ABSTRACT: The study aims to define gene expression changes associated with mithramycin treatment of Ewing Sarcoma cell lines. The data consist of 12 arrays. Two cell lines, TC71 and TC32, were treated with solvent control or with mithramycin, and RNA was extracted at 6 hours. Three biological replicates per cell line/treatment.
Project description:The investigation of the function of SUPT6H, SF3B1 and HNRNPH1 in Ewing sarcoma The expression profiles of TC32 Ewing sarcoma cells were determine 48 hours post siRNA-transfected. Groups consisted of untreated cells (three samples), and negative control (siNeg) transfected-TC32 cells (six samples), and TC32 cells in which either SUPT6H, SF3B1 or HNRNPH1 were silenced; three different siRNAs corresponding to each gene were assessed separately.
Project description:Ewing Sarcoma is caused by a pathognomonic genomic translocation that places an N-terminal EWSR1 gene in approximation with one of several ETS genes (typically FLI1). This aberration, in turn, alters the transcriptional regulation of more than five hundred genes and perturbs a number of critical pathways that promote oncogenesis, cell growth, invasion, and metastasis. Among them, translocation-mediated up-regulation of the insulin-like growth factor receptor 1 (IGF-1R) and mammalian target of rapamycin (mTOR) are of particular importance since they work in concert to facilitate IGF-1R expression and ligand-induced activation, respectively, of proven importance in ES transformation. When used as a single agent in Ewing sarcoma therapy, IGF-1R or mTOR inhibition leads to rapid counter-regulatory effects that blunt the intended therapeutic purpose. Therefore, identify new mechanisms of resistance that are used by Ewing sarcoma to evade cell death to single-agent mTOR inhibition might suggest a number of therapeutic combinations that could improve its clinical activity. TC32 and TC71 ES clones with acquired resistance to ridaforolimus (MK8669, mTOR inhibitor) were generated by maintaining the corresponding parental cell lines with increasing concentrations of the agents (up to 50 μM using ridaforolimus) for 7 months. All parental and acquired drug resistant cell lines were tested twice per year for mycoplasma contamination using the MycoAlert Detection Kit (Lonza Group Ltd.) according to the manufacturerâ??s protocol and validated using short-tandem repeat fingerprinting with an AmpFLSTR Identifier kit as previously described. Herein, we determine subtle differences in acquired mechanism of resistance by promising small molecule inhibitor of mTOR, were evaluated using in vitro assays to decipher the mechanism(s) by which IGF-1R inhibition induces drug resistance in Ewing sarcoma cells. The preparation of extracted proteins from sensitive and acquired resistant Ewing sarcoma cells to ridaforolimus for reverse-phase protein lysate array (RPPA) analysis were prepared using the same array. Lysates were processed, spotted onto nitrocellulose-coated FAST slides, probed with 115 validated primary antibodies, and detected using a DakoCytomation-catalyzed system with secondary antibodies. MicroVigene software program (VigeneTech) was used for automated spot identification, background correction, and individual spot-intensity determination. Expression data was normalized for possible unequal protein loading, taking into account the signal intensity for each sample for all antibodies tested. Log2 values were media-centered by protein to account for variability in signal intensity by time and were calculated using the formula log2 signal â?? log2 median. Principal component analysis was used to check for a batch effect and feature-by-feature two-sample t-tests were used to assess differences between sensitive and resistant cell lines to drug treatments. We also used feature-by-feature one-way analysis of variance (ANOVA) followed by the Tukey test to perform pair comparisons for all groups. Beta-uniform mixture models were used to fit the resulting p value distributions to adjust for multiple comparisons. The cutoff p values and number of significant proteins were computed for several different false discovery rates (FDRs). Biostatistical analyses comparing two groups were performed using an unpaired t-test with Gaussian distribution followed by the Welch correction. To distinguish between treatment groups, we used one-way ANOVA with the Geisser-Greenhouse correction. Differences with p values <0.05 were considered significant. Within clustered image maps (CIM), unsupervised double hierarchical clustering used the Pearson correlation distance and Wardâ??s linkage method as the clustering algorithm to link entities (proteins) and samples.
Project description:Identification of genes and pathways altered by PlaB, a bacterial natural product that acts as a spliceosome modulator by targeting the SF3b subunit of the spliceosome SKNMC, TC32, TC71, and RD-ES Ewing sarcoma cell lines were treated with 0.1% (v/v) DMSO vehicle or 5nM PlaB for 24 hours. Three samples in each group were analyzed.
Project description:Ewing Sarcoma is caused by a pathognomonic genomic translocation that places an N-terminal EWSR1 gene in approximation with one of several ETS genes (typically FLI1). This aberration, in turn, alters the transcriptional regulation of more than five hundred genes and perturbs a number of critical pathways that promote oncogenesis, cell growth, invasion, and metastasis. Among them, translocation-mediated up-regulation of the insulin-like growth factor receptor 1 (IGF-1R) and mammalian target of rapamycin (mTOR) are of particular importance since they work in concert to facilitate IGF-1R expression and ligand-induced activation, respectively, of proven importance in ES transformation. When used as a single agent in Ewing sarcoma therapy, IGF-1R or mTOR inhibition leads to rapid counter-regulatory effects that blunt the intended therapeutic purpose. Therefore, identify new mechanisms of resistance that are used by Ewing sarcoma to evade cell death to single-agent IGF-1R inhibition might suggest a number of therapeutic combinations that could improve its clinical activity. TC32 and TC71 ES clones with acquired resistance to OSI-906 or NVP-ADW-742 were generated by maintaining the corresponding parental cell lines with increasing concentrations of the agents (up to 2.3 μM for OSI-906, 1.5 μM for NVP-ADW-742) for 7 months. All parental and acquired drug resistant cell lines were tested twice per year for mycoplasma contamination using the MycoAlert Detection Kit (Lonza Group Ltd.) according to the manufacturerâs protocol and validated using short-tandem repeat fingerprinting with an AmpFLSTR Identifier kit as previously described. Herein, we determine subtle differences in acquired mechanism of resistance by two promising small molecule inhibitors of IGF-1R/IR-α. OSI-906, which inhibits IGF-1R and IR, and NVP-ADW-742, which inhibits only IGF-1R, were evaluated using in vitro assays to decipher the mechanism(s) by which IGF-1R inhibition induces drug resistance in Ewing sarcoma cells. The preparation of extracted proteins from sensitive and acquired resistant Ewing sarcoma cells to OSI-906 and NVP-ADW-742 for reverse-phase protein lysate array (RPPA) analysis were prepared using the same array. Lysates were processed, spotted onto nitrocellulose-coated FAST slides, probed with 115 validated primary antibodies, and detected using a DakoCytomation-catalyzed system with secondary antibodies. MicroVigene software program (VigeneTech) was used for automated spot identification, background correction, and individual spot-intensity determination. Expression data was normalized for possible unequal protein loading, taking into account the signal intensity for each sample for all antibodies tested. Log2 values were media-centered by protein to account for variability in signal intensity by time and were calculated using the formula log2 signal â log2 median. Principal component analysis was used to check for a batch effect and feature-by-feature two-sample t-tests were used to assess differences between sensitive and resistant cell lines to drug treatments. We also used feature-by-feature one-way analysis of variance (ANOVA) followed by the Tukey test to perform pair comparisons for all groups. Beta-uniform mixture models were used to fit the resulting p value distributions to adjust for multiple comparisons. The cutoff p values and number of significant proteins were computed for several different false discovery rates (FDRs). Biostatistical analyses comparing two groups were performed using an unpaired t-test with Gaussian distribution followed by the Welch correction. To distinguish between treatment groups, we used one-way ANOVA with the Geisser-Greenhouse correction. Differences with p values <0.05 were considered significant. Within clustered image maps (CIM), unsupervised double hierarchical clustering used the Pearson correlation distance and Wardâs linkage method as the clustering algorithm to link entities (proteins) and samples.
Project description:Microarray gene expression analysis conducted from cell lines in each of three cohorts: (1) Resistant ES cell lines, (2) Sensitive parental ES cell lines treated with YK-4-279 for 72 hours, and (3) untreated sensitive parental ES cell lines (Three replicates from TC32 & TC71 original parental cell lines) We used microarrays to detail the global programme of gene expression underlying mechanism of resistance to YK-4-279 within parental sensitive and resistant selected Ewing's Sarcoma cell lines. We identified distinct classes of up-regulated genes during this process. Total RNA was extracted from the cell lines using the Qiagen miRNeasy Mini kit. RNA quality was assessed to have an RNA integrity number before amplification and labeling using AffymetrixM-bM-^@M-^Ys GeneChip GeneChipM-BM-.3M-bM-^@M-^Y IVT Express Kit following manufacturerM-bM-^@M-^Ys instructions. Amplified and biotinylated cRNAs were hybridized onto Affymetrix GeneChip Human Genome U133A 2.0 cartridge arrays, Washed, and Stained with kit following manufacturerM-bM-^@M-^Ys instructions. Arrays were scanned using Affymetrix GeneChip Scanner 3000 and the initial raw data were extracted using Affymetrix GeneChip Command Console-Expression software. The statistical analyses of scanned data were performed using default setting on GeneSpring GX 12.1 software (Agilent).
Project description:The synthesis and processing of mRNA, from transcription to translation initiation, often requires splicing of intragenic material. The final mRNA composition varies based upon proteins that modulate splice site selection. EWS-FLI1 is an Ewing sarcoma (ES) oncogene with an interactome that we demonstrate to have multiple partners in spliceosomal complexes. We evaluate EWS-FLI1 upon post-transcriptional gene regulation using both exon array and RNA-seq. Genes that potentially regulate oncogenesis including CLK1, CASP3, PPFIBP1, and TERT validate as alternatively spliced by EWS-FLI1. EWS-FLI1 also alters splicing by directly binding to known splicing factors including DDX5, hnRNPK, and PRPF6. Reduction of EWS-FLI1 produces an isoform of g-TERT that has increased telomerase activity compared to WT TERT. The small molecule YK-4-279 is an inhibitor of EWS-FLI1 oncogenic function that disrupts specific protein interactions including DDX5 and RNA helicase A (RHA) that alters RNA splicing ratios. As such, YK-4-279 validates the splicing mechanism of EWS-FLI1 showing alternatively spliced gene patterns that significantly overlap with EWS-FLI1 reduction and WT human mesenchymal stem cells. Exon array analysis of 75 ES patient samples show similar isoform expression patterns to cell line models expressing EWS-FLI1, supporting the clinical relevance of our findings. These experiments establish systemic alternative splicing as an oncogenic process modulated by EWS-FLI1. EWS-FLI1 modulation of mRNA splicing may provide insight into the contribution of splicing towards oncogenesis, and reciprocally, EWS-FLI1 interactions with splicing proteins may inform the splicing code. Alternative splicing of RNA allows a limited number of coding regions in the human genome to produce proteins with diverse functionality. Alternative splicing has also been implicated as an oncogenic process. Identifying aspects of cancer cells that differentiate them from non-cancer cells remains an ongoing challenge and our research suggests that alternatively spliced mRNA and subsequent protein isoforms will provide new anti-cancer targets. We determined that the key oncogene of Ewing sarcoma (ES), EWS-FLI1, regulates alternative splicing in multiple cell line models. These experiments establish oncogenic aspects of splicing which are specific to cancer cells and thereby illuminate potentially oncogenic splicing shifts as well as provide a useful stratification mechanism for ES patients. We analyzed three models of EWS-FLI1 using Affymetrix GeneChip Human Exon 1.0 ST microarray: (i) Ewing's sarcoma TC32 wild-type cells expressing EWS-FLI1, and TC32 cells where EWS-FLI1 was reduced with a lentiviral shRNA; (ii) A673i, which has a doxycycline-inducible shRNA to reduce EWS-FLI1 expression, and wild-type EWS-FLI1 to screen for alternative splicing as measured by exon-specific expression changes; and (iii) human mesenchymal stem cells (hMSC), a putative cell of origin of Ewing's sarcoma, exogenously expressing EWS-FLI1, and hMSC wild-type cells without EWS-FLI1. Three biological replicates were included for each condition. The Bioconductor package "oligo" in the R programming language was used for normalization and background correction. Analysis was carried out using only core probesets, as defined by the manufacturer.
Project description:Identification of genes and pathways that were influenced by knock-down PRKDC in TC32 Ewing sarcoma and HCT116 colorectal carcinoma cell lines.
Project description:Analysis of miR-130b regulated genes in TC71 Ewing Sarcoma cells. The hypothesis tested being that overexpression of miR-130b increases metastasis in Ewing Sarcoma.
Project description:Identification of EWSR1 and EWS-FLI1 transcript variants associated with the silencing of HNRNPH1 in TC32 Ewing sarcoma and 293T human embryonic kidney (HEK-293T) cells.