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[Cancer Research 63, 1602-1607, April 1, 2003]
© 2003 American Association for Cancer Research

Molecular Biology and Genetics

Gene Expression-based Classification of Malignant Gliomas Correlates Better with Survival than Histological Classification1

Catherine 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. Louis2

Molecular Neuro-Oncology Laboratory and Molecular Pathology Unit, Department of Pathology and Neurosurgical Service [C. L. N., U. P., C. H., T. T. B., D. N. L.], and Brain Tumor Center, Department of Neurology [T. T. B.], Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114; Whitehead Institute/Massachusetts Institute of Technology Center for Genome Research, Cambridge, Massachusetts 02139 [D. R. M., P. T., C. L., T. R. G.]; Departments of Pathology [M. E. M.] and Neurosurgery [P. M. B.], Brigham and Women’s Hospital and Division of Neuroscience, Department of Neurology, Children’s Hospital [S. L. P.], and Department of Biostatistics, Harvard School of Public Health [R. A. B.], Dana-Farber Cancer Institute and Harvard Medical School [T. R. G.], Boston, Massachusetts 02115; Department of Oncology and Clinical Neurological Sciences, University of Western Ontario and London Regional Cancer Centre, London, Ontario N6A 4L6, Canada [J. G. C.]; and Department of Neuropathology, Charité Hospital, Humboldt University, Berlin, Germany [A. v. D.]

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 ~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 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.




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