Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes.
Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes.
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DOI：
10.1158/0008-5472.can-08-2100
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发表时间：
2009-03-01
期刊：
影响因子：
11.2
通讯作者：
Fine HA
中科院分区：
文献类型：
--
作者：
Li A;Walling J;Ahn S;Kotliarov Y;Su Q;Quezado M;Oberholtzer JC;Park J;Zenklusen JC;Fine HA
胶质瘤是成人最常见的原发脑肿瘤类型，也是癌症相关死亡的重要原因。根据客观的基因和分子特征确定胶质瘤的亚型可能会为未来的治疗提供一种更合理的、针对患者的治疗方法。过去曾尝试过基于基因表达数据的分类，但取得了不同的成功，研究之间只有一些一致性，这可能是由于通过使用分析方法在分类之前对基因进行先验选择而引入的固有偏见。为了克服这种潜在的偏差来源，我们将两种无监督的机器学习方法应用于159个胶质瘤的全基因组基因表达谱，从而建立了一个仅依赖于分子数据的稳健的胶质瘤分类模型。该模型预测了两种主要的胶质瘤组(富含少突胶质瘤和富含胶质母细胞瘤的组)，可分为六种层级嵌套的亚型。然后，我们确定了六组分类器，可以用来将任何给定的胶质瘤分配到相应的亚型，并使用内部(189个额外的独立样本)和两个外部数据集(341名患者)来验证这些分类器。将分类系统应用于外部胶质瘤数据集，使我们能够识别先前发表的数据和癌症基因组图谱中胶质母细胞瘤样本中先前未识别的预后组，以及与不同胶质瘤亚型相关的不同生物学途径，为每个亚型中的肿瘤提供潜在的发病机制线索和可能的治疗靶点。
Gliomas are the most common type of primary brain tumors in adults and a significant cause of cancer-related mortality. Defining glioma subtypes based on objective genetic and molecular signatures may allow for a more rational, patient-specific approach to therapy in the future. Classifications based on gene expression data have been attempted in the past with varying success and with only some concordance between studies, possibly due to inherent bias that can be introduced through the use of analytic methodologies that make a priori selection of genes before classification. To overcome this potential source of bias, we have applied two unsupervised machine learning methods to genome-wide gene expression profiles of 159 gliomas, thereby establishing a robust glioma classification model relying only on the molecular data. The model predicts for two major groups of gliomas (oligodendroglioma-rich and glioblastoma-rich groups) separable into six hierarchically nested subtypes. We then identified six sets of classifiers that can be used to assign any given glioma to the corresponding subtype and validated these classifiers using both internal (189 additional independent samples) and two external data sets (341 patients). Application of the classification system to the external glioma data sets allowed us to identify previously unrecognized prognostic groups within previously published data and within The Cancer Genome Atlas glioblastoma samples and the different biological pathways associated with the different glioma subtypes offering a potential clue to the pathogenesis and possibly therapeutic targets for tumors within each subtype.