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Current Cancer Drug Targets

Editor-in-Chief

ISSN (Print): 1568-0096
ISSN (Online): 1873-5576

Review Article

Malignant Glioma In Vitro Models: On the Utilization of Stem-like Cells

Author(s): Rutger K. Balvers, Clemens M.F. Dirven, Sieger Leenstra and Martine L.M. Lamfers

Volume 17, Issue 3, 2017

Page: [255 - 266] Pages: 12

DOI: 10.2174/1568009616666160813191809

Price: $65

Abstract

Recent publications on the molecular characterization of malignant glioma have had profound impact on the appreciation of tumoral heterogeneity within and between patients. Both these phenomena are implicated in the variability in clinical outcome between patients, as well as the inevitable recurrence of these tumors after conventional treatment. The advent of selective cell culture protocols for the propagation of patient-derived glioma stem-like cells (GSCs) provides researchers the ability to selectively study the cells that could be at the root of tumor proliferation and resistance to therapy. As these techniques are widely applied in contemporary studies and becoming the preferred in vitro model, molecular characterization of GSCs is considered pivotal for the identification and advancement of novel therapies for this devastating disease.

This review aims to provide an overview of canonical molecular alterations defining subtypes of malignant glioma as derived from genotypic, transcriptomic and epigenetic profiling in relation to their representation in GSC models. The distribution of these hallmark alterations as found in characterization studies of GSCs is compared between publications. Finally, conclusions of these studies with respect to coverage of driving alterations and translational relevance are provided. By doing so, we provide a contemporary overview of scientific results derived from GSC models and hopefully create appreciation of the advantages and caveats of utilizing these models for studying malignant glioma.

Keywords: Glioblastoma, in vitro models, GSC, tumor profiling, TCGA, cell culture, tumor heterogeneity.

Graphical Abstract


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