我们分析了大规模的翻译后修饰（PTM）数据，描绘了在十个肺癌细胞系中酪氨酸激酶抑制剂（TKI）影响的细胞信号通路。我们利用顺序富集后翻译后修饰（SEPTM）蛋白质组学同时鉴定酪氨酸磷酸化、赖氨酸泛素化和赖氨酸乙酰化蛋白质。我们使用机器学习来识别代表对TKI响应的功能模块的PTM聚类。为了在蛋白质水平上建立肺癌信号的模型，我们使用PTM聚类来构建一个共聚同步网络（CCCN），并从一个大型被筛选的蛋白质相互作用网络中选择蛋白质相互作用（PPI）来创建一个被聚类筛选的网络（CFN）。随后，我们通过连接NCATS BioPlanet中成员蛋白质具有共同PTM的通路来构建路径交叉网络（PCN）。分别及组合地对CCCN、CFN和PCN进行审查，可以深入了解肺癌细胞对TKI的反应情况。我们突出了涉及EGFR和ALK的细胞信号通路与BioPlanet通路之间的交叉反应的例子：小分子跨膜转运和糖酵解和糖异生。这些数据揭示了受体酪氨酸激酶（RTK）信号转导和肺癌致癌代谢重编程之间已知和以前未被赞赏的联系。与以前多PTM分析肺癌细胞系生成的CFN的比较显示出热休克/伴侣蛋白、代谢酶、细胞骨架组分和RNA结合蛋白的PPI共同核心。识别不同PTM所采用的信号通路之间交叉反应的环节可揭示新的潜在药物靶点和联合用药攻击的候选药物。版权所有：©2023 Ross等人。本文是在知识共享署名许可下发布的开放获取文章，允许在任何媒介中无限制使用、分发和复制，前提是原作者和出处得到了贷记。

We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy.Copyright: © 2023 Ross et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.