WES data from 286 diffuse gliomas under the 2021 WHO Classification of Tumors of the Central Nervous System
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PROVIDER: PRJNA893567 | ENA |
REPOSITORIES: ENA
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Project description:Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 WHO Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient’s clinical and pathological information and used it to annotate their genetic data. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.
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Project description:Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient's clinical and pathological information and used it to annotate their genetic data. All patients were diagnosed and classified by neuro-pathologist under the 2021 WHO classification. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.
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Project description:Glial cell-derived brain tumors (gliomas) are devastating diseases without effective curative therapies. Gliomas are classified according to various schemes including the cancer-initiating cell type, World Health Organization tumor grades, and genetic markers such as Isocitrate dehydrogenase (IDH) mutations and chromosomal 1p/19q codeletion status. Genomics, transcriptomics and methylomics approaches are emerging as powerful means to refine classification. However, various aspects of cancer biology are solely reflected on the proteomelevel. Here, we employ mass spectrometry (MS) to characterize the proteomes and phospho-proteomes of IDHmut glioma entities with and without 1p/19q codeletion, IDHwt gliomas and control tissue from a total of 42 patients. Our data-dependent acquisition MS workflow on average quantifies more than 5000 proteins and 3000 phospho-sites per sample of formalin-fixed paraffin-embedded (FFPE) brain sections. The tumor proteomes reflected genetic alterations such as the loss of chromosome 1p/19q proteins, the loss of ATRX in non-codeletion IDHmut glioma, and the frequent loss and amplification of chromosomes 10 and 7, respectively,in IDH-wild type glioma. Nevertheless, 1p/19q codeletion status was not the major determinant of IDHmut glioma proteomes. Instead, the proteome profiles suggest an alternative classification of IDHmut gliomas that correlates with the loss of mitochondrial DNA-encoded proteins, the abundance of respiratory chain proteins, a distinct tumor suppressor and oncoprotein profile, an extracellular matrix signature, and alterations in the phospho-proteome. Moreover, the glioma association of numerous proteins implicated in other cancers by this dataset provides a resource for further mechanistic investigation of glioma genesis and progression.
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Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology. louis-00379 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG_U95Av2 Organism: Homo sapiens (ncbitax) Material Types: total_RNA, synthetic_RNA, organism_part, whole_organism Disease States: Classic anaplastic oligodendroglioma, Non-classic glioblastoma, Classic glioblastoma, Non-classic anaplastic oligodendroglioma
2016-05-28 | E-GEOD-82009 | biostudies-arrayexpress
Project description:This phase I trial studies the side effects and best dose of pembrolizumab and to see how well it works in treating younger patients with high-grade gliomas (brain tumors that are generally expected to be fast growing and aggressive), diffuse intrinsic pontine gliomas (brain stem tumors), brain tumors with a high number of genetic mutations, ependymoma or medulloblastoma that have come back (recurrent), progressed, or have not responded to previous treatment (refractory). Immunotherapy with monoclonal antibodies, such as pembrolizumab, may induce changes in the body’s immune system, and may interfere with the ability of tumor cells to grow and spread.
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Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology.
2016-05-28 | GSE82009 | GEO
Project description:Background: Molecular profiling of diffuse gliomas has provided significant insights into the pathogenesis, classification and prognostication of these malignancies. However, previous molecular studies of glioma have largely focused on genomic readouts and targeted proteomic profiling technologies. Consequently, proteomic and downstream functional landscape of gliomas in general, and molecular subgroups in particular, remains largely unexplored. Here, we utilize liquid chromatography tandem mass spectrometry to profile genomically-defined cohorts of gliomas spanning the full range of World Health Organization (WHO) grades. Methods: Bulk frozen tissue and purified micro-dissected regions from formalin-fixed paraffin-embedded (FFPE) tissues were assembled and utilized to define robust proteomic signatures of both low grade, infiltrative and high-grade tumors. As a final analysis, primary tumor tissue was compared with both IDH-mutated and IDH-wildtype glioblastoma stem cell (GSC) lines to further overcome tissue heterogeneity and pinpoint proteins differences likely arising in the relevant glial cellular drivers of tumor development. Results: In aggregate, 5,496 unique proteins over 3 glioma cohorts were identified, and span common molecular subclasses based on IDH and 1p19q co-deletion status and all four WHO grades. Supervised clustering highlights substantial proteome and systems-level pathway differences between different genetically defined glioma subtypes and WHO grades. By using bulk tumor statistical analysis, 833 proteins distinguish different WHO grade tumors, while FFPE tumor dissection reveals 287 proteins in GBMs with abundance changes according to IDH mutation status. Using our integrative approach, calcium signaling, proteins of the endoplasmic reticulum and extracellular integrin proteins are most conserved proteomic markers that distinguish aggressive, IDH-wt, from IDH-mut GBM tumors in primary and tissue culture models gliomagenesis. Conclusions: This proteomic survey provides the largest and most diverse unbiased protein-based brain tumor resource to date. Current treatments for glial tumors are largely non-specific and overlap between genomic subtypes and WHO grades. Our analysis provides early insight into the vast downstream and epigenetic protein-level differences within this molecular framework. Given the central position proteins occupy in driving biology and phenotype, further characterization of the substantial proteomic diversity that exist between the molecular subtypes and grades of gliomas, proteomics may help define more personalized prognostic and predictive biomarkers for precision care.
2019-07-30 | PXD010133 | Pride
Project description:Purpose: More than 90% of children with diffuse intrinsic pontine glioma (DIPG) die within 2 years of diagnosis. There is a dire need to identify therapeutic targets, however lack of patient material for research has limited progress. We evaluated a large cohort of diffuse intrinsic pontine gliomas (DIPGs) to identify recurrent genomic abnormalities and gene expression signatures underlying DIPG. Patients and Methods: We used single nucleotide polymorphism arrays to evaluate genomic copy number imbalances in 43 DIPGs from 40 patients and in 8 low-grade exophytic brainstem gliomas. Gene expression arrays were used to evaluate expression signatures from 27 DIPGs, 6 low-grade exophytic brainstem gliomas and 66 low-grade gliomas arising outside the brainstem. Results: Frequencies of specific large-scale and focal imbalances varied significantly between DIPGs and pediatric glioblastomas outside the brainstem. Focal amplifications of genes within the receptor tyrosine kinase-Ras-PI3-kinase signaling pathway were found in 47% of DIPG, with PDGFRA and MET showing the highest frequency. 30% of DIPG contained focal amplifications of cell-cycle regulatory genes controlling RB phosphorylation, and 21% had concurrent amplification of genes from both pathways. Some tumors showed heterogeneity in amplification patterns. DIPGs showed distinct gene expression signatures relating to developmental processes compared to pediatric glioblastomas arising outside the brainstem, while expression signatures of low-grade exophytic brainstem gliomas were similar to low-grade gliomas outside the brainstem. Copy number analaysis: 43 DIPG samples, 8 Low Grade Gliomas using SNP6.0. Available matched normals are also profiled with SNP6.0. Expression analysis: 29 DIPG samples, 6 Low grade samples Please contact Suzanne Baker at Suzanne.Baker@stjude.org for CEL files and genotype calls.
2011-09-20 | E-GEOD-26576 | biostudies-arrayexpress