternative and independent way of visualizing the cluster structure of the data a multidimensional scaling analysis was done. To deal with both the large range and the negative values of the expression data we took as the distance function 1/2(1 – r), where r is the Spearman rank-order correlation coefficient. The multidimensional scaling was done using S-PLUS38Venables WN Ripley BD Modern Applied Statistics with S-PLUS. Springer-Verlag, New York1999Crossref Google Scholar projecting the data into three dimensions. The ability of a machine-learning algorithm to correctly classify each tumor type was measured using SVM analysis with hold-one-out cross-validation.29Brown MP Grundy WN Lin D Cristianini N Sugnet CW Furey TS Ares Jr, M Haussler D Knowledge-based analysis of microarray gene expression data by using support vector machines.Proc Natl Acad Sci USA. 2000; 97: 262-267Crossref PubMed Scopus (1845) Google Scholar, 30Furey TS Cristianini N Duffy N Bednarski DW Schummer M Haussler D Support vector machine classification and validation of cancer tissue samples using microarray expression data.Bioinformatics. 2000; 16: 906-914Crossref PubMed Scopus (1985) Google Scholar In brief, during the training phase the SVM takes as input a microarray data matrix, and labels each sample as either belonging to a given class (positive) or not (negative). The SVM treats each sample in the matrix as a point in a high-dimensional feature space, where the number of genes on the microarray determines the dimensionality of the space. The SVM learning algorithm then identifies a hyperplane in this space that best separates the positive and negative training examples. The trained SVM can then be used to make predictions about a test sample's membership in the class. This approach allows us to collect unbiased measurements of the ability of the SVM to classify each sample. We used a standard “hold-one-out” training/testing scheme, in which the SVM is trained separately on training sets made up of all but one of the samples, and then tested on the single “held out” sample. Because a classifier's performance can be hindered by the inclusion of irrelevant data, we used feature selection to identify genes that are most important for classification. The genes in the training data set were ranked in order of their proposed importance in distinguishing the positives from the negatives, as described in more detail in the next section, and the top N genes were taken for each trial. The value N was varied in 12 powers of 2, ranging from 4 to 8192. Thus, the SVM was run 51 times on each of 12 different numbers of features (genes), for each of the tumor classes. Each held-out test sample was counted as either a false positive, false negative, true positive, or true negative. To select genes that were the most informative for the SVM, we tested a variety of methods including the Fisher score method30Furey TS Cristianini N Duffy N Bednarski DW Schummer M Haussler D Support vector machine classification and validation of cancer tissue samples using microarray expression data.Bioinformatics. 2000; 16: 906-914Crossref PubMed Scopus (1985) Google Scholar and parametric and nonparametric statistics. Data reported here were derived from Student's t-test, because it yielded the best SVM performance overall. Each gene in each training data set was subjected to the following procedure. A standard Student's t-test was used to compare the expression in one tumor type to that in the remaining samples. The resulting P values were then used to rank the genes, and the desired number of genes was then selected for use. The corresponding data from the training set was used to train the SVM, and the same genes were used for the test data. It is important to note that the genes were selected solely on the basis of the training data. Finally, a t-test statistic as determined for all samples was used to provide an overall ranking of the genes in order of relevance for each tumor classification. This ranking was used to provide an overview of the most important genes for distinguishing the class. We determined the gene expression profile of 51 adult soft tissue sarcomas using 12,559 oligonucleotide probe sets on the U95A GeneChip from Affymetrix. Tumor specimens included nine different histological subtypes, which taken together cover more than 75% of STS cases diagnosed in the United States. We explored three approaches to data analysis. In the first, we used unsupervised cluster analysis to identify groups of tumors related by similarity in overall gene expression profile using all genes represented on the U95A GeneChip (Figure 1). We identified two principal clusters that discriminate specimens by karyotypic and morphological features. STS characterized by non-recurrent genetic aberrations and karyotypic complexity show poor overall similarity in both gene expression profile and bootstrap analyses. In contrast, STS characterized by single recurrent genetic events clustered distinctly in strong groups. This was shown for all cases of GIST, synovial sarcoma, clear-cell sarcoma and round-cell liposarcoma. Similarly, visualized using multidimensional scaling analysis once again using all genes represented on the U95A GeneChip (MDS) (Figure 2).Figure 2Multidimensional scaling analysis of 51 soft tissue sarcoma specimens. The plot displays the position of each tumor specimen in three-dimensional space, where the distance between cases reflects their approximate degree of correlation. Two views of this three-dimensional figure demonstrated separate groups of clear-cell sarcoma (blue), round-cell liposarcoma (yellow), GIST (green) and synovial sarcoma (brown). Several fibrosarcomas (purple) were seen in close proximity to the synovial sarcoma cluster. Pleomorphic specimens were poorly distinguished using this data visualization technique.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Five of 8 conventional fibrosarcomas were observed to cluster in close proximity to the synovial sarcoma cluster. These 5 specimens were retrospectively tested for the presence of SYT-SSX fusion transcript by RT-PCR, and were found to be negative. Similarly, a single case of pleomorphic liposarcoma was observed to cluster in proximity to the round-cell liposarcoma group and was shown to be negative for the TLS-CHOP fusion transcript (data not shown). Although the pleomorphic STS were not strongly related overall by gene expression profile, predominant groups were observed on hierarchical cluster analysis in concordance with histological classification. In particular, 5 of 6 leiomyosarcoma specimens (S20-S24) co-clustered with a de-differentiated liposarcoma (S29). This de-differentiated liposarcoma was noted previously to contain divergent leiomyosarcomatous differentiation on routine histological and immunohistochemical assessment. These 6 specimens were designated as “genomic leiomyosarcoma group #1” for further discussion. Similarly, 9 of 11 MFH specimens (S36-S40, S43-S46), including 5 of 6 lesions with myxoid features, clustered together with a single fibrosarcoma (S5). This was designated as “genomic MFH group” for further discussion. The remaining specimens appeared heterogeneous. Our second approach incorporated the use of SVM analysis to explore the outcome of genomic diagnosis in both previously-defined histological subtypes and potential novel genomic groups. Specimens were divided into two groups to establish training classes for each diagnostic category. The positive class contained all specimens that belong to a specific category. The negative class contained the remaining specimens. We performed hold-one-out cross-validation, in which one specimen was hidden from the SVM during training and was subsequently given to the “machine” as a test specimen. This was performed over a range of gene numbers to identify the range in which the “machine” operates optimally in diagnosing an unknown specimen. The outcome of the analysis was compared to the predicted subtype of the test specimen and indicated as true/false positive or true/false negative. SVM analysis achieved both high sensitivity and high specificity in GIST, synovial sarcoma, round-cell liposarcoma, and clear-cell sarcoma. In the case of MFH, leiomyosarcoma, and de-differentiated liposarcoma, genomic reclassification of these tumors by cluster analysis improved SVM performance (Figure 3). Interestingly, de-differentiated liposarcomas were diagnosed accurately using as few as four genes, but only up to 64 genes. This limited range of sensitivity is consistent with a genomic-based relationship over few genes that is sufficient for SVM diagnosis yet insufficient to generate clusters using global gene expression. In the case of leiomyosarcoma, the designated “genomic leiomyosarcoma group #1” behaved poorly in SVM analysis, as observed by consistent misclassifications as false positive and false negative. We explored this further by hypothesizing an alternative “genomic leiomyosarcoma group #2” which included the outlier leiomyosarcoma specimens S26. This hypothetical cluster gained support by demonstrating consistently perfect SVM performance over a large range in the number of genes used. These results, taken together, demonstrate the efficacy of a diagnostic algorithm in validating and, in particular, exploring the outcome of cluster analysis techniques. Our third approach to data analysis was the identification of genes, consistent with each tumor subtype for the purpose of useful biological discovery (Figure 4). In the case of MFH, leiomyosarcoma, and de-differentiated liposarcoma, genomic classification was used. This was performed using Student's t-test analysis and cross-referencing the top scoring 500 genes against both the published literature and the gene ontology consortium database (http://www.geneontology.org/) using NetAffx (http://www.affymetrix.com). We further limited this analysis to the top 50 genes for any particular STS subtype. We identified the known genetic markers for distinct subtypes of STS, including KIT (GIST), SYT-SSX (synovial sarcoma), PPARγ (round-cell liposarcoma) and MITF (clear-cell sarcoma). In addition, we discovered several genes that are implicated in diverse biological processes, pathways, and states of differentiation. GISTs were characterized by genes involved in receptor tyrosine kinase signal pathways, including KIT, putative G protein-coupled receptor, and activin type II A receptor. We similarly observed genes encoding ion channels, as well as the neuropeptide precursor preproenkephalin. Enkephalin has been implicated in gastrointestinal motili