基于新型放射性药物18F-MPG的非小细胞肺癌EGFR-TKI/PD-L1抑制剂联合治疗机制可视化及疗效预判

免疫检查点抑制剂联合分子靶向药物是肺癌诊疗领域的最新治疗理念和策略，并获得80%客观缓解率。然而临床深入应用发现，EGFR突变非小细胞肺癌患者存在PD-L1抑制剂免疫应答差的瓶颈问题，这极大制约了患者从联合治疗中获益。因此如何实现PD-L1调控EGFR通路的关键分子可视化和两药治疗疗效的在体精准预判，将是突破EGFR突变患者群免疫治疗失败的关键科学问题。我团队前期研发的新型放射性药物18F-MPG，已在临床受试中明确了其在精准监测EGFR突变丰度中的重要价值。本项目拟拓展18F-MPG的应用领域，建立独特的PD-L1人源性小鼠荷瘤鼠模型，模拟临床患者PD-L1高表达的肿瘤微环境，同时结合分子成像和分子病理等技术，验证PD-L1抑制剂调控EGFR通路激活的分子机制，从而及时预判两药联用的治疗效果。本项目将为肺癌精准治疗优势人群筛选、方案优化制订及联合治疗疗效预判提供全新技术方法和评估策略。

Immunocheckpoint inhibitors combined with molecular-targeted drugs are the latest treatment concepts and strategies in the field of lung cancer diagnosis and treatment, and have achieved an impressive 80% objective response rate. However, after the clinical application, it is found that patients with EGFR-mutated non-small cell lung cancer have a severe problem of poor immune response to PD-L1 inhibitors, which greatly restricts patients' benefit from the combination therapy. Therefore, how to realize the visualization of the key molecules of PD-L1 regulating the EGFR pathway and the accurate prediction of the efficacy of combination therapy in vivo will be a key scientific problem that breaks through the failure of immunotherapy in NSCLC patients with EGFR mutations. The novel radioactive drug 18F-MPG developed successfully by our team in the early stage has been identified its important value in the accurate monitoring of EGFR mutation abundance in clinical subjects. This project intends to expand the application field of 18F-MPG, establish a unique PD-L1 human-derived mouse tumor-bearing mouse model, simulate the tumor microenvironment with high PD-L1 expression in clinical patients, and combine molecular imaging and molecular pathology technologies, to verify the molecular mechanism of PD-L1 inhibitors regulating the activation of the EGFR pathway, so as to predict the therapeutic effect of the combination of the two drugs in time. The project will provide a new technical method and evaluation strategy for the selection of the dominant population for the precise treatment of lung cancer, the formulation of the optimal plan and the prediction of the combined treatment effect.