Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy
Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigen...
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Online Access: | http://dx.doi.org/10.1080/2162402X.2020.1868130 |
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doaj-c96423e775ad49e891b21b02b46d94592021-01-26T11:50:11ZengTaylor & Francis GroupOncoImmunology2162-402X2021-01-0110110.1080/2162402X.2020.18681301868130Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapyPeng Bai0Yongzheng Li1Qiuping Zhou2Jiaqi Xia3Peng-Cheng Wei4Hexiang Deng5Min Wu6Sanny K. Chan7John W. Kappler8Yu Zhou9Eric Tran10Philippa Marrack11Lei Yin12Wuhan UniversityWuhan UniversityWuhan UniversityWuhan UniversityNational Jewish HealthWuhan UniversityWuhan UniversityNational Jewish HealthNational Jewish HealthWuhan UniversityEarle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Cancer Institute, Portland, USANational Jewish HealthWuhan UniversityGenetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the “NP” rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better understand this rule, we solved several neoantigen/MHC structures. These structures showed that neoantigens that follow this rule not only increase peptide-MHC binding affinity but also create new TCR-binding features. These molecular insights highlight the value of immune-based classification in neoantigen studies and may enable the design of more effective cancer immunotherapies.http://dx.doi.org/10.1080/2162402X.2020.1868130cancer immunologymajor histocompatibility complex (mhc)vaccineneoantigen predictionantigen immunogenicity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peng Bai Yongzheng Li Qiuping Zhou Jiaqi Xia Peng-Cheng Wei Hexiang Deng Min Wu Sanny K. Chan John W. Kappler Yu Zhou Eric Tran Philippa Marrack Lei Yin |
spellingShingle |
Peng Bai Yongzheng Li Qiuping Zhou Jiaqi Xia Peng-Cheng Wei Hexiang Deng Min Wu Sanny K. Chan John W. Kappler Yu Zhou Eric Tran Philippa Marrack Lei Yin Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy OncoImmunology cancer immunology major histocompatibility complex (mhc) vaccine neoantigen prediction antigen immunogenicity |
author_facet |
Peng Bai Yongzheng Li Qiuping Zhou Jiaqi Xia Peng-Cheng Wei Hexiang Deng Min Wu Sanny K. Chan John W. Kappler Yu Zhou Eric Tran Philippa Marrack Lei Yin |
author_sort |
Peng Bai |
title |
Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy |
title_short |
Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy |
title_full |
Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy |
title_fullStr |
Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy |
title_full_unstemmed |
Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy |
title_sort |
immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy |
publisher |
Taylor & Francis Group |
series |
OncoImmunology |
issn |
2162-402X |
publishDate |
2021-01-01 |
description |
Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the “NP” rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better understand this rule, we solved several neoantigen/MHC structures. These structures showed that neoantigens that follow this rule not only increase peptide-MHC binding affinity but also create new TCR-binding features. These molecular insights highlight the value of immune-based classification in neoantigen studies and may enable the design of more effective cancer immunotherapies. |
topic |
cancer immunology major histocompatibility complex (mhc) vaccine neoantigen prediction antigen immunogenicity |
url |
http://dx.doi.org/10.1080/2162402X.2020.1868130 |
work_keys_str_mv |
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