生物信号转导网络对于生命各个领域的信息处理和基因表达调节至关重要。众所周知，失调会导致多种疾病，包括癌症。在这里，我介绍自洽信号转导分析，它利用基因组规模的组学数据（特别是转录组学和/或蛋白质组学）来以个性化的方式预测通过这些网络的信息流。我将该方法应用于乳腺癌患者的内分泌治疗研究，结果表明，抑制雌激素受体 α 的药物可引起广泛的抗肿瘤作用，并且它们最具临床影响力的作用是通过调节控制细胞增殖的信号。基因 GREB1、HK1、AKT1、MAPK1、AKT2 和 NQO1。这种方法为研究人员提供了一个宝贵的工具，可以帮助他们了解失调如何以及为何发生，以及网络扰动（例如靶向治疗）如何影响网络本身，并最终影响患者的结果。© 2024。作者。

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.© 2024. The Author(s).