Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissu
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RESEARCH
Open Access
Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissue sarcomas Keith M Skubitz1,2*, Amy PN Skubitz2,3, Wayne W Xu2,4, Xianghua Luo2,5, Pauline Lagarde6, Jean-Michel Coindre6 and Frédéric Chibon6
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. Keywords: Microarray, Sarcoma, Gene expression, Heterogeneity, Subgroups, Metastasis, Prognosis
Background Soft tissue sarcomas (STS) represent a diverse group of malignancies with different clinical behaviors. Adult STS can be grouped into two broad categories. One category has simple genomic profiles and specific cytogenetic changes, such as a point mutation or translocation (for example SYT-SSX in synovial sarcoma). The second category is comprised of tumors with more complex genomic
* Correspondence: [email protected] 1 Department of Medicine, University Hospital, Minneapolis, MN, USA 2 Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA Full list of author information is available at the end of the article
patterns characterized by multiple gains and losses, including many leiomyosarcomas (LMS), pleomorphic liposarcomas, and undifferentiated pleomorphic sarcomas (UPS) (previously termed malignant fibrous histiocytomas) [1-5]. Although UPS may represent a distinct tumor entity, many UPS have mRNA expression profiles tha
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