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1 Institute of Pathology, Heinrich-Heine-University, Dusseldorf, Germany; 2 Gerhard-Domagk-Institute of Pathology, 3 Institute of Arteriosclerosis Research, and 4 Laboratory for Experimental Orthopaedic Research, Department of Orthopaedic Surgery, University of Muenster, Muenster, Germany; 5 Department of Hematology and Oncology, University Children’s Hospital of Heidelberg, Heidelberg, Germany; 6 Children’s Cancer Research Institute, St. Anna Kinderspital, Vienna, Austria; 7 Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan, Republic of China; 8 Department of Biology, Hong Kong University of Science and Technology, Kowloon, Hong Kong S.A.R. China; 9 Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands; and 10 Department of Neuropathology, Heinrich-Heine-University, Dusseldorf, Germany
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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12,000 genes to generate the expression profiles of the CCSST cell lines GG-62, DTC-1, KAO, MST2, MST3, and Su-CC-S1 in comparison with 8 neuroblastoma, 7 Ewing tumor, and 6 osteosarcoma cell lines. Subsequent hierarchical clustering of microarray data clearly separated all four of the tumor types from each other and identified differentially expressed transcripts, which are characteristically up-regulated in CCSST. Statistical analysis revealed a group of 331 probe sets, representing 300 significant (P < 0.001) differentially regulated genes, which clearly discriminated between the CCSST and other tumor samples. Besides genes that were already known to be highly expressed in CCSST, like S100A11 (S100 protein) or MITF (microphthalmia-associated transcription factor), this group shows an obvious portion of genes that are involved in cyclic AMP response or regulation, in pigmentation processes, or in neuronal structure and signaling. Comparison with other expression profile analyses on neuroectodermal childhood tumors confirms the high robustness of this strategy to characterize tumor entities based on their gene expression. We found the avian erythroblastic leukemia viral oncogene homologue 3 (ERBB3) to be one of the most dramatically up-regulated genes in CCSST. Quantitative real-time PCR and Northern blot analysis verified the mRNA abundance and confirmed the absence of the inhibitory transcript variant of this gene. The protein product of the member of the epidermal growth factor receptor family ERBB3 could be shown to be highly present in all of the CCSST cell lines investigated, as well as in 18 of 20 primary tumor biopsies. In conclusion, our data demonstrate new aspects of the phenotype and the biological behavior of CCSST and reveal ERBB3 to be a useful diagnostic marker. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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1% of all malignancies of the musculoskeletal system (1, 2, 3)
. CCSST, which usually affects adolescents and young adults, is commonly associated with tendons and aponeuroses and is believed to be derived from neuroectodermal tissues (4)
. The tumor cells are typified by the presence of a balanced t(12;22)(q13;q12) rearrangement: the result is a fusion between the Ewing sarcoma gene (EWSR1) and activating transcription factor 1 (ATF1), which permits the expression of an EWS-ATF1 oncoprotein (5)
. ATF1, along with cyclic AMP (cAMP)-responsive element binding protein and modulator (CREM), comprise a bZIP subfamily of transcription factors, which regulate gene expression via homo- or heterodimeric binding to cAMP response elements (CREs; Ref. 6
). EWS-ATF1 activates transcription independently of cAMP induction due to a partial deletion of the ATF1 kinase-inducible domain (7)
; moreover, the EWS-ATF1 fusion protein has been shown to act as a potent activator of several cAMP-inducible promoters (8
, 9) . Therefore, EWS-ATF1 may exert its oncogenic properties via the induction or deregulation of genes that govern transcription, cell division, and signal transduction. According to their histological appearance CCSST can resemble other malignant mesenchymal tumors of childhood and adolescence. Immunostaining for S100 and especially markers for melanocytic differentiation (including melan-A, the microphthalmia-associated transcription factor, or the melanosomal matrix protein Pmel17, which is detected by HMB-45 monoclonal antibodies) is usually included in the examination of tumor biopsies to arrive at a reliable diagnosis (10) . Because cutaneous, deeply invasive spindle-cell melanomas that lack a demonstrable primary tumor and CCSST share a broad panel of these histological and immunohistologic features, the distinction of these two lesions should include proving the presence or absence of the t(12;22) translocation, which is only found in CCSST (11 , 12) .
Besides the diagnosis, the treatment of CCSST patients may represent a serious challenge to the physician. The tumor often presents as a painless, slowly growing mass, which can radiologically be mistaken as a benign process (13
, 14)
. Nevertheless, CCSST represents a fully malignant neoplasm with the tendency to metastasize to regional lymph nodes and the lung, and the likelihood of local or distant recurrences is high as evidenced by 5-year survival rates of 50%. Only a few data are available on the clinical management of this rare soft tissue lesion. One major problem in the treatment of CCSST is the resistance of this entity to chemotherapeutic drugs, which in other soft tissue tumors are reported to be at least partially effective (14
, 15) .
To additionally understand the processes that control this rare, slowly growing but highly malignant cancer type and obtain new insights for new potentially diagnostic markers or therapeutic targets, we report here the microarray analysis of 6 CCSST cell lines and 21 cell lines derived from other solid tumors affecting children and young adults.
Using this powerful tool (16 , 17) in combination with statistical analysis we were able to identify characteristic multigene expression patterns, which point to new regulatory mechanisms, cellular functions, and the molecular biological phenotype of CCSST.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The cell lines we studied are listed in Table 1
. Our experiments included 27 cell lines: 8 neuroblastoma (NB), 7 Ewing’s tumor (ET), 6 CCSST, and 6 osteosarcoma (OS). All of the cell lines were maintained using standard procedures as described previously (18)
. Absence of Mycoplasma contamination was determined by the PCR-based VenorGeM Mykoplasma Detection kit (Minerva Biolabs GmbH, Berlin, Germany) according to the manufacturer’s protocols.
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MST2 was derived from a 60-year-old Taiwanese male who had noted a slowly growing mass at his right knee for 5 years. Because of aggravated pain, the tumor (measuring 6 cm x 4 cm x 3 cm) was excised in May 1994. The initial histopathological diagnosis was synovial sarcoma. Magnetic resonance imaging performed 2 weeks after initial surgery revealed residual tumor. After radical excision, histopathological diagnosis at a different department revealed a spindle cell neoplasm with tumor cells exhibiting focally clear cytoplasm, large nuclei with prominent nucleoli, and melanin granules. Immunohistochemically, the tumor cells were positive for HMB-45. The diagnosis was changed to CCSST. In November 1994, after radical surgery followed by two cycles of chemotherapy using the British Columbia Drug Treatment Program regimen {carmustine [1,3-bis(2-chloroethyl)-1-nitrosourea], cisplatin, dacarbazine [5-(3,3-dimethyl-1-triazeno)-imidazole-4-carboxamide], and tamoxifen}, multiple pigmented nodular tumors developed in the right thigh. Histopathology revealed metastasis of the CCSST, and the MST2 cell line was established from this biopsy. Additional tumor growth into the pelvis and abdominal cavity caused ascites and intestinal obstruction. The patient died in January 1995.
MST3 was established from the tumor of a 34-year-old Taiwanese male, who had suffered from a slowly growing mass in his groin for 10 years, which suddenly enlarged rapidly. Excisional biopsy in February 1996 revealed a 6 cm x 4 cm tumor in the groin and a 1 cm x 1 cm satellite nodule on the lateral site. Histopathology showed tumors composed of clear cells with large nuclei and distinct nucleoli. Immunohistochemically, the tumor cells were positive for HMB-45 and S100 protein, but negative for cytokeratins (AE1/AE3) and epithelial membrane antigen. Therefore, the diagnosis of CCSST was established. At this time, no additional treatment was performed. In November 1996, tumor relapse in the groin was noted. The tumor and regional lymph nodes were excised. Histopathologically, tumor metastases were found in 17 of 25 lymph nodes. Despite adjuvant chemotherapy (cisplatin and carmustine) another tumor relapse occurred in March 1997. The tumor did not respond to additional chemotherapy (British Columbia Drug Treatment Program regimen), which was performed in April 1997. The patient died in March 1998 due to tumor metastases in the lungs, skeletal system, and lymph nodes.
Detection of the EWS-ATF1 Fusion Transcript.
The EWS-ATF1 gene fusion was detected by a reverse transcription-PCR assay, which has been described previously (19)
.
Complementary RNA Preparation and Oligonucleotide-Microarray Hybridization.
The preparation and processing of labeled and fragmented cRNA targets, as well as GeneChip hybridization and scanning was carried out according to the manufacturer’s protocol (Affymetrix, Santa Clara, CA) as described elsewhere (20)
.
Microarray Data Analysis.
To translate the scanned images into expression analysis files Micro-Array-Suite 5 Software was used generating a unique Signal value for each probe set together with a "Detection P" based on the one-sided Wilcoxon’s signed rank test, which indicates whether a transcript is reliably detected (Present) or not detected (Absent). Probe sets characterized by Ps < 0.04 were considered to be present. Only probe sets called present in at least 2 of the 27 samples were included in the following statistical analysis.
To compensate for differing hybridization efficiencies, we used Micro-Array-Suite 5 and normalized all of the chip data to a single scaling factor target of 1000.
Each individual microarray was then standard normalized using the reduced gene set. The data were centered to zero by subtracting the overall mean of the chip and scaled to a range of multiples of the SD (21) . To identify the differentially expressed genes we computed t tests (two-sample, two sided t tests with a test on homogeneous variances) and nonparametric Wilcoxon-rank-sum tests (22 , 23) .
We used the Cluster 2.11 software for similarity analyses, and TreeView 1.50 permitted visualization of the results (24) .
For interpretation of data in terms of chromosomal localization of up-regulated genes GenMAPP v1.0, MAPPFinder v1.0, and MAPPBuilder 1.0 from the Gladstone Institutes/University of California at San Francisco11 were used. From the list of genes used for our analysis (see "Results") MAPPBuilder assigned all of the genes to separated MAPPs according to their affiliation to individual chromosomal arms. These MAPPs were used by MAPPFinder to calculate significant differences in the distribution of gene activity among the genome. A z-score is used to quantify the gene activity difference of an individual MAPP with regard to the average gene activity of the data set. Being the difference between observed and expected activity normalized with the SD of the hypergeometric distribution, a z-score of 0 states no activity difference, and a z-score of 1 means one SD more activity than expected. Negative z-scores indicate less activity than expected.
Quantitative Real-Time PCR.
For a selected set of genes the microarray-derived expression data were evaluated by quantitative PCR using the LightCycler system (Roche Diagnostics, Mannheim, Germany). Glyceraldehyde-3-phosphate dehydrogenase (GAPD) cDNA (NM 002046) was amplified using primers GAPD-1 (5'- GAGTCCACTGGCGTCTTCA -3') and GAPD-2 (5'- GGGGTGCTAAGCAGTTGGT -3'). Avian erythroblastic leukemia viral oncogene homologue 3 (ERBB3) cDNA (NM 001982) was amplified using primers ERBB3–1 (5'-ATGGTGCATAGAAACCTGGC-3') and ERBB3–2 (5'-ACTCCCAAACTGTCACACCA-3'), and primers SILV-1 (5'-AGCTGGCCAAGTGCCTACTA-3') and SILV-2 (5'-GGCACCTTCTCAGGTGTCAT-3') were used for quantifying the "silver like gene" (SILV; NM 006928). For all of the primer pairs an initial denaturation at 95°C for 2 min was followed by 35 cycles of denaturation at 94°C for 1 s, annealing at 60°C for 10 s, and extension at 72°C for 10 s.
Quantitative analysis was performed using the LightCycler Software, and a relative quantification method is described elsewhere (25) .
Northern Blot Analysis.
Total RNA was prepared according to the peqGOLD TriFast protocol (peqlab, Erlangen, Germany). Four µg of RNA were loaded onto a 1.2% formaldehyde-agarose gel and transferred onto positively charged nylon membranes (Roche Diagnostics) using the VacuGene XL Vacuum blotting System (Amersham Pharmacia Biotech) according to the manufacturer’s protocol. For detection of GAPD and ERBB3 RNA, antisense probes were generated by in vitro transcription (Biotin- or DIG-RNA labeling kit for GAPD and ERBB3, respectively; Roche Diagnostics). DNA templates for in vitro transcription were generated by PCR using primers 5'-CACCCATGGCAAATTCCATGGC-3' and 5'-TAATACGACTCACTATAGGGAGGCATTGCTGATGATCTTGAGGCT-3' for GAPD, and 5'-TTCAATGACAGTGGAGCCTG-3' and 5'-TAATACGACTCACTATAGGGAGCCGTACTGTCC-GGAAGACAT-3' for ERBB3 exons 7–10 (T7 promoter sequences are italicized). The membranes were hybridized overnight at 63°C, and chemiluminescent detection was carried out with anti-DIG-alkaline phosphatase [1:10,000] (Roche Diagnostics) and CSPD [1:100] (Roche Diagnostics) according to the supplier’s instructions. Signals were obtained by exposing X-ray film to the membranes for 6 h. For GAPD detection, the membranes were reprobed with the biotinylated antisense RNA, and chemiluminescent detection was achieved using the Streptavidin-biotinylated alkaline phosphatase (StreptABComplex/AP; DAKO A/S;1:30,000).
Western Blot Analysis and Immunohistochemistry.
For Western blotting, cells or tissues were lysed in sample buffer containing 12.5% glycerol, 0.5% SDS, 31 mM Tris (pH 6.8), and 1.25% ß-mercaptoethanol. Proteins were separated in an 8% SDS-PAGE and transferred onto prewetted Protran 0.2-µm nitrocellulose membranes (Schleicher & Schuell, Dassel, Germany) with transfer buffer (31 mM Tris-base, 233 mM glycine, and 25% methanol). Benchmark prestained protein ladder (Invitrogen, Karlsruhe, Germany) was used for size estimation. The ERBB3 protein was detected by the ErbB-3 (C-17) antibody (rabbit polyclonal IgG; Santa Cruz Biotechnology, Heidelberg, Germany) together with a goat antirabbit IgG peroxidase-conjugated secondary antibody (Pierce, Rockford, IL). Whole cell lysate from breast cancer cell line SK-BR-3 (Santa Cruz Biotechnology) was used as a positive control.
Immunohistochemistry was performed using rabbit polyclonal antibody HER-3 antibody-10 at a dilution of 1:100 (Neomarkers, Westinghouse, CA), and monoclonal melanosomal antibody HMB-45 at 1:200. Peroxidase staining was done using the DAB Chromogen/DAB Substrate kit from ScyTek Laboratories (Logan, UT) according to the manufacturer’s protocol.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Gene Expression Patterns, Cluster Analysis, and Statistical Analysis.
We investigated differential gene expression in 27 human cancer cell lines using Affymetrix Human Genome U95Av2 Arrays carrying 12,626 probe sets representing 12,000 different genes. These cell lines included 6 CCSST, 7 ETs, 6 OSs, and 8 NBs. After removing all of the probe sets, which were not called "present" for at least 2 of the 27 samples by the Micro-Array-Suite 5 software (Affymetrix), the remaining 6,358 probe sets were used in an unsupervised hierarchical clustering algorithm as described elsewhere (26)
, and the results were visualized with the TreeView program.
In the hierarchical cluster analysis (Fig. 1)
all of the cell lines were readily grouped according to their respective tumor tissue of origin with the exception of the NB cell line, SHEP-SF, clustering with the osteosarcomas.
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For each statistic we computed Ps to assess the strength of the evidence (statistical significance) against the null hypothesis of an average equal expression in CCSST and "non-CCSST" (27) . Using a cutoff level for the P of 0.05 we found 88% common genes in both groups.
Among the 6358 probe sets used for statistical analysis the Wil-coxon rank-sum test identified 2697 probe sets to be significantly (P < 0.05) up- or down-regulated in CCSST including 1527 probe sets showing Ps < 0.01 and 331 probe sets characterized by Ps < 0.001. Lists of complete raw-data and processed data including statistical analysis, "GeneOntology" annotations and "LocusLink" chromosomal localizations are available.13 ,14
To obtain an overview of the expression profile of CCSST with respect to the entire human genome, we first sought to identify chromosomal regions showing elevated gene activity. Merging all genes of an individual chromosomal arm into one map, MAPPFinder calculated the impact of differences of the distribution pattern. For this analysis probe sets showing at least a 2-fold increased median expression level in CCSST samples together with a P < 0.05 in the Wilcoxon test were considered to be activated (337 genes). Chromosomal arms showing at least 1.0 SD more activity than expected were 8p and q (z score = 3.153 and 4.742), 18p and q (z score = 3.504 and 2.376), 6q (z score = 2.528), 17q (z score = 1.707), 7p and q (z score = 1.505 and 1.343), and 10p (z score = 1.167).
The 54 most highly overexpressed genes (fold change >5; P < 0.0005; 54 genes) are summarized in Table 2
, and their chromosomal localization is illustrated in Fig. 2
. In addition to the characteristically up-regulated genes a list comprising the most significantly down-regulated genes (P < 0.005; fold change <0.25) is given in Table 3
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were also used for the analysis of biological processes that might be of importance for biology of CCSST. By Onto-Express (28)
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we translated the up-regulated genes into functional profiles, using information available from GeneOntology database. Sufficient information on biological processes was available for 37 of the 54 highly significant genes. Focusing on ontology terms for which more than one up-regulated gene was annotated we observed over-representation of genes especially involved in "amino acid metabolism" (CTH and SLC7A4; P = 0.024) and "regulation of transcription from Pol II promoter" (PRKAR1A, STAT5A, LRRFIP1, and SOX10; P = 0.028; Ps corrected for multiple testing).
Marker Genes in Neuroblastoma and Ewing’s Tumors.
We additionally sought to compare our GeneChip data with a former expression profiling study on neuroblastoma and Ewing’s tumors together with rhabdomyosarcoma and Burkitt lymphoma by Khan et al. (29)
. Using artificial neuronal networks the authors established a list of 96 ranked genes that could be used to classify their tumor samples according to the tissue type of origin. From this list of 96 ranked genes 56 were found to: (a) represent fully annotated genes (no expressed sequence tag); and (b) be also included in our list of probe sets, which were at least twice called "present." We applied the 
2 test to determine whether genes found to be overexpressed in Ewing’s tumors or neuroblastoma, respectively, by Khan et al. (29)
were also up-regulated at least 1.5-fold (calculated by median values) in our tumor cell lines. If a gene was represented by more than one probe set, the median value of all of the corresponding probe sets was used for this purpose. We calculated Ps of 0.0000026 for the comparison of up-regulated Ewing’s tumor genes and P = 0.0000415 for the neuroblastoma samples using Microsoft Excel. Characteristic genes concordantly found in both studies were APLP1, AF1Q, CDH2, FHL1, GAP43, GATA2, MAP1B, SFRP1, NFIB, and PIG3 for NB and CCND1, DPYSL2, ANXA1, CAV1, DAPK1, FCGRT, FVT1, GYG2, MIC2, PTPN13, SELENBP1, TLE2, TNFAIP6, TUBB5, MYC, GAS1, and MN1 for EFT. All of the genes used for this analysis are assembled in Table 4
.
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). For both genes we could confirm that they are obviously up-regulated in CCSST compared with the ET, NB, and OS samples.
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Is ERBB3 a Target Gene of EWS/ATF1?
Because constitutively active EWS-ATF1 may mimic cAMP-induced gene activation, the promotor region (transcription start sites from –1000 to +50) of ERBB3 (NM 001982), in comparison to all of the other up-regulated genes of Table 2
were extracted using "PROMOSER"16
and were analyzed for CREs (consensus TGACGTCA) using the web-accessible "cis-element Cluster Finder" analysis software "CISTER".17
Because CISTER looks for clusters of potential transcription factor binding sites, besides CREs, the DNA sequences were scanned additionally for TATA, Sp1, CCAAT, AP1, LSF, and GATA elements. A probability score of <0.01 was delivered indicating that ERBB3 is not likely to be directly affected by a CRE-binding transcription factor. Somatostatin (SST) and c-fos (FOS), both known to transcriptionally respond to cAMP, deliver probability scores of 0.12 and 0.12, respectively. Overall, 13 of the 50 (26%) most up-regulated genes of CCSST show potential CREs near to their transcription start point. Using the list of genes found by Khan et al. (29)
to be characteristically regulated in ET and/or NB (Table 4)
as a reference panel reveals CREs for only 8 (17%) of these 46 genes (17%; for 10 of the 56 genes no unique reference cDNA sequence could be obtained) even if this difference does not reach statistical significance (2 test).
ERBB3 Northern Analysis.
For the ERBB3 gene, alternatively spliced variants are known and only the full-length transcript codes for the functional receptor, which is composed of the extracellular ligand binding domain, the transmembrane domain, and the cytoplasmic effector domain. Shorter versions of the ERBB3 mRNA only code for the extracellular domain, and these are considered to have an antagonistic effect.
To evaluate whether the truncated antagonistic forms of ERBB3 were also present in CCSST cells, an exon 7–10 spanning antisense probe, designed to detect all of the previously observed variants of ERBB3 mRNA, was used for Northern analysis. We found a strong hybridization signal at 6.2 kb in all of the six CCSST cell lines, which was expected for the full-length receptor mRNA. No abundant amounts of shorter variants of the ERBB3 mRNA could be detected (Fig. 4)
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170 kDa found in both the CCSST as well as the breast cancer cell line SK-BR-3, which is known to overexpress ERBB3, indicated that not only the mRNA but also the protein product is produced at elevated levels. Lower amounts of the ERBB3 protein were detected in the 1 of 5 OS (OST), in 2 of 4 ET (WE-68 and VH-64), and a weak signal in 1 of 2 NB (SHSY-5Y; Fig. 5
).
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showing specific ERBB3 expression also in this primary tumor sample. The latter, together with 19 additional CCSST specimens, was examined by immunohistochemistry for SILV and ERBB3 protein expression. Two osteosarcoma, 2 neuroblastoma, and 2 Ewing sarcoma samples were also included in the immunohistochemical analysis. We found 19 of 20 CCSST samples to be positive for SILV/HMB-45 (the negative sample could be shown to harbor an EWS-ATF1 rearrangement), and 18 of 20 clinical CCSST samples revealed abundant expression of the ERBB3 protein (Fig. 6, B and C)
. As expected, none of the other 6 tumor specimens (osteosarcomas, neuroblastomas, and Ewing sarcomas) or other negative controls (e.g., tonsil) showed positive staining for SILV or ERBB3.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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We have characterized six cell lines derived from CCSST by their expression profiles in comparison to 21 cell lines derived from other solid extracranial tumors affecting children and young adults. All of the CCSST cell lines were known or could be shown here to harbor the characteristic t(12;22) translocation, resulting in an EWS-ATF1 gene fusion transcript type 1 (30) .
Applying two-dimensional hierarchical cluster analysis based on a subset of 6000 reliably measured genes, the 27 cell lines analyzed here were grouped in the first dimension according to their four tumor entities. Only the NB cell line SHEP-SF was found to be more related to the OSs than the NBs regarding their expression profiles. This aberrant clustering of SHEP-SF apart from all of the other 7 NBs was not entirely unexpected, because SHEP-SF has been characterized as an atypical neuroblastoma exhibiting epithelial differentiation (31)
. Moreover, we have observed previously SHEP-SF being clustered apart from the NB cell lines Kelly, NGP, and SH-SY5Y in a gene expression analysis of NB and ET cell lines using a different type of oligonucleotide microarray (20)
. Despite this single case, our data clearly confirm that expression profile analysis is a valuable tool to define tumors based on their molecular biological characteristics. In fact, there are examples published where expression profiling was the basis to discover improperly diagnosed tumors and led to the reclassification of these malignancies (19
, 32)
. In addition, comparison of our GeneChip data to a former expression profiling study on EFT and NB showed strong correlation between these two data sets regardless of the type of microarray used (Affymetrix versus Image cDNA array) or the used numerical analysis (nonparametric Wilcoxon rank-sum tests versus artificial neuronal network).
Several of the genes we found to be significantly (P < 0.05) overexpressed in CCSST had already been characterized in this entity by other methods including the S100 protein (S100A11), the microphthalmia-associated transcription factor (MITF; Ref. 33
), or the silver (mouse homologue)-like melanosomal protein Pmel17 (SILV), which is detected by the antibody HMB-45 in routine immunohistochemical examination of CCSST. These findings, together with the comparison of the real-time PCR data (Fig. 3)
prove the reliability of GeneChips and confirm them as a valuable molecular genetic tool even if the annotation of individual probe sets on the U95Av2 Chip may lead to conflicting results whereas measuring target gene mRNA abundance was shown here for one of the four probe sets representing ERBB3. In this case the probe set 1742 at was designed to identify a rare splice variant of the ERBB3 mRNA, which was originally observed in gastric cancer cell line MNK45 (GenBank S61953). Another source of conflicting data may arise from incorrect annotations of probe sets. For example we found probe sets representing the gene "transcription factor 8" (TCF8) in both, the list of up- and down-regulated genes. Reanalysis of the sequences used to generate the probes of set 33439 at clearly showed that they belong to the "SNF1-like kinase" gene rather than TCF8.
While this article was under review Segal et al. (34)
described the discrimination of four CCSST tumor specimens from other soft tissue tumors and cutaneous melanoma by computational analysis of U95Av2-data. In their Fig. 4
the authors present a list of 55 probe sets representing 41 different genes that were the most significantly overexpressed genes in their CCSST tumor tissues. Three of these probe sets did not meet our criteria of being "present" in at least 2 of the samples in our study. Of the remaining 52 probe sets, 45 (representing 34 genes) were observed to be differentially (Wilcoxon and/or t test) regulated in our samples. Keeping in mind that these were two completely separate studies comparing different tumor entities against CCSST, the observed concordance powerfully underlines the robustness of GeneChip expression profiling and the suitability of tumor cell lines to serve as a model system in many respects.
Analyzing our 54 most activated genes (Table 2)
by OntoExpress reveals that tight gene expression regulation seems to be a critical factor for malignant CCSST growth. Because sufficient information was available for only 37 of the 54 genes in the GO database, Table 2
was supplemented by the records found in the GeneCards database. Merging this information, we found CCSST to be largely characterized by the overexpression of genes that are involved in the response or extinguishing of cAMP signals (MITF, CREM, and PRKAR1A), in signaling or structure maintenance of neuronal tissue (PLP1, MBP, KCNAB2, and VATI) or which are critical for pigmentation (SILV, OCA2, and MITF).
The chimeric EWS-ATF1 protein was shown previously to constitutively deregulate promoters harboring an ATF1-binding site (8) . These findings could be endorsed by our computational analysis of the promoter region of the most up-regulated genes in CCSST showing a higher (even if not significantly) proportion of genes harboring a potential ATF1-binding site when compared with characteristically up-regulated genes in Ewing’s tumors or neuroblastoma. On the other hand, CCSST cells are deficient, at least to some extent, to respond transcriptionally to cAMP stimulation (7) .
We observed increased expression of MITF (microphthalmia-associated transcription factor, mainly the melanocyte specific variant; Ref. 35
), the CREM, and the protein kinase, cAMP-dependent, regulatory subunit, type I, 
(tissue specific extinguisher 1; PRKAR1A).
CREM and PRKAR1A play a role in extinguishing cAMP-induced signals. In untransformed cells, the cAMP-dependent transcription factors ATF1 and CRE binding protein are activated by phosphorylation by protein kinase A (PKA). In the absence of cAMP induction, the PKA catalytic subunits are sequestered and inactivated by regulatory subunits encoded by PRKAR1A. However, because EWS-ATF1 proteins lack a complete ATF1 NH2-terminal kinase-inducible domain, they function independently of activation by PKA. Therefore, the PRKAR1A up-regulation in CCSST cells may indicate a futile attempt by the cells to counter the constitutive activity of EWS-ATF1. The CREM variant inducible cAMP early repressor is transcriptionally activated by cAMP (and EWS-ATF1?) but, at the same time, functions as a powerful repressor of cAMP-induced transcription by binding to CREs within its own and other gene promoter regions (36) . Therefore, we speculate that the constitutive overexpression of CREM considerably contributes to the deficiency of EWS-ATF1-positive cells to respond to cAMP by gene transcription.
In melanocytes and melanoma of the skin there is a well-established link between cAMP signaling and induction of melanogenesis (37)
. Elevation of intracellular cAMP leads to PKA activation and finally results in the transcription of MITF; MITF is essential for the expression of tyrosinase, the rate-limiting enzyme in melanogenesis. Whereas the regulatory subunits, type I, of PKA and MITF are both dramatically up-regulated in our CCSST cell lines, only a subset of these cells also expresses tyrosinase mRNA and protein (35)
. This may explain the histological presence of immature melanosomal structures but absence of detectable melanin pigment in most CCSST cases. It will be interesting to determine whether the melanocytic phenotype of CCSST is just an epiphenomenon due to severe disturbance of cAMP pathways or is based on a close etiological relation to melanoma of the skin defining CCSST as a subtype of melanoma as suggested by Segal et al. (34)
.
When we focused on the 54 most up-regulated genes we observed a small cluster of three activated genes on the same chromosomal band as the ATF1 breakpoint (12q13). This co-up-regulation of ERBB3, CDK2, and SILV in CCSST, which can also be deduced from the data by Segal et al. (34) for their four tumor tissue specimens, is momentous because it distinguishes this tumor not only from ET, NB, and OS, but also from malignant melanoma of the skin. In 17 cutaneous melanoma cell lines, Walker and Hayward (38) found strong expression of both SILV and CDK2 in just 6 samples (35%). SILV or CDK2 alone were each expressed in 3 cell lines, and, in the remaining 5 cell lines, both genes were down-regulated. In addition, immunohistochemistry could detect only a weak ERBB3 expression in a minor subset of primary cutaneous melanoma biopsies (12 of 30; Ref. 39 ).
One of the most significant findings of our study is the dramatic and characteristic overexpression of ERBB3, encoding a member of the epidermal growth factor receptor family, in CCSST at both the mRNA and protein levels. The knowledge of this characteristically overexpressed growth factor receptor offers new possibilities to further improve the diagnosis of CCSST.
In addition, the understanding of the multiple processes that modulate epidermal growth factor receptor signal transduction, such as heterodimerization, tyrosine kinase activity, and endocytosis, together with the discovery that certain receptors are selectively overexpressed in special cancer types, has revealed new opportunities in the development of modern anticancer therapeutics (reviewed in Ref. 40 ). One example is the development of agents consisting of a targeting ligand coupled to a potent toxin (41) .
This is of special interest because CCSST are highly resistant to adjuvant chemotherapy. It remains to be elucidated whether ERBB3 overexpression is not only a new marker of the CCSST phenotype but also a prerequisite for malignant growth of this tumor type. Our ongoing studies have already excluded the presence of secreted isoforms of the ERBB3 receptor, produced from an alternatively spliced mRNA transcript, which are known to antagonize the activation of ERBB heterodimers (42) .
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Requests for reprints: Christopher Poremba, Institute of Pathology, Heinrich-Heine-University Duesseldorf, Germany, Moorenstrasse 5, 40225 Duesseldorf, Germany. Phone: 49-211-8118492; Fax: 49-211-8118353; E-mail: poremba{at}med.uni-duesseldorf.de
11 Internet address: http://www.genmapp.org. 
12 Internet address: http://bioinformatics.weizmann.ac.il/cards/.
13 Internet address: http://www-public.rz.uni-duesseldorf.de/k-sch00l/download/CCST-DATA-processed.XLS.
14 Internet address: http://www-public.rz.uni-duesseldorf.de/k-sch001/download/CCSST-DATA-raw.XLS.
15 Internet address: http://vortex.cs.wayne.edu/projects.htm#Onto-Express.
16 Internet address: http://biowulf.bu.edu/zlab/promoser/.
17 Internet address: http://zlab.bu.edu/mfrith/cister.shtml.
Received 3/28/03. Revised 2/ 9/04. Accepted 3/ 4/04.
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) as well as LightCycler data (
) are expressed as percentage with respect to the highest-expressing cell line.
-DNA was used as size-marker (SM). Only the 6.2 kb full-length ERBB3 mRNA was detectable, and no abundant amounts of shorter variants of the ERBB3 mRNA could be seen.
