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Published on: August 21, 2017
Neoantigen prioritization based on antigen processing and presentation
Serina Tokita1,2, Takayuki Kanaseki1,2, Toshihiko Torigoe1
1Department of Pathology, Sapporo Medical University, Sapporo, Japan.
Abstract:
Somatic mutations in tumor cells give rise to mutant proteins, fragments of which are often presented by MHC and serve as neoantigens. Neoantigens are tumor-specific and not expressed in healthy tissues, making them attractive targets for T-cell-based cancer immunotherapy. On the other hand, since most somatic mutations differ from patient to patient, neoantigen-targeted immunotherapy is personalized medicine and requires their identification in each patient. Computational algorithms and machine learning methods have been developed to prioritize neoantigen candidates. In fact, since the number of clinically relevant neoantigens present in a patient is generally limited, this process is like finding a needle in a haystack. Nevertheless, MHC presentation of neoantigens is not random but follows certain rules, and the efficiency of neoantigen detection may be further improved with technological innovations. In this review, we discuss current approaches to the detection of clinically relevant neoantigens, with a focus on antigen processing and presentation.
Insights
Identifying tumor-specific neoantigens is crucial for personalized cancer immunotherapy. This review explores methods for detecting these neoantigens, focusing on antigen processing and presentation for effective T-cell targeting.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Somatic mutations in tumors create unique neoantigens, which are ideal targets for T-cell-based cancer immunotherapy due to their tumor-specificity.
- Neoantigen-targeted immunotherapy represents a personalized medicine approach, necessitating individual patient neoantigen identification.
Purpose of the Study:
- To review current methodologies for detecting clinically relevant neoantigens.
- To emphasize the importance of antigen processing and presentation in neoantigen detection strategies.
Main Methods:
- Discussion of computational algorithms and machine learning for prioritizing neoantigen candidates.
- Exploration of technological innovations aimed at improving neoantigen detection efficiency.
Main Results:
- Neoantigen presentation by MHC molecules follows specific rules, not random occurrence.
- The number of clinically relevant neoantigens per patient is typically limited, posing a detection challenge.
Conclusions:
- Accurate neoantigen identification is key to advancing personalized cancer vaccines and immunotherapies.
- Further technological advancements in understanding antigen processing and presentation will enhance neoantigen detection and therapeutic efficacy.
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