Regional heterogeneity of glioblastoma upon single-cell RNA sequencing
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE188772 | GEO | 2022/01/15
REPOSITORIES: GEO
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Project description:Glioblastoma (GBM) is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance, recurrence and progression. While regional and single cell transcriptomic variations of GBM have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned to specific niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to GBM’s hallmark histomorphologic niches across 20 patients and define distinct molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Importantly, we show using multiple cohorts, that GBMs with an enhanced KRAS component harbor a more clinically aggressive and infiltrative phenotype. Conversely, tumor cells enriched along the MYC axis where mutually exclusive and had a distinct proliferative program. Moreover, by applying both experimental and computational approaches to link each of these distinct molecular axes with potential pharmacological therapies, we highlight differential drug sensitivities and a notable relative chemoresistance in GBM cell lines with enhanced KRAS programs. Importantly, pharmacological differences were less evident when using traditional expression-based subgroups supporting thattopographic phenotypic mapping ofGBM, and the proposed axes may provide new insights for targeting heterogeneity and overcoming therapy resistance in this aggressive disease.
2021-11-02 | PXD029290 | Pride
Project description:Regional heterogeneity of glioblastoma upon single-cell RNA sequencing
| PRJNA796656 | ENA
Project description:The purpose of this study is to determine whether the combination of two agents, INC280 and bevacizumab, is safe and effective when administered to patients with Glioblastoma Multiforme (GBM) who have progressed after receiving prior therapy or who have unresectable GBM.
| 2185625 | ecrin-mdr-crc
Project description:BackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.
| S-EPMC5193022 | biostudies-literature
Project description:Glioblastoma (GBM) is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance, recurrence and progression. While the regional and single cell-level genetic variation of GBM have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned to specific niches. Here, we leverage laser capture microdissection and mass spectrometry-based proteomics to assign 4794 proteins to GBM’s hallmark histomorphologic niches across 20 patients. Importantly, this analysis defined 1360 regionally enriched proteins, including 502 which are proteogenomically concordant. We validate a subset of identified niche-specific markers using orthogonal immunohistochemical approaches and make the associated atlas publicly available as an online resource (https://www.brainproteinatlas.org/dash/apps/GPA). Spatial resolution of the molecular landscape of GBM, operational at the protein level, aims to further facilitate and refine our biological understanding and treatment approaches for this aggressive disease.
2021-11-02 | PXD019381 | Pride
Project description:Background: A feature of glioblastoma (GBM) is the cellular and molecular heterogeneity, both within and between tumors. This variability results in a risk for sampling bias and potential tumor escape from future targeted therapy. Heterogeneous gene expression within GBM is well documented, but little is known regarding the epigenetic heterogeneity. We therefore aimed to profile the intra-tumor DNA methylation heterogeneity in GBM. Methods: 3-4 biopsies per tumor from spatially separated regions were collected from 12 GBM patients. We performed genome-wide DNA methylation analysis (~850,000 CpG sites) and compared inter- and intra-tumor variation. Results: All samples were classified as GBM IDH wt or IDH mutated by DNA methylation profiling, but the GBM subtype differed within five tumors. Some GBM samples exhibited higher DNA methylation differences within tumors than between, and many CpG sites (mean: 17,000) had different methylation levels within the tumors. Conclusions: We demonstrated that intra-tumor DNA methylation heterogeneity is a feature of GBM. Although all biopsies were classified as GBM IDH wt/mutated by DNA methylation analysis, the assigned subtype differed in samples from the same patient. The observed DNA methylation heterogeneity within tumors is important to consider for methylation-based biomarkers and future improvements in stratification of GBM patients.
2019-02-27 | GSE116298 | GEO
Project description:One of the hallmarks of glioblastoma (GBM) is extensive neovascularization. In addition to supplying blood and nutrients, vascular endothelial (VE) cells provide trophic support to GBM cells via paracrine signaling, the precise mechanisms of which are being unraveled. Here, using patient-derived GBM and VE cells as well as orthotopic GBM mouse models, we report that Endocan (ESM1), an endothelial-secreted proteoglycan, confers enhanced proliferative, migratory, and angiogenic properties to GBM cells and regulates their spatial identity. Mechanistically, Endocan exerts at least part of its functions via direct binding and activation of the PDGFRA receptor. Subsequent downstream signaling enhances chromatin accessibility of the Myc promoter and upregulates Myc expression inducing highly stable phenotypic changes in GBM cells. Furthermore, Endocan confers a radioprotection phenotype in GBM cells, both in vitro and in vivo. Inhibition of Endocan-PDGFRA signaling with ponatinib increases survival in the Esm1 wild-type but not in the Esm1 knock-out mouse GBM model. Our findings identify Endocan and its downstream signaling axis as a potential target to subdue the recurrence of GBM and further highlight the importance of vascular to tumor cell signaling for GBM biology.
2024-10-30 | GSE277014 | GEO
Project description:One of the hallmarks of glioblastoma (GBM) is extensive neovascularization. In addition to supplying blood and nutrients, vascular endothelial (VE) cells provide trophic support to GBM cells via paracrine signaling, the precise mechanisms of which are being unraveled. Here, using patient-derived GBM and VE cells as well as orthotopic GBM mouse models, we report that Endocan (ESM1), an endothelial-secreted proteoglycan, confers enhanced proliferative, migratory, and angiogenic properties to GBM cells and regulates their spatial identity. Mechanistically, Endocan exerts at least part of its functions via direct binding and activation of the PDGFRA receptor. Subsequent downstream signaling enhances chromatin accessibility of the Myc promoter and upregulates Myc expression inducing highly stable phenotypic changes in GBM cells. Furthermore, Endocan confers a radioprotection phenotype in GBM cells, both in vitro and in vivo. Inhibition of Endocan-PDGFRA signaling with ponatinib increases survival in the Esm1 wild-type but not in the Esm1 knock-out mouse GBM model. Our findings identify Endocan and its downstream signaling axis as a potential target to subdue the recurrence of GBM and further highlight the importance of vascular to tumor cell signaling for GBM biology.
2024-10-30 | GSE277011 | GEO
Project description:Background: Glioblastoma (GBM) is the most aggressive and currently incurable brain tumor with a median survival of one year in adult patients. Elucidating novel transcriptomic and epigenetic contributors to the molecular heterogeneity underlying its aggressiveness may lead to improved clinical outcomes. Methods: To identify GBM-associated 5-hydroxymethylcytosine (5hmC) and transcriptomic features as well as their cross-talks, genome-wide 5hmC and transcriptomic profiles of tissue samples from 61 patients with GBM and 9 normal controls were obtained for differential and co-regulation/co-modification analyses. Prognostic models on overall survival (OS) based on transcriptomic features and the 5hmC modifications summarized over genic regions (promoters, gene bodies) and brain-derived histone marks were developed using machine learning algorithms. Results: Despite global reduction, the majority of differential 5hmC features showed higher modification levels in GBM tumors as compared to normal controls. In addition, the bi-directional correlations between 5hmC modifications over promoter regions or gene bodies and gene expression were greatly disturbed in GBM tumors regardless of IDH1 mutation status. Phenotype-associated co-regulated 5hmC-5hmC modules and 5hmC-mRNA modules not only are enriched with different molecular pathways that are indicative of the pathogenesis of GBM, but also are of prognostic significance comparable to IDH1 mutation status. Lastly, the best-performing 5hmC model can predict patient survival at a much higher accuracy (c-index = 74%) when compared to conventional prognostic factor IDH1 (c-index = 57%), capturing the molecular characteristics of tumors that are independent of IDH1 mutation status and gene expression-based molecular subtypes. Conclusions: The 5hmC-based prognostic model offers a robust tool to predict survival in GBM patients, outperforming existing prognostic factors such as IDH1 mutations. The crosstalk between 5hmC and gene expression revealed another layer of complexity underlying the molecular heterogeneity in GBM, offering opportunities for identifying novel therapeutic targets.
2023-08-23 | GSE196533 | GEO
Project description:Single cell derived clones were extracted from four glioblastoma tumors using fluorescence activated cell sorting with CD15 and CD133 markers. Unsorted bulk tumors were also derived. Gene expression analysis of clones of tumor GBM-482 involved comparison between TMZ resistant (n=5) and sensitive (n=2) clones.
2015-01-03 | E-MTAB-2693 | biostudies-arrayexpress