Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE212666 | GEO | 2022/10/10
REPOSITORIES: GEO
Similar Datasets
Project description:We report DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, proteomic, and single-cell approaches to show meningiomas are comprised of 3 groups with distinct clinical outcomes, biological drivers, and therapeutic vulnerabilities. Merlin-intact meningiomas have the best outcomes and are distinguished by NF2/Merlin regulation of glucocorticoid signaling, apoptosis, and susceptibility to cytotoxic therapy. Immune-enriched meningiomas have intermediate outcomes and are distinguished by immune infiltration, HLA expression, and lymphatic vessels. Hypermitotic meningiomas have the worst outcomes and are distinguished by convergent genetic and epigenetic mechanisms driving the cell cycle and resistance to cytotoxic therapy. Our results establish a framework for understanding meningioma biology, and provide a foundation for new meningioma treatments.
2022-01-10 | GSE183647 | GEO
Project description:Meningiomas are the most common primary brain tumor. Though typically benign with a low mutational burden, histopathologic analysis has poor predictive value for malignant behavior and there are no proven chemotherapies. Although DNA methylation patterns distinguish subgroups of meningiomas and have higher predictive value for tumor behavior than histologic classification, little is known about differences in DNA methylation between meningiomas and surrounding normal dura tissue. Using multimodal studies of meningioma/dura pairs, we identified 4 distinct DNA methylation patterns. Diffuse DNA hypomethylation of malignant meningiomas readily facilitated their identification from lower grade tumors by unsupervised clustering. All clusters and 12/12 meningioma-dura pairs exhibited hypomethylation of the gene promoters of a module associated with the craniofacial patterning transcription factor FOXC1 and its upstream lncRNA FOXCUT. Furthermore, we identified an epigenetic continuum of increasing hypermethylation of polycomb repressive complex target promoters with increased histopathologic grade suggesting progressive epigenetic dysregulation is associated with increasing tumor aggressiveness. These findings are a starting point for future investigations of the role of epigenetic dysregulation of FOXC1 and cranial patterning genes in early stages of meningioma formation as well as studies of the utility of polycomb inhibitors for treatment of aggressive meningiomas.
2023-06-22 | GSE178139 | GEO
Project description:Meningiomas are the most common primary brain tumor. Though typically benign with a low mutational burden, histopathologic analysis has poor predictive value for malignant behavior and there are no proven chemotherapies. Although DNA methylation patterns distinguish subgroups of meningiomas and have higher predictive value for tumor behavior than histologic classification, little is known about differences in DNA methylation between meningiomas and surrounding normal dura tissue. Using multimodal studies of meningioma/dura pairs, we identified 4 distinct DNA methylation patterns. Diffuse DNA hypomethylation of malignant meningiomas readily facilitated their identification from lower grade tumors by unsupervised clustering. All clusters and 12/12 meningioma-dura pairs exhibited hypomethylation of the gene promoters of a module associated with the craniofacial patterning transcription factor FOXC1 and its upstream lncRNA FOXCUT. Furthermore, we identified an epigenetic continuum of increasing hypermethylation of polycomb repressive complex target promoters with increased histopathologic grade suggesting progressive epigenetic dysregulation is associated with increasing tumor aggressiveness. These findings are a starting point for future investigations of the role of epigenetic dysregulation of FOXC1 and cranial patterning genes in early stages of meningioma formation as well as studies of the utility of polycomb inhibitors for treatment of aggressive meningiomas.
2023-06-22 | GSE176457 | GEO
Project description:Methylation analysis of meningioma samples. Genomic DNA isolated directly from fresh-frozen human meningioma specimens.
2022-03-30 | GSE168726 | GEO
Project description:Methylation analysis of meningioma samples. Genomic DNA isolated directly from fresh-frozen human meningioma specimens.
2021-07-14 | GSE180061 | GEO
Project description:To characterize DNA methylation-based subgroups in colorectal cancer, we performed genome-scale DNA methylation profiling of 125 colorectal tumor samples and 29 histologically normal-adjacent colonic tissue samples using the Illumina Infinium DNA methylation assay, which assesses the DNA methylation status of 27,578 CpG sites located at the promoter regions of 14,495 protein-coding genes. We identified four DNA methylation-based subgroups of CRC using model-based cluster analyses. Each subtype shows characteristic genetic and clinical features, indicating that they represent biologically distinct subgroups.
2011-06-03 | GSE25062 | GEO
Project description:WHO classification for tumors of the central nervous system strongly endorses molecular tests for the precise diagnosis of diffuse gliomas. While alterations in the DNA methylation status of gliomas are already well documented and used in specialised clinical centers to distinguish between brain tumor entities, changes to the epigenetic layer at the level of histone modifications are only poorly characterised. Here, we applied a recently developed data-independent acquisition (DIA) - mass spectrometry method to generate a comprehensive histone epi-proteomic map that documents the abundance of almost all characterized and many uncharacterized histone modifications to a series of IDH-mutant oligodendroglioma and astrocytoma samples. Our analysis documented significant abundance differences in almost one-third of the 144 quantified histone peptides. Among them are lower abundance levels of the polycomb repressive mark H3K27me3 in oligodendroglioma samples compared to astrocytomas. We validated this finding by immunohistochemistry using the C36B11 antibody. Surprisingly, we observed inconsistencies with another widely applied H3K27me3 antibody (07-449), providing a warning flag for immunohistochemistry of brain cancers. An unbiased unsupervised clustering analysis of the proteomic dataset separated the two IDH-mutant glioma subtypes in full accordance to the EPIC DNA methylation classifier and the 1p/19q status. The clustering also revealed at least two histone epi-proteomic subgroups of oligodendroglioma, a feature not observable in the DNA methylation dataset. Our results indicate that histone epi-proteomic profiling at the depth of the current method has the capacity to identify clinically-relevant glioma sub-groups. In addition to being of use for diagnostic purposes, this could also provide novel insights in glioma biology and may identify new therapeutic targets.
2020-01-17 | PXD015151 | Pride
Project description:To characterize DNA methylation-based subgroups in colorectal cancer, we performed genome-scale DNA methylation profiling of 125 colorectal tumor samples and 29 histologically normal-adjacent colonic tissue samples using the Illumina Infinium DNA methylation assay, which assesses the DNA methylation status of 27,578 CpG sites located at the promoter regions of 14,495 protein-coding genes. We identified four DNA methylation-based subgroups of CRC using model-based cluster analyses. Each subtype shows characteristic genetic and clinical features, indicating that they represent biologically distinct subgroups. Bisulfite converted DNA from fresh frozen 125 colorectal tumors and 29 adjacent normal tissues were hybridized to the Illumina Infinium 27k Human Methylation Beadchip v1.2
2011-06-03 | E-GEOD-25062 | biostudies-arrayexpress
Project description:Purpose: Clustering of meningiomas using DNA methylation and RNA-sequencing data yields groups which predict clinical behavior than the current clinical standard of histopathologic grading. Both techniques segregate many common genomic features seen in meningiomas similarly, raising the question of whether they were identifying the same underyling groups. Methods: DNA methylation profiling (EPIC 850K array) and RNA-sequencing of 110 primary meningiomas was performed. Unsupervised clustering was performed using each type of data, followed by computational modeling which explored correlations of promoter methylation and copy number variation (CNV) with gene expression. Results: We performed unsupervised clustering of the DNA methylation data and gound three clusters to be optimal. These clusters aligned closely with our prior transcriptional classification (Patel et al. PNAS 2019). These groups also closely aligned with groups defined by large-scale cytogenetic changes and merlin expression/genomic instability, suggesting the presence of three biologic groups of meningioma that can be identified by multiple methods. Computational modeling demonstrated that both promoter methylation and CNV correlated closely with gene expression differences seen in meningioma and our biologic groups, although cytogenetic changes (particularly chr 1p loss) was predominant in the clinically aggressive tumors. Conclusions: Our analysis suggests the presence of three biologic groups of meningioma (MenG A, B, and C) which can be accurately identified through DNA methylation, RNA-seq, and cytogenetic profiling.
2021-11-30 | GSE189521 | GEO
Project description:Purpose: Clustering of meningiomas using DNA methylation and RNA-sequencing data yields groups which predict clinical behavior than the current clinical standard of histopathologic grading. Both techniques segregate many common genomic features seen in meningiomas similarly, raising the question of whether they were identifying the same underyling groups. Methods: DNA methylation profiling (EPIC 850K array) and RNA-sequencing of 110 primary meningiomas was performed. Unsupervised clustering was performed using each type of data, followed by computational modeling which explored correlations of promoter methylation and copy number variation (CNV) with gene expression. Results: We performed unsupervised clustering of the DNA methylation data and gound three clusters to be optimal. These clusters aligned closely with our prior transcriptional classification (Patel et al. PNAS 2019). These groups also closely aligned with groups defined by large-scale cytogenetic changes and merlin expression/genomic instability, suggesting the presence of three biologic groups of meningioma that can be identified by multiple methods. Computational modeling demonstrated that both promoter methylation and CNV correlated closely with gene expression differences seen in meningioma and our biologic groups, although cytogenetic changes (particularly chr 1p loss) was predominant in the clinically aggressive tumors. Conclusions: Our anaylsis suggests the presence of three biologic groups of meningioma (MenG A, B, and C) which can be accurately identified through DNA methylation, RNA-seq, and cytogenetic profiling.
2021-11-30 | GSE189672 | GEO