[CANCER RESEARCH 63, 1602–1607, April 1, 2003] Gene Expression-based Classification of Malignant Gliomas Correlates Better with Survival than Histological Classification 1 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. Louis 2 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, Charite ´ Hospital, Humboldt University, Berlin, Germany [A. v. D.] 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 chal- lenging; 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 expres- sion 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 classi- fied 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 oligo- dendroglioma 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 reproduc- ibly. Moreover, the model provided a more accurate predictor of prog- nosis 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. INTRODUCTION Malignant gliomas are the most common primary brain tumor and result in an estimated 13,000 deaths each year in the United States 3 . Glial tumors are classified histologically, with pathological diagnosis affecting prognostic estimation and therapeutic decisions more than any other variable. Among high-grade gliomas, anaplastic oligoden- drogliomas have a more favorable prognosis than glioblastomas (1). Moreover, although glioblastomas are resistant to most available therapies, anaplastic oligodendrogliomas are often chemosensitive, with approximately two-thirds of cases responding to procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea, and vincristine (2, 3). Paradoxically, recognition of the clinical importance of diagnosing anaplastic oligodendroglioma has blurred the histopathological line separating glioblastoma and oligodendroglioma; to ensure that pa- tients are not deprived of effective chemotherapy, pathologists have loosened their criteria for anaplastic oligodendroglioma. Indeed, this diagnostic promiscuity has recently been described as a “contagion” (4). As such, there is a critical need for an objective, clinically relevant method of glioma classification. The most widely used histological system of brain tumor classifi- cation is that of the WHO (1). Gliomas are classified according to defined histological features characteristic of the presumed normal cell of origin. Tumors of classic histology clearly display these fea- tures and resemble typical depictions in standard textbooks (5, 6); these cases would be diagnosed similarly by nearly all pathologists. Unfortunately, there are situations in which the WHO classification system is problematic, primarily because pathological diagnosis re- mains subjective (7); intratumoral histological variability is common, and high-grade gliomas can display little cellular differentiation, thus lacking defining histological features. The diagnosis of tumors with such nonclassic histology is often controversial. Consequently, diag- nostic accuracy and reproducibility are jeopardized, and significant interobserver variability can occur. Coons et al. (8) found that com- plete diagnostic concordance among four neuropathologists reviewing gliomas over four sessions peaked at 69%. Giannini et al. (9), in a study of seven neuropathologists and six surgical pathologists scoring histological features of oligodendroglioma, found that agreement for identifying features ranged from 0.05 to 0.8, confirming that numer- ous classification parameters are not easily reproduced. To develop more objective approaches to glioma classification, recent investigations have focused on molecular genetic analyses. Sasaki et al. (10) demonstrated loss of chromosome 1p in 86% of oligodendrogliomas with classic histology and maintenance of both 1p alleles in 73% of “oligodendrogliomas” with astrocytic features. Interestingly, tumor genotypes more closely predicted chemosensitiv- ity, demonstrating an ability of tumor genotype to augment standard pathology. Burger et al. (11) also demonstrated close correlation between classic low-grade oligodendroglioma appearance and allelic losses of 1p and 19q. In gene expression studies, Lu et al. (12) suggested that expression of oligodendrocyte lineage genes (Olig1 and 2) might augment identification of oligodendroglial tumors. Sim- ilarly, Popko et al. (13) found three of four myelin transcripts signif- icantly more often in oligodendrogliomas than in astrocytomas. The advent of expression microarray techniques now allows simul- taneous analysis of thousands of genes. We hypothesized that this approach could identify molecular markers capable of refining the Received 10/11/02; accepted 2/3/03. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. 1 Supported in part by NIH Grant CA57683 (D. N. L.), Affymetrix and Bristol-Myers Squibb (Whitehead Institute/MIT Center for Genome Research), NIH Grant NS35701 (S. L. P.), and Canadian Institutes of Health Research MOP37849 (J. G. C.). 2 To whom requests for reprints should be addressed, at Molecular Pathology Labo- ratory, CNY7, Massachusetts General Hospital, 149 13 th Street, Charlestown, MA 02129. Phone: (617) 726-5690; Fax: (617) 726-5079; E-mail: dlouis@partners.org. 3 Internet address: http://www.cbtrus.org. 1602 Research. on August 10, 2015. © 2003 American Association for Cancer cancerres.aacrjournals.org Downloaded from