A mass spectrometry-based proteome map of drug action in lung cancer cell lines Part 3

Mass spectrometry-based discovery proteomics is an essential tool for the proximal read-out of cellular drug action. Here, we used a robust proteomic workflow to rapidly and systematically profile the proteomes of five cell lines in response to > 50 drugs. We found that aggregating 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 which frequently responded to drugs and the aggregation of proteome changes across multiple cell lines resolved compound effects on proteostasis. These characteristics were further leveraged to demonstrate efficient target identification of protein degraders. Moreover, we followed up on selected proteomic findings and showed that the inhibition of mitochondrial function is an off-target mechanism of the clinical MEK inhibitor PD184352 and that Ceritinib, an FDA approved drug in lung cancer, modulates autophagy. Overall, this study demonstrates that large-scale proteome perturbation profiling can be a useful addition to the drug discovery toolbox.

基于质谱的发现蛋白质组学（mass spectrometry-based discovery proteomics）是精准读出细胞药物作用近端效应的核心工具。本研究采用稳健的蛋白质组学工作流程，对5种细胞系在50余种药物处理后的蛋白质组进行了快速且系统性的图谱绘制。研究发现，整合数百万条定量蛋白质-药物关联数据，可显著优化单一化合物的作用机制（MoA）解卷积工作。例如，去除频繁响应药物的蛋白质后，作用机制的特异性得以提升；而整合多细胞系的蛋白质组变化数据，则可解析化合物对蛋白质稳态的影响。我们进一步利用上述特性，实现了蛋白质降解剂的高效靶点识别。此外，我们针对筛选出的蛋白质组学发现进行了验证实验，结果显示：线粒体功能抑制是临床用MEK抑制剂PD184352的脱靶作用机制；而美国食品药品监督管理局（FDA）批准用于肺癌治疗的药物色瑞替尼（Ceritinib）可调控细胞自噬。综上，本研究证明，大规模蛋白质组扰动图谱分析可作为药物研发工具库中极具价值的补充手段。