Anaplastic Meningioma WGS X10
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ABSTRACT:
PROVIDER: EGAS00001000859 | EGA |
REPOSITORIES: EGA
Similar Datasets
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
2021-05-04 | GSE173825 | GEO
Project description:Anaplastic meningiomas are a rare, malignant variant of meningioma. At present there is no effective treatment for this cancer. The aim of the study is to identify somatic mutations in anaplastic meningiomas. We plan to sequence a set of 500 known cancer genes in 50 anaplastic meningioma and corresponding peripheral blood DNA samples. Bioinformatics will be used to analyse the results to assess the probability of these mutations being causal and so likely of critical importance for the tumour growth. Identification of these mutations will guide selection of appropriate compounds to effectively treat the disease.
| EGAD00001001267 | EGA
Project description:Meningiomas are common brain tumors that are classified into three World Health Organization grades (Grade I: benign, Grade II: atypical and Grade III: malignant) and are molecularly ill-defined tumors. The purpose of this study was identify microRNA (miRNA) molecular signatures unique to the different grades of meningiomas correlating them to prognosis. We have used a miRNA expression microarray to show that meningiomas of all three grades fall into two main molecular groups designated “benign” and “malignant” meningiomas. While all typical meningiomas fall into the benign group and all anaplastic meningiomas fall into the malignant group, atypical meningiomas distribute into either one of these groups. We have identified a miRNA signature that distinguishes benign meningiomas from malignant meningiomas. We studied the gene expression profiles of 340 mammalian miRNAs in 37 primary meningioma tumors by means of DNA microarrays.
2011-01-02 | E-GEOD-19720 | biostudies-arrayexpress
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.
2014-03-06 | E-GEOD-55609 | biostudies-arrayexpress
Project description:Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.
2024-06-12 | MTBLS4938 | MetaboLights
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.
2020-06-17 | GSE151067 | GEO
Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.
2011-05-11 | GSE16156 | GEO
Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.
2011-05-11 | GSE16153 | GEO
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.
2014-12-31 | GSE50641 | GEO
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.
2024-07-04 | PXD053578 | Pride