Multiscale profiling of protease activity in cancer

The activity of multiple enzymes is dysregulated in cancer, but this cannot always be measured through enzyme expression. Here, the authors develop methods to measure protease activity across the organism, tissue, and single cell scales, and identify protease dysregulation in lung cancer and its response to targeted therapy. Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. High-fidelity methods to profile enzyme activity are therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we present an integrated set of methods for measuring specific protease activities across scales, and deploy these methods to study treatment response in an autochthonous model of Alk-mutant lung cancer. We leverage multiplexed nanosensors and machine learning to analyze in vivo protease activity dynamics in lung cancer, identifying significant dysregulation that includes enhanced cleavage of a peptide, S1, which rapidly returns to healthy levels with targeted therapy. Through direct on-tissue localization of protease activity, we pinpoint S1 cleavage to the tumor vasculature. To link protease activity to cellular function, we design a high-throughput method to isolate and characterize proteolytically active cells, uncovering a pro-angiogenic phenotype in S1-cleaving cells. These methods provide a framework for functional, multiscale characterization of protease dysregulation in cancer.

Automated summary

This study develops methods to measure protease activity in cancer and identifies protease dysregulation in lung cancer. By analyzing protease activity dynamics using multiplexed nanosensors and machine learning, the researchers discover significant dysregulation associated with lung cancer. They also localize the dysregulated activity to tumor vasculature and uncover a pro-angiogenic phenotype in cells with specific protease activity.