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Cell Stem Cell
Article . 2021 . Peer-reviewed
License: Elsevier Non-Commercial
Data sources: Crossref
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Harnessing features of adaptive NK cells to generate iPSC-derived NK cells for enhanced immunotherapy

Authors: Karrune V. Woan; Hansol Kim; Ryan Bjordahl; Zachary B. Davis; Svetlana Gaidarova; John Goulding; Brian Hancock; +24 Authors

Harnessing features of adaptive NK cells to generate iPSC-derived NK cells for enhanced immunotherapy

Abstract

Select subsets of immune effector cells have the greatest propensity to mediate antitumor responses. However, procuring these subsets is challenging, and cell-based immunotherapy is hampered by limited effector-cell persistence and lack of on-demand availability. To address these limitations, we generated a triple-gene-edited induced pluripotent stem cell (iPSC). The clonal iPSC line was engineered to express a high affinity, non-cleavable version of the Fc receptor CD16a and a membrane-bound interleukin (IL)-15/IL-15R fusion protein. The third edit was a knockout of the ecto-enzyme CD38, which hydrolyzes NAD+. Natural killer (NK) cells derived from these uniformly engineered iPSCs, termed iADAPT, displayed metabolic features and gene expression profiles mirroring those of cytomegalovirus-induced adaptive NK cells. iADAPT NK cells persisted in vivo in the absence of exogenous cytokine and elicited superior antitumor activity. Our findings suggest that unique subsets of the immune system can be modeled through iPSC technology for effective treatment of patients with advanced cancer.

Country
Norway
Keywords

Killer Cells, Natural, Neoplasms, Induced Pluripotent Stem Cells, 610, Humans, Immunotherapy, Immunotherapy, Adoptive, Cells, Cultured

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    149
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
149
Top 1%
Top 10%
Top 0.1%
Green
Related to Research communities
Cancer Research