Project description:DNA methylation profiling of human FFPE meningioma samples These samples were processed as part of a study developing and validating a targeted gene expression biomarker of meningioma outcomes and benefit from radiotherapy.
Project description:DNA methylation from human meningioma samples that were also profiled for spatial heterogeneity analysis. Some samples represent spatially distinct regions, punched using a 2mm core punch from FFPE blocks in a given tumor. Other samples represent serial tumor samples at index treatment and then recurrence.
Project description:Multiple stereotatically separate sites from human meningioma were processed for methlyation profiling Meningiomas are the most common primary intracranial tumors, but the molecular drivers of meningioma tumorigenesis are poorly understood. We hypothesized that investigating intratumor heterogeneity in meningiomas would elucidate biologic drivers and reveal new targets for molecular therapy. To test this hypothesis, we performed multiplatform molecular profiling of 86 spatially-distinct samples from 13 human meningiomas. Our data reveal that regional alterations in chromosome structure underlie clonal transcriptomic, epigenomic, and histopathologic signatures in meningioma. Stereotactic co-registration of sample coordinates to preoperative magnetic resonance images further demonstrated that high apparent diffusion coefficient (ADC) distinguished meningioma regions with proliferating cells enriched for developmental gene expression programs. To understand the function of these genes in meningioma, we developed a human cerebral organoid model of meningioma and validated the high ADC marker genes CDH2 and PTPRZ1 as potential targets for meningioma therapy using live imaging, single cell RNA sequencing, CRISPR interference, and pharmacology.
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.
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.
Project description:Genome wide DNA methylation profiling of formalin-fixed paraffin-embedded (FFPE) meningioma samples. The Illumina Infinium MethylationEPIC BeadChip was used to obtain DNA methylation profiles across approximately 850,000 methylation sites across the genome at single-nucleotide resolution.
