MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.
Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR 97201, USA.
Cell Syst. 2017 Jan 25;4(1):73-83.e10. doi: 10.1016/j.cels.2016.11.013. Epub 2016 Dec 22.
Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.
受体酪氨酸激酶下游的信号转导网络是研究最为广泛的生物网络之一，但需要新的方法来阐明网络组件之间的因果关系，并了解这种关系如何受到生物背景和疾病的影响。在这里，我们使用反相蛋白阵列时间过程测定和网络分析方法，在因果概念框架内研究信号转导网络的上下文特异性。我们关注的是一组在五种激酶抑制剂抑制下，在 32 种情况下（四种乳腺癌细胞系 MCF7、UACC812、BT20 和 BT549 在八种刺激条件下）进行了很好定义的信号蛋白的情况。这些数据涵盖了多个途径，包含约 70000 个磷酸化蛋白和约 260000 个蛋白测量值，提供了大量可测试的、特定于上下文的假设，其中有几个我们进行了实验验证。此外，这些数据为计算方法的发展提供了一个独特的资源，允许在复杂的哺乳动物环境中对因果网络学习进行实证评估。
