Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single cell data from IDH mutant astroxytoma and IDH wildtype GBM tumor specimen

ABSTRACT: Single cell data from IDH mutant astroxytoma and IDH wildtype GBM tumor specimen

PROVIDER: PRJNA961045 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Single cell data from IDH mutant astroxytoma and IDH wildtype GBM tumor specimen

Project description:Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Adhesion G protein-coupled receptors (aGPCRs) have attracted interest for their functional role in gliomagenesis and their potential as treatment targets. To identify therapeutically targetable opportunities among aGPCR family members in an unbiased fashion, we analyzed expression levels of all aGPCRs in GBM and non-neoplastic brain tissue. Using bulk and single cell transcriptomic and proteomic data, we show that CD97 (ADGRE5), an aGPCR previously implicated in GBM pathogenesis, is the most promising aGPCR target in GBM, by virtue of its abundance in all GBM tumors and its de novo expression profile in GBM compared to normal brain tissue and neural progenitors. CD97 knockdown or knockout significantly reduces the tumor initiation capacity of patient-derived GBM cells (PDGC) in vitro and in vivo. Transcriptomic and metabolomic data from PDGCs suggest that CD97 promotes glycolytic metabolism. The oncogenic and metabolic effects of CD97 are mediated by the MAPK pathway. Activation of MAPK signaling depends on phosphorylation of the cytosolic C-terminus of CD97 and recruitment of -arrestin. Using single-cell RNA-sequencing and biochemical assays, we demonstrate that THY1/CD90 is the most likely CD97 ligand in GBM. Lastly, we show that targeting of GBM cells with an anti-CD97 antibody-drug conjugate in vitro selectively kills tumor cells but not human astrocytes or neural stem cells. Our studies identify CD97 as an important regulator of tumor metabolism in GBM, elucidate mechanisms of receptor activation and signaling, and provide strong scientific rationale for developing biologics to target CD97 in GBM.

2023-12-01 | GSE230389 | GEO

Safety and Efficacy of NEO212 in Patients With Astrocytoma IDH-mutant, Glioblastoma IDH-wildtype or Brain Metastasis

Project description:This multi-site, Phase 1/2 clinical trial is an open-label study to identify the safety, pharmacokinetics, and efficacy of a repeated dose regimen of NEO212 for the treatment of patients with radiographically-confirmed progression of Astrocytoma IDH-mutant, Glioblastoma IDH-wildtype, and the safety, pharmacokinetics and efficacy of a repeated dose regimen of NEO212 when given with select SOC for the treatment of solid tumor patients with radiographically confirmed uncontrolled brain metastasis. The study will have three phases, Phase 1, Phase 2a and Phase 2b.

| 92735 | ecrin-mdr-crc

Mutant IDH modulates suppressive myeloid populations in malignant glioma

Project description:RNA-sequencing for myeloid inflammation-related genes was conducted on primary tumor samples from patients with IDH-wildtype glioblastoma (GBM) and grade 4 IDH-mutant astrocytoma (G4IMA). In addition, the IDH-wildtype murine glioma cell line GL261 and a strain of IDH-mutant GL261 were also sequenced using the murine counterpart of the RNA-sequencing myeloid innate immunity panel.

2024-05-29 | GSE268118 | GEO

single cell RNA-seq analysis of adult and paediatric IDH-wildtype Glioblastomas

Project description:To understand the diversity of expression states in IDH-wildtype Glioblastomas, we profiled 24,131 single cells from 28 patients with GBM by single-cell RNA sequencing (7,930 cells by Smartseq2 and 16,201 by 10X).

2019-07-07 | GSE131928 | GEO

Molecular landscape of IDH-wildtype, H3-wildtype glioblastomas of adolescents and young adults (AYA)

Project description:Genome wide DNA methylation profiling of IDH-wildtype, H3-wildtype AYA glioblastomas

2022-02-12 | GSE196490 | GEO

Gene expression changes in IDH mutant or IDH wildtype gliomas treated with All-trans Retinoic Acid

Project description:IDH mutant cells are deficient in retinoic acid production, which impacts downstream gene expression and pathways. We looked at the effect of supplementing IDH mutant glioma cells with ATRA on gene expression and downstream pathways. We compared gene expression changes with IDH WT cells that were also treated with ATRA

2019-12-11 | GSE141789 | GEO

Gene expression changes in IDH mutant or IDH wildtype gliomas treated with All-trans Retinoic Acid

Project description:Gene expression changes in IDH mutant or IDH wildtype gliomas treated with All-trans Retinoic Acid

| PRJNA594739 | ENA

Transcriptional profiling of a patient-matched cohort of glioblastoma (IDH-wildtype) for therapeutic target and repurposing drug identification

Project description:Cohort of patient matched initial and recurrent IDH-wt Glioblastoma GBM. Profiled using Clariom D Human array.

2023-05-03 | GSE212067 | GEO

Genotyping by array of 123 IDH WT glioblastomas

Project description:Glioblastoma (GBM) is the most frequent and most aggressive form of diffuse glioma. The prognosis is very poor, with a median overall survival of 15 months after maximum safe resection and radiochemotherapy.GBM is one of the most genetically unstable cancers. It is characterized by numerous chromosome (chr) copy number alterations (CNA), such as chr 7 gain, chr 9p loss, and chr 10 loss, along with CDKN2A homozygous deletion (chr 9p21) and EGFR amplification (chr 7p11).Chromosome instability (CIN) may be the cause or the consequence of GBM development. In high-grade diffuse gliomas (HGG), CIN may initiate tumorigenesis. To identify recurrent genomic abnormalities in IDH WT glioblastomas, SNP arrays (Illumina 850K CytoSNP) were analyzed for 123 IDH WT GBM cases.

2018-02-27 | E-MTAB-6371 | biostudies-arrayexpress

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.

Project description:Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

| S-EPMC8069494 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data