Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene expression profiles of Sunitinib-treated but not untreated short-term serum-free cultures predict treatment response of human high-grade gliomas in vitro

ABSTRACT: High-grade gliomas are amongst the most deadly human tumors. Treatment results are overall disappointing. Nevertheless, in several trials around 20% of patients respond to therapy. Diagnostic strategies to identify those patients that will ultimately profit from a specific targeted therapy are urgently needed. Gene expression profiling of untreated tumors is a well established approach for identifying biomarkers or diagnostic signatures. However, reliable signatures predicting treatment response in gliomas do not exist. Here we suggest a novel strategy for developing diagnostic signatures. We postulate that predictive gene expression patterns emerge only after tumor cells have been treated with the agent in vitro. Moreover, we postulate that enriching specimens for tumor initiating cells sharpens predictive expression patterns. Here, we report on the prediction of treatment response of cancer cells in vitro. As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated but not from untreated glioma cells allowed to predict therapy-induced impairment of proliferation of glioma cells in vitro. Prediction can be achieved with as little as 6 genes allowing for a straightforward translation into the clinic once the predictive power of the signature is shown also in vivo. Our strategy of using expression profiles from in vitro treated BTIC-enriched cultures opens new ways for trial design for patients with malignant gliomas. 72 samples; 18 brain tumor initiating cells (BTICs); 4 treatment conditions: 1 ÂµM Sunitinib or 0.00025% DMSO with and without the combination of recombinant growth factors VEGF and PDGF-AB for 6 hours

ORGANISM(S): Homo sapiens

SUBMITTER: Christian Kohler

PROVIDER: E-GEOD-51305 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Response-predictive gene expression profiling of glioma progenitor cells in vitro.

Moeckel Sylvia S Meyer Katharina K Leukel Petra P Heudorfer Fabian F Seliger Corinna C Stangl Christina C Bogdahn Ulrich U Proescholdt Martin M Brawanski Alexander A Vollmann-Zwerenz Arabel A Riemenschneider Markus J MJ Bosserhoff Anja-Katrin AK Spang Rainer R Hau Peter P

PloS one 20140930 9

<h4>Background</h4>High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist.<h4>Methods</h4>In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a) to enrich specimens for brain tumor initiating c ...[more]

PMID: 25268354

Similar Datasets

Project description:Aims: Novel cancer therapies leading to increased survivorship of cancer patients have been negated by a concomitant rise in cancer therapies-related cardiovascular toxicities. Sunitinib, a first line multi receptor tyrosine kinase inhibitor (TKI), has been reported to cause vascular dysfunction although the initiating mechanisms contributing to this side effect remain unknown. Long non-coding RNAs (lncRNAs) are emerging regulators of biological processes in endothelial cells (ECs); however, their roles in cancer therapies-related vascular toxicities remain underexplored. Methods and Results: We performed lncRNA expression profiling to identify potential lncRNAs that are dysregulated in human induced pluripotent stem cells-derived ECs (iPSC-ECs) treated with sunitinib. We show that the lncRNA hyaluronan synthase 2 antisense 1 (HAS2-AS1) is significantly diminished in sunitinib-treated iPSC-ECs. Sunitinib was found to downregulate HAS2-AS1 by an epigenetic mechanism involving hypermethylation. Depletion of HAS2-AS1 recapitulated sunitinib-induced detrimental effects on iPSC-ECs, whereas CRISPR-mediated activation of HAS2-AS1 reversed sunitinib-induced dysfunction. We confirm that HAS2-AS1 stabilizes the expression of its sense gene HAS2 via an RNA/mRNA heteroduplex formation. Knockdown of HAS2-AS1 led to reduced synthesis of hyaluronic acid (HA) and upregulation of ADAMTS5, an enzyme involved in extracellular matrix degradation, resulting in disruption of the endothelial glycocalyx which is critical for ECs. In vivo, sunitinib-treated mice showed reduced coronary flow reserve, accompanied by a reduction in has2os and degradation of the endothelial glycocalyx. Finally, we identify that treatment with high molecular-weight HA can prevent the deleterious effects of sunitinib both in vitro and in vivo by preserving the endothelial glycocalyx. Conclusions: Our findings highlight the importance of lncRNA-mediated regulation of the endothelial glycocalyx as an important determinant of sunitinib-induced vascular toxicity and reveal potential novel therapeutic avenues to attenuate sunitinib-induced vascular dysfunction.

Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas

Project description:Purpose: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell carcinoma (MRCC) and to validate them in a cellular model. Selected microRNAs were studied in serum from MRCC patients and healthy individuals. Methods: We screened 673 microRNAs using TaqMan Low-density Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n=41). Differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenoytpes selected from an independent cohort (n=117). Results were validated in a cellular model of sunitinib resistance and studied in serum from healthy individuals and MRCC patients. Results: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p=0.0074). High expression of miR-942, miR-133a, miR484, and miR-628-5p was significantly associated with decreased time-to-progression and overall survival. These microRNAs were overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive parental cell line. Serum levels of miR-942, miR-133a, miR-484, miR-146a-5p, miR-374a and miR-486-5p were significantly reduced in MRCC patients compared to healthy controls. Conclusions: Our strategy identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity and resistance to sunitinib. Mir-942 was the best predictor of efficacy. Results were confirmed in a cellular model of sunitinib resistance. We also identified exosome derived serum microRNAs differentially expressed in MRCC patients and healthy individuals. Taqman Low Density Array for 6 FFPE tissues obtained from extreme phenotype MRCC patients, (n=3 marked resistance to sunitinib treatment patients and n=3 marked sensitivity to sunitinib treatment patients), was performanced to screen 667 microRNAs.

Dataset Information

Gene expression profiles of Sunitinib-treated but not untreated short-term serum-free cultures predict treatment response of human high-grade gliomas in vitro

Publications

Response-predictive gene expression profiling of glioma progenitor cells in vitro.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets