Bioinformatic analysis of patient-derived ASPS gene expressions and ASPL-TFE3 fusion transcript levels identify potential therapeutic targets

• Published in: PloS one 2012-11, Vol.7 (11), p.e48023
• Main Authors: Covell, David G, Wallqvist, Anders, Kenney, Susan, Vistica, David T
• Format: Article
• Language: English
• Subjects: Aberration; Adhesion; Adult; Analysis; Basic Helix-Loop-Helix Leucine Zipper Transcription Factors - genetics; Biological effects; Biology; Biopsy; c-Met protein; Cancer therapies; Carbon tetrachloride; CCL4 protein; Cell Adhesion; Cell Cycle; Cell Cycle Proteins - genetics; Cell division; Cell growth; Cell Line, Transformed; Chemokine CCL4 - genetics; Chemokines; CHK1 protein; Chromosomes; Chromosomes, Human, Pair 17; Clustering; Computational Biology; Data analysis; Data Mining; Data processing; DNA-Binding Proteins - genetics; Gene Expression; Gene fusion; Gene Regulatory Networks; Genes; Genetic research; Genomics; Health aspects; Humans; Information management; Inhibitor of Apoptosis Proteins - genetics; Insulin; Insulin-like growth factors; Kinases; Laboratories; Medical research; Medicine; Meiosis; Metastasis; Mitosis; Models, Genetic; Muscle Neoplasms - genetics; Oncogene Proteins, Fusion - genetics; Pathways; Principal components analysis; Protein-tyrosine kinase; Proteins; Proto-Oncogene Proteins c-met - genetics; rho Guanine Nucleotide Dissociation Inhibitor alpha - genetics; RNA, Messenger - genetics; Sarcoma, Alveolar Soft Part - genetics; Signatures; Target recognition; Transcription; Transcription factors; Translocation; Translocation, Genetic; Tumors; Tyrosine; Vascular Endothelial Growth Factor Receptor-1 - genetics
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0048023

Gene expression data, collected from ASPS tumors of seven different patients and from one immortalized ASPS cell line (ASPS-1), was analyzed jointly with patient ASPL-TFE3 (t(X;17)(p11;q25)) fusion transcript data to identify disease-specific pathways and their component genes. Data analysis of the pooled patient and ASPS-1 gene expression data, using conventional clustering methods, revealed a relatively small set of pathways and genes characterizing the biology of ASPS. These results could be largely recapitulated using only the gene expression data collected from patient tumor samples. The concordance between expression measures derived from ASPS-1 and both pooled and individual patient tumor data provided a rationale for extending the analysis to include patient ASPL-TFE3 fusion transcript data. A novel linear model was exploited to link gene expressions to fusion transcript data and used to identify a small set of ASPS-specific pathways and their gene expression. Cellular pathways that appear aberrantly regulated in response to the t(X;17)(p11;q25) translocation include the cell cycle and cell adhesion. The identification of pathways and gene subsets characteristic of ASPS support current therapeutic strategies that target the FLT1 and MET, while also proposing additional targeting of genes found in pathways involved in the cell cycle (CHK1), cell adhesion (ARHGD1A), cell division (CDC6), control of meiosis (RAD51L3) and mitosis (BIRC5), and chemokine-related protein tyrosine kinase activity (CCL4).