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识别癌症信号网络中激酶依赖性的方法。

Approaches to identify kinase dependencies in cancer signalling networks.

作者信息

Dermit Maria, Dokal Arran, Cutillas Pedro R

机构信息

Cell Signalling & Proteomics Group, Barts Cancer Institute (CRUK Centre), Queen Mary University of London, UK.

出版信息

FEBS Lett. 2017 Sep;591(17):2577-2592. doi: 10.1002/1873-3468.12748. Epub 2017 Jul 25.

Abstract

Cells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine.

摘要

细胞通过由蛋白质和脂质激酶等蛋白质活性驱动的复杂生化反应网络,将细胞外信号整合为适当的反应。为了理解这种复杂性,最近已经开发了新的实验和计算方法,旨在识别调节激酶并推断它们在信号网络背景下的激活状态。在这里,我们回顾这些方法,特别关注基于磷酸化蛋白质组学的方法。从磷酸化蛋白质组学数据推断的激酶活性测量值与其他“组学”数据集的整合,开始被用于识别生化网络中的调节节点。这些方法未来可能用于识别患者特异性靶点,从而推动个性化癌症医学的发展。

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