Nutt Catherine L, Mani D R, Betensky Rebecca A, Tamayo Pablo, Cairncross J Gregory, Ladd Christine, Pohl Ute, Hartmann Christian, McLaughlin Margaret E, Batchelor Tracy T, Black Peter M, von Deimling Andreas, Pomeroy Scott L, Golub Todd R, Louis David N
Department of Pathology and Neurosurgical Service, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.
Cancer Res. 2003 Apr 1;63(7):1602-7.
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.
在现代临床神经肿瘤学中，组织病理学诊断对治疗决策和预后评估的影响比其他任何变量都更大。在高级别胶质瘤中，组织学上典型的胶质母细胞瘤和间变性少突胶质细胞瘤遵循明显不同的临床病程。不幸的是，许多恶性胶质瘤在诊断上具有挑战性；这些非典型病变难以通过组织学特征进行分类，导致观察者间差异很大且诊断可重复性有限。由此产生的初步病理诊断造成了严重的临床困惑。我们研究了基因表达谱分析结合分类预测方法是否可用于以比标准病理学更客观、明确和一致的方式对高级别胶质瘤进行分类。使用微阵列分析来确定一组50例胶质瘤（28例胶质母细胞瘤和22例间变性少突胶质细胞瘤）中约12000个基因的表达。采用监督学习方法，基于14例具有典型组织学的胶质母细胞瘤和7例间变性少突胶质细胞瘤构建二类预测模型。与病理诊断相比，一个20特征的k近邻模型在留一法交叉验证中正确分类了21例典型病例中的18例。然后使用该模型预测临床上常见的、组织学非典型样本的分类。根据病理学对肿瘤进行分类时，非典型胶质母细胞瘤和非典型间变性少突胶质细胞瘤患者的生存率无显著差异（P = 0.19）。然而，根据模型进行的分类与生存结果显著相关（P = 0.05）。该分类预测模型能够客观且可重复地对高级别非典型胶质瘤进行分类。此外，对于这些非典型病变，该模型比病理分类提供了更准确的预后预测指标。这些数据表明，基于确定分子谱的分类预测模型，以一种比标准病理学与临床结果相关性更好的方式，对诊断具有挑战性的恶性胶质瘤进行分类。
