Meningioma DNA methylation grouping reveals biologic drivers and therapeutic vulnerabilities (array)
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ABSTRACT: 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:Meningiomas are the most common primary intracranial tumors and are associated with inactivation of the tumor suppressor NF2/Merlin, but one-third of meningiomas retain Merlin expression and typically have favorable clinical outcomes. Biochemical mechanisms underlying Merlin-intact meningioma growth are incompletely understood, and non-invasive biomarkers that predict meningioma outcomes and could be used to guide treatment de-escalation or imaging surveillance of Merlin-intact meningiomas are lacking. Here we integrate single-cell RNA sequencing, proximity-labeling proteomic mass spectrometry, mechanistic and functional approaches, and magnetic resonance imaging (MRI) across meningioma cells, xenografts, and human patients to define biochemical mechanisms and an imaging biomarker that distinguish Merlin-intact meningiomas with favorable clinical outcomes from meningiomas with unfavorable clinical outcomes. We find Merlin drives meningioma Wnt signaling and tumor growth through a feed-forward mechanism that requires Merlin dephosphorylation on serine 13 (S13) to attenuate inhibitory interactions with β-catenin and activate the Wnt pathway. Meningioma MRI analyses of xenografts and human patients show Merlin-intact meningiomas with S13 phosphorylation and favorable clinical outcomes are associated with high apparent diffusion coefficient (ADC) on diffusionweighted imaging. In sum, our results shed light on Merlin posttranslational modifications that regulate meningioma Wnt signaling and tumor growth in tumors without NF2/Merlin inactivation. To translate these findings to clinical practice, we establish a non-invasive imaging biomarker that could be used to guide treatment de-escalation or imaging surveillance for patients with favorable meningiomas.
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:Comparison of the gene expression profiles with meningiomas of different grading. 24 primary meningioma cultures from surgical specimen were maintained to primary meningioma cultures.
Project description:Genomic profiling of anaplastic meningioma can inform prognostic gene level alterations in lower-grade meningiomas, potentially reflecting evolution of anaplastic meningioma from lowergrade precursor tumours. Larger scale studies in paired primary and recurrent meningiomas are warranted to unravel the evolutionary path to anaplastic meningiomas and prognostic genomic alterations in detail
Project description:This study reports clinical, genetic, epigenetic, gene expression, and cellular features distinguishing meningioma DNA methylation subgroups within meningioma DNA methylation groups. We present these data in the context of molecular and clinical models predicting postoperative outcomes across 565 meningiomas with comprehensive clinical follow-up from independent discovery and validation institutions. By discovering meningioma DNA methylation subgroups within meningioma DNA methylation groups, we provide an opportunity to resolve inconsistencies in the recent literature. In doing so, our study establishes an architecture that unifies opposing biological theories for the most common primary intracranial tumor
Project description:Although meningiomas are one of the most frequent primary intracranial tumors, there are only a few studies dealing with gene regulation processes in meningiomas. MiRNAs are key regulators of gene expression and regulation and miRNA profiles offer themselves as biomarkers for cancer development and progression. To investigate the role of miRNAs during meningioma growth and progression, we compared expression of 1205 miRNAs in 55 meningioma samples of different tumor grades and histological subtypes. We were able to classify histological subtypes in WHO grade I meningiomas with up to 97% accuracy (meningothelial versus fibroblastic) and different WHO grades with up to 93% accuracy (WHO I versus WHO III). We found significant downregulation of miRNAs on chromosome 1p36 and within two large miRNA clusters on 14q32 in high grade meningiomas, two regions that are yet associated with meningioma progression. We also identified several miRNAs associated with epithelial to mesenchymal transition differentially expressed in meningothelial meningioma compared to fibroblastic meningioma. Combined, our data show that meningiomas of different WHO grades and histological subtypes show a specific miRNA expression profile. Some individual miRNAs can also serve as potential biomarkers for meningioma progression.
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.
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:The majority of meningiomas are benign tumors associated with favorable outcomes; however, the less common aggressive variants with unfavorable outcomes often recur and may be due to sub-populations of less-differentiated cells residing within the tumor. These sub-populations of tumor cells, termed tumor-initiating cells, may be isolated from heterogeneous tumors when sorted or cultured in defined medium designed for enrichment of the tumor-initiating cells. We report the isolation and characterization of a population of tumor-initiating cells derived from an atypical meningioma. These meningioma-initiating cells (MICs) self-renew, differentiate, and can recapitulate the histological characteristics of the parental tumor when transplanted into athymic nude mice. Immunohistochemistry reveals protein expression patterns similar to neural stem and progenitor cells while genomic profiling verified the isolation of cancer cells (with defined meningioma chromosomal aberrations) from the bulk tumor. Furthermore, microarray analysis of gene expression reveals that many epithelial to mesenchymal transition genes are upregulated in the MICs, consistent with the presence of both neural stem cell and mature neural cell molecular markers seen in the derived cultures. Pathway analysis identifies biochemical processes and gene networks related to aberrant cell cycle progression, particularly the loss of heterozygosity of tumor suppressor genes CDKN2A (p16INK4A), p14ARF, and CDKN2B (p15INK4B). Flow cytometric analysis revealed the expression of CD44 and activated leukocyte adhesion molecule (ALCAM/CD166); these may prove to be markers able to identify this cell type. In conclusion, we identify a tumor-initiating population from an atypical meningioma that displays a unique phenotype and these results provide increased understanding of atypical meningioma progression. Part 1 of 2: Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from primary tissue and their counterpart cell lines Part 2 of 2: Illumina gene expression array analysis was performed according to the manufacturer's directions on RNA extracted from cultured primary Meningioma and neural stem cell lines