Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics.
Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics.
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DOI：
10.1186/1752-0509-8-12
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发表时间：
2014-02-05
影响因子：
--
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Penrod NM;Moore JH
对新型分子靶向药物的需求将继续上升，因为我们朝着个性化癌症治疗的目标前进，以个体肿瘤的分子特征。然而，可以安全有效地调节的靶标和靶标组合的鉴定是药物发现过程面临的最大挑战之一。一个有前途的方法是使用生物网络，根据它们彼此之间的相对位置来确定目标的优先级，这一特性影响它们保持网络完整性和传播信息流的能力。在这里，我们介绍了影响网络，并演示了如何使用它们来生成影响分数，作为基于网络的指标来将基因列为潜在的药物靶点。我们使用这种方法来优先考虑基因作为药物靶点候选人在一组ER +乳腺肿瘤样本中收集的过程中的芳香酶抑制剂来曲唑的新辅助治疗。我们发现，有影响力的基因，那些具有高影响力的分数，往往是必不可少的，包括更高比例的必要基因比那些优先级的基础上，他们的位置（即枢纽或瓶颈）在同一个网络。此外，我们发现有影响力的基因代表了治疗ER +乳腺癌的新的生物学相关药物靶点。此外，我们证明了基因的影响在未经治疗的肿瘤和已经适应药物治疗的残留肿瘤之间是不同的。通过这种方式，影响分数捕获了基因的背景依赖性功能，并提供了设计利用肿瘤适应过程的组合治疗策略的机会。影响网络有效地发现必需基因作为有希望的药物靶点和靶点组合，以告知分子靶向药物的开发及其使用。
The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use.
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影响因子：
2
作者：
Hemmi, Kazunori;Ma, Dongping;Tsuchiya, Kayoko
通讯作者：
Tsuchiya, Kayoko
影响因子：
3.3
作者：
Fortes, M. R. S.;Reverter, A.;Hawken, R. J.
通讯作者：
Hawken, R. J.
影响因子：
3
作者：
Jeffery, Ian B.;Higgins, Desmond G.;Culhane, Aedin C.
通讯作者：
Culhane, Aedin C.
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
影响因子：
2.8
作者：
Miller, W. R.;Larionov, A.;Dixon, J. M.
通讯作者：
Dixon, J. M.