4.7 Article

Induction of neoantigen-reactive T cells from healthy donors

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NATURE PROTOCOLS
卷 14, 期 6, 页码 1926-1943

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41596-019-0170-6

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资金

  1. Stiftelsen Kristian Gerhard Jebsen
  2. South-Eastern Regional Health Authority Norway
  3. Research Council of Norway
  4. Norwegian Cancer Society
  5. University of Oslo
  6. Oslo University Hospital
  7. Queen Wilhelmina Cancer Research Award

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The identification of immunogenic neoantigens and their cognate T cells represents the most crucial and rate-limiting steps in the development of personalized cancer immunotherapies that are based on vaccination or on infusion of T cell receptor (TCR)-engineered T cells. Recent advances in deep-sequencing technologies and in silico prediction algorithms have allowed rapid identification of candidate neoepitopes. However, large-scale validation of putative neoepitopes and the isolation of reactive T cells are challenging because of the limited availablity of patient material and the low frequencies of neoepitope-specific T cells. Here we describe a standardized protocol for the induction of neoepitopereactive T cells from healthy donor T cell repertoires, unaffected by the potentially immunosuppressive environment of the tumor-bearing host. Monocyte-derived dendritic cells (DCs) transfected with mRNA encoding candidate neoepitopes are used to prime autologous naive CD8(+) T cells. Antigen-specific T cells that recognize endogenously processed and presented epitopes are detected using peptide-MHC (pMHC) multimers. Single multimer-positive T cells are sorted for the identification of TCR sequences, after an optional step that includes clonal expansion and functional characterization. The time required to identify neoepitope-specific T cells is 15 d, with an additional 2-4 weeks required for clonal expansion and downstream functional characterization. Identified neoepitopes and corresponding TCRs provide candidates for use in vaccination and TCR-based cancer immunotherapies, and datasets generated by this technology should be useful for improving algorithms to predict immunogenic neoantigens.

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