Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology

ABSTRACT: Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology. 276 glioma samples of all histology, 8 control samples

ORGANISM(S): Homo sapiens

SUBMITTER: Pim French

PROVIDER: E-GEOD-16011 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology.

Gravendeel Lonneke A M LA Kouwenhoven Mathilde C M MC Gevaert Olivier O de Rooi Johan J JJ Stubbs Andrew P AP Duijm J Elza JE Daemen Anneleen A Bleeker Fonnet E FE Bralten Linda B C LB Kloosterhof Nanne K NK De Moor Bart B Eilers Paul H C PH van der Spek Peter J PJ Kros Johan M JM Sillevis Smitt Peter A E PA van den Bent Martin J MJ French Pim J PJ

Cancer research 20091117 23

Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes ...[more]

PMID: 19920198

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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.

Dataset Information

Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology

Publications

Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology.

Similar Datasets

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