Gene expression profiling of gliomas strongly predicts survival

In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. W...

Full description

Saved in:

Bibliographic Details
Published in:	Cancer research (Chicago, Ill.) Vol. 64; no. 18; pp. 6503 - 6510
Main Authors:	FREIJE, William A, CASTRO-VARGAS, F. Edmundo, ZIXING FANG, HORVATH, Steve, CLOUGHESY, Timothy, LIAN, Linda M, MISCHEL, Paul S, NELSON, Stanley F
Format:	Journal Article
Language:	English
Published:	Philadelphia, PA American Association for Cancer Research 15-09-2004
Subjects:	Adolescent Adult Aged Antineoplastic agents Biological and medical sciences Brain Neoplasms - genetics Cluster Analysis Female Gene Expression Profiling Glioma - genetics Glioma - pathology Humans Male Medical sciences Middle Aged Oligonucleotide Array Sequence Analysis Pharmacology. Drug treatments Predictive Value of Tests Prognosis Reverse Transcriptase Polymerase Chain Reaction Survival Rate Tumors Tumor Nervous system diseases Glioma Survival Central nervous system disease Gene expression profile
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0008-5472 1538-7445
DOI:	10.1158/0008-5472.can-04-0452