title: Gene expression-based classification of malignant gliomas correlates better with survival than histological classification.
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.
authors.affiliation: Department of Pathology and Neurosurgical Service, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.
authors.forename: Catherine L
authors.initials: CL
authors.lastname: Nutt
authors.forename: D R
authors.initials: DR
authors.lastname: Mani
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Betensky
authors.forename: Pablo
authors.initials: P
authors.lastname: Tamayo
authors.forename: J Gregory
authors.initials: JG
authors.lastname: Cairncross
authors.forename: Christine
authors.initials: C
authors.lastname: Ladd
authors.forename: Ute
authors.initials: U
authors.lastname: Pohl
authors.forename: Christian
authors.initials: C
authors.lastname: Hartmann
authors.forename: Margaret E
authors.initials: ME
authors.lastname: McLaughlin
authors.forename: Tracy T
authors.initials: TT
authors.lastname: Batchelor
authors.forename: Peter M
authors.initials: PM
authors.lastname: Black
authors.forename: Andreas
authors.initials: A
authors.lastname: von Deimling
authors.forename: Scott L
authors.initials: SL
authors.lastname: Pomeroy
authors.forename: Todd R
authors.initials: TR
authors.lastname: Golub
authors.forename: David N
authors.initials: DN
authors.lastname: Louis
mesh_terms: D001932:Brain Neoplasms / Q000145:classification* / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D020869:Gene Expression Profiling; D005910:Glioma / Q000145:classification* / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D006801:Humans; D008957:Models, Genetic; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D015996:Survival Rate