4.8 Article

Alarmin-painted exosomes elicit persistent antitumor immunity in large established tumors in mice

Journal

NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-020-15569-2

Keywords

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Funding

  1. National Key RAMP
  2. D Program of China [2017YFC1001902]
  3. National Natural Science Foundation of China [81803416, 81672124, 81501531, 81671528]
  4. Science AMP
  5. Technology Development Fund of Tianjin Education Commission for Higher Education [2017KJ222]
  6. Tianjin Municipal 13th five-year plan (Tianjin Medical University Talent Project)

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Treating large established tumors is challenging for dendritic cell (DC)-based immunotherapy. DC activation with tumor cell-derived exosomes (TEXs) carrying multiple tumor-associated antigen can enhance tumor recognition. Adding a potent adjuvant, high mobility group nucleosome-binding protein 1 (HMGN1), boosts DCs' ability to activate T cells and improves vaccine efficiency. Here, we demonstrate that TEXs painted with the functional domain of HMGN1 (TEX-N1ND) via an exosomal anchor peptide potentiates DC immunogenicity. TEX-N1ND pulsed DCs (DCTEX-N1ND) elicit long-lasting antitumor immunity and tumor suppression in different syngeneic mouse models with large tumor burdens, most notably large, poorly immunogenic orthotopic hepatocellular carcinoma (HCC). DCTEX-N1ND show increased homing to lymphoid tissues and contribute to augmented memory T cells. Importantly, N1ND-painted serum exosomes from cancer patients also promote DC activation. Our study demonstrates the potency of TEX-N1ND to strengthen DC immunogenicity and to suppress large established tumors, and thus provides an avenue to improve DC-based immunotherapy. The use of tumour exosome-activated dendritic cell (DC) immunotherapy shows promise for the treatment of large established tumours. Here, the authors generate alarmin HMGN1-attached tumour exosomes which significantly improve therapy efficacy by boosting DC activation in several preclinical mouse models.

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