Gene Expression Profiling of Human Sarcomas: Insights Into Sarcoma Biology

Research Article

Gene Expression Profiling of Human Sarcomas: Insights into Sarcoma Biology

Kristin Baird,1 Sean Davis,1 Cristina R. Antonescu,3 Ursula L. Harper,1 Robert L. Walker,1 Yidong Chen,1 Arthur A. Glatfelter,1 Paul H. Duray,2 and Paul S. Meltzer1

1Cancer Genetics Branch, National Human Genome Research Institute; 2Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, Maryland; 3Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York

Abstract changes. This latter group occurs more frequently in older adults and includes several types of sarcomas that lack known disease- Sarcomas are a biologically complex group of tumors of specific chromosome translocations or mutations, but may contain mesenchymal origin. By using gene expression microarray mutations in RB1, CDKN2A, and TP53. Investigation of these and analysis, we aimed to find clues into the cellular differenti- other aspects of sarcoma biology have provided insights into ation and oncogenic pathways active in these tumors as well broadly relevant fundamental mechanisms of oncogenesis (for as potential biomarkers and therapeutic targets. We exam- review, see ref. 1) and may have profound implications in the ined 181 tumors representing 16 classes of human bone and development of therapeutic intervention. This is exemplified by the soft tissue sarcomas on a 12,601-feature cDNA microarray. successful treatment of gastrointestinal stromal tumors, which Remarkably, 2,766 probes differentially expressed across this frequently have activating mutations of KIT, with the tyrosine sample set clearly delineated the various tumor classes. kinase inhibitor, imatinib mesylate. Although genetic alterations, Several genes of potential biological and therapeutic interest particularly fusion genes arising from translocations, have been were associated with each sarcoma type, including specific identified in many sarcomas, the function of the fusion gene tyrosine kinases, transcription factors, and homeobox genes. products are not well understood and their downstream targets We also identified subgroups of tumors within the lip- have not been fully identified (1). Additionally, in tumors that lack osarcomas, leiomyosarcomas, and malignant fibrous histio- specific chromosomal translocations, there may well be additional cytomas. We found significant gene ontology correlates for oncogenic mutations to be described. Furthermore, ‘‘second hits’’ each tumor group and identified similarity to normal tissues or additional genetic mutations, thought to be essential in cancer by Gene Set Enrichment Analysis. Mutation analysis done on development, have rarely been identified in sarcomas (1). 275 tumor samples revealed that the high expression of Progress in the evaluation of sarcomas has been limited not only epidermal growth factor receptor (EGFR) in certain tumors by their rarity, but also by their histologic diversity and genetic was not associated with gene mutations. Finally, to further complexity, making high-throughput tumor profiling a critical tool the investigation of human sarcoma biology, we have created to advance understanding of sarcoma tumor biology. Studies of an online, publicly available, searchable database housing the human sarcoma samples using microarray technology first began data from the gene expression profiles of these tumors with a report on seven alveolar rhabdomyosarcoma on a 1,238- (http://watson.nhgri.nih.gov/sarcoma), allowing the user to feature microarray (2). Since then, there have been remarkable interactively explore this data set in depth. (Cancer Res 2005; advances in microarray technology leading to a growing body of 65(20): 9226-35) gene expression studies focused on characterizing this complex group of tumors (3–5). Recent studies have described gene Introduction signatures associated with poor clinical outcome in leiomyosar- Sarcomas are malignant tumors of mesenchymal origin with coma (6) and Ewing’s sarcoma (7), diagnostic classification (8), and f15,000 soft tissue and bone sarcomas newly diagnosed in the novel biomarkers in dermatofibrosarcoma protuberans (9) and United States annually. Although sarcomas represent only 1% of all clear cell sarcoma (10). human malignancies, they have distinctive biological character- Previous studies have generally focused on a limited number of istics, which include a high incidence of aggressive local behavior histotypes and have typically separated bone and soft tissue and a predilection for metastasis. Several sarcomas such as Ewing’s sarcomas. We sought to generate a technically uniform gene sarcoma, synovial sarcoma, alveolar rhabdomyosarcoma, and expression data set that would allow a broad view of the more myxoid liposarcoma tend to occur in younger patients and are frequent bone and soft tissue sarcoma types. In this study, we characterized by tumor-specific chromosomal translocations. In utilized gene expression array analysis and denaturing high- contrast, other sarcomas such as leiomyosarcoma and malignant performance liquid chromatography and immunohistochemistry fibrous histiocytoma lack specific translocations and have a chaotic on tissue microarray to evaluate the largest set of human sarcoma karyotype accompanied by frequent chromosome copy number samples studied by high-throughput genetic techniques to date. Gene expression data was processed by integrating standard cluster analysis methods with more recently developed approaches, such as gene ontology analysis and gene set enrichment analysis. Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). Our primary goal was to present an in depth evaluation of the Requests for reprints: Paul S. Meltzer, National Human Genome Research expression profiles found in human sarcomas which could be used Institute, NIH, 50 South Drive, Bethesda, MD 20892-8000. Phone: 301-594-5283; as a basis for the identification of their key biological features. To Fax: 301-402-3281; E-mail: [email protected]. I2005 American Association for Cancer Research. achieve this goal, we have established tumor specific profiles with doi:10.1158/0008-5472.CAN-05-1699 highly significant gene lists, created gene ontology profiles,

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Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2005 American Association for Cancer Research. Gene Expression Profiling of Human Sarcomas identified expression of potentially critical genes and pathways and Reference cell lines. Reference RNA was derived from a pooled group of developed a searchable database which makes these data available five sarcoma cell lines: SK-LMS-1, HT-1080, RMS-13, OSA-Cl, and TC-32. The to the sarcoma community. Through this comprehensive approach, SK-LMS-1 and the HT-1080 cell lines were obtained from the American Type we gained additional insight into the genetic diversity and Culture Collection and grown in RPMI 1640 + 10% fetal bovine serum (FBS) + penicillin-streptomycin + nonessential amino acids. The RMS-13 complexity of sarcomas, including clues regarding their origin, and OSA cell lines were obtained from St. Jude Children’s Research Hospital differentiation and pathophysiology. (Memphis, TN) and grown in RPMI 1640 + 10% FBS + penicillin- streptomycin + L-glutamine. The TC-32 cell line was obtained from the Materials and Methods Laboratory of Pathology at National Cancer Institute/NIH (Bethesda, MD) Frozen tissue samples and pathologic diagnosis. Gene expression and grown in RPMI 1640 + 15% FBS. data represent 181 tumors taken from a sarcoma frozen tissue tumor bank RNA labeling and hybridization. RNA was extracted from tumors and (À80jC) located at the Cancer Genetics Branch of the National Human cell lines via standard methods using tissue homogenization in Trizol Genome Research Institute (Table 1). The tumors were accessioned through reagent (Invitrogen, Carlsbad, CA), and purified using RNeasy midi kits (Qiagen, Valencia, CA), quality checked on 2% agarose gel. RNA labeling and the Cooperative Human Tissue Network, the National Cancer Institute, the 4 Norwegian Radium Hospital, and the Memorial Sloan-Kettering Cancer hybridization was done as previously described (2, 11). Approximately 100 A A Center over an f15-year period. All samples had original pathology reports to 120 g of tumor RNA and 75 to 100 g of control RNA were oligo(dT)- available and the majority, 108 of 181 tumors, had H&E reviewed from primed and labeled using reverse transcription with either Cy3 dUTP accompanying paraffin blocks of tissue by one of us with expertise in (control) or Cy5 dUTP (sample; Amersham Pharmacia Biotech, Piscataway, sarcoma pathology (C.R. Antonescu). The central pathology review was NJ). Microarrays contained 12,601 PCR-amplified cDNAs from sequence- done in a semiblinded fashion, based on one representative H&E slide and verified IMAGE consortium clones deposited on poly-L-lysine–coated glass limited clinical information (age, sex, and anatomic site), in the absence of slides using ink jet technology by Agilent Technologies (Palo Alto, CA). Gene names were assigned according to the UniGene human sequence the original pathology report or immunoprofile. An additional 38 tumors 5 had their diagnosis confirmed by the presence of a chromosomal collection. translocation (Ewing’s sarcoma, synovial sarcoma, alveolar rhabdomyosar- Image analysis and statistical analysis. Microarray slides were scanned coma), or gene mutations such as KIT and PDGFRA in gastrointestinal on an Agilent DNA Microarray Scanner. Image analyses were done using stromal tumor. DeArray software (Scanalytics, Fairfax, VA; ref. 12). Using mean red intensity of >200 as a general quality threshold, 7,788 probes were selected and subjected to several data analysis algorithms.6 For hierarchical clustering, Table 1. Number of samples represented per tumor type we selected the 1,527 most highly variable genes across the data set with a in each stage of analysis, gene expression microarray, standard deviation of the log of the calibrated ratio of >0.35. The ratio tissue microarray, and denaturing high-performance liquid values were log transformed and clustered using the Pearson correlation chromatography coefficient. Further hierarchical clustering was done on several of the larger tumor subsets: malignant fibrous histiocytomas, leiomyosarcomas, and liposarcomas. Similar parameters were applied, using a mean red intensity Tumor type Gene array TMA DNA for of >200 and a standard deviation of the log of the calibrated ratio >0.35 DHPLC within the subset of tumors (1,250 probes for malignant fibrous histiocytoma, 1,246 probes for liposarcomas, and 1,305 probes for Â ASPS 1 1 leiomyosarcoma). Finally, using the same parameters, calibrated ratio Â Clear cell sarcoma 1 1 values were log transformed and multidimensional scaling plots and Chondrosarcoma 1 22 15 weighted gene lists were generated as previously described (13).4 Weighted DFSP 5 8 6 gene lists were derived by t test for two-group comparison, whereas EWS 19 8 1 f-statistic (one-way ANOVA) was used for comparison of sets of more than FIBRO 7 13 13 two groups. Weighted gene list P values were derived from 10,000 random GIST 5 20 4 permutations. All algorithms were implemented in MATLAB (Mathworks, Malignant HPC 6 4 5 Inc. Natik, MA). LMS 17 36 24 Tissue microarray. A sarcoma tumor tissue microarray was created LIPO 33 97 87 using paraffin blocks from a subset of 374 sarcoma samples supplied by the MFH 38 82 55 Cooperative Human Tissue Network (123 samples overlap with expression Â Mixed Mullerian 2 2 analysis) using techniques as described previously (14). The sarcoma tissue OS 5 23 8 microarray is comprised of two to three 0.6 mm tissue cores from each MPNST 6 11 14 sample and consisted of 14 tumor types (Table 1). Benign schwannoma 3 4 2 Immunohistochemistry. Five-micrometer-cut sections were transferred RMS 6 9 3 via a tape transfer system onto adhesive-coated glass slides (Instrumedics, SS 16 18 7 Hackensack, NJ). Slides were deparaffinized using a xylene-ethanol series. NOS/MISC 10 19 27 Antigen retrieval was done by steaming for 30 minutes in antigen retrieval Total 181 374 275 buffer (Vector Laboratories, Burlington, Ontario, Canada) followed by horseradish peroxidase/3,3V-diaminobenzidine immunohistochemistry using UltraVision Detection System (Lab Vision Corporation, Fremont, Abbreviations: TMA, tissue microarray; DHPLC, denaturing high- CA) for rabbit and mouse antibodies, and Immunocruz Staining System performance liquid chromatography; ASPS, alveolar soft parts (Santa Cruz Biotechnology, Inc., Santa Cruz, CA) for goat antibodies. The sarcoma; DFSP, dermatofibrosarcoma; EWS, Ewing’s sarcoma; FIBRO, following primary antibodies were used: FTL1, EPHB4, JAK1 (Santa Cruz fibrosarcoma; GIST, gastrointestinal stromal tumor; HPC, hemangio- Biotechnology); EGFR (Cell Signaling Technology); WNT5A, FZD1 (R&D pericytoma; LMS, leiomyosarcoma; LIPO, liposarcoma; MFH, malignant fibrous histiocytoma; OS, osteosarcoma; MPNST, malignant peripheral nerve sheath tumor; RMS, rhabdomyosarcoma; SS, synovial sarcoma; NOS/MISC, previously unclassified sarcomas. 4 http://research.nhgri.nih.gov/microarray. 5 http://www.ncbi.nlm.nih.gov/UniGene/. 6 http://arrayanalysis.nih.gov/. www.aacrjournals.org 9227 Cancer Res 2005; 65: (20). October 15, 2005

Systems, Minneapolis, MN). Primary antibodies were applied to tissue intermingled with the malignant fibrous histiocytoma cluster. The microarray slides and incubated at 4jC overnight. Slides were counter- majority of liposarcomas that formed the tightly clustered group stained with hematoxylin and fixed. All tissue microarrays were reviewed were considered lower grade and were predominantly myxoid or and scored on an eight-point scoring system by one of us (P.H. Duray). The round cell (17 samples) or well differentiated (1 sample). Only three scale for scoring spots are based on 0 to 4 points for antibody intensity members in this group were identified as high grade, dediffer- (0 = no staining, 1 = weakly staining, 2 = moderate staining, 3 = heavy staining, 4 = very heavy staining) and 0 to 4 points for percentage of the entiated (one sample), or pleomorphic (two samples). Conversely, tumor cells staining positive (0 = 0% of cells staining positive, 1 = 1-25%, the majority of those liposarcoma tumors dispersed among the 2 = 26-50%, 3 = 51-75%, and 4 = 76-100%). malignant fibrous histiocytoma tumors were considered higher- Denaturing high-performance liquid chromatography. DNA was grade tumors with six samples classified as dedifferentiated and extracted from a total of 275 tumors from the same set of sarcoma frozen three samples as pleomorphic. Only one tumor from this second tissue tumor bank (À80jC; Table 1). Total genomic DNA was isolated by group was characterized as a myxoid tumor. Finally, three groups, ethanol precipitation from the interphase of primary tumor samples the peripheral nerve sheath tumors (which was comprised of three homogenized in Trizol and purified by phenol-chloroform extractions, benign schwannomas and five malignant peripheral nerve sheath precipitated, and further purified over a Qiagen column. PCR products were tumors), the fibrosarcomas, and the miscellaneous tumors [not analyzed on a Transgenomic Nucleic Acid Fragment Analysis System otherwise specified (NOS)] formed no distinct clusters from the (Omaha, NE). All available samples were screened for mutations in KIT exons 9, 11, and 13, PDGFRA exons 12 and 18, and EGFR all exons (1-28). Samples other groups. The three benign schwannomas were found scattered with potential mutations detected by denaturing high-performance liquid along the dendrogram and did not separate from the malignant chromatography were subsequently sequenced. Primer sequences are tumors. Many of the remaining tumors (four of eight peripheral available in Supplementary Table S5.7 nerve sheath tumors, two of seven fibrosarcoma tumors, and three Gene ontology and gene set enrichment analysis. Gene ontology of ten NOS tumors) intermingled within the main malignant fibrous provides structured functional, localization, and biological process informa- histiocytoma cluster of tumors. Two of the seven fibrosarcoma 8 tion about individual genes. We used the GoHyperG function from the samples clustered with the synovial sarcoma, similar to a previous bioconductor project to find overrepresented ontology categories from the expression study of soft tissue sarcomas where the majority of their weighted lists of genes associated with each tumor type. For each tumor fibrosarcoma samples (five of eight) clustered with the synovial type, we obtained a list of gene ontology categories, ranked by hypergeometric test P value.9 Gene set enrichment analysis is ‘‘designed to detect sarcoma (3). Finally, one malignant peripheral nerve sheath tumor modest but coordinate changes in the expression of groups of functionally clustered within the synovial sarcoma, which was also seen in a related genes’’ (15). In our implementation of the gene set enrichment prior study of synovial sarcoma, where synovial sarcoma and analysis scan, we first obtained the raw data from the Genomics Institute of malignant peripheral nerve sheath tumor tumors clustered tightly the Novartis Foundation (16) compendium of normal tissues consisting of 79 (detailed dendrogram; Supplementary Fig. S1; ref. 19). normal tissues assayed in duplicate for 22,000 features representing over 13,000 unique LocusLink IDs using the Affymetrix hgU133A array. These Weighted Gene Analysis data were normalized using robust multiarray averaging (17). For each We next subjected the data to supervised cluster analysis to normal tissue, all genes were ranked by t statistic using a pooled variance generate a weighted gene list. Because the gene selection algorithm using limma, a technique particularly suited to small numbers of samples per is based on determination of homogeneity within a group, the tissue type (18). Gene set enrichment analysis scanning of the weighted following criteria were applied in selecting the evaluable: the tumor gene lists for each tumor against these ranked lists for each normal tissue had a specific diagnosis confirmed by pathologic review; the tumor produces one value for each tumor-normal tissue pair; mappings between clustered on the same branch as the other members of the cDNA and Affymetrix platforms was via LocusLink IDs. High values represent similarity in expression between the tumor and normal tissue. We computed diagnostic group; and there was more than one member in the rough statistical significance by randomly resampling genes from the group. Of the original 181 tumors, 134 met these conditions—three sarcoma array 10,000 times per sarcoma group for each normal tissue type. were eliminated for only having one group member (clear cell sarcoma, alveolar soft part sarcoma, chondrosarcoma), 10 tumors Results and Discussion were undiagnosed (NOS), and 34 tumors either did not cluster with their diagnostic group (24 samples) or had an uncertain diagnosis Hierarchical Clustering Analysis on pathology review (10 samples). For the two diagnoses that did To gain an overview of the data, we first did unsupervised not form a major cluster by hierarchical clustering (malignant hierarchical clustering of 181 tumors using the 1,527 most variably peripheral nerve sheath tumor, fibrosarcoma), the tumors were expressed genes within the data set (log SD > 0.35). The dendrogram evaluated if both the original pathology report and the diagnosis by divided into two main branches one containing tight clusters of our pathologic review were in concordance. All five of the many of the sarcoma subgroups, in particular the gastrointestinal malignant peripheral nerve sheath tumors and five of seven stromal tumor, osteosarcomas, rhabdomyosarcoma, Ewing’s sarco- fibrosarcoma met these criteria. ma, synovial sarcoma, hemangiopericytoma, dermatofibrosarcoma This gene selection analysis produced a highly statistically protuberans, and myxoid liposarcomas. The second branch significant list of 2,766 probes of the 7,788 submitted for analysis contained the leiomyosarcoma, the majority of the malignant with P < 0.0001, including 562 probes with weights >10 and P < fibrous histiocytoma, and the remaining liposarcoma tumors, which 0.000001 (Fig. 1B; Supplementary Fig. S2A and S2B; Supplementary clustered more loosely (Fig. 1A; Supplementary Fig. S1). The Table S1). This represented 36% of those probes submitted liposarcoma group separated into two distinct populations, one of reflecting the extreme diversity of these tumors. Each group which formed a tightly clustered group, and the remainder exhibited a highly informative list of associated genes, including numerous genes not previously associated with sarcoma. Many genes previously associated with specific tumor types were among 7 http://watson.nhgri.nih.gov/sarcoma/. 8 http://www.geneontology.org. those most highly weighted for example: the muscle markers MYLK, 9 http://www.bioconductor.org. CNN1, and ACTG2 in leiomyosarcoma; KIT in gastrointestinal

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Figure 1. A, hierarchical clustering dendrogram. Tight clustering is seen with most tumor types. The left side of the dendrogram is populated primarily by the more poorly differentiated or dedifferentiated tumors and the typically adult sarcomas with the major branches being formed by the malignant fibrous histiocytoma (MFH) tumors, leiomyosarcoma (LMS), dermatofibrosarcoma (DFSP), hemangiopericytoma (HPC), and liposarcoma (Lipo). Conversely, the right side of the dendrogram has several tightly clustered groups and favors the pediatric sarcomas, Ewing’s sarcoma (EWS), synovial sarcoma (SS), osteosarcoma (OS), and rhabdomyosarcoma (RMS). B, a heat map showing the 2,766 most significant genes by weighted gene analysis for each of the tumor groups. Pseudocolored blocks, SD of the ratio intensity of the sample compared with the mean of the entire sample set. Red blocks, increased level of expression; green blocks, decreased level of expression (scale shown below). The distribution of probes from the list of 2,766 weighted genes was as follows: dermatofibrosarcoma 234 probes, Ewing’s sarcoma 293 probes, fibrosarcoma (Fibro) 100 probes, gastrointestinal stromal tumor (Gist) 295 probes, hemangiopericytoma 227 probes, leiomyosarcoma 251 probes, liposarcoma 204 probes, mixed Mullerian 139 probes, malignant fibrous histiocytoma 343 probes, malignant peripheral nerve sheath tumor 165 probes, osteosarcoma 133 probes, rhabdomyosarcoma 189 probes, and synovial sarcoma 193 probes. stromal tumor tumors; SSX1 in synovial sarcoma; and PDGFB in HCC-4, NRP1, and several collagen family members. SPRY2 is an dermatofibrosarcoma protuberans (4, 20–23). interesting gene that encodes a putative signaling molecule that is The top discriminators for the dermatofibrosarcoma protuber- involved in the regulation of the EGF, FGF, and Ras/MAPK signaling ans include the most highly associated probe arylsulfatase G pathways. NRP1 is also noteworthy in that it encodes a membrane- (ARSG), as well as NBEA, KCTD12, RRM2, LGALS1, NOTCH2, SPRY2, bound coreceptor to a tyrosine kinase receptor for both vascular www.aacrjournals.org 9229 Cancer Res 2005; 65: (20). October 15, 2005

Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2005 American Association for Cancer Research. Cancer Research endothelial growth factor and semaphorin family members and expression profiles of the two divergent groups of liposarcomas plays a role in angiogenesis, cell survival, migration, and invasion from the unsupervised hierarchical clustering to weighted gene (24). The top discriminators for the Ewing’s sarcoma tumors include analysis to determine the distinguishing features of these groups. the most highly associated probe FVT1, DCC, and DKK2. DKK2 is a The weighted gene list and corresponding P values were generated member of the Dickkopf family of genes that are involved in using a t test with 10,000 random permutations as previously regulation of WNT signaling. Interestingly, 9 of the top 20 described. This generated a list of 1,038 probes with P values discriminators in the Ewing’s sarcoma weighted list are genes or <0.001. Interestingly, among the genes most highly associated with isoforms of genes predominantly expressed in brain or neuronal the higher grade tumors were several genes associated with cell tissue. The most highly associated identified probe for the adhesion, invasion, cell death, chemotherapy resistance, and fibrosarcoma tumors was PMP22. The protein tyrosine phosphatase metastases, including GSTTLp28, PLAT, PLAU, FN1, THBS2, MCAM, PTPRZ1 and fibronectin1 (FN1) were also highly expressed and FGFR4, FAPA, FGFR2, FGF18 (Fig. 2A; Supplementary Table S2; heavily weighted in the fibrosarcoma tumors. The top discriminators refs. 28, 31–39). for the gastrointestinal stromal tumors include the oncogene KIT Leiomyosarcoma. In contrast to the liposarcoma group, there and other top ranking genes—ZNF41, HRASLS3, PLAT, GHR, FHL2, was surprisingly little variability within the leiomyosarcoma group. IGF2, and FAT. The top discriminators for the hemangiopericytomas Using unsupervised clustering, no discernible distinction among include the most highly associated probe TLE3, which is involved anatomic site, tumor grade, or metastatic lesions was observed. in epithelial differentiation, as well as CELSR2, COL13A1, and SVIL. Supervised analysis comparing uterine or vaginal-based leiomyo- The genes most highly associated with the liposarcoma tumors sarcoma against other anatomic sites revealed only 25 genes with discriminating them from the other sarcoma types include several P < 0.001 discriminating these two groups (Supplementary Table genes associated with adipocyte differentiation and fatty acid S3). These genes were primarily related to tissue type and included metabolism, PPARG, FABP4, FALC5, as well as SH3KBP1, a mediator regulators of urogenital differentiation, development, and growth: of apoptotic cell death, and the candidate tumor suppressor gene ESR1, HOXA10, PBX1, and FAT (40–43). AIM1. Many of the top leiomyosarcoma discriminators were related Malignant fibrous histiocytoma. The malignant fibrous to muscle structure and function such as MYLK, potassium/calcium histiocytoma group of tumors, which are poorly differentiated and channel components, CCN1, and SLMAP. However, IL4R, the of uncertain histogenesis, proved to be a rather complex group. developmental gene WDR1, and the cell signaling molecule RAB23 Although this diagnostic category is controversial, it is generally also heavily discriminated for leiomyosarcoma. Although the accepted to be a distinct subset of soft tissue sarcoma and remains malignant fibrous histiocytomas did not exhibit a cohesive group the most common diagnosis of adult soft tissue sarcomas. There are by unsupervised cluster analysis, this group does display an four histologic subsets of malignant fibrous histiocytoma described, intriguing group of associated genes when subjected to weighted which include storiform-pleomorphic, myxoid (myxofibrosarcoma), gene analysis. Among the most heavily weighted, highly expressed inflammatory, and giant cell types (44). Although malignant fibrous genes in the malignant fibrous histiocytoma set are several histiocytoma can exhibit some muscle-associated proteins by transcription factors, genes involved in motility, adhesion, and immunohistochemistry, it is controversial whether this reflects proteolysis: RAB32, PLAU, MSN, RUNX1, and DSC2 (25–28). myofibroblastic differentiation or derivation from smooth muscle Although malignant peripheral nerve sheath tumors did not form tissue. Commonly malignant fibrous histiocytoma tumors are a tight cluster on unsupervised hierarchical analysis, there was a difficult to distinguish from other pleomorphic sarcomas and robust list of associated genes identified by supervised, weighted diagnostic certainty is difficult (44). Accordingly, on unsupervised gene analysis. This included the most highly associated probes clustering, the majority of malignant fibrous histiocytoma tumors IRAK1, DOK1, QSN6, and COL6A3. Also found among the top coclustered with the more poorly differentiated tumors, forming a malignant peripheral nerve sheath tumor discriminators were ARHC large branch that included dedifferentiated and pleomorphic and MMP2, both of which have been shown to be highly expressed in liposarcomas, malignant peripheral nerve sheath tumors, and the metastatic phenotypes (29, 30). The top discriminators for the sarcomas NOS. Interestingly, the adjacent branch on the unsuper- osteosarcoma include PPFIBP2, S100A13, and PTHR1, as well as vised clustering dendrogram contained the leiomyosarcoma (Fig. 1). several collagen, cartilage, osteoid-associated genes: P4HA2, PLOD, Similar to the leiomyosarcoma, neither supervised nor unsuper- COL5A1, LUM. Several fibroblast growth factor receptors (FGFR) vised clustering analysis of malignant fibrous histiocytoma led to were also highly associated with osteosarcoma: FGFR3, FGFR2, and significant distinction among anatomic site, tumor grade, or FGFR1. The top discriminators for the rhabdomyosarcoma include metastasis. However, unsupervised hierarchical cluster analysis of both functional and structural genes including the most highly the malignant fibrous histiocytoma tumors alone identified two associated probe MYL4 as well as several cell cycle and cell signaling groups of nearly equal size. When these two groups were subjected molecules and transcription factors: GAB1, MYCL1, MYOIE, IGF2, to weighted gene analysis and gene ontology profiling, an CCND2, PTPRF, PPFIA1, CDK6, FGFR4, GPC3, and POU4F1. Finally, interesting pattern emerged. A list of 279 differentially expressed the top discriminators for the synovial sarcoma include FGF11, genes with P < 0.001 was generated with 41 genes up-regulated COL4A5, PBX3, BCE-1, TLE1, PDGFRA, EFNB3, NRP2, CXCL2, within the first group and the remainder within the second group SHANK2, and EGFR. The entire ranked weighted gene list can be (Fig. 2B; Supplementary Table S4). The group of genes associated found in spreadsheet format and detailed figure form in the Sup- with the first group carried a muscle profile with the genes myosin plementary Data (Supplementary Table S1; Supplementary Fig. S2A). X, sarcoglycan b, and tenascin C among the 10 most significantly expressed genes. In contrast, the second group of tumors revealed In-depth Tumor-specific Profiles an abundance of immune regulatory genes with HEM1, MX1, Liposarcoma. We next examined the more common sarcomas, DAP10, PLCG2, and FOLR3, constituting the five most highly liposarcoma, leiomyosarcoma, and malignant fibrous histiocytoma weighted genes. We then mapped these two gene lists to the for heterogeneity within groups. We first submitted the gene biological process gene ontology classification. Overwhelmingly,

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Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2005 American Association for Cancer Research. Gene Expression Profiling of Human Sarcomas the genes expressed in the first group belonged to the ontologic both groups of tumors. However, the distinction of malignant family of motor activity and the second group of genes belonged to fibrous histiocytoma with myogenic differentiation versus inflam- the ontologic family of immune activity and cell adhesion (Fig. 2C). matory characteristics could ultimately have substantial clinical Storiform-pleomorphic and myxoid subtypes equally populate relevance. The presence of myogenic differentiation in malignant

Figure 2. A, heat map showing weighted gene analysis for the two subgroups of liposarcoma. The higher-grade liposarcoma, indicated by the red bar on the left portion of the heat map, have 752 probes differentially expressed, with P < 0.001. Among the most highly weighted genes associated with the higher grade liposarcoma are several genes known to be associated with cell adhesion, invasion, and metastases. The myxoid/round cell liposarcoma are labeled in green, well and dedifferentiated liposarcomas are labeled in red, and pleomorphic liposarcoma are labeled blue. B, heat map from the weighted gene analysis of the two malignant fibrous histiocytoma groups. Group 1 on the left side of the diagram (red) has 42 probes differentially expressed with P < 0.001 and exhibits a predominance of muscle related genes, whereas group 2 (blue) has 237 probes differentially expressed with P < 0.001 and exhibits an inflammatory profile. C, ontologic evaluation of the two groups of malignant fibrous histiocytoma tumors at the level 1 of molecular function ontology is shown above. To determine gene ontology categories of interest, the differentially expressed genes for each group were tested for enrichment of gene ontology categories by computing their significance with the hypergeometric test. The first group (red column) has an abundance of genes associated with muscle and motor activity, whereas the second group (blue column) carries an inflammatory profile. Graphs are plotted based on the negative log of the P value of the hypergeometric distribution. www.aacrjournals.org 9231 Cancer Res 2005; 65: (20). October 15, 2005

Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2005 American Association for Cancer Research. Cancer Research fibrous histiocytoma and undifferentiated sarcomas has been highly weighted gene. These observations were confirmed by shown to correlate to a poor clinical prognosis (45). The signi- correspondingly intense antibody staining by immunohistochemis- ficance of the inflammatory signature we find in almost half try, with 8 of 16 evaluable tumors on the tissue microarray having of our malignant fibrous histiocytoma is less clear. Although there high intensity staining of >6, two tumors having moderate-intensity is a subclassification of inflammatory-type malignant fibrous scores ranging from 3 to 5, two having low-intensity scores ranging histiocytoma, they are rare and this classification is quite from 1 to 2, and five with no detectable EGFR staining (Supple- controversial and more recently is thought to encompass misdiag- mentary Fig. S3D). However, when the complete panel of 275 nosed dedifferentiated liposarcomas (46). It is unlikely that the sarcoma DNAs (Table 1) was screened for mutations in all 28 exons inflammatory signature we found represents this subclass of tumors. via denaturing high-performance liquid chromatography and sequencing, no mutations were identified. This suggests that EGFR Further Evaluation of Selected Pathways inhibition may not be an optimal therapeutic strategy for sarcomas. Tyrosine kinases. As exemplified by the success of imatinib in gastrointestinal stromal tumor, the receptor tyrosine kinases are WNT Signaling Pathway attractive therapeutic targets (47) and there are currently clinical The WNT signaling pathway, involved in the development of and preclinical trials evaluating various receptor tyrosine kinase brain and the peripheral nervous system, has been found to play a inhibitors (48). We found highly expressed tyrosine kinases or critical role in the formation of several cancers (50), including receptor tyrosine kinases associating with half of the tumor groups. synovial sarcoma (3, 19, 51). We also found evidence implicating Along with known highly expressed kinases, such as KIT in this pathway in synovial sarcoma. WNT5A expression was gastrointestinal stromal tumor and PDGFRB in dermatofibrosar- pronounced, as was the expression of the WNT signaling targets coma protuberans, we additionally found JAK1 in Ewing’s sarcoma, FZD1, CDH4, EN2, TLE4, and TLE1. TLE1, WNT5A, FZD1, and TLE4 FLT1 in hemangiopericytoma, EGFR and PDGFRA in synovial were ranked among the top 50 discriminating genes for the sarcoma, and several FGFR in osteosarcoma (Table 2). Because of synovial sarcoma, and both WNT5A and FZD1 showed heavy the potential clinical relevance, we chose to evaluate several of staining on the tissue microarray as well. All of the synovial these proteins on our tissue microarray. Corresponding immuno- sarcoma samples stained positive for WNT5A with 11 of 16 tumors histochemistry confirmed the presence of many of these proteins staining >6+ for WNT5A, and 9 of 16 tumors staining >6+ for by staining on the array (see Supplementary Data for all Frizzled (Supplementary Table S1; Supplementary Fig. S4). immunohistochemistry images). For example, all of the Ewing’s sarcoma (eight tumors) on the tissue microarray stained for JAK1 Homeobox and Early Developmental Genes with a minimum intensity of 3+. Similarly, each hemangiopericy- The homeobox genes are involved in early embryonic develop- toma (three tumors) stained heavily for FLT1 with intensity of 7+ or ment and the determination of cell fate. There is interest in greater. All of the 13 evaluable synovial sarcoma had moderate to defining the relationship between deviant expression of these early heavy staining for EPHB4 with 9 of 13 with intensity of >6+ and the developmental genes and their role in cancer. Specifically, aberrant remaining four tumors staining with 3 to 5+ intensity. In the homeobox gene expression has been linked to the development of biphasic synovial tumors, the glandular epithelial components leukemia, testicular cancer, breast carcinoma, as well as several stained most intensely for EPHB4 (Supplementary Fig. S3A-C). other tumors (52). The possibility that sarcomas are derived from Since the development of the EGFR inhibitor, gefitinib, there have early mesenchymal stem cells or pluripotent progenitor cells (53) been several clinical trials evaluating its efficacy and subsequent makes this group of genes particularly intriguing. Several studies evaluating whether EGFR expression is predictive for homeobox genes have been identified in the gene expression response to gefitinib. In lung cancer, rather than high expression, profiles of various sarcomas: MEOX2 in synovial sarcoma (3), it is the presence of activating mutations in the EGFR gene that HOXA5 in liposarcoma (5), and MEOX1 in dermatofibrosarcoma predicts response (49). In our data set, we found high EGFR protuberans (54). In our study, we confirmed these associations expression in several of the tumors, particularly the synovial and identify several other highly expressed and statistically sarcoma, where on weighted analysis EGFR was the 25th most significantly associated homeobox genes (Table 3).

Table 2. Tyrosine kinases and receptor tyrosine kinases associated with individual tumor groups (shown are the mostly highly weighted)

Tumor type Gene name Clone title Weight P

DFSP PDGFRB Platelet-derived growth factor receptor, h polypeptide 9.049879 0.000002 EWS JAK1 Janus kinase 1 (a protein tyrosine kinase) 17.694458 0.000001 GIST KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homologue 16.598318 0.000001 LYN v-yes-1 Yamaguchi sarcoma viral related oncogene homologue 10.054924 0.000001 HPC FLT1 Fms-related tyrosine kinase 1 (VEGFR1) 9.646015 0.000002 OS FGFR3 fibroblast growth factor receptor 3 15.948823 0.000001 FGFR2 Fibroblast growth factor receptor 2 8.1758 0.000005 FGFR1 Fibroblast growth factor receptor 1 7.209751 0.00001 RMS FGFR4 Fibroblast growth factor receptor 4 15.539065 0.000001 SS PDGFRA Platelet-derived growth factor receptor, a polypeptide 15.28 0.000001 EGFR Epidermal growth factor receptor 12.682508 0.000001 EPHB4 EphB4 5.609882 0.000042

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Gene Ontology Gene Set Enrichment Analysis To examine more global differences in gene expression between We used gene set enrichment analysis to further explore sarcoma, we mapped the genes from the weighted gene list for similarities between sarcoma gene expression and normal tissues each tumor type to the biological process gene ontology using the publicly available normal tissue gene expression data classification and obtained a list of gene ontology categories, from Genomics Institute of the Novartis Foundation. Gene set ranked by hypergeometric test P. There were multiple categories enrichment analysis generated a table of 936 P values (13 tumor for many tumor types of both biological and statistical interest types Â 72 normal tissues). The Àlog10 (P value) of the 936 (complete gene ontology tables can be found on the Supplemen- members of the P value matrix were used to construct a heatmap tary Data). In addition to providing a high-level description of by clustering tumor types on the x-axis and normal tissues on the some of the biological processes involved in differentiating these y-axis using the Euclidian distance metric (Fig. 3). Some rather tumors from each other, many of the gene ontology lists suggest a striking tumor-normal tissue similarities characterize some but not tissue-of-origin effect. Tissue-type influences are clear where all tumor types. Particularly interesting is the unanticipated angiogenesis and blood vessel development are seen in heman- similarity between the profiles of the Ewing’s sarcoma and synovial giopericytoma, muscle development and regulation of muscle sarcoma, which both exhibit similarity to several brain tissues, contraction in rhabdomyosarcoma and leiomyosarcoma, fatty which supports the position that these tumor groups exhibit acid metabolism in liposarcoma, skeletal development in features of neuroectodermal differentiation. Other similarities exist osteosarcoma, and neurogenic regulation in malignant peripheral between the malignant peripheral nerve sheath tumor and nerve sheath tumor. In those tumors where tissue of origin is malignant fibrous histiocytoma tumors as well as the dermato- unknown, Ewing’s sarcoma and synovial sarcoma, no clear tissue fibrosarcoma protuberans and fibrosarcoma tumors. The inflam- association emerges. There are also cellular pathway categories matory component of the malignant fibrous histiocytoma tumors prominent within the gene ontology classification such as is also apparent by gene set enrichment analysis. phospholipase C activation in dermatofibrosarcoma protuberans, FGFR signaling in rhabdomyosarcoma, and transcription regula- Conclusions tion and the WNT signaling pathway in synovial sarcoma. WNT The rich, highly informative profiles that emerged from this large signaling, phospholipase C, and FGFR signaling are all thought to study of multiple sarcoma histotypes are extraordinary. Comparison of play key roles in the oncogenesis and metastatic potential of our results with previously published studies reinforces the impor- tumors (50). tance of certain specific pathways. For instance, the Wnt-frizzled

Table 3. Homeobox and early developmental genes differentially expressed in sarcoma subgroups (all are highly weighted with P < 0.00001)

Tumor type Gene name Clone Weight P

DFSP MEOX1 Mesenchyme homeobox 1 7.218517 0.00001 SHOX2 Short stature homeobox 2 7.885787 0.000006 EWS EST ESTs, moderately similar to homeobox protein HOX-D10 10.022562 0.000002 PAX3 Paired box gene 3 (Waardenburg syndrome 1) 7.319926 0.000009 ZFHX1B Zinc finger homeobox 1B 6.658276 0.000017 HOXB13 Homeobox B13 5.572331 0.000046 HOXD9* Homeobox D9 — — GIST PBX3 Pre–B-cell leukemia transcription factor 3 7.439544 0.000008 HPC DLX4 Distal-less homeobox 4 10.166724 0.000001 HOXA10 Homeobox A10 5.572109 0.000046 LMS PBX1 Pre–B-cell leukemia transcription factor 1 11.449719 0.000001 LIPO HOXA5 Homeobox A5 11.797409 0.000001 HESX1 Homeobox (expressed in embryonic stem cells) 1 6.984505 0.000013 HOXA4 Homeobox A4 6.11153 0.000026 MFH DLX2 Distal-less homeobox 2 7.224304 0.00001 OS TLX3 Homeobox 11-like 2 6.536942 0.000018 RMS PROX1 Prospero-related homeobox 1 11.575021 0.000001 TGIF TGFB-induced factor (TALE family homeobox) 6.942119 0.000013 PITX2 Paired-like homeodomain transcription factor 2 5.22564 0.000062 SS PBX3 Pre–B-cell leukemia transcription factor 3 17.864521 0.000001 EN2 Engrailed homologue 2 7.922731 0.000006 TGIF2 TGFB-induced factor 2 (TALE family homeobox) 5.961361 0.000032 GBX2 Gastrulation brain homeobox 2 5.731002 0.000038 MEOX2* Mesenchyme homeobox 2 — —

*Ratios are based on a normalized red ratio. These clones failed in the control channel and were not used into the original f-statistic analysis because of poor performance of this clone overall. However, the spot did well with high expression values for HOXD9 in Ewing’s sarcoma and for MEOX2 in synovial sarcoma.

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Figure 3. Gene set enrichment analysis heat map shows tumors along the x axis and normal tissue groups along the y axis. The higher degree of homology is indicated by the lighter (yellow) color (see key in inset). Notable is the similarity in pattern between Ewing’s sarcoma and synovial sarcoma with both groups showing significant overlap with brain tissues (amygdala, occipital lobe, hypothalamus, prefrontal cortex, and fetal brain). There is a high degree on homology found between the malignant fibrous histiocytoma group of tumors and many of the immune system tissues such as thymus, dendritic cells, lymph nodes, monocytes, and others. As expected, there is similarity between adipocytes and liposarcoma tumors and uterine tissue and leiomyosarcoma.

signaling pathway (WNT5A, FZD1, TLE1)andMEOX2 (3), EPHB3 (19), To facilitate exploration of these data, we have created an on- SALL2 (55), have been found in the profiles of synovial sarcoma in line, publicly available, searchable database housing the data from previous gene array studies and are again found to be heavily weighted the gene expression profiles of the 134 tumors profiled through and highly expressed in our synovial sarcoma samples. Similarly, weighted gene analysis.7 This database allows the user to browse HOXA5 in liposarcoma (5), PRKCQ in gastrointestinal stromal through the expression profiles of the 7,788 probes of highest tumor (4), MEOX1 and EGR2 in dermatofibrosarcoma protuberans quality from the gene expression array. The user can search for (54), and IGF2 in rhabdomyosarcoma (56) have also been implicated genes of interest, plot their profiles, and identify genes with in previous studies and are prominent in our profiles as well. similar expression patterns. The database also allows searches Although clinical data was not available for these samples, the based on tumor subclasses, and chromosome regions. The expression profiles of these tumors provide information that is complete raw data are also available through the Gene Expression highly relevant to translational research. The cell signaling pathways, Omnibus (GEO) data repository,10 GEO accession number GSE2553. cell surface adhesion molecules, receptor tyrosine kinases, growth Mining this information provides an opportunity to identify new factors, transcription factors, and early developmental genes markers for sarcoma classification and to identify potential expressed in these tumors identify potential candidates for prognostic indicators, and oncogenic pathways, which may therapeutic intervention and diagnostic development. In this report, ultimately guide the development of targeted therapeutics. In we have highlighted several of the most interesting aspects of these data. However, many more facets merit further examination, particularly in the context of newly developed targeted therapies. 10 http://www.ncbi.nlm.nih.gov/geo/.

Cancer Res 2005; 65: (20). October 15, 2005 9234 www.aacrjournals.org

Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2005 American Association for Cancer Research. Gene Expression Profiling of Human Sarcomas summary, we have presented the largest collection of expression Acknowledgments profiles from human soft tissue and bone sarcomas to date. We have highlighted some of the most interesting and potentially clinically Received 5/25/2005; revised 7/19/2005; accepted 8/1/2005. The costs of publication of this article were defrayed in part by the payment of page relevant features of these data based on hierarchical clustering, charges. This article must therefore be hereby marked advertisement in accordance weighted gene analysis, gene ontology, and gene set enrichment with 18 U.S.C. Section 1734 solely to indicate this fact. analysis. We have also made available a database that will allow We thank the following individuals for their efforts in this study: Suzanne Allander, Michael Bittner, Peter Borys, Jenna Cherchio, Yu-Waye Chu, Robert Cornelison, Javed other investigators to utilize this data set to continue the process of Khan, John Kakareka, Marc Ladanyi, Darryl Leja, John Leuders, Greg Maher, Livia gene discovery in these tumors. Mezinos, Ola Myklebost, Lauren Schuler, Jun Wei, and Michael Wu.

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