Gene expression profiling of liposarcoma identifies distinct biological types/subtypes and potential therapeutic targets in well-differentiated and dedifferentiated liposarcoma
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Soft Tissue Neoplasms
Classification of liposarcoma into three biological types encompassing five subtypes, (a) well-differentiated/dedifferentiated, (b) myxoid/round cell, and (c) pleomorphic, based on morphologic features and cytogenetic aberrations, is widely accepted. However, diagnostic discordance remains even among expert sarcoma pathologists. We sought to develop a more systematic approach to liposarcoma classification based on gene expression analysis and to identify subtype-specific differentially expressed genes that may be involved in liposarcoma genesis/progression and serve as potential therapeutic targets. A classifier based on gene expression profiling was able to distinguish between liposarcoma subtypes, lipoma, and normal fat samples. A 142-gene predictor of tissue class was derived to automatically determine the class of an independent validation set of lipomatous samples and shows the feasibility of liposarcoma classification based entirely on gene expression monitoring. Differentially expressed genes for each liposarcoma subtype compared with normal fat were used to identify histology-specific candidate genes with an in-depth analysis of signaling pathways important to liposarcoma pathogenesis and progression in the well-differentiated/dedifferentiated subset. The activation of cell cycle and checkpoint pathways in well-differentiated/dedifferentiated liposarcoma provides several possible novel therapeutic strategies with MDM2 serving as a particularly promising target. We show that Nutlin-3a, an antagonist of MDM2, preferentially induces apoptosis and growth arrest in dedifferentiated liposarcoma cells compared with normal adipocytes. These results support the development of a clinical trial with MDM2 antagonists for liposarcoma subtypes which overexpress MDM2 and show the promise of using this expression dataset for new drug discovery in liposarcoma.