Nakayama R, Nemoto T, Takahashi H, Ohta T, Kawai A, Seki K et al.. Gene expression analysis of soft tissue sarcomas: characterization and reclassification of malignant fibrous histiocytoma. Mod Pathol 20: 749-759

Cancer Transcriptome Project, National Cancer Center Research Institute, Tokyo, Japan.
Modern Pathology (Impact Factor: 6.19). 04/2007; 20(7):749-759. DOI: 10.1038/modpathol.3800794


In soft tissue sarcomas, the diagnosis of malignant fibrous histiocytoma (MFH) has been a very controversial issue, and MFH is now considered to be reclassified into pleomorphic subtypes of other sarcomas. To characterize MFH genetically, we used an oligonucleotide microarray to analyze gene expression in 105 samples from 10 types of soft tissue tumors. Spindle cell and pleomorphic sarcomas, such as dedifferentiated liposarcoma, myxofibrosarcoma, leiomyosarcoma, malignant peripheral nerve sheath tumor (MPNST), fibrosarcoma and MFH, showed similar gene expression patterns compared to other tumors. Samples from those five sarcoma types could be classified into respective clusters based on gene expression by excluding MFH samples. We calculated distances between MFH samples and other five sarcoma types (dedifferentiated liposarcoma, myxofibrosarcoma, leiomyosarcoma, MPNST and fibrosarcoma) based on differentially expressed genes and evaluated similarities. Three of the 21 MFH samples showed marked similarities to one of the five sarcoma types, which were supported by histological findings. Although most of the remaining 18 MFH samples showed little or no histological resemblance to one of the five sarcoma types, 12 of them showed moderate similarities in terms of gene expression. These results explain the heterogeneity of MFH and show that the majority of MFHs could be reclassified into pleomorphic subtypes of other sarcomas. Taken together, gene expression profiling could be a useful tool to unveil the difference in the underlying molecular backgrounds, which leads to a rational taxonomy and diagnosis of a diverse group of soft tissue sarcomas.Keywords: gene expression, malignant fibrous histiocytoma, myxofibrosarcoma, soft tissue sarcoma, reclassification, undifferentiated pleomorphic sarcoma

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    • "(B) Top: relative expression of CDK4, MDM2, NUP107, and OS9 in 15 WD/DDLPS samples compared to a pool of 116 other sarcomas. Bottom: the same analysis using an independent set of 105 sarcomas, including 18 WD/DDLPS samples (National Center for Biotechnology Information Data Set Browser record GDS2736; Nakayama et al., 2007). Box-and-whisker plots show log 2 gene expression distribution (**** p < 0.0001, two-tailed t test). "
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    • "Gene expression patterns may be useful in the subclassification of STS, both for diagnosis and for prediction of clinical behavior [2,7-16]. In some cases, gene expression patterns may correlate better with biological behavior than histology, and some studies have suggested that gene expression patterns may correlate with metastatic potential in some high-grade STS [11,12,14,17]. "
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    ABSTRACT: Background The biologic heterogeneity of soft tissue sarcomas (STS), even within histological subtypes, complicates treatment. In earlier studies, gene expression patterns that distinguish two subsets of clear cell renal carcinoma (RCC), serous ovarian carcinoma (OVCA), and aggressive fibromatosis (AF) were used to separate 73 STS into two or four groups with different probabilities of developing metastatic disease (PrMet). This study was designed to confirm our earlier observations in a larger independent data set. Methods We utilized these gene sets, hierarchical clustering (HC), and Kaplan-Meier analysis, to examine 309 STS, using Affymetrix chip expression profiling. Results HC using the combined AF-, RCC-, and OVCA-gene sets identified subsets of the STS samples. Analysis revealed differences in PrMet between the clusters defined by the first branch point of the clustering dendrogram (p = 0.048), and also among the four different clusters defined by the second branch points (p < 0.0001). Analysis also revealed differences in PrMet between the leiomyosarcomas (LMS), dedifferentiated liposarcomas (LipoD), and undifferentiated pleomorphic sarcomas (UPS) (p = 0.0004). HC of both the LipoD and UPS sample sets divided the samples into two groups with different PrMet (p = 0.0128, and 0.0002, respectively). HC of the UPS samples also showed four groups with different PrMet (p = 0.0007). HC found no subgroups of the LMS samples. Conclusions These data confirm our earlier studies, and suggest that this approach may allow the identification of more than two subsets of STS, each with distinct clinical behavior, and may be useful to stratify STS in clinical trials and in patient management.
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