Title:
ENGINEERED ACTIVITY SENSORS FOR PREDICTIVE IMMUNE MONITORING
ENGINEERED ACTIVITY SENSORS FOR PREDICTIVE IMMUNE MONITORING
Author(s)
Mac, Quoc Duy
Advisor(s)
Kwong, Gabriel A.
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Abstract
Immunotherapies are transforming the treatment of immunological disorders for patients with intractable diseases, for instance through the activation of anti-tumor immunity or the suppression of host reactivity against organ transplants. However, modest response rates and treatment resistance remain clinical barriers, driving efforts to improve response monitoring to better guide clinical decision-making. Most current standards to assess immunotherapy responses rely on evaluation of disease burden by either the core biopsy (e.g., to detect transplant rejection) or radiographic imaging (e.g., to assess tumor regression), yet these approaches primarily focus on morphological features downstream of the immune response. There remains a need for early on-treatment biomarkers to identify patients that may benefit from treatment continuation, alleviate the risks of immune-mediated toxicity, and provide opportunities to treat resistant patients with alternative therapies. Biomarkers of T cell immunity have the potential to monitor the onset of therapeutic responses as elevation of T cell activity in the tumor microenvironment drives tumor control, and suppression of host T cell reactivity towards donor cells promotes transplant tolerance.
Proteases are important mediators of immunity and diseases, providing an opportunity to predict responses to immunotherapy early on-treatment. Of note, T cell killing occurs via the classic perforin and granzyme-mediated pathway – the latter of which comprises a family of potent serine proteases – while proteases like matrix-degrading and inflammatory proteases are implicated in major disease hallmarks such as angiogenesis and inflammation. In this thesis, I engineer activity sensors of T cell immunity for two important clinical problems: detecting transplant rejection and monitoring tumor responses during immunotherapy. These sensors monitor the activity of proteases during T cell responses and produce a remote readout in urine. I first develop activity-based nanosensors monitoring granzyme B (GzmB) as noninvasive biomarkers of T cell-mediated acute transplant rejection. Using a skin graft mouse model of organ transplantation, I demonstrate that GzmB nanosensors detect the onset of rejection and indicate allograft failure in recipients treated with subtherapeutic immunosuppression. Then, to noninvasively assess response and resistance to cancer immunotherapy, I design ImmuNe Sensors for monItorinG cHeckpoint blockade Therapy (INSIGHT) by conjugating activity sensors to checkpoint antibodies (e.g., αPD1). In tumor models of immune checkpoint blockade (ICB) response, I show that αPD1-GzmB sensor conjugates retain therapeutic efficacy while producing increased urine signals indicative of early on-treatment responses. Additionally, a multiplexed INSIGHT library sensing tumor and immune proteases enables the development of machine learning classifiers based on urinary outputs to accurately stratify two mechanisms of ICB resistance. This thesis motivates the development of in vivo immune monitoring technologies to maximize the precision and benefit of immunotherapy.
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Date Issued
2021-08-02
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Text
Resource Subtype
Dissertation