Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy
Abstract Neoantigens, which are tumor-specific peptides generated by malignant cells, can be presented to T cells to elicit immune responses. Owing to their tumor-specific properties, neoantigens have emerged as one of the most promising biomarkers and targets for cancer immunotherapy. Previous stud...
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BMC
2025-07-01
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| Series: | Biomarker Research |
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| Online Access: | https://doi.org/10.1186/s40364-025-00808-9 |
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| author | Zhenchang Wang Yu Gu Xiao Sun Hao Huang |
| author_facet | Zhenchang Wang Yu Gu Xiao Sun Hao Huang |
| author_sort | Zhenchang Wang |
| collection | DOAJ |
| description | Abstract Neoantigens, which are tumor-specific peptides generated by malignant cells, can be presented to T cells to elicit immune responses. Owing to their tumor-specific properties, neoantigens have emerged as one of the most promising biomarkers and targets for cancer immunotherapy. Previous studies have demonstrated their capacity to mediate tumor-specific immune responses in targeting and eliminating tumor cells while preserving normal cellular function. Driven by advancements in high-throughput sequencing technologies, mass spectrometry, and artificial intelligence, researchers have developed a growing interest in establishing more accurate neoantigen prediction algorithms. Here, we presented a comprehensive review of integrated neoantigen prediction algorithms, encompassing task definition, theoretical developments, benchmark datasets, cutting-edge applications, and future research directions. We systematically evaluated recent advancements in neoantigen source characterization and prediction algorithms, with particular emphasis on innovative methods for HLA-peptide binding and TCR recognition developed. Additionally, we explored the cutting-edge applications of neoantigens in personalized cancer vaccine design and adoptive cell therapies. We delineated potential research directions and the future prospects for neoantigen-based therapies, including integrating multi-omics data to discover universal neoantigens, addressing algorithmic generalization challenges and diversifying neoantigen validation methods. |
| format | Article |
| id | doaj-art-7ccc1bb9898348eebe68d3ef5ee2c994 |
| institution | Kabale University |
| issn | 2050-7771 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Biomarker Research |
| spelling | doaj-art-7ccc1bb9898348eebe68d3ef5ee2c9942025-08-20T04:02:54ZengBMCBiomarker Research2050-77712025-07-0113112010.1186/s40364-025-00808-9Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapyZhenchang Wang0Yu Gu1Xiao Sun2Hao Huang3Institute of Microphysiological Systems, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast UniversityInstitute of Microphysiological Systems, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast UniversityInstitute of Microphysiological Systems, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast UniversityInstitute of Microphysiological Systems, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast UniversityAbstract Neoantigens, which are tumor-specific peptides generated by malignant cells, can be presented to T cells to elicit immune responses. Owing to their tumor-specific properties, neoantigens have emerged as one of the most promising biomarkers and targets for cancer immunotherapy. Previous studies have demonstrated their capacity to mediate tumor-specific immune responses in targeting and eliminating tumor cells while preserving normal cellular function. Driven by advancements in high-throughput sequencing technologies, mass spectrometry, and artificial intelligence, researchers have developed a growing interest in establishing more accurate neoantigen prediction algorithms. Here, we presented a comprehensive review of integrated neoantigen prediction algorithms, encompassing task definition, theoretical developments, benchmark datasets, cutting-edge applications, and future research directions. We systematically evaluated recent advancements in neoantigen source characterization and prediction algorithms, with particular emphasis on innovative methods for HLA-peptide binding and TCR recognition developed. Additionally, we explored the cutting-edge applications of neoantigens in personalized cancer vaccine design and adoptive cell therapies. We delineated potential research directions and the future prospects for neoantigen-based therapies, including integrating multi-omics data to discover universal neoantigens, addressing algorithmic generalization challenges and diversifying neoantigen validation methods.https://doi.org/10.1186/s40364-025-00808-9Neoantigen discoveryCancer immunotherapyArtificial intelligence |
| spellingShingle | Zhenchang Wang Yu Gu Xiao Sun Hao Huang Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy Biomarker Research Neoantigen discovery Cancer immunotherapy Artificial intelligence |
| title | Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy |
| title_full | Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy |
| title_fullStr | Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy |
| title_full_unstemmed | Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy |
| title_short | Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy |
| title_sort | computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy |
| topic | Neoantigen discovery Cancer immunotherapy Artificial intelligence |
| url | https://doi.org/10.1186/s40364-025-00808-9 |
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