Identification of Distinct and Common Subpopulations of Myxoid Liposarcoma and Ewing Sarcoma Cells Using Self-Organizing Maps

Abstract

Myxoid liposarcoma and Ewing sarcoma are the two most common tumor types that are characterized by the FET (FUS, EWSR1 and TAF15) fusion oncogenes. These FET fusion oncogenes are considered to have the same pathological mechanism. However, the cellular similarities between cells from the different tumor entities remain unknown. Here, we profiled individual myxoid liposarcoma and Ewing sarcoma cells to determine common gene expression signatures. Five cell lines were analyzed, targeting 76 different genes. We employed unsupervised clustering, focusing on self-organizing maps, to identify biologically relevant subpopulations of tumor cells. In addition, we outlined the basic concepts of self-organizing maps. Principal component analysis and a t-distributed stochastic neighbor embedding plot showed gradual differences among all cells. However, we identified five distinct and robust subpopulations using self-organizing maps. Most cells were similar to other cells within the same tumor entity, but four out of five groups contained both myxoid liposarcoma and Ewing sarcoma cells. The major difference between the groups was the overall transcriptional activity, which could be linked to cell cycle regulation. We conclude that self-organizing maps are useful tools to define biologically relevant subpopulations and that myxoid liposarcoma and Ewing sarcoma exhibit cells with similar gene expression signatures.

Keywords

• Ewing sarcoma
• myxoid liposarcoma
• self-organizing maps
• single-cell analysis
• unsupervised grouping