|Application:||Peptide array analysis|
|Number of samples:||13|
|Release date:||Jun 1 2016|
|Last update date:||Sep 6 2016|
|Diseases:||Sarcoma, Sarcoma, Ewing|
|Dataset link||In vitro proteomic expression changes in Ewing sarcoma cell lines after mTOR blockade|
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 manufacturer’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 – 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 Ward’s linkage method as the clustering algorithm to link entities (proteins) and samples.
Joseph A. Ludwig