Neoantigen-Specific Adoptive Cell Therapies for Cancer: Making T-Cell Products More Personal.


Journal

Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960

Informations de publication

Date de publication:
2020
Historique:
received: 31 03 2020
accepted: 15 05 2020
entrez: 23 7 2020
pubmed: 23 7 2020
medline: 13 4 2021
Statut: epublish

Résumé

Mutation-derived neoantigens are taking central stage as a determinant in eliciting effective antitumor immune responses following adoptive T-cell therapies. These mutations are patient-specific, and their targeting calls for highly personalized pipelines. The promising clinical outcomes of tumor-infiltrating lymphocyte (TIL) therapy have spurred interest in generating T-cell infusion products that have been selectively enriched in neoantigen (or autologous tumor) reactivity. The implementation of an isolation step, prior to T-cell

Identifiants

pubmed: 32695101
doi: 10.3389/fimmu.2020.01215
pmc: PMC7333784
doi:

Substances chimiques

Antigens, Neoplasm 0
Biomarkers 0
Cytokines 0
Epitopes, T-Lymphocyte 0
HLA Antigens 0

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1215

Informations de copyright

Copyright © 2020 Bianchi, Harari and Coukos.

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Auteurs

Valentina Bianchi (V)

Department of Oncology, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.

Alexandre Harari (A)

Department of Oncology, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.

George Coukos (G)

Department of Oncology, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.

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Classifications MeSH