Rembrandt: helping personalized medicine become a reality through integrative translational research.
Rembrandt: helping personalized medicine become a reality through integrative translational research.
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DOI：
10.1158/1541-7786.mcr-08-0435
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发表时间：
2009-02
影响因子：
5.2
通讯作者：
Buetow, Kenneth
中科院分区：
文献类型：
--
作者：
Madhavan, Subha;Zenklusen, Jean-Claude;Kotliarov, Yuri;Sahni, Himanso;Fine, Howard A.;Buetow, Kenneth
由于缺乏在大样本集中一致获得的分子数据，以及整合来自不同来源的生物医学数据的能力，从而能够将治疗方法从工作台转换到床边，阻碍了寻找更好的治疗脑瘤的方法。因此，促进生物医学研究和临床翻译的一个关键因素是数据可以在功能领域内和跨功能领域进行集成、重新分配和分析。新的生物医学信息学基础设施和工具对于根据每个患者肿瘤中的特定基因组签名开发个性化的患者治疗至关重要。在这里，我们介绍了伦勃朗，分子脑肿瘤数据仓库，癌症临床基因组数据库和一个基于网络的数据挖掘和分析平台，旨在通过连接临床信息和基因组特征数据之间的点来促进发现。到目前为止，伦勃朗包含了通过胶质瘤分子诊断倡议产生的数据，这些数据来自874个胶质瘤样本，包括近566个基因表达阵列、834个拷贝数阵列和13,472个临床表型数据点。可以跨所有数据平台查询和可视化所选基因的数据，或所选平台中多个基因的数据。此外，基因集可以局限于临床上重要的注释，包括分泌的、激酶、膜和已知的基因-异常对，以促进新的生物标志物和治疗靶点的发现。我们相信，伦勃朗代表了一个原型，展示了如何将高通量基因组数据和临床数据以一种允许快速有效地将实验室发现转化为临床的方式整合在一起。
Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set, and ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient’s tumor. Here we present Rembrandt, Repository of Molecular BRAin Neoplasia DaTa, a cancer clinical genomics database and a web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising nearly 566 gene expression arrays, 834 copy number arrays and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that REMBRANDT represents a prototype of how high throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic.