Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Glucocorticoid Resistance Gene Profiling in T-ALL Cell Lines

ABSTRACT: Baseline glucocorticoid resistance profiles were measured in a panel of 15 paediatric T-Cell acute lymphoblastic leukaemia cell lines and correlated with Affymetrix Gene expression to identify genes and pathways associated with drug resistance in T-ALL. The cell lines' resistance to Methylprednisolone (MPRED) and Dexamethasone (DEX) was then tested using the MTT assay and IC50 values (drug concentration lethal to 50% of cells) for each drug were calculated.

ORGANISM(S): Homo sapiens

SUBMITTER: Alex Beesley

PROVIDER: E-MTAB-1208 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Glucocorticoid resistance in T-lineage acute lymphoblastic leukaemia is associated with a proliferative metabolism.

Beesley A H AH Firth M J MJ Ford J J Weller R E RE Freitas J R JR Perera K U KU Kees U R UR

British journal of cancer 20090512 12

Glucocorticoids (GCs) are among the most important drugs for acute lymphoblastic leukaemia (ALL), yet despite their clinical importance, the exact mechanisms involved in GC cytotoxicity and the development of resistance remain uncertain. We examined the baseline profile of a panel of T-ALL cell lines to determine factors that contribute to GC resistance without prior drug selection. Transcriptional profiling indicated GC resistance in T-ALL is associated with a proliferative phenotype involving ...[more]

PMID: 19436302

Publication: 1/2

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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 Î¼M for OSI-906, 1.5 Î¼M 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-Î±. 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.

Dataset Information

Glucocorticoid Resistance Gene Profiling in T-ALL Cell Lines

Publications

Glucocorticoid resistance in T-lineage acute lymphoblastic leukaemia is associated with a proliferative metabolism.

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