WO2003099998A2

WO2003099998A2 - Gene expression profiling of adult soft tissue sarcoma

Info

Publication number: WO2003099998A2
Application number: PCT/US2003/016023
Authority: WO
Inventors: Neil S. Segal; Carlos Cordon-Cardo; Murray Brennan
Original assignee: Sloan Kettering Institute For Cancer Research
Priority date: 2002-05-21
Filing date: 2003-05-20
Publication date: 2003-12-04
Also published as: AU2003233626A8; AU2003233626A1; WO2003099998A3

Abstract

The present invention develops a genome-based classification scheme for adult soft tissue sarcomas. Synovial sarcomas, round cell//myxoid liposarcomas, clear cell sarcomas and gastrointestinal stromal tumors displayed remarkably distinct and homogenous gene expression profiles. Notably, a subset of malignant fibrous histiocytomas, controversial histologic subtype, was identified as a distinct genomic group. The classification scheme would have implications for diagnosis and provides potential new therapeutic targets of soft tissue sarcoma.

Description

GENE EXPRESSION PROFILING OF ADULT SOFT TISSUE SARCOMA

Cross-reference to Related Application This non-provisional patent application claims benefit of provisional patent application U.S. Serial number 60/382,098, filed May 21, 2002, now abandoned.

Federal Funding Legend

This invention was produced in part using funds obtained through a National Institutes of Health grant CA-47179 and a National Science Foundation grant IIS-0093302. Consequently, the federal government has certain rights in this invention. BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates generally to the field of cancer research. More specifically, the present invention relates to gene expression profiling of adult soft tissue sarcomas.

Description of the Related Art

Soft tissue sarcomas (STS) defines a group of histologically and genetically diverse cancers that account for approximately one percent of all adult malignancies with an annual incidence in the United States of approximately 8,000 cases. There are over 50 subtypes of this disease, currently diagnosed by genetic and morphological criteria. Those most frequently seen include liposarcoma, leiomyosarcoma, malignant fibrous histiocytoma (MFH), fibrosarcoma and synovial sarcoma.

The molecular classification of soft tissue sarcomas includes two major categories on the basis of (1) non-recurrent genetic aberrations, which form part of a complex abnormal aryotype, and (2) a single recurrent genetic alteration, such as chromosomal translocations (synovial sarcoma, myxoid/round cell liposarcoma, clear cell sarcoma), or activating mutation (KIT).

It is possible to classify some soft tissue sarcomas by their recurrent chromosomal translocations or somatic mutation, such as the presence of SYT-SSX fusion transcript in synovial sarcoma, EWS-ATF1 in clear cell sarcoma, TLS-CHOP in myxoid/round cell liposarcoma and ASPL-TFE3 in alveolar soft part sarcoma. Most of these translocations produce chimeric transcription factors that presumably deregulate the expression of several target genes. In the case of gastrointestinal stromal tumors (GIST), a distinct somatic mutation has been described in KIT, 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, nonrecurrent chromosomal alterations includes malignant fibrous histiocytoma ( MFH ) , conventional fibrosarcoma, leiomyosarcoma, dedifferentiated liposarcoma and pleomorphic liposarcoma. In particular, the diagnosis of malignant fibrous histiocytoma has been long controversial. Originally described in the 1960s as a fibrous xanthoma, malignant fibrous histiocytoma 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 malignant fibrous histiocytoma should be reclassified as pleomorphic fibrosarcoma. Others consider malignant fibrous histiocytoma to be a final common pathway for certain types of soft tissue sarcomas and represent tumor progression or dedifferentiation.

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. 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 (Brown et al., Furey et al., 2000; Ramaswamy et al., 2001)

The prior art is deficient in a genome-based classification scheme for soft tissue sarcomas. The present invention fulfills this need in the art.

SUMMARY OF THE INVENTION

Adult soft tissue sarcomas are a heterogeneous group of tumors, including well-described subtypes defined by histologic and genotypic criteria, and pleomorphic tumors typically characterized by non-recurrent genetic aberrations and karyotypic heterogeneity. The latter types pose a diagnostic challenge even to experienced pathologists. It is an object of the present invention to develop a genome-based classification scheme for adult soft tissue sarcomas. The classification scheme would have implications for diagnosis and treatment.

RNA samples from 51 pathologically confirmed cases, representing nine different histologic 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 controversial histologic 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. Hence, gene expression profiling is proved to be useful in classification and diagnosis of soft tissue sarcoma, providing insights into pathogenesis and pointing to potential new therapeutic targets of soft tissue sarcoma.

Other and further aspects, features, and advantages of the present invention will be apparent from the following description of the presently preferred embodiments of the invention. These embodiments are given for the purpose of disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1: Hierarchical cluster analysis of 51 soft tissue sarcoma specimens using global gene expression profiles revealed distinct clusters for gastrointestinal stromal tumors (GIST), synovial sarcoma, clear cell sarcoma and round cell liposarcoma. Several conventional fibrosarcoma were observed in close proximity to synovial sarcoma, albeit with weaker correlation. Pleomorphic specimens exhibited weak overall correlation and consistency by bootstrap analysis. Predominant clusters were similarly observed for malignant fibrous histiocytoma (MFH) and leiomyosarcoma. Numbers represent consistency obtained in 100 bootstrap iterations. Cases are shown on the X-axis.

Figure 2: Multidimensional 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 3-dimensional figure demonstrated separate groups of clear cell sarcoma (blue), round cell liposarcoma (yellow), gastrointestinal stromal tumors (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.

Figure 3: Diagnostic performance of support vector machine analysis for histologic and genomic subtypes of soft tissue sarcoma is shown as true positive outcome versus true negative as indicators of the sensitivity and specificity respectively. Perfect performance was achieved over a large range of genes in gastrointestinal stromal tumors (GIST), round cell liposarcoma (RCL), clear cell sarcoma (CSS) and synovial sarcoma (SS). Poor sensitivity and specificity were observed for fibrosarcoma (Fib) and the histologic group of malignant fibrous histiocytoma (MFH-H). The latter improved using a genomic- based classification scheme (MFH-G). A similar improvement in support vector machine performance was shown for dedifferentiated liposarcoma (D.Lipo-H vs. D.Lipo-G) that demonstrated sensitivity using up to 64 genes. In the case of leiomyosarcoma (Leio-H), the introduction of genomic classification by cluster analysis (Leio-G#l) further improved support vector machine outcome by reintroducing a histologic specimen that did not group together with remaining leiomyosarcoma specimens on hierarchical cluster analysis (Leio- #2). P.Lipo, pleomorphic liposarcoma; Blue circle, predicted true positive; Orange circle, predicted false positive; Vertical line, actual true positive.

Figure 4: Identification of genes for biological discovery. The thumbnail panels represent the top 500 genes for each tumor type scored by Student's t-test analysis and sorted by increasing p-value (shown as negative log p-value). The second column shows rank according to p-value where a higher value corresponds to a lower p-value; the first value indicates rank within genes that discriminate the particular tumor subtype; the second value indicates rank within all genes that discriminate any tumor subtype. Light to dark color variation in the left panel represents high to low levels of expression. Annotated genes were selected according to biological interest from the top 500 genes that discriminate any soft tissue sarcomas subtype. All genes for fibrosarcoma failed to satisfy this criterion. DETAILED DESCRIPTION OF THE INVENTION

The present invention discloses genomic profiling of adult soft tissue sarcoma using oligonucleotide array analysis. This study attempts to provide an overall molecular perspective on the similarities and differences, as well as unique characteristics of soft tissue sarcoma. The inventors have sought to clarify relationships across the spectrum of histologic distinctness from that of the well-defined gastrointestinal stromal tumor to the more controversial malignant fibrous histiocytoma lesions.

In this study, the gene expression profiles of 51 high grade soft tissue sarcomas representing nine different histologic subtypes were investigated. The investigation 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 support vector machine analysis, the present invention determined the molecular relationship of soft tissue sarcomas and compared that to the current histologic classification for the purpose of establishing a novel biology based model of soft tissue sarcomas.

Data from the instant disclosure demonstrates that soft tissue sarcoma characterized by specific translocations display remarkably homogenous and distinct global gene expression profiles, as evident in the case of synovial sarcomas, round cell liposarcomas and clear cell sarcomas. This phenomenon was similarly observed in gastrointestinal stromal tumor characterized by recurrent genetic mutations in KIT. The observation of distinct gene expression profiles in these tumors is striking, in particular their consistent ability to cluster under different algorithms analysis. The finding in gastrointestinal stromal tumor (GIST) is consistent with a previous study that showed 13 gastrointestinal stromal tumors displayed a distinct gene expression profile relative to six spindle cell sarcomas. Furthermore, the gastrointestinal stromal tumors separated from leiomyosarcoma, including intra-abdominal tumors, in support of their different histogenesis.

The present study also describes the use of a supervised learning algorithm, support vector machine analysis, in the diagnosis of soft tissue sarcoma. The diagnosis of tumors characterized by specific genetic events was highly accurate using as few as between four and 32 genes. Errors were predominantly confined to reduced specificity at low gene numbers and an eventual drop off in sensitivity between 1,000 and 8,000 genes. These findings suggest that, aside from pathognomonic genetic changes that have been reported for these tumors, collective information from an extremely diverse number of genes may be considered in their diagnosis and underlying biology.

Data from this report reveal that soft tissue sarcoma characterized by pleomorphic phenotypes and complex karyotypes display relatively inconsistent gene expression profiles, in keeping with their cytogenetic heterogeneity. However, within this group of pleomorphic soft tissue sarcoma, leiomyosarcoma and a subset of malignant fibrous histiocytoma were distinguished by their ability to cluster. This particular finding prompted the inventors to explore the possibility of diagnosing these tumors using a genomic platform. Support vector machine analysis attained perfect performance over a limited range in gene number when diagnosing genomic MFH compared to histologic malignant fibrous histiocytoma. This observation supports the claim that the genomic group malignant fibrous histiocytoma is distinct and amenable to objective diagnosis.

Since malignant fibrous histiocytoma is diagnosed at different rates by different pathologists, it is not known nationwide or worldwide if specific drugs are better for this subtype or not, beyond just using doxorubicin, ifosfamide, DTIC or combinations thereof. The identification of a subset of malignant fibrous histiocytoma with a particular characteristic expression profile could potentially facilitate an objective diagnosis of this tumor type and assist in subsequent therapeutic studies.

Unlike genomic malignant fibrous histiocytoma, improved support vector machine performance with specimens selected by genomic classification was not initially shown for leiomyosarcoma. The above findings were intriguing for two reasons. First, it provided further support that the ability to diagnose the genomic malignant fibrous histiocytoma group by support vector machine analysis was not only a consequence of their ability to cluster, but in fact demonstrated that the other tumors in this study were sufficiently different so as not to be misdiagnosed as malignant fibrous histiocytoma by support vector machine analysis. Second, the observation of a consistent misclassification of genomically defined leiomyosarcoma prompted us to repeat this support vector machine analysis including the specimen that was excluded on cluster analysis. This removed the false positive occurrence in support vector machine analysis and also improved overall performance.

The observations that support vector machine performance improved when diagnosing genomic groups versus histologic groups was not surprising as these tumors were selected largely on the basis of genomic correlation. However, this finding was significant and demonstrated an important and logical extension of genomic profiling. It illustrated that genomic correlation between tumors may be exploited in order to recognize novel classifications, against which meaningful biological/clinical correlates may be considered. It is concluded that genomic classification by cluster analysis of adult soft tissue sarcoma and support vector machine support is feasible and presents a user-independent, reproducible mechanism by which to establish biology based classification of soft tissue sarcoma.

Inspection of the gene lists that discriminate subtypes of soft tissue sarcoma was particularly informative for biological discovery. In particular the present invention identifies features consistent with autocrine growth loops including SCF and KIT in a subset of gastrointestinal stromal tumor. In synovial sarcoma, WNT5a and components of the downstream signaling pathway such as FRIZZLED- 1 were identified.

Mutations in the KIT occur somatically in many sporadic gastrointestinal stromal tumor. These mutations activate the tyrosine kinase activity of KIT and induce constitutive signaling. Inhibition of the tyrosine kinase activity of KIT by imatinib mesylate induces tumor regression in gastrointestinal stromal tumor. The finding of SCF, also known as KIT ligand, in subset of gastrointestinal stromal tumor is a novel and noteworthy finding that may have implications in understanding potential autocrine growth effects in gastrointestinal stromal tumor involving the KIT pathway.

Results of this analysis suggest additional therapeutic considerations. These include blockade of PI-3 kinase with wortmannin or similar compounds in gastrointestinal stromal tumor, and the use of retinoid agonists/antagonists or blockade of WNT signaling in synovial sarcoma.

In summary, the present invention approaches the challenge of sarcoma classification using a combination of clustering techniques to propose novel groups and supervised diagnostic techniques to test the proposed grouping. This combined approach allows consideration of distinction between groups of tumors in terms of diagnostic sensitivity and specificity, rather than by similarity in gene expression profile alone. The classification of soft tissue sarcoma will continue to evolve as additional subtypes of this disease are introduced into the molecular classification scheme. More detailed analysis of the gene expression profiles of each of the more than 50 subtypes of soft tissue sarcoma (STS) will clarify the biological differences within soft tissue sarcoma and will propose therapies specific for each subclass of soft tissue sarcoma, if not therapy specific for an individual patient's tumor. The present study also proposes multiple molecular pathways that may become potential targets for therapeutic intervention, and represents one step toward a comprehensive molecular understanding of this rare and heterogeneous group of diseases.

Thus, the present invention is directed to a method of detecting gastrointestinal stromal tumor. The method involves isolating nucleic acid samples from an individual and performing statistical analysis on the gene expression levels of a group of genes comprising KIT gene, putative G protein-coupled receptor gene, activin type II A receptor gene, ion channels gene, and neuropeptide precursor preproenkephalin gene. The group of gene may further include phosphatidylinositol 3 kinase γ gene and stem cell factor gene. Statistically significant values obtained from the statistical analysis in comparison with normal individual would indicate that such individual has gastrointestinal stromal tumor. In general, gene expression can be examined by DNA micro array and statistical analysis such as hierarchical cluster analysis or support vector machine analysis can be used.

In another embodiment of the present invention, there is provided a method of detecting synovial sarcomas based on differential gene expression of a group of genes comprising TLE1, FZD1, WNT5A, JAG2, SIX1, MEOX2, SALL2, MYC, and retinoic acid receptor γ gene.

In yet another embodiment of the present invention, there is provided a method of detecting dedifferentiated liposarcoma based on differential gene expression of a group of genes comprising CDK4 and MDM2.

The following examples are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion. One skilled in the art will appreciate readily that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those objects, ends and advantages inherent herein. Changes therein and other uses which are encompassed within the spirit of the invention as defined by the scope of the claims will occur to those skilled in the art.

EXAMPLE 1

Tumor Specimen

Tumor specimens, obtained from 51 patients undergoing surgery at Memorial Sloan-Kettering Cancer Center, included malignant fibrous histiocytoma (n=l l), conventional fibrosarcoma (n=8) leiomyosarcoma (n=6), round cell liposarcoma (n=4) , pleomorphic liposarcoma (n=3), dedifferentiated liposarcoma (n=5), clear cell sarcoma (n=4), synovial sarcoma (n=5) and gastrointestinal stromal tumor (n=5). 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 histologic diagnosis. Round cell liposarcoma, dedifferentiated 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 since tumors did not cluster differently after prior treatment.

In all cases, histologic slides were available from the primary resection specimen and were reviewed independently by two soft tissue pathologists. Histologic 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 (Kawai et al., 1998), myxoid/round cell liposarcoma (Antonescu et al., 2001 ), and clear cell sarcoma (Antonescu et al., 2002) respectively. All gastrointestinal stromal tumors were tested for the presence of KIT mutations, using PCR amplification of genomic DNA followed by direct sequencing (Lasota et al., 2000).

For gene expression profiling, 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 CIP and hybridized to HG_U95A oligonucleotide arrays (Affymetrix Inc., Santa Clara, CA). Fluorescence was measured by laser confocal scanner (Agilent) and converted to signal intensity by means of Affymetrix Microarray Suite v4.0 software.

EXAMPLE 2

Hierarchical Cluster Analysis

The gene expression profile of 51 adult soft tissue sarcomas was examined using 12,559 oligonucleotide probe sets on the U95A GeneChip from Affymetrix®. Tumor specimens included nine different histologic subtypes that cover more than

75% of soft tissue sarcoma cases diagnosed in the United States.

Hierarchical cluster analysis was performed using Xcluster. A centered Pearson correlation coefficient distance metric and average linkage were used to measure cluster distances during partitioning (Eisen et al., 1998). A nonparametric bootstrap was used to estimate confidence of the cluster structure (Felsenstein, 1985). 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. Initially, unsupervised cluster analysis was used to identify groups of tumors related by similarity in overall gene expression profile using all genes represented on the U95A GeneChip® (Figure 1). Two principal clusters were identified that discriminate specimens by karyotypic and morphological features. Soft tissue sarcoma characterized by non-recurrent genetic aberrations and karyotypic complexity show poor overall similarity in both gene expression profile and bootstrap analyses. In contrast, soft tissue sarcoma characterized by single recurrent genetic events clustered distinctly in strong groups. This was shown for all cases of gastrointestinal stromal tumor, synovial sarcoma, clear cell sarcoma and round cell liposarcoma.

As an alternative and independent way of visualizing the cluster structure of the data, a multidimensional scaling analysis was done (Figure 2). In order to deal with both the large range and the negative values of the expression data, the distance function was ( 1 - r), where r is the Spearman rank-order correlation coefficient. The multidimensional scaling was- done using S-PLUS (Venables and Ripley, 1999) projecting the data into three dimensions.

Five of eight conventional fibrosarcomas were observed to cluster in close proximity to the synovial sarcoma cluster. These five 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 soft tissue sarcoma were 5 not strongly related overall by gene expression profile, predominant groups were observed on hierarchical cluster analysis in concordance with histologic classification. In particular, five of six leiomyosarcoma specimens (S20-S24) co- clustered with a dedifferentiated liposarcoma (S29). This 10 dedifferentiated liposarcoma was noted previously to contain divergent leiomyosarcomatous differentiation on routine histologic and immunohistochemical assessment. These six specimens were designated as 'genomic leiomyosarcoma --:--- -- group#l' for further discussion. Similarly, nine of eleven 15 malignant fibrous histiocytoma (MFH) specimens (S36-S40,S43- S46), including five of six 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. 0

EXAMPLE 3

Support Vector Machine Analysis

The ability of a machine-learning algorithm to 5 correctly classify each tumor type was measured using support vector machine analysis with hold-one-out cross-validation (Furey et al., 2000; Brown et al., 2000). Briefly, during the training phase the support vector machine takes as input a microarray data matrix, and labels each sample as either belonging to a given class (positive) or not (negative). The support vector machine 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 support vector machine learning algorithm then identifies a hyperplane in this space that best separates the positive and negative training examples. The trained support vector machine can then be used to make predictions about a test sample's membership in the class.

A standard 'hold-one-out' training/testing scheme was used, in which the support vector machine is trained separately on training sets made up of all but one of the samples, and then tested on the single 'held out' sample. This approach allows us to collect unbiased measurements of the ability of the support vector machine to classify each sample. Because a classifier's performance can be hindered by the inclusion of irrelevant data, feature selection was used 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 below, 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 support vector machine 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. Support vector machine analysis achieved both high sensitivity and high specificity in gastrointestinal stromal tumor, synovial sarcoma, round cell liposarcoma and clear cell sarcoma. In the case of malignant fibrous histiocytoma, leiomyosarcoma and dedifferentiated liposarcoma genomic reclassification of these tumors by cluster analysis improved support vector machine performance (Figure 3). Interestingly, dedifferentiated 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 support vector machine diagnosis yet insufficient to generate clusters using global gene expression.

In the case of leiomyosarcoma, the designated 'genomic leiomyosarcoma group #1' behaved poorly in support vector machine analysis, as observed by consistent misclassifications as false positive and false negative. It is further explored by hypothesizing an alternative 'genomic leiomyosarcoma group #2' which included the outlier leiomyosarcoma specimens S26. This hypothetical cluster gained support by demonstrating consistently perfect support vector machine 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. EXAMPLE 4

Genes with Potential Biological and Therapf .ric Relevance.

To select genes that were the most informative for the support vector machine analysis, a variety of methods including the Fisher score method (Furey et al., 2000) and parametric and nonparametric statistics were used. Data reported here were derived from the Student's t-test, because it yielded the best support vector machine 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 support vector machine, 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 (see Figure 4).

Student's T-test analysis was performed and the top scoring 500 genes were cross-referenced against both the published literature and the gene ontology consortium database using NetAffx®. The analysis was further limited to the top 50 genes for any particular soft tissue sarcoma subtype. Known genetic markers for distinct subtypes of soft tissue sarcoma, including KIT (for gastrointestinal stromal tumor), SYT-SSX (for synovial sarcoma), PPARγ (for round cell liposarcoma) and MITF (for clear cell sarcoma) were identified. In addition, several genes that are implicated in diverse biological processes, pathways and states of differentiation were discovered.

Gastrointestinal stromal tumor were characterized by genes involved in receptor tyrosine kinase signal pathways, including KIT, putative G protein-coupled receptor and activin type II A receptor. Other genes include genes encoding ion channels, as well as the neuropeptide precursor preproenkephalin. Enkephalin has been implicated in gastrointestinal motility, consistent with gastrointestinal stromal tumor deriving from the interstitial cell of Cajal (ICC). Next, the inventors searched for genes that are selectively expressed in the KIT pathway and identified phosphatidylinositol 3 (PT3) kinase γ in five out of five specimens and the KIT ligand, stem cell factor (SCF), in 2 out of 5 specimens (SI 5, SI 7). This finding was not related to any particular mutation in KIT (table 1).

TABLE 1

Gene mutation in KIT and KIT_f SCF expression

Specimen KIT mutation KIT SCF expression expression

S13 Exon 11 del: 557,558 WK +

S14 Exon 9 ins: 6bp +

S15 Exon 11 del: + 557,558 WK

S17 Exon 11 ins: 45bp + duplication

S18 Exon llpoint + mutation:559, V to G

Synovial sarcomas were characterized by genes expressed in early developmental pathways involving WNT and notch signaling, including TLE1, FZD1, WNT5A and JAG2. Several developmentally related homeobox genes, such as SIXl, MEOX2 and SALL2 were also identified. Other genes of interest in synovial sarcoma included the retinoic acid receptor γ and MYC oncogene. Clear cell sarcomas demonstrated several genes associated with their melanocytic lineage, including SOX10, gplOO and MITF.

Dedifferentiated liposarcoma were characterized by genes located on 12q, including CDK4 and MDM2. Round cell liposarcomas were characterized by lipid metabolism and adipogenic profiles and included several homeobox genes. Leiomyosarcomas were characterized by genes implicated in the smooth muscle phenotype.

The following references were cited herein: Antonescu et al., 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. 7:3977-3987 (2001).

Antonescu et al., 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. 4:44-52 (2002). Brown et al., Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc.

Natl. Acad. Sci. USA 97:262-267 (2000). Eisen et al., Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95:14863- 14868 (1998).

Feisenstein, Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783-791 (1985). Furey et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16:906-914 (2000).

Kawai et al., SYT-SSX gene fusion as a determinant of morphology and prognosis in synovial sarcoma. N. Engl. J. Med.

338:153-160 (1998). Lasota et al., Mutations in exons 9 and 13 of KIT gene are rare events in gastrointestinal stromal tumors. A study of 200 cases. Am. J. Pathol. 157:1091-1095 (2000). Ramaswamy et al., Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98:15149-

15154 (2001). Venables and Ripley, Modern Applied Statistics with S-PLUS. New York, Springer-Verlag, 1999.

Any patents or publications mentioned in this specification are indicative of the levels of those skilled in the art to which the invention pertains. Further, these patents and publications are incorporated by reference herein to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference.

Claims

WHAT IS CLAIMED IS:

1. A method of detecting gastrointestinal stromal tumor, comprising the steps of: isolating nucleic acid samples from an individual; examining gene expression levels of a group of genes comprising KIT gene, putative G protein-coupled receptor gene, activin type II A receptor gene, ion channels gene, and neuropeptide precursor preproenkephalin gene; and performing statistical analysis on the expression levels of said genes as compared to those in normal individual, wherein statistically significant values obtained from said analysis indicate that said individual has gastrointestinal stromal tumor.

2. The method of claim 1, wherein said gene expression is examined by DNA microarray.

3. The method of claim 1, wherein said group of gene further comprises phosphatidylinositol 3 kinase γ gene and stem cell factor gene.

4. The method of claim 1, wherein said statistical analysis is hierarchical cluster analysis or support vector machine analysis.

5. A method of detecting synovial sarcomas, comprising the steps of: isolating nucleic acid samples from an individual; examining gene expression levels of a group of genes comprising TLE1, FZD1, WNT5A, JAG2, SIXl, MEOX2, SALL2, MYC, and retinoic acid receptor γ gene; and performing statistical analysis on the expression levels of said genes as compared to those in normal individual, wherein statistically significant values obtained from said analysis indicate that said individual has synovial sarcomas.

6. The method of claim 5, wherein said gene expression is examined by DNA microarray.

7. The method of claim 5, wherein said statistical analysis is hierarchical cluster analysis or support vector machine analysis.

8. A method of identifying dedifferentiated liposarcoma, comprising the steps of: isolating nucleic acid samples from an individual; examining gene expression levels of a group of genes comprising CDK4 and MDM2; and performing statistical analysis on the expression levels of said genes as compared to those in normal individual, wherein statistically significant values obtained from said analysis indicate that said individual has dedifferentiated liposarcoma.

9. The method of claim 8, wherein said gene expression is examined by DNA microarray.

10. The method of claim 8, wherein said statistical analysis is hierarchical cluster analysis or support vector machine analysis.