Trained Immunity-Promoting Nanobiologic Therapy Suppresses Tumor Growth and Potentiates Checkpoint Inhibition
- PMID: 33125893
- PMCID: PMC8074872
- DOI: 10.1016/j.cell.2020.09.059
Trained Immunity-Promoting Nanobiologic Therapy Suppresses Tumor Growth and Potentiates Checkpoint Inhibition
Abstract
Trained immunity, a functional state of myeloid cells, has been proposed as a compelling immune-oncological target. Its efficient induction requires direct engagement of myeloid progenitors in the bone marrow. For this purpose, we developed a bone marrow-avid nanobiologic platform designed specifically to induce trained immunity. We established the potent anti-tumor capabilities of our lead candidate MTP10-HDL in a B16F10 mouse melanoma model. These anti-tumor effects result from trained immunity-induced myelopoiesis caused by epigenetic rewiring of multipotent progenitors in the bone marrow, which overcomes the immunosuppressive tumor microenvironment. Furthermore, MTP10-HDL nanotherapy potentiates checkpoint inhibition in this melanoma model refractory to anti-PD-1 and anti-CTLA-4 therapy. Finally, we determined MTP10-HDL's favorable biodistribution and safety profile in non-human primates. In conclusion, we show that rationally designed nanobiologics can promote trained immunity and elicit a durable anti-tumor response either as a monotherapy or in combination with checkpoint inhibitor drugs.
Keywords: cancer; checkpoint inhibitors; immunotherapy; innate immunity; melanoma; myeloid cells; nanobiologics; nanomedicine; nanotechnology; trained immunity.
Copyright © 2020 Elsevier Inc. All rights reserved.
Conflict of interest statement
Declaration of Interests W.J.M.M., L.A.B.J., J.O., Z.A.F., and M.G.N. are scientific co-founders of and have equity in Trained Therapeutix Discovery. W.J.M.M. and Z.A.F. have consulting agreements with Trained Therapeutix Discovery.
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Comment in
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Nanobiologic trains innate immunity for anticancer responses.Nat Rev Immunol. 2020 Dec;20(12):718-719. doi: 10.1038/s41577-020-00476-w. Nat Rev Immunol. 2020. PMID: 33173162 No abstract available.
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