Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein–drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

基于质谱的发现蛋白质组学是近端读出细胞药物作用的重要工具。在这里，我们应用强大的蛋白质组学工作流程来快速分析 5 种肺癌细胞系对 50 多种药物的反应的蛋白质组。数百万个定量蛋白质-药物关联的整合显着改善了单一化合物的作用机制 (MoA) 反卷积。例如，在去除经常对药物有反应的蛋白质后，MoA 特异性增加，并且跨细胞系的蛋白质组变化的聚集解决了对蛋白质稳态的复合效应。我们利用这些发现来证明化学蛋白质降解剂的有效目标识别。聚合跨细胞系的药物反应还表明，四分之一的化合物调节了它们已知蛋白质靶标之一的丰度。最后，蛋白质组学数据使我们发现抑制线粒体功能是 MAP2K1/2 抑制剂 PD184352 的脱靶机制，而 ALK 抑制剂色瑞替尼调节自噬。