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Published on: November 17, 2018
Computational methods and data resources for predicting tumor neoantigens
Xiaofei Zhao1, Lei Wei1, Xuegong Zhang1,2
1MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China.
Abstract:
Neoantigens are tumor-specific antigens presented exclusively by cancer cells. These antigens are recognized as nonself by the host immune system, thereby eliciting an antitumor T-cell response. This response is significantly enhanced through neoantigen-based immunotherapies, such as personalized cancer vaccines. The repertoire of neoantigens is unique to each cancer patient, necessitating neoantigen prediction for designing patient-specific immunotherapies. This review presents the computational methods and data resources used for neoantigen prediction, as well as the prediction-associated challenges. Neoantigen prediction typically uses human leukocyte antigen typing, RNA-seq transcript quantification, somatic variant calling, peptide-major histocompatibility complex (pMHC) presentation prediction, and pMHC recognition prediction as the main computational steps. The immunoinformatics tools used for these steps and for the overall prediction of neoantigens are systematically summarized and detailed in this review.
Insights
Neoantigen prediction identifies unique cancer targets for personalized immunotherapies. Computational methods analyze tumor-specific antigens to guide the development of effective cancer vaccines and treatments.
Area of Science:
- Computational immunology and bioinformatics
- Cancer immunotherapy research
Background:
- Neoantigens are tumor-specific antigens recognized by the immune system, triggering anti-cancer T-cell responses.
- Neoantigen-based immunotherapies, including personalized cancer vaccines, significantly enhance anti-tumor immunity.
- The unique neoantigen repertoire in each patient necessitates accurate prediction for tailored treatments.
Purpose of the Study:
- To review computational methods and data resources for neoantigen prediction.
- To detail the challenges associated with neoantigen prediction.
- To systematically summarize immunoinformatics tools used in neoantigen prediction.
Main Methods:
- Human leukocyte antigen (HLA) typing
- RNA-seq transcript quantification
- Somatic variant calling
- Peptide-major histocompatibility complex (pMHC) presentation and recognition prediction
Main Results:
- Summarizes key computational steps in neoantigen prediction.
- Details various immunoinformatics tools applicable to neoantigen discovery.
- Highlights challenges inherent in predicting neoantigens.
Conclusions:
- Accurate neoantigen prediction is crucial for developing patient-specific cancer immunotherapies.
- Computational approaches and bioinformatics tools are essential for identifying neoantigens.
- This review provides a comprehensive overview of the field for researchers and clinicians.
