Lung cancer is the leading cause of cancer-related death, and patients most commonly present with incurable advanced-stage disease. U.S. national guidelines recommend screening for high-risk patients with low-dose computed tomography, but this approach has limitations including high false-positive rates. Activity-based nanosensors can detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease activity. Here, we demonstrate the translational potential of activity-based nanosensors for lung cancer by coupling nanosensor multiplexing with intrapulmonary delivery and machine learning to detect localized disease in two immunocompetent genetically engineered mouse models. The design of our multiplexed panel of sensors was informed by comparative transcriptomic analysis of human and mouse lung adenocarcinoma datasets and in vitro cleavage assays with recombinant candidate proteases. Intrapulmonary administration of the nanosensors to a Kras- and Trp53-mutant lung adenocarcinoma mouse model confirmed the role of metalloproteases in lung cancer and enabled accurate detection of localized disease, with 100% specificity and 81% sensitivity. Furthermore, this approach generalized to an alternative autochthonous model of lung adenocarcinoma, where it detected cancer with 100% specificity and 95% sensitivity and was not confounded by lipopolysaccharide-driven lung inflammation. These results encourage the clinical development of activity-based nanosensors for the detection of lung cancer.

肺癌是癌症相关死亡的主要原因，患者最常出现无法治愈的晚期疾病。美国国家指南建议使用低剂量计算机断层扫描对高危患者进行筛查，但这种方法存在局限性，包括高假阳性率。基于活动的纳米传感器可以检测体内失调的蛋白酶并释放报告分子以提供疾病活动的尿液读数。在这里，我们通过将纳米传感器多路复用与肺内给药和机器学习相结合，在两种具有免疫能力的基因工程小鼠模型中检测局部疾病，证明了基于活性的纳米传感器对肺癌的转化潜力。通过人类和小鼠肺腺癌数据集的比较转录组分析和重组候选蛋白酶的体外切割测定，我们的多路传感器面板的设计得到了信息。将纳米传感器肺内给药Kras和Trp53突变肺腺癌小鼠模型证实了金属蛋白酶在肺癌中的作用，并能够准确检测局部疾病，具有 100% 的特异性和 81% 的敏感性。此外，这种方法推广到肺腺癌的另一种本土模型，它以 100% 的特异性和 95% 的灵敏度检测癌症，并且不受脂多糖驱动的肺部炎症的影响。这些结果促进了用于检测肺癌的基于活性的纳米传感器的临床开发。