Please note that we have stopped the regular imports of Gene Expression Omnibus (GEO) data into ArrayExpress. This may not be the latest version of this experiment.
E-GEOD-16011 - Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology
Released on 26 April 2010, last updated on 27 March 2012
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
transcription profiling by array
Pim French <email@example.com>, Andrew P Stubbs, Anneleen Daemen, Bart De Moor, Fonnet E Bleeker, J E Duijm, Johan J de Rooi, Johan M Kros, Linda B Bralten, Lonneke A Gravendeel, Martin J van den Bent, Mathilde C Kouwenhoven, Nanne Kloosterhof, Olivier Gevaert, Peter A Sillevis Smitt, Peter J van der Spek, Pim J French
Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Gravendeel LA, Kouwenhoven MC, Gevaert O, de Rooi JJ, Stubbs AP, Duijm JE, Daemen A, Bleeker FE, Bralten LB, Kloosterhof NK, De Moor B, Eilers PH, van der Spek PJ, Kros JM, Sillevis Smitt PA, van den Bent MJ, French PJ. , PMID:19920198