Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis
Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis
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DOI：
10.1158/1078-0432.ccr-06-2789
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发表时间：
2007-12-15
影响因子：
11.5
通讯作者：
Kato, Kikuya
中科院分区：
文献类型：
--
作者：
Shirahata, Mitsuaki;Iwao-Koizumi, Kyoko;Kato, Kikuya
目的：目前基于形态学的胶质瘤分类方法不能充分反映胶质瘤的复杂生物学，从而限制了其预后能力。在这项研究中，我们集中在间变性少突胶质细胞瘤和胶质母细胞瘤，这通常遵循不同的临床过程。我们的目标是构建一个临床上有用的分子诊断系统的基础上基因表达profiling.Experimental设计：32例患者，其中12例和20例有明显不同的间变性少突胶质细胞瘤和胶质母细胞瘤，分别为3,456个基因的表达，通过PCR阵列测量。接下来的无监督的方法，我们做了监督分析，使用加权投票算法构建一个诊断系统区分间变性少突胶质细胞瘤胶质母细胞瘤。该系统的诊断准确性进行了评价留一交叉验证。的临床效用进行了测试，基于微阵列的数据集50恶性胶质瘤从以前的study.Results：无监督分析显示，两个肿瘤类之间的分歧全球基因表达模式。一个有监督的二元分类模型显示100%（95%置信区间，89.4-100%）的诊断准确率留一法交叉验证使用168个诊断基因。应用于先前研究的基因表达数据集，我们的模型与结果的相关性优于组织学诊断，并且与原始文章中用于这些组织学上有争议的胶质瘤的分子分类方案具有96.6%（29个中的28个）的一致性。此外，我们观察到，组织学诊断的胶质母细胞瘤样本具有间变性少突胶质细胞瘤分子特征，往往与更长的生存期相关。结论：我们的分子诊断系统显示出可重复的临床实用性和预后能力，上级传统的恶性胶质瘤组织病理学诊断。
Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling.Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study.Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival.Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.