Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissue sarcomas

• Published in: Journal of translational medicine Vol. 12; no. 1; p. 176
• Main Authors: Skubitz, Keith M, Skubitz, Amy P N, Xu, Wayne W, Luo, Xianghua, Lagarde, Pauline, Coindre, Jean-Michel, Chibon, Frédéric
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20-06-2014; BioMed Central
• Subjects: Analysis; Care and treatment; Development and progression; Diagnosis; Gene Expression; Genetic aspects; Genetic Heterogeneity; Humans; Medical research; Medicine, Experimental; Metastasis; Neoplasm Metastasis - genetics; Patient outcomes; Probability; Risk factors; Sarcoma - classification; Sarcoma - genetics; Sarcoma - pathology; Sequence Tagged Sites
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/1479-5876-12-176

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.
We utilized these gene sets, hierarchical clustering (HC), and Kaplan-Meier analysis, to examine 309 STS, using Affymetrix chip expression profiling.
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.
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.