Engineering Protease Activity Sensors and Machine Learning Methods to Detect and Characterize Disease

Abstract

Precision medicine promises the ability to intelligently tailor clinical interventions to individual patient needs. Personalizing clinical management will necessitate access to high quality, accurate, and functional information about disease state. However, traditional noninvasive diagnostics such as imaging rely on static measurements of tissue morphology, while molecular features of disease can often only be discerned through invasive methods like biopsy. To acquire functional, personalized information about a disease, a promising approach would be to engineer sensing probes that can directly query disease activity to generate amplified signals that can be analyzed externally. To this end, we have engineered responsive nanosensors that probe tissue microenvironments and detect dysregulated protease activity as a functional biomarker of disease. Because proteases directly contribute to multiple disease processes, including cancer, these sensing probes have the potential to enable quantitative, dynamic, and personalized monitoring of disease activity. In this thesis, we designed new classes of these protease activity sensors and machine learning methods to accurately detect cancer and to functionally profile its biology, aiming to promote the utility of these tools for precision medicine. We first established diagnostic platforms that leverage responsive nanoparticles to measure protease activity in vivo and release reporters that can be detected in the urine. We exploited this basic framework to invent a simple, fast color change urinary readout, and further demonstrated its translational potential by integrating nanosensor multiplexing and machine learning to accurately detect localized lung cancer in two genetically engineered mouse models of lung adenocarcinoma. We then sought to establish generalizable pipelines to rationally design such tools and to functionally dissect the biology of protease dysregulation in cancer. We designed an integrated suite of ex vivo activity sensors that enabled the bottom-up design of a protease-activated diagnostic and demonstrated their ability to reveal new insights into the critical roles of proteases in cancer, encouraging their utility for precision medicine applications and for translation to human disease. Finally, we explored the potential for machine learning methods to close analytical and design loops relevant to precision medicine. Integration of our in vivo and ex vivo protease activity sensors together with machine learning enabled rapid and functional profiling of drug response in a mouse model of lung cancer undergoing treatment with targeted therapy. Last, we developed generalizable algorithms for uncertainty quantification in complex, non-linear neural networks and demonstrated their ability to facilitate high-confidence therapeutic discovery. Together, this thesis provides a framework for the development of protease activity sensors as next-generation diagnostic tools for precision medicine.