Dedifferentiated cells obtained from glioblastoma cell lines are an easy and robust model for mesenchymal glioblastoma stem cells studies.

Glioblastoma is an aggressive brain tumor with a poor prognosis. Glioblastoma Stem Cells (GSC) are involved in glioblastoma resistance and relapse. Effective glioblastoma treatment must include GSC targeting strategy. Robust and well defined in vitroGSC models are required for new therapies evaluation. In this study, we extensively characterized 4 GSC models obtained by dedifferentiation of commercially available glioblastoma cell lines and compared them to 2 established patient derived GSC lines (Brain Tumor Initiating Cells). Dedifferentiated cells formed gliospheres, typical for GSC, with self-renewal ability. Gene expression and protein analysis revealed an increased expression of several stemness associated markers such as A2B5, integrin α6, Nestin, SOX2 and NANOG. Cells were oriented toward a mesenchymal GSC phenotype as shown by elevated levels of mesenchymal and EMT related markers (CD44, FN1, integrin α5). Dedifferentiated GSC were similar to BTIC in terms of size and heterogeneity. The characterization study also revealed that CXCR4 pathway was activated by dedifferentiation, emphasizing its role as a potential therapeutic target. The expression of resistance-associated markers and the phenotypic diversity of the 4 GSC models obtained by dedifferentiation make them relevant to challenge future GSC targeting therapies.

Identification of Dysregulated Expression of G Protein Coupled Receptors in Endocrine Tumors by Bioinformatics Analysis: Potential Drug Targets?

BACKGROUND: Many studies link G protein-coupled receptors (GPCRs) to cancer. Some endocrine tumors are unresponsive to standard treatment and/or require long-term and poorly tolerated treatment. This study explored, by bioinformatics analysis, the tumoral profiling of the GPCR transcriptome to identify potential targets in these tumors aiming at drug repurposing. METHODS: We explored the GPCR differentially expressed genes (DEGs) from public datasets (Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)). The GEO datasets were available for two medullary thyroid cancers (MTCs), eighty-seven pheochromocytomas (PHEOs), sixty-one paragangliomas (PGLs), forty-seven pituitary adenomas and one-hundred-fifty adrenocortical cancers (ACCs). The TCGA dataset covered 92 ACCs. We identified GPCRs targeted by approved drugs from pharmacological databases (ChEMBL and DrugBank). RESULTS: The profiling of dysregulated GPCRs was tumor specific. In MTC, we found 14 GPCR DEGs, including an upregulation of the dopamine receptor (DRD2) and adenosine receptor (ADORA2B), which were the target of many drugs. In PGL, seven GPCR genes were downregulated, including vasopressin receptor (AVPR1A) and PTH receptor (PTH1R), which were targeted by approved drugs. In ACC, PTH1R was also downregulated in both the GEO and TCGA datasets and was the target of osteoporosis drugs. CONCLUSIONS: We highlight specific GPCR signatures across the major endocrine tumors. These data could help to identify new opportunities for drug repurposing.

G Protein-coupled Receptors in Radioiodine-refractory Thyroid Cancer in the Era of Precision Medicine.

CONTEXT: Radioiodine-refractory thyroid cancers have poor outcomes and limited therapeutic options (tyrosine kinase inhibitors) due to transient efficacy and toxicity of treatments. Therefore, combinatorial treatments with new therapeutic approaches are needed. Many studies link G protein-coupled receptors (GPCRs) to cancer cell biology. OBJECTIVE: To perform a specific atlas of GPCR expression in progressive and refractory thyroid cancer to identify potential targets among GPCRs aiming at drug repositioning. METHODS: We analyzed samples from tumor and normal thyroid tissues from 17 patients with refractory thyroid cancer (12 papillary thyroid cancers [PTCs] and 5 follicular thyroid cancers [FTCs]). We assessed GPCR mRNA expression using NanoString technology with a custom panel of 371 GPCRs. The data were compared with public repositories and pharmacological databases to identify eligible drugs. The analysis of prognostic value of genes was also performed with TCGA datasets. RESULTS: With our transcriptomic analysis, 4 receptors were found to be downregulated in FTC (VIPR1, ADGRL2/LPHN2, ADGRA3, and ADGRV1). In PTC, 24 receptors were deregulated, 7 of which were also identified by bioinformatics analyses of publicly available datasets on primary thyroid cancers (VIPR1, ADORA1, GPRC5B, P2RY8, GABBR2, CYSLTR2, and LPAR5). Among all the differentially expressed genes, 22 GPCRs are the target of approved drugs and some GPCRs are also associated with prognostic factors. DISCUSSION: For the first time, we performed GPCR mRNA expression profiling in progressive and refractory thyroid cancers. These findings provide an opportunity to identify potential therapeutic targets for drug repositioning and precision medicine in radioiodine-refractory thyroid cancer.

Formalizing and enriching phenotype signatures using Boolean networks.

In order to predict the behavior of a biological system, one common approach is to perform a simulation on a dynamic model. Boolean networks allow to analyze the qualitative aspects of the model by identifying its steady states and attractors. Each of them, when possible, is associated with a phenotype which conveys a biological interpretation. Phenotypes are characterized by their signatures, provided by domain experts. The number of steady states tends to increase with the network size and the number of simulation conditions, which makes the biological interpretation difficult. As a first step, we explore the use of Formal Concept Analysis as a symbolic bi-clustering technics to classify and sort the steady states of a Boolean network according to biological signatures based on the hierarchy of the roles the network components play in the phenotypes. FCA generates a lattice structure describing the dependencies between proteins in the signature and steady-states of the Boolean network. We use this lattice (i) to enrich the biological signatures according to the dependencies carried by the network dynamics, (ii) to identify variants to the phenotypes and (iii) to characterize hybrid phenotypes. We applied our approach on a T helper lymphocyte (Th) differentiation network with a set of signatures corresponding to the sub-types of Th. Our method generated the same classification as a manual analysis performed by experts in the field, and was also able to work under extended simulation conditions. This led to the identification and prediction of a new hybrid sub-type later confirmed by the literature.