Cancer Program Publication

Gene expression-based classification of malignant gliomas correlates better with survival than histological classification
ProjectBrain Cancer
Abstract 
In modern clinical neuro-oncology, histopathological diagnosis affects therapeutic decisions and prognostic estimation more than any other variable. Among high grade gliomas, for example, histologically classic glioblastomas and anaplastic oligodendrogliomas follow markedly different clinical courses. Unfortunately, many malignant gliomas are diagnostically challenging; these non-classic lesions are difficult to classify by histological features, generating considerable interobserver variability and limited diagnostic reproducibility. The resulting tentative pathological diagnoses create significant clinical confusion. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to classify high grade gliomas in a manner more objective, explicit and consistent than standard pathology. Microarray analysis was used to determine the expression of approximately 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 out of the 21 classic cases in leave-one-out cross validation when compared to pathological diagnoses. This model was then used to predict the classification of clinically common, histologically non-classic samples. When tumors were classified according to pathology, the survival of patients with non-classic glioblastoma and non-classic 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, non-classic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these non-classic 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.
AuthorsCatherine L. Nutt, D. R. Mani, Rebecca A. Betensky, Pablo Tamayo, J. Gregory Cairncross, Christine Ladd, Ute Pohl, Christian Hartmann, Margaret E. McLaughlin, Tracy T. Batchelor, Peter M. Black, Andreas von Deimling, Scott L. Pomeroy, Todd R. Golub, and David N. Louis
Publication Date04/01/2003
Contact emails nutt@helix.mgh.harvard.edu
Publication URLhttp://cancerres.aacrjournals.org/
CitationCancer Research 63(7):1602-1607
 
Supplemental Information
Files
DescriptionFile
PaperNutt et al - revised manuscript with references.doc
FiguresFigures 1, 2 and 3.ppt
SupplementSupplementary Information - Cancer Research.doc
Classics Res FileBrain_Classics.res
Classics Class FileBrain_Classics.cls
NonClassics Res FileBrain_NonClassics.res
NonClassics Class FileBrain_NonClassics.cls
CEL FilesGlioma CEL Files.zip