Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE136661 | GEO | 2019/11/13
REPOSITORIES: GEO
Similar Datasets
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.
2011-01-02 | GSE19720 | GEO
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:Meningiomas are typically considered a benign tumor that can be cured by complete surgical resection; however, a percentage of patients have recurrent disease, even after apparently complete resections. These patients require additional surgeries, radiation therapy, chemotherapy, or a combination of all three. The ability to recognize these patients prior to recurrence would promote earlier use of adjuvant therapy, thus improving overall patient outcome. Unfortunately, identification of meningiomas with this more aggressive phenotype is difficult, and standard histopathological techniques rarely suffice. The identification of genetic and molecular parameters that can help to define these more aggressive tumors would improve prognostication and treatment planning for patients with meningiomas. 1. Establish gene profiles for benign (grade 1) and aggressive (grades 2 and 3) meningiomas. 2. Determine if there are particular expression profiles that can help differentiate between benign and aggressive meningiomas. 3. Determine if there is/are specific gene(s) whose expression is/are altered in benign vs aggressive tumors. 4. Determine if there is a correlation between specific genetic abnormalities in these tumors (as analyzed by fluorescent in situ hybridization; FISH) and gene expression profiles. Our overall hypothesis is that there are molecular and biochemical changes that can be used to identify meningiomas that will have a more aggressive clinical course. Specific Aims 1 and 2: RNA from flash frozen or RNA-later preserved tissue (from all three grades of meningiomas) has been used for RNA isolation using standard protocols. RNA quantity has been determined using a RiboGreen RNA quantitation Kit (Molecular Probes), and RNA quality has been demonstrated using standard formaldehyde gels. These samples will be sent to the NINDS/NIMH microarray consortium for Affymetrix microarray analyses. Data analysis will be done using GeneSpring software (Silicon Genetics, Inc.) with assistance from consortium personnel. Specific Aim 3: Differentially expressed genes identified through microarray analyses will be analyzed using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Real time qRT-PCR is a standard technique used in our laboratory for gene expression analysis. Specific Aim 4: FISH analyses of paraffin-embedded tissue has been completed for 77 tumors. We have frozen tissue from a number of these patients. RNA from these samples will be used for microarray analyses (Specific Aims 1-3). The results of Speicifc Aims 1 and 2 will affect how we perform our correlation analyses. This will be done with the assistance of contracted statistical personnel
2008-06-13 | E-GEOD-4780 | 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:Meningiomas are the most common primary intracranial tumor. However, surgical resection and radiation frequently fail to eliminate high grade tumors, leading to significant morbidity and mortality. Predicting which tumors will recur rapidly is critical to effective treatment strategies. To address the prognostic challenges and dearth of therapeutic targets, we interrogated the enhancer landscape of a diverse cohort of meningiomas. Enhancers robustly stratified meningiomas into three biologically distinct groups and identified a subset of tumors with a poor prognosis, independent of histological grading. Integrating enhancer networks with transcriptional profiles revealed unique lineage transcriptional regulators associated with each subgroup. A strong hormonal epidemiologic association is well-characterized in meningiomas, but mechanistic insight remains lacking. We identified differential hormonal regulators that stratified between subgroups, and implicated progesterone receptor in maintaining the super enhancer network of a subset of tumors. Super enhancers marked critical and druggable dependencies across a panel of meningioma models.
2021-01-01 | GSE139651 | GEO
Project description:Meningiomas are the most common primary intracranial tumor. However, surgical resection and radiation frequently fail to eliminate high grade tumors, leading to significant morbidity and mortality. Predicting which tumors will recur rapidly is critical to effective treatment strategies. To address the prognostic challenges and dearth of therapeutic targets, we interrogated the enhancer landscape of a diverse cohort of meningiomas. Enhancers robustly stratified meningiomas into three biologically distinct groups and identified a subset of tumors with a poor prognosis, independent of histological grading. Integrating enhancer networks with transcriptional profiles revealed unique lineage transcriptional regulators associated with each subgroup. A strong hormonal epidemiologic association is well-characterized in meningiomas, but mechanistic insight remains lacking. We identified differential hormonal regulators that stratified between subgroups, and implicated progesterone receptor in maintaining the super enhancer network of a subset of tumors. Super enhancers marked critical and druggable dependencies across a panel of meningioma models.
2021-01-01 | GSE139650 | GEO
Project description:Meningiomas are the most common primary intracranial tumors in humans. While most of these tumors are benign, some are malignant, rapidly recur after multimodal treatment with surgery and radiotherapy, and can ultimately be fatal. The current WHO grade system does not always identify high risk meningiomas, therefore better characterizations of the biology of aggressive tumors are needed. In order to address these challenges, we combined 13 bulk RNA-Seq datasets, corrected for batch effects, and applied Uniform Manifold Approximation and Projection (UMAP) to create a reference landscape of ~1300 meningioma tumors. Our analyses revealed multiple distinct meningioma subtypes with specific biological signatures. Clinical metadata, mutations, copy number alterations, and gene-fusion data effectively correlated with regions of the UMAP. Notably, regional distribution of time to recurrence identified major clusters as well as intra-cluster differences of meningiomas with varying patient outcomes. The most aggressive subtype, characterized by an enrichment of higher WHO grades, frequent tumor recurrences, and shorter time to recurrence, exhibited elevated proliferation rates and RNA expression resembling muscle development. To facilitate clinical applications, we developed a cross-validated nearest-neighbors-based algorithm that accurately maps new patients onto this UMAP landscape. Our study highlights the utility of transcriptomic analysis in discerning meningioma heterogeneity as well as successful combination of multiple datasets from various sources. We provide a valuable tool for understanding the disease, predicting tumor biology and patient prognosis. This resource is accessible via the open source, interactive online tool Oncoscape, where the scientific community can explore the landscape and mine clinical and genomic metadata.
2024-04-26 | GSE252291 | GEO
Project description:Variability of meningioma growth, even within benign subgroup, makes some remain unaltered, while others grow fast despite an appended radiotherapy. Biomarkers that differentiate between less and more aggressive meningiomas would therefore have a significant clinical benefit in prediction of their biological behaviour. The aim of the study was to identify new candidate miRNAs enabling better and proper prediction of meningioma recurrence. We used miRNA 4.0 array (Applied Biosystems, Foster City, CA, USA) to detect miRNA profiles in 44 primary meningioma patients (19 with subsequent recurrence) and 5 healthy controls. Moreover, miRNA profiles were analyzed in 15 secondary meningiomas.
2019-02-15 | GSE126563 | GEO
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
2010-07-09 | E-GEOD-22577 | biostudies-arrayexpress
Project description:Meningiomas are typically considered a benign tumor that can be cured by complete surgical resection; however, a percentage of patients have recurrent disease, even after apparently complete resections. These patients require additional surgeries, radiation therapy, chemotherapy, or a combination of all three. The ability to recognize these patients prior to recurrence would promote earlier use of adjuvant therapy, thus improving overall patient outcome. Unfortunately, identification of meningiomas with this more aggressive phenotype is difficult, and standard histopathological techniques rarely suffice. The identification of genetic and molecular parameters that can help to define these more aggressive tumors would improve prognostication and treatment planning for patients with meningiomas. 1. Establish gene profiles for benign (grade 1) and aggressive (grades 2 and 3) meningiomas. 2. Determine if there are particular expression profiles that can help differentiate between benign and aggressive meningiomas. 3. Determine if there is/are specific gene(s) whose expression is/are altered in benign vs aggressive tumors. 4. Determine if there is a correlation between specific genetic abnormalities in these tumors (as analyzed by fluorescent in situ hybridization; FISH) and gene expression profiles. Our overall hypothesis is that there are molecular and biochemical changes that can be used to identify meningiomas that will have a more aggressive clinical course. Specific Aims 1 and 2: RNA from flash frozen or RNA-later preserved tissue (from all three grades of meningiomas) has been used for RNA isolation using standard protocols. RNA quantity has been determined using a RiboGreen RNA quantitation Kit (Molecular Probes), and RNA quality has been demonstrated using standard formaldehyde gels. These samples will be sent to the NINDS/NIMH microarray consortium for Affymetrix microarray analyses. Data analysis will be done using GeneSpring software (Silicon Genetics, Inc.) with assistance from consortium personnel. Specific Aim 3: Differentially expressed genes identified through microarray analyses will be analyzed using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Real time qRT-PCR is a standard technique used in our laboratory for gene expression analysis. Specific Aim 4: FISH analyses of paraffin-embedded tissue has been completed for 77 tumors. We have frozen tissue from a number of these patients. RNA from these samples will be used for microarray analyses (Specific Aims 1-3). The results of Speicifc Aims 1 and 2 will affect how we perform our correlation analyses. This will be done with the assistance of contracted statistical personnel.
2008-06-12 | E-GEOD-4039 | biostudies-arrayexpress