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Blocking distinct interactions between Glioblastoma cells and their tissue microenvironment: A novel multi-targeted therapeutic approach.

ABSTRACT: Due to the highly invasive nature of Glioblastoma (GB), complete surgical resection is not feasible, while motile tumour cells are often associated with several specific brain structures that enhance treatment-resistance. Here, we investigate the therapeutic potential of Disulfiram and Carbenoxolone, that inhibit two distinct interactions between GB and the brain tissue microenvironment: stress-induced cell-matrix adhesion and gap junction mediated cell-cell communication, respectively. Increase in cell numbers of tumour-initiating cells, which are cultured in suspension as cell clusters, and adherent differentiated cells can be blocked to a similar extent by Carbenoxolone, as both cell populations form gap junctions, but the adherent differentiated cells are much more sensitive to Disulfiram treatment, which - via modulation of NF-?B signalling - interferes with cell-substrate adhesion. Interestingly, inducing adhesion in tumour-initiating cells without differentiating them does not sensitize for Disulfiram. Importantly, combining Disulfiram, Carbenoxolone and the standard chemotherapeutic drug Temozolomide reduces tumour size in an orthotopic mouse model. Isolating GB cells from their direct environment within the brain represents an important addition to current therapeutic approaches. The blockage of cellular interactions via the clinically relevant substances Disulfiram and Carbenoxolone, has distinct effects on different cell populations within a tumour, potentially reducing motility and/or resistance to apoptosis.

SUBMITTER: Mettang M

PROVIDER: S-EPMC5882900 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

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Blocking distinct interactions between Glioblastoma cells and their tissue microenvironment: A novel multi-targeted therapeutic approach.

Mettang Melanie M Meyer-Pannwitt Viola V Karpel-Massler Georg G Zhou Shaoxia S Carragher Neil O NO Föhr Karl Josef KJ Baumann Bernd B Nonnenmacher Lisa L Enzenmüller Stefanie S Dahlhaus Meike M Siegelin Markus D MD Stroh Sebastien S Mertens Daniel D Fischer-Posovszky Pamela P Schneider E Marion EM Halatsch Marc-Eric ME Debatin Klaus-Michael KM Westhoff Mike-Andrew MA

Scientific reports 20180403 1

Due to the highly invasive nature of Glioblastoma (GB), complete surgical resection is not feasible, while motile tumour cells are often associated with several specific brain structures that enhance treatment-resistance. Here, we investigate the therapeutic potential of Disulfiram and Carbenoxolone, that inhibit two distinct interactions between GB and the brain tissue microenvironment: stress-induced cell-matrix adhesion and gap junction mediated cell-cell communication, respectively. Increase ...[more]

PMID: 29615749

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Project description:Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.

Dataset Information

Blocking distinct interactions between Glioblastoma cells and their tissue microenvironment: A novel multi-targeted therapeutic approach.

Publications

Blocking distinct interactions between Glioblastoma cells and their tissue microenvironment: A novel multi-targeted therapeutic approach.

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