Covalent inhibitors of the KRAS^G12C oncoprotein have recently been developed and are being evaluated in clinical trials. Resistance to targeted therapies is common and may limit long-term efficacy of KRAS inhibitors (KRASi). To identify pathways of adaptation to KRASi and predict drug combinations that circumvent resistance, we use mass-spectrometry-based quantitative temporal proteomics to profile the proteomic response to KRASi in pancreatic and lung cancer 2D and 3D cellular models. We quantify 10,805 proteins, representing the most comprehensive KRASi proteome (https://manciaslab.shinyapps.io/KRASi/). Our data reveal common mechanisms of acute and long-term response between KRAS^G12C-driven tumors. Based on these proteomic data, we identify potent combinations of KRASi with phosphatidylinositol 3-kinase (PI3K), HSP90, CDK4/6, and SHP2 inhibitors, in some instances converting a cytostatic response to KRASi monotherapy to a cytotoxic response to combination treatment. Overall, using quantitative temporal proteomics, we comprehensively characterize adaptations to KRASi and identify combinatorial regimens with potential therapeutic utility.

最近已经开发出KRAS ^G12C癌蛋白的共价抑制剂，并正在临床试验中对其进行评估。对靶向疗法的耐药性很普遍，可能会限制KRAS抑制剂（KRASi）的长期疗效。为了确定适应KRASi的途径并预测可避免耐药性的药物组合，我们使用基于质谱的定量时间蛋白质组学来分析在胰腺癌和肺癌2D和3D细胞模型中对KRASi的蛋白质组反应。我们量化了10805种蛋白质，它们代表了最全面的KRASi蛋白质组（https://manciaslab.shinyapps.io/KRASi/）。我们的数据揭示了KRAS ^G12C之间急性和长期反应的常见机制驱动的肿瘤。基于这些蛋白质组学数据，我们确定了KRASi与磷脂酰肌醇3激酶（PI3K），HSP90，CDK4 / 6和SHP2抑制剂的有效组合，在某些情况下，将对KRASi单一疗法的抑制作用转化为对联合治疗的细胞毒性反应。总体而言，使用定量的时间蛋白质组学，我们全面表征了对KRASi的适应性，并确定了具有潜在治疗作用的组合方案。