Classification of prostate cancer using a protease activity nanosensor library

Significance Prostate cancer is the most common noncutaneous cancer in men, but there is a need for better biomarkers that can identify aggressive disease. Here, we describe a bottom-up approach to design nanosensors to detect and classify prostate cancer. We used transcriptomic and proteomic analysis to identify proteolytic enzymes that are dysregulated in human prostate cancer and built a library of nanosensors to measure their activity in vivo using multiplexed urinary readouts. In mouse models, we demonstrated that these nanosensors could classify aggressive prostate cancer and outperform a serum biomarker. This library could be deployed as a screening test to identify patients with higher-risk prostate cancer at the time of screening.