Noninvasive lung cancer detection via pulmonary protease profiling

Abstract

Lung cancer is the leading cause of cancer-related death and patients most commonly present with incurable metastatic disease. National guidelines recommend screening for high-risk patients with low-dose computed tomography (LDCT), but this approach has limitations including high false positive rates. Activity-based nanosensors (ABNs) detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease activity. Here, we demonstrate the translational potential of ABNs by coupling ABN multiplexing with intrapulmonary delivery to detect early-stage lung cancer in an immunocompetent, genetically engineered mouse model (GEMM). The design of the multiplexed panel of sensors was informed by comparative transcriptomic analysis of human and mouse lung adenocarcinoma data sets and in vitro cleavage assays with recombinant candidate proteases. When employed in a Kras and Trp53 mutant lung adenocarcinoma mouse model, this approach confirmed the role of metalloproteases in lung cancer and enabled accurate early detection of disease, with 92% sensitivity and 100% specificity.