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

@article{citeulike:2821209, 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 [~]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.}, author = {Nutt, Catherine L. and Mani, D. R. and Betensky, Rebecca A. and Tamayo, Pablo and Cairncross, Gregory J. and Ladd, Christine and Pohl, Ute and Hartmann, Christian and Mclaughlin, Margaret E. and Batchelor, Tracy T. and Black, Peter M. and von Deimling, Andreas and Pomeroy, Scott L. and Golub, Todd R. and Louis, David N. }, citeulike-article-id = {2821209}, journal = {Cancer Res}, keywords = {brain\_cancer, classification, microarray}, month = {April}, number = {7}, pages = {1602--1607}, posted-at = {2008-05-21 21:14:00}, priority = {2}, title = {Gene Expression-based Classification of Malignant Gliomas Correlates Better with Survival than Histological Classification}, url = {http://cancerres.aacrjournals.org/cgi/content/abstract/63/7/1602}, volume = {63}, year = {2003} }

TY - JOUR ID - citeulike:2821209 L3 - citeulike-article-id:2821209 TI - Gene Expression-based Classification of Malignant Gliomas Correlates Better with Survival than Histological Classification JF - Cancer Res VL - 63 IS - 7 SP - 1602 EP - 1607 N2 - 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. KW - brain_cancer KW - classification KW - microarray AU - Nutt, Catherine AU - Mani, DR AU - Betensky, Rebecca AU - Tamayo, Pablo AU - Cairncross, Gregory AU - Ladd, Christine AU - Pohl, Ute AU - Hartmann, Christian AU - Mclaughlin, Margaret AU - Batchelor, Tracy AU - Black, Peter AU - von Deimling, Andreas AU - Pomeroy, Scott AU - Golub, Todd AU - Louis, David PY - 2003/04/01/ UR - http://cancerres.aacrjournals.org/cgi/content/abstract/63/7/1602 ER -