Dataset Information

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

ABSTRACT:

Background

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.

Methods

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 cells and (b) to confront cells with a therapeutic agent before expression profiling.

Results

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 progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro.

Conclusion

For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.

SUBMITTER: Moeckel S

PROVIDER: S-EPMC4182559 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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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

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Project description:Tumor heterogeneity of high-grade glioma (HGG) is recognized by four clinically relevant subtypes based on core gene signatures. However, molecular signaling in glioma stem cells (GSCs) in individual HGG subtypes is poorly characterized. Previously we identified and characterized two mutually exclusive GSC subtypes with distinct activated signaling pathways and biological phenotypes. One GSC subtype presented with a gene signature resembling Proneural (PN) HGG, whereas the other was similar to Mesenchymal (Mes) HGG. Classical HGG-derived GSCs were sub-classified as either one of these two subtypes. Differential mRNA expression analysis of PN and Mes GSCs identified 5,796 differentially expressed genes, revealing a pronounced correlation with the corresponding PN or Mes HGGs. Mes GSCs displayed more aggressive phenotypes in vitro and as intracranial xenografts in mice. Further, Mes GSCs were markedly resistant to radiation compared with PN GSCs. Expression of ALDH1A3 - one of the most up-regulated Mes representative genes and a universal cancer stem cell marker in non-brain cancers - was associated with self-renewal and a multi-potent stem cell population in Mes but not PN samples. Moreover, inhibition of ALDH1A3 attenuated the growth of Mes but not PN GSCs in vitro. Lastly, radiation treatment of PN GSCs up-regulated Mes-associated markers and down-regulated PN-associated markers, whereas inhibition of ALDH1A3 attenuated an irradiation-induced gain of Mes identity in PN GSCs in vitro. Taken together, our data suggest that two subtypes of GSCs, harboring distinct metabolic signaling pathways, represent intertumoral glioma heterogeneity and highlight previously unidentified roles of ALDH1A3-associated signaling that promotes aberrant proliferation of Mes HGGs and GSCs. Inhibition of ALDH1A3-mediated pathways therefore might provide a promising therapeutic approach for a subset of HGGs with the Mes signature. Here, we describe the gene expression analysis, including pre-processing methods for the data published by Mao and colleagues in PNAS [1], integration of microarray data from this study with The Cancer Genome Atlas (TCGA) glioblastoma data and also with another published study.

Project description:BACKGROUND: Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs. RESULTS: The predictive integration of gene expression data and standardized functional similarity information enabled us to identify new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement and evaluate an integrated prediction approach based on the concept of k-nearest neighbours learning (kNN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures. The resulting integrated kNN system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating characteristic curve>0.8). We also showed that the proposed models can outperform those discovered by standard gene expression analysis. Furthermore, we report an initial independent in vitro experimental follow-up, which provides additional evidence of the potential validity of the top biosignature. CONCLUSION: Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker discovery problems.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

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