Genetic Engineering of Primary Human Natural Killer (NK) Cells for Enhanced Cancer Immunotherapy

Pomeroy, Emily
2021-06
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Genetic Engineering of Primary Human Natural Killer (NK) Cells for Enhanced Cancer Immunotherapy

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2021-06

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Natural killer (NK) cells are a critical component of the innate immune system due to their ability to kill a variety of target cells, including cancer cells. This innate anti-tumor phenotype has driven intense interest in the use of NK cells for cancer immunotherapy, but this has seen limited success in the clinic. Enhancing NK cell cytotoxicity by augmenting activating signals or eliminating inhibitory signals could significantly improve NK-based cancer immunotherapy. We have developed highly efficient methods for editing the genome of human NK cells. Specifically, to target inhibitory signals for elimination, we have developed methods for CRISPR-Cas9-based gene knockout. We have also created platforms for delivery of activating signals using either CRISPR-Cas9 in combination with recombinant adeno-associated virus (rAAV) and a non-viral approach for engineering using DNA transposons. We targeted relevant genes (ADAM17 and PDCD1) for knockout and delivering activating receptors CD16a and a CD19-specific chimeric antigen receptor (CAR). Importantly, we show direct functional consequences of engineering steps, using preclinical in vitro and in vivo models. Furthermore, we demonstrate the clinical scalability of all methods. The focus of this work was to develop methods for engineering primary human NK cells, with the goal of creating clinical products to treat human disease. Future work will focus on combining approaches to generate NK cells expertly equipped to kill cancer.

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University of Minnesota Ph.D. dissertation. June 2021. Major: Microbiology, Immunology and Cancer Biology. Advisor: Branden Moriarity. 1 computer file (PDF); viii, 108 pages.

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Pomeroy, Emily. (2021). Genetic Engineering of Primary Human Natural Killer (NK) Cells for Enhanced Cancer Immunotherapy. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/224657.

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