Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcriptomal Diversity of Long-Term Glioblastoma Survivors

ABSTRACT: Glioblastoma (GBM) is a highly aggressive type of glioma with poor prognosis. However, a small number of patients live much longer than the median survival. A better understanding of these long-term survivors (LTS) may provide important insight into the biology of GBM. We identified 7 patients with GBM treated at Memorial Sloan-Kettering Cancer Center (MSKCC) with survival greater than 48 months. We characterized the transcriptome of each patient and determined rates of MGMT promoter methylation and IDH1 and IDH2 mutational status. We identified LTS in two independent cohorts (TCGA and REMBRANDT) and analyzed the transcriptomal characteristics of these LTS. The median overall survival of our cohort was 62.5 months. LTS were distributed between the proneural (n=2), neural (n=2), classical (n=2) and mesenchymal (n=1) subtypes. Similarly, LTS in the TCGA and REMBRANDT cohorts demonstrated diverse transcriptomal subclassification identity. The majority of the MSKCC LTS (71%) were found to have methylation of the MGMT promoter. None of the patients had an IDH1 or IDH2 mutations, and IDH mutation occurred in a minority of the TCGA LTS as well. A set of 42 genes was found to be differentially expressed in the MSKCC and TCGA LTS. While IDH mutant proneural tumors impart a better prognosis in the short-term, survival beyond 4 years does not require IDH mutation and is not dictated by a single transcriptional subclass. In contrast, MGMT methylation continues to have strong prognostic value for survival beyond 4 years. These findings have substantial impact for understanding GBM biology and progression. All tumors (n = 7) were obtained following surgical resection at the MSKCC as part of routine clinical care, and snap frozen. Tumors were obtained in accordance with Institutional Review Board policies at the MSKCC. Each sample was examined histologically by 3 independent neuropathologists and confirmed to be World Health Organization (WHO) grade IV glioma (GBM). Before analysis, tumors were sectioned and microdissected. Genomic DNA or RNA was extracted using the DNeasy kit (Qiagen) or RNeasy Lipid Tissue Mini Kit (Qiagen) as per the manufacturer’s instructions. Expression analysis of tumors was performed using the Affymetrics U133 2.0 microarray (Affymetrix). Affymetrix CEL files were imported into the Partek Genomics Suite (Partek). The TCGA gene expression data (HT-HG-U133A) was accessed from the TCGA data repositories (http://cancergenome.nih.gov, date of download 12/2013).

ORGANISM(S): Homo sapiens

SUBMITTER: Naamit Gerber

PROVIDER: E-GEOD-54077 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Transcriptional diversity of long-term glioblastoma survivors.

Gerber Naamit K NK Goenka Anuj A Turcan Sevin S Reyngold Marsha M Makarov Vladimir V Kannan Kasthuri K Beal Kathryn K Omuro Antonio A Yamada Yoshiya Y Gutin Phillip P Brennan Cameron W CW Huse Jason T JT Chan Timothy A TA

Neuro-oncology 20140323 9

<h4>Background</h4>Glioblastoma (GBM) is a highly aggressive type of glioma with poor prognosis. However, a small number of patients live much longer than the median survival. A better understanding of these long-term survivors (LTSs) may provide important insight into the biology of GBM.<h4>Methods</h4>We identified 7 patients with GBM, treated at Memorial Sloan-Kettering Cancer Center (MSKCC), with survival >48 months. We characterized the transcriptome of each patient and determined rates of ...[more]

PMID: 24662514

Similar Datasets

Project description:Temozolomide (TMZ) is an important first-line treatment for glioblastoma (GBM), but there are limitations to TMZ response in terms of durability and dependence on the promoter methylation status of the DNA repair gene O6-methylguanine DNA methyltransferase (MGMT). MGMT-promoter-hypermethylated (MGMT-M) GBMs are more sensitive to TMZ than MGMT-promoter-hypomethylated (MGMT-UM) GBMs. Moreover, TMZ resistance is inevitable even in TMZ-sensitive MGMT-M GBMs. Hence, epigenetic reprogramming strategies are desperately needed in order to enhance TMZ response in both MGMT-M and MGMT-UM GBMs. In this study, we present novel evidence that the epigenetic reactivation of Tumor Suppressor Candidate 3 (TUSC3) can reprogram sensitivity of GBM stem cells (GSCs) to TMZ irrespective of MGMT promoter methylation status. Interrogation of TCGA patient GBM datasets confirmed TUSC3 promoter regulation of TUSC3 expression and also revealed a strong positive correlation between TUSC3 expression and GBM patient survival. Using a combination of loss-of-function, gain-of-function and rescue studies, we demonstrate that TUSC3 reactivation is associated with enhanced TMZ response in both MGMT-M and MGMT-UM GSCs. Further, we provide novel evidence that the demethylating agent 5-Azacitidine (5-Aza) reactivates TUSC3 expression in MGMT-M GSCs, whereas the combination of 5-Aza and MGMT inhibitor Lomeguatrib is necessary for TUSC3 reactivation in MGMT-UM GSCs. Lastly, we propose a pharmacological epigenetic reactivation strategy involving TUSC3 that leads to significantly prolonged survival in MGMT-M and MGMT-UM orthotopic GSCs models. Collectively, our findings provide a framework and rationale to further explore TUSC3-mediated epigenetic reprogramming strategies that could enhance TMZ sensitivity and outcomes in GBM. Mechanistic and translational evidence gained from such studies could contribute towards optimal design of impactful trials for MGMT-UM GBMs that currently do not have good treatment options.

Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated. Gene expression profiling experiments were conducted for 25 patient-derived xenograft glioblastoma samples using Affymetrix Human Gene 1.0 ST arrays according to manufacturer's protocol.

Project description:Angiogenesis inhibitors, such as sunitinib, represent a promising strategy to improve glioblastoma (GBM) tumor response. In this study, we used the O6-methylguanine methyltransferase (MGMT)-negative GBM cell line U87MG stably transfected with MGMT (U87/MGMT) to assess whether MGMT expression affects the response to sunitinib. We showed that the addition of sunitinib to standard therapy (temozolomide [TMZ] and radiation therapy [RT]) significantly improved the response of MGMT positive but not of MGMT-negative cells. Gene expression profiling revealed alterations in the angiogenic profile, as well as differential expression of several receptor tyrosine kinases targeted by sunitinib. MGMT-positive cells displayed higher levels of vascular endothelial growth factor receptor 1 (VEGFR-1) compared with U87/EV cells, whereas they displayed decreased levels of VEGFR-2. Depleting MGMT using O6-benzylguanine suggested that the expression of these receptors was directly related to the MGMT status. Also, we showed that MGMT expression was associated with a dramatic increase in the soluble VEGFR-1/VEGFA ratio, thereby suggesting a decrease in bioactive VEGFA and a shift towards an antiangiogenic profile. The reduced angiogenic potential of MGMT-positive cells is supported by: (i) the decreased ability of their secreted factors to induce endothelial tube formation in vitro and (ii) their low tumorigenicity in vivo compared with the MGMT-negative cells. Our study is the first to show a direct link between MGMT expression and decreased angiogenicity and tumorigenicity of GBM cells and suggests the combination of sunitinib and standard therapy as an alternative strategy for GBM patients with MGMT-positive tumors. RNA was isolated from parental U87 (U87_EV) and MGMT-transfected U87 (U87_MGMT) triplicate. Pairwise gene expression differences were compared between the two cell lines. Features selected had more than 2-fold up-or down-regulated (P values of >05, unpaired Studentâs t-test with Bonferroni multiple testing correction) were identified. Database for annotation, visualization, and integrated discovery23 was used to identify enriched gene ontology (GO) biological themes.24 The GO data mining was conducted at a term specificity level 3.25 The EASE score was set at 0.05 and the minimum number of genes in a category was 5.

Dataset Information

Transcriptomal Diversity of Long-Term Glioblastoma Survivors

Publications

Transcriptional diversity of long-term glioblastoma survivors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets