Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.1054231/full |
| _version_ | 1828068700395143168 |
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| author | Yu Cai Rui Chen Shenghan Gao Wenqing Li Yuru Liu Guodong Su Mingming Song Mengju Jiang Chao Jiang Xi Zhang |
| author_facet | Yu Cai Rui Chen Shenghan Gao Wenqing Li Yuru Liu Guodong Su Mingming Song Mengju Jiang Chao Jiang Xi Zhang |
| author_sort | Yu Cai |
| collection | DOAJ |
| description | The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed. |
| first_indexed | 2024-04-11T00:08:07Z |
| format | Article |
| id | doaj.art-d514ff0851274a65bbcd42740135c392 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T00:08:07Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-d514ff0851274a65bbcd42740135c3922023-01-09T10:16:49ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-01-011210.3389/fonc.2022.10542311054231Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapyYu Cai0Rui Chen1Shenghan Gao2Wenqing Li3Yuru Liu4Guodong Su5Mingming Song6Mengju Jiang7Chao Jiang8Xi Zhang9School of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaDepartment of Neurology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, ChinaSchool of Medicine, Northwest University, Xi’an, Shaanxi, ChinaThe field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.https://www.frontiersin.org/articles/10.3389/fonc.2022.1054231/fullneoantigen predictioncancer neoantigencancer immunotherapyartificial intelligencenext generation sequencing |
| spellingShingle | Yu Cai Rui Chen Shenghan Gao Wenqing Li Yuru Liu Guodong Su Mingming Song Mengju Jiang Chao Jiang Xi Zhang Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy Frontiers in Oncology neoantigen prediction cancer neoantigen cancer immunotherapy artificial intelligence next generation sequencing |
| title | Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy |
| title_full | Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy |
| title_fullStr | Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy |
| title_full_unstemmed | Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy |
| title_short | Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy |
| title_sort | artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy |
| topic | neoantigen prediction cancer neoantigen cancer immunotherapy artificial intelligence next generation sequencing |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.1054231/full |
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