Dataset Information

Glioblastoma Neurovascular Progenitor Orchestrates Tumor Cell Type Diversity.

ABSTRACT: Glioblastoma (GBM) is the deadliest form of primary brain tumor with limited treatment options. Recent studies have profiled GBM tumor heterogeneity, revealing numerous axes of variation that explain the molecular and spatial features of the tumor. Here, we seek to bridge descriptive characterization of GBM cell type heterogeneity with the functional role of individual populations within the tumor. Our lens leverages a gene program-centric meta-atlas of published transcriptomic studies to identify commonalities between diverse tumors and cell types in order to decipher the mechanisms that drive them. This approach led to the discovery of a tumor-derived stem cell population with mixed vascular and neural stem cell features, termed a neurovascular progenitor (NVP). Following in situ validation and molecular characterization of NVP cells in GBM patient samples, we characterized their function in vivo. Genetic depletion of NVP cells resulted in altered tumor cell composition, fewer cycling cells, and extended survival, underscoring their critical functional role. Clonal analysis of primary patient tumors in a human organoid tumor transplantation system demonstrated that the NVP has dual potency, generating both neuronal and vascular tumor cells. Although NVP cells comprise a small fraction of the tumor, these clonal analyses demonstrated that they strongly contribute to the total number of cycling cells in the tumor and generate a defined subset of the whole tumor. This study represents a paradigm by which cell type-specific interrogation of tumor populations can be used to study functional heterogeneity and therapeutically targetable vulnerabilities of GBM.

SUBMITTER: Fazzari E

PROVIDER: S-EPMC11291138 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Glioblastoma Neurovascular Progenitor Orchestrates Tumor Cell Type Diversity.

Fazzari Elisa E Azizad Daria J DJ Yu Kwanha K Ge Weihong W Li Matthew X MX Nano Patricia R PR Kan Ryan L RL Tum Hong A HA Tse Christopher C Bayley Nicholas A NA Haka Vjola V Cadet Dimitri D Perryman Travis T Soto Jose A JA Wick Brittney B Raleigh David R DR Crouch Elizabeth E EE Patel Kunal S KS Liau Linda M LM Deneen Benjamin B Nathanson David A DA Bhaduri Aparna A

bioRxiv : the preprint server for biology 20240724

Glioblastoma (GBM) is the deadliest form of primary brain tumor with limited treatment options. Recent studies have profiled GBM tumor heterogeneity, revealing numerous axes of variation that explain the molecular and spatial features of the tumor. Here, we seek to bridge descriptive characterization of GBM cell type heterogeneity with the functional role of individual populations within the tumor. Our lens leverages a gene program-centric meta-atlas of published transcriptomic studies to identi ...[more]

PMID: 39091877

Similar Datasets

Project description:BackgroundAccurately predicting the outcome of isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) remains hitherto challenging. This study aims to Construct and Validate a Robust Prognostic Model for IDH wild-type GBM (COVPRIG) for the prediction of overall survival using a novel metric, gene-gene (G × G) interaction, and explore molecular and cellular underpinnings.MethodsUnivariate and multivariate Cox regression of four independent trans-ethnic cohorts containing a total of 800 samples. Prediction efficacy was comprehensively evaluated and compared with previous models by a systematic literature review. The molecular underpinnings of COVPRIG were elucidated by integrated analysis of bulk-tumor and single-cell based datasets.ResultsUsing a Cox-ph model-based method, six of the 93,961 G × G interactions were screened to form an optimal combination which, together with age, comprised the COVPRIG model. COVPRIG was designed for RNA-seq and microarray, respectively, and effectively identified patients at high risk of mortality. The predictive performance of COVPRIG was satisfactory, with area under the curve (AUC) ranging from 0.56 (CGGA693, RNA-seq, 6-month survival) to 0.79 (TCGA RNAseq, 18-month survival), which can be further validated by decision curves. Nomograms were constructed for individual risk prediction for RNA-seq and microarray-based cohorts, respectively. Besides, the prognostic significance of COVPRIG was also validated in GBM including the IDH mutant samples. Notably, COVPRIG was comprehensively evaluated and externally validated, and a systemic review disclosed that COVPRIG outperformed current validated models with an integrated discrimination improvement (IDI) of 6-16%. Moreover, integrative bioinformatics analysis predicted an essential role of METTL1+ neural-progenitor-like (NPC-like) malignant cell in driving unfavorable outcome.ConclusionThis study provided a powerful tool for the outcome prediction for IDH wild-type GBM, and preliminary molecular underpinnings for future research.

Dataset Information

Glioblastoma Neurovascular Progenitor Orchestrates Tumor Cell Type Diversity.

Publications

Glioblastoma Neurovascular Progenitor Orchestrates Tumor Cell Type Diversity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets