Systematic name M2577
Brief description Top 50 marker genes for anaplastic oligodendroglioma (AO), a class of high grade glioma.
Full description or abstract In modern clinical neuro-oncology, histopathological diagnosis affects therapeutic decisions and prognostic estimation more than any other variable. Among high-grade gliomas, histologically classic glioblastomas and anaplastic oligodendrogliomas follow markedly different clinical courses. Unfortunately, many malignant gliomas are diagnostically challenging; these nonclassic 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 12000 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.
Collection C2: Curated
      CGP: Chemical and Genetic Perturbations
Source publication Pubmed 12670911   Authors: Nutt CL,Mani DR,Betensky RA,Tamayo P,Cairncross JG,Ladd C,Pohl U,Hartmann C,McLaughlin ME,Batchelor TT,Black PM,von Deimling A,Pomeroy SL,Golub TR,Louis DN
Exact source Suppl. Data: High Grade Glioma Class Markers, AO
Related gene sets (show 1 additional gene sets from the source publication)

(show 345 gene sets from the same authors)
External links
Filtered by similarity ?
Source species Homo sapiens
Contributed by Arthur Liberzon (MSigDB Team)
Source platform or
identifier namespace
Dataset references  
Download gene set format: grp | gmt | xml | json | TSV metadata
Compute overlaps ? (show collections to investigate for overlap with this gene set)
Compendia expression profiles ? NG-CHM interactive heatmaps
(Please note that clustering takes a few seconds)
GTEx compendium
Human tissue compendium (Novartis)
Global Cancer Map (Broad Institute)
NCI-60 cell lines (National Cancer Institute)

Legacy heatmaps (PNG)
GTEx compendium
Human tissue compendium (Novartis)
Global Cancer Map (Broad Institute)
NCI-60 cell lines (National Cancer Institute)
Advanced query Further investigate these 46 genes
Gene families ? Categorize these 46 genes by gene family
Show members (show 50 source identifiers mapped to 46 genes)
Version history 3.0: First introduced

See MSigDB license terms here. Please note that certain gene sets have special access terms.