Dataset Information

Metabolism-based isolation of invasive glioblastoma cells with specific gene signatures and tumorigenic potential.

ABSTRACT: Background:Glioblastoma (GBM) is a highly aggressive brain tumor with rapid subclonal diversification, harboring molecular abnormalities that vary temporospatially, a contributor to therapy resistance. Fluorescence-guided neurosurgical resection utilizes the administration of 5-aminolevulinic acid (5-ALA) generating individually fluorescent tumor cells within a background population of non-neoplastic cells in the invasive tumor region. The aim of the study was to specifically isolate and interrogate the invasive GBM cell population using a novel 5-ALA-based method. Methods:We have isolated the critical invasive GBM cell population by developing 5-ALA-based metabolic fluorescence-activated cell sorting. This allows purification and study of invasive cells from GBM without an overwhelming background "normal brain" signal to confound data. The population was studied using RNAseq, real-time PCR, and immunohistochemistry, with gene targets functionally interrogated on proliferation and migration assays using siRNA knockdown and known drug inhibitors. Results:RNAseq analysis identifies specific genes such as SERPINE1 which is highly expressed in invasive GBM cells but at low levels in the surrounding normal brain parenchyma. siRNA knockdown and pharmacological inhibition with specific inhibitors of SERPINE1 reduced the capacity of GBM cells to invade in an in vitro assay. Rodent xenografts of 5-ALA-positive cells were established and serially transplanted, confirming tumorigenicity of the fluorescent patient-derived cells but not the 5-ALA-negative cells. Conclusions:Identification of unique molecular features in the invasive GBM population offers hope for developing more efficacious targeted therapies compared to targeting the tumor core and for isolating tumor subpopulations based upon intrinsic metabolic properties.

SUBMITTER: Smith SJ

PROVIDER: S-EPMC7462276 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metabolism-based isolation of invasive glioblastoma cells with specific gene signatures and tumorigenic potential.

Smith Stuart James SJ Rowlinson Jonathan J Estevez-Cebrero Maria M Onion David D Ritchie Alison A Clarke Phil P Wood Katie K Diksin Mohammed M Lourdusamy Anbarasu A Grundy Richard Guy RG Rahman Ruman R

Neuro-oncology advances 20200101 1

<h4>Background</h4>Glioblastoma (GBM) is a highly aggressive brain tumor with rapid subclonal diversification, harboring molecular abnormalities that vary temporospatially, a contributor to therapy resistance. Fluorescence-guided neurosurgical resection utilizes the administration of 5-aminolevulinic acid (5-ALA) generating individually fluorescent tumor cells within a background population of non-neoplastic cells in the invasive tumor region. The aim of the study was to specifically isolate and ...[more]

PMID: 32904996

Similar Datasets

Project description:Despite the functional role of serglycin as an intracellular proteoglycan, a variety of malignant cells depends on its expression and constitutive secretion to advance their aggressive behavior. Serglycin arose to be a biomarker for glioblastoma, which is the deadliest and most treatment-resistant form of brain tumor, but its role in this disease is not fully elucidated. In our study we suppressed the endogenous levels of serglycin in LN-18 glioblastoma cells to decipher its involvement in their malignant phenotype. Serglycin suppressed LN-18 (LN-18shSRGN) glioblastoma cells underwent astrocytic differentiation characterized by induced expression of GFAP, SPARCL-1 and SNAIL, with simultaneous loss of their stemness capacity. In particular, LN-18shSRGN cells presented decreased expression of glioma stem cell-related genes and ALDH1 activity, accompanied by reduced colony formation ability. Moreover, the suppression of serglycin in LN-18shSRGN cells retarded the proliferative and migratory rate, the invasive potential in vitro and the tumor burden in vivo. The lack of serglycin in LN-18shSRGN cells was followed by G2 arrest, with subsequent reduction of the expression of cell-cycle regulators. LN-18shSRGN cells also exhibited impaired expression and activity of proteolytic enzymes such as MMPs, TIMPs and uPA, both in vitro and in vivo. Moreover, suppression of serglycin in LN-18shSRGN cells eliminated the activation of pro-tumorigenic signal transduction. Of note, LN-18shSRGN cells displayed lower expression and secretion levels of IL-6, IL-8 and CXCR-2. Concomitant, serglycin suppressed LN-18shSRGN cells demonstrated repressed phosphorylation of ERK1/2, p38, SRC and STAT-3, which together with PI3K/AKT and IL-8/CXCR-2 signaling control LN-18 glioblastoma cell aggressiveness. Collectively, the absence of serglycin favors an astrocytic fate switch and a less aggressive phenotype, characterized by loss of pluripotency, block of the cell cycle, reduced ability for ECM proteolysis and pro-tumorigenic signaling attenuation.

Project description:ObjectivesGlioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.MethodsWe first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.ResultsWe identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.ConclusionsIn this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

Dataset Information

Metabolism-based isolation of invasive glioblastoma cells with specific gene signatures and tumorigenic potential.

Publications

Metabolism-based isolation of invasive glioblastoma cells with specific gene signatures and tumorigenic potential.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure