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Neoantigen identification: Technological advances and challenges.
- Source :
-
Methods in cell biology [Methods Cell Biol] 2024; Vol. 183, pp. 265-302. Date of Electronic Publication: 2023 Sep 19. - Publication Year :
- 2024
-
Abstract
- Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.<br />Competing Interests: Conflicts of interest The authors declare that there is no conflict of interest regarding the publication of this paper.<br /> (Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)
Details
- Language :
- English
- ISSN :
- 0091-679X
- Volume :
- 183
- Database :
- MEDLINE
- Journal :
- Methods in cell biology
- Publication Type :
- Academic Journal
- Accession number :
- 38548414
- Full Text :
- https://doi.org/10.1016/bs.mcb.2023.06.005