Gene expression profiling of alveolar soft-part sarcoma (ASPS)

<p>Abstract</p> <p>Background</p> <p>Alveolar soft-part sarcoma (ASPS) is an extremely rare, highly vascular soft tissue sarcoma affecting predominantly adolescents and young adults. In an attempt to gain insight into the pathobiology of this enigmatic tumor, we performed the first genome-wide gene expression profiling study.</p> <p>Methods</p> <p>For seven patients with confirmed primary or metastatic ASPS, RNA samples were isolated immediately following surgery, reverse transcribed to cDNA and each sample hybridized to duplicate high-density human U133 plus 2.0 microarrays. Array data was then analyzed relative to arrays hybridized to universal RNA to generate an unbiased transcriptome. Subsequent gene ontology analysis was used to identify transcripts with therapeutic or diagnostic potential. A subset of the most interesting genes was then validated using quantitative RT-PCR and immunohistochemistry.</p> <p>Results</p> <p>Analysis of patient array data versus universal RNA identified elevated expression of transcripts related to angiogenesis (ANGPTL2, HIF-1 alpha, MDK, c-MET, VEGF, TIMP-2), cell proliferation (PRL, IGFBP1, NTSR2, PCSK1), metastasis (ADAM9, ECM1, POSTN) and steroid biosynthesis (CYP17A1 and STS). A number of muscle-restricted transcripts (ITGB1BP3/MIBP, MYF5, MYF6 and TRIM63) were also identified, strengthening the case for a muscle cell progenitor as the origin of disease. Transcript differentials were validated using real-time PCR and subsequent immunohistochemical analysis confirmed protein expression for several of the most interesting changes (MDK, c-MET, VEGF, POSTN, CYP17A1, ITGB1BP3/MIBP and TRIM63).</p> <p>Conclusion</p> <p>Results from this first comprehensive study of ASPS gene expression identifies several targets involved in angiogenesis, metastasis and myogenic differentiation. These efforts represent the first step towards defining the cellular origin, pathogenesis and effective treatment strategies for this atypical malignancy.</p

Bioinformatic Analysis of Patient-Derived ASPS Gene Expressions and ASPL-TFE3 Fusion Transcript Levels Identify Potential Therapeutic Targets

<div><p>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).</p> </div

Three-dimensional histogram for count of pathways (pw cnt) shared from the GSEA analysis of gene expressions derived from the patient pooled/ASPS-1 data (axis labels DEND meta-clade, with clade letters A–J) and the individual patient data (axis labeled SOM meta-clades, with meta-clades numbered 1–10).

<p>Genes designated with an asterisk are common to pathways identified using the ASPS-tissue: ASPS-1 data set and the individual patient dataset.</p

Data Analysis Workflow.

<p>The expression of 54,675 genes, done in duplicate, was measured for ASPS tissue biopsies and the ASPS-1 tumor cell. Selecting only present (P) calls trimmed this starting set to 17,698 gene expressions. Next, these ∼18 k candidate genes were analyzed using Principal Component Analysis (PCA). PCA identified 1244 genes that distinguished the pooled patient data from the ASPS-1 tumor cell data. From this point forward these 1244 genes were analyzed, in parallel, for the pooled patient/ASPS-1 tumor cell data (left-most path), and the individual patient gene expressions (right-most path). The left-most path used conventional hierarchical clustering to identify gene clusters. Clustered genes were then used to reveal a set of ASPS-specific pathways. The right-most path analyzed the individual patient measures of these same 1244 gene expressions, using self-organizing maps (SOMs), to cluster genes according to similarities in gene expressions across patient samples. These gene clusters were also analyzed to identify their set of ASPS-specific pathways. The final step in this process selected only pathways and their constituent genes that are shared amongst the ASPS-specific pathways identified from each parallel analyses.</p

Top left panel displays the SOM for the 1244 individual patient gene expressions from ASPS-tissue and ASPS-1 (1244 genes).

<p>SOM colors indicate the similarity of gene measurements between SOM clusters (dark∶ similar light∶less similar). Dendrogram (clipped at 10 SOM meta-clades) from hierarchical clustering of SOM codebook vectors is displayed in the upper right panel. Corresponding SOM meta-clades are identified on the SOM by the white boundary lines. Matching labels appear on the SOM regions and SOM dendrogram. Lower left panel displays the gene expression measurements for the 1244 ASPS-tissue genes for 7 patients, done in replicate. Records are ordered from top to bottom from the left to right meta-clades of the dendrogram. Record boundaries are: SOM meta-clade 7; rows 1–41, SOM meta-clade 6; rows 42–256, SOM meta-clade 10; rows 257–340, SOM meta-clade 2; rows 341–425, SOM meta-clade 9; rows 426–526, SOM meta-clade 1; rows 527–646), SOM meta-clade 3; rows 647–787, SOM meta-clade 5; rows 788–934, SOM meta-clade 8; rows 935–1143, SOM meta-clade 4; rows 1144–1244, Lower right panel displays the histogram for the ratio of counts of genes in the ASPS-tissue set to counts of genes in the ASPS-1 set. Dashed horizontal line defines cases where equal fractions of ASPS-tissue and ASPS-cell genes occur in a meta-clade. Here SOM meta-clades (7, 6, 10, 3 and 5) represent ASPS-tissue genes greater in abundance than found for ASPS-1 genes.</p

Top Panel, dendrogram for ASPS-1 gene expression.

<p>Clades are colored to identify samples associated with five meta-clade memberships (DEND meta-clades F-J). DEND meta-clade members with gene expressions lower than their universal RNA are highlighted in pink. Bottom Panel, ASPS-tissue versus ASPS-1 scatter plot where meta-clade memberships are color-coded to match the dendrogram displayed in the top panel.</p

GSEA results for DEND meta-clade H (red∶pink) for ASPS-1 over expressed genes relative to ASPS-tissue.

<p>Column 1 provides a short description of the pathway, column 2 the pathway identifier, column 3 the number of genes from ASPS-1 in this sub-clade that occurs within each pathway, column 4 the statistical significance of this occurrence.</p

Top Panel: Scatter plot of pooled ASPS-tissue (y-axis) versus ASPS-1 (x-axis) differential gene expression measurements.

<p>Data trimming (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048023#s2" target="_blank">Methods</a>: Statistical Analysis) reduced the original 54,798 measurements to 17,698 differentially expressed genes. The diagonal line represents the first principal component (1^st PC) from PCA analysis and accounts for 91% of the variation in this data set. The lower panel displays the 1244 gene expressions not associated with the 1^st PC. Points in red and green, respectively, correspond to differential expressions relatively higher in the pooled ASPS-tissue versus ASPS-1 gene expressions, and vice-versa. Consistent with the PCA analysis, the 1^st PC exactly bisects the pooled ASPS-tissue versus ASPS-1 datasets.</p

Convergent pathways where the SOM meta-clades overlap with SOM clades found to be most positively correlated with the ASPL-TFE3 fusion transcript data.

<p>Pathways extracted from genes associated with SOM meta-clades 3, 5, 3 and 9.</p

Details for genes associated with Chromosomes 17 and X.

<p>Columns include a) HUGO name, b) chromosome position, c) GSEA probability for genes in same cytoband, d) brief description of gene, and e) GSEA pathway.</p