Harness the tools and design principles of synthetic biology to develop novel therapies that can overcome key challenges in cancer immunotherapy

Cancer immunotherapy has shown tremendous success, however several major challenges such as the lack of ideal targetable tumor surface antigens, tumor-mediated immunosuppression, and the potential for severe immune-mediated toxicity, still limit the application of cancer immunotherapy. Our vision is to develop transformative therapies that can challenge the status quo and overcome these outstanding challenges. 

We are leveraging the power of synthetic biology, bioinformatics, and machine learning, to develop effective cancer immunotherapies. Synthetic biology is a field that applies rigorous engineering paradigms from electrical engineering and computer science (i.e., modularity, orthogonality, tunability, and composability) to genetic engineering. It aims to program living cells with novel functions, such as Boolean operations, analog computing, signal integration, and event recording, to enable sophisticated user-defined functions for sense-and-respond adaptive therapies – medicines that can change their behaviors in response to the disease condition. Combining synthetic biology with bioinformatics and machine learning enables us to identify ideal targetable disease signatures, explore a very large circuit design space, and reliably predict circuit performance.   

We are taking two distinct and synergistic approaches: CAR-T cell-based therapy and gene circuit-based therapy. To increase the efficacy and safety of CAR-T cell-based therapy, our strategies include creating novel pan-tumor targeting Boolean-logic controlled CAR-T cells, small molecule/light-inducible split CAR-T cells, and immunosuppressive signal resistant T cells utilizing sense-and-respond circuits. For gene circuit-based therapy, we are focusing on translating our current gene circuit-based cancer immunotherapies into the clinic and developing novel circuits. We have developed cancer-targeting gene circuits that can sense the activity of two cancer-associated transcription factors (overcoming lack of targetable surface antigen), and trigger tumor-localized combinatorial immunotherapy (addressing systemic toxicity and overcoming immune suppression). This approach effectively turns cancer cells against themselves by reprogramming and force them to produce immunotherapeutic agents. Importantly, we have shown that this platform can cure ovarian cancer in mouse models. This work is particularly important as cancer-targeting gene circuits would be highly synergistic with the current FDA approved immunotherapies and could serve as an integral part of future combination therapies. We envision that the integration of synthetic biology, computational biology, and immunology will enable us to develop the next generation of cancer immunotherapy.