一种用于分子网络重建的可扩展方法鉴定急性髓系白血病中靶点和突变的性质
A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia
摘要：
系统生物学的一个关键目标是重建分子网络，但是我们还没有一个网络可以整合来自特定临床条件的所有数据集的信息。这部分是由于现有算法在所需计算时间和功率方面的可扩展性有限。网络重建方法也应该是可扩展的，允许来自不同背景的科学家有效地整合额外的数据。我们提出了一个急性髓细胞白血病（AML）的网络模型。在当前版本（AML 2.1）中，我们使用了来自五项不同研究的基因表达数据（微阵列和RNA-seq），包括总共771个AML样本和一个蛋白质相互作用数据集。我们的可扩展的网络重建方法部分是基于相互作用的分子之间的基因表达相关性的众所周知的属性。区分直接和间接相互作用的困难是解决优化变异系数的基因表达，直接相互作用的验证金标准数据集。与其他网络重建方法相比，计算时间大大减少。一个关键特征是研究独立临床数据集中发现的相互作用的再现性。对最重要的聚类和常见AML突变的网络属性（集内效率、度、介数中心性和PageRank）的分析表明了该网络的生物学意义。11例AML患者的原始细胞对激酶抑制剂库的反应的统计分析提供了网络的实验验证。网络和实验数据的组合将CDK 1、CDK 2、CDK 4和CDK 6以及其他激酶确定为AML的潜在治疗靶点。
英文摘要：
A key aim of systems biology is the reconstruction of molecular networks, however we do not yet have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1) we have used gene expression data (both microarray and RNA-seq) from five different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed optimizing the coefficient of variation of gene expression, using a validated gold standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from eleven AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4 and CDK6 and other kinases as potential therapeutic targets in AML.
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