Abstract
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).
Highlights
Identifying disease-specific genetic signatures offers the potential for selecting therapeutic targets and discovering small molecules that affect the roles of these targets in cancer cell survival
Gene Set Enrichment Analysis (GSEA) Results for pooled ASPS-tissue and ASPS-1 genes The pooled ASPS-tissue versus ASPS-1 Principal Component Analysis (PCA) divided the 1244 gene expressions into 542 genes lying above and 702 genes lying below the 1st principal components (PCs)
Genes above the 1st PC represent relatively greater gene expression in ASPStissue samples when compared to ASPS-1
Summary
Identifying disease-specific genetic signatures offers the potential for selecting therapeutic targets and discovering small molecules that affect the roles of these targets in cancer cell survival. The second component of this methodology analyzes the individual (i.e. unpooled) microarray readouts from individual patient tissue biopsies to determine clusters of genes, and their associated pathways, that exhibit consistent expression patterns for these patients These clustering results serve as a check of the isolated tumor cell and pooled in situ derived disease-specific genetic signatures that were found in the first step of the methodology. The over-arching theme of this methodology is to construct a means to jointly analyze microarray readouts from pooled and individual tissue biopsies, with microarray readouts from an isolated tumor cell, combined with patient-derived RT-PCR measures of a diagnostic marker, to identify a small set of genetic features that collectively span this data and may have potential as therapeutic targets
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