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Programmable bacteria synergize with PD-1 blockade to overcome cancer cell–intrinsic immune resistance mechanisms

Tumors employ a variety of genetic resistance mechanisms to evade immune responses and immunotherapies such as PD-1 blockade. The pleiotropic cytokine interferon-gamma (IFNγ) is a potent immune effector and critical for patient response to PD-1 blockade, yet conventional systemic delivery is hindered by severe dose limiting toxicities. As such, the effects of exogenously introduced IFNγ either as monotherapy or in combination with PD-1 blockade in the context of different tumor genetic background remain poorly understood.

Synthetic biology allows programming of microbes for tumor-specific delivery of therapeutic candidates that are otherwise not possible using conventional administration strategies. Herein, we engineered a strain of probiotic bacteria that home to tumors and locally release IFNγ. We validated the efficacy of our therapeutic strain, either as monotherapy or in combination with PD-1 blockade, in multiple murine tumor models.

Within this dissertation, we demonstrate that a single intratumoral injection of these IFNγ-producing bacteria is sufficient to drive systemic tumor antigen–specific antitumor immunity, without observable toxicity. Although cancer cells employ various resistance mechanisms to evade immune responses, bacteria-derived IFNγ additionally overcomes primary resistance to PD-1 blockade via activation of cytotoxic CD4⁺Foxp3⁻ and CD8⁺ T cells. Moreover, by activating NK cells, bacteria-derived IFNγ also overcome acquired resistance mechanisms to PD-1 blockade, specifically loss of function mutations in IFNγ signaling and antigen presentation pathways. Collectively, this dissertation highlights the promise of combining IFNγ-producing bacteria with PD-1 blockade as a therapeutic strategy for overcoming immunotherapy-resistant, locally advanced, and metastatic disease.

Identiferoai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/hnwg-dn50
Date January 2024
CreatorsLi, Fangda
Source SetsColumbia University
LanguageEnglish
Detected LanguageEnglish
TypeTheses

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