Genomic estimates of aneuploid content in glioblastoma multiforme and improved classification.
Genomic estimates of aneuploid content in glioblastoma multiforme and improved classification.
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DOI：
10.1158/1078-0432.ccr-12-1427
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发表时间：
2012-10-15
期刊：
影响因子：
--
通讯作者：
Li JZ
中科院分区：
文献类型：
--
作者：
Li B;Senbabaoglu Y;Peng W;Yang ML;Xu J;Li JZ
多形性胶质母细胞瘤（GBM）的准确分类对于了解其生物多样性并为诊断和治疗提供信息至关重要。癌症基因组图谱 (TCGA) 项目使用基因表达数据确定了四个 GBM 类别，并分别使用甲基化数据确定了三个类别。我们试图在 GBM 分类中整合多种数据类型，了解新定义的亚型的生物学特征，并与先前的研究相一致。我们使用等位基因特异性拷贝数数据来估计每个肿瘤的非整倍体含量，并将肿瘤内异质性的这种测量纳入类别发现中。我们使用已知神经元细胞类型的参考数据集估计了各个亚型以及整倍体和非整倍体分数的潜在细胞起源。非整倍体含量与观察到的肿瘤间表达模式多样性之间存在意想不到的相关性。在从头开始类别发现中联合使用 DNA 和 mRNA 数据揭示了一个独特的群体，类似于另一项研究中描述的 Proneural 亚型和基于甲基化数据的 G-CIMP+ 类别。还确定了另外三种亚型：经典亚型、增殖亚型和间充质亚型，并修改了许多样本的分配。修订显示患者结果存在更大差异，细胞类型特异性特征更清晰。间充质 GBM 具有较高的整倍体含量，可能是由小胶质细胞/巨噬细胞浸润所致。我们澄清了关于“前神经”亚型的困惑，该亚型在不同的先前研究中定义不同。推断肿瘤内异质性的能力改进了类别发现，导致更接近 GBM 基础生物学的新亚型。
Accurate classification of Glioblastoma Multiforme (GBM) is crucial for understanding its biological diversity, and informing diagnosis and treatment. The Cancer Genome Atlas (TCGA) project identified four GBM classes using gene expression data, and separately, identified three classes using methylation data. We sought to integrate multiple data types in GBM classification, understand biological features of the newly defined subtypes, and reconcile with prior studies. We used allele-specific copy number data to estimate the aneuploid content of each tumor, and incorporated this measure of intratumor heterogeneity in class discovery. We estimated the potential cell of origin of individual subtypes and the euploid and aneuploid fractions using reference datasets of known neuronal cell types. There exists an unexpected correlation between aneuploid content and the observed among-tumor diversity of expression patterns. Joint use of DNA and mRNA data in ab initio class discovery revealed a distinct group that resembles the Proneural subtype described in a separate study and the G-CIMP+ class based on methylation data. Three additional subtypes, Classical, Proliferative, and Mesenchymal, were also identified, and revised the assignment for many samples. The revision showed stronger differences in patient outcome and clearer cell type-specific signatures. Mesenchymal GBMs had higher euploid content, potentially contributed by microglia/macrophage infiltration. We clarified the confusion regarding the "Proneural" subtype that was defined differently in different prior studies. The ability to infer within-tumor heterogeneity improved class discovery, leading to new subtypes that are closer to the fundamental biology of GBM.