Integration and analysis of genome-scale data from gliomas.
Integration and analysis of genome-scale data from gliomas.
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DOI：
10.1038/nrneurol.2011.100
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发表时间：
2011-07-05
期刊：
影响因子：
--
通讯作者：
Fine HA
中科院分区：
文献类型：
--
作者：
Riddick G;Fine HA
原发性脑肿瘤是年轻人和儿童癌症相关死亡的主要原因。最常见的原发性恶性脑肿瘤，神经胶质瘤，中位生存期仅为14个月。两个主要的多机构计划，胶质瘤分子诊断计划和癌症基因组图谱，已经采用各种技术对大量临床胶质瘤样本进行了全面的基因组表征，以测量基因表达，染色体拷贝数改变，体细胞和种系突变，DNA甲基化，microRNA和蛋白质组学变化。基于基因表达的神经胶质瘤分类揭示了六种主要亚型，并提供了对每种亚型的潜在生物学的见解。来自不同技术的全基因组数据的整合已被用于识别这种疾病中的许多潜在蛋白质靶点，同时提高了结果的可靠性和生物学可解释性。将基因组变化映射到已知和推断的细胞网络上代表了下一个分析水平，并产生了在肿瘤发生中具有关键作用的蛋白质。最终，从这些方法中获得的信息将用于根据个体肿瘤的独特基因组特征为每位患者创建定制的治疗方案。在这篇综述中，我们描述了使用基因组数据表征神经胶质瘤的努力，并考虑如何从这些分析中获得的见解有望增加对疾病生物学基础的理解，并为新的治疗策略开辟道路。
Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.
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