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Machaca V, Goyzueta V, Cruz MG, Sejje E, Pilco LM, López J, Túpac Y. Transformers meets neoantigen detection: a systematic literature review. J Integr Bioinform 2024; 0:jib-2023-0043. [PMID: 38960869 DOI: 10.1515/jib-2023-0043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 03/20/2024] [Indexed: 07/05/2024] Open
Abstract
Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.
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Affiliation(s)
| | | | | | - Erika Sejje
- Universidad Nacional de San Agustín, Arequipa, Perú
| | | | | | - Yván Túpac
- 187038 Universidad Católica San Pablo , Arequipa, Perú
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2
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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3
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Li X, You J, Hong L, Liu W, Guo P, Hao X. Neoantigen cancer vaccines: a new star on the horizon. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0395. [PMID: 38164734 PMCID: PMC11033713 DOI: 10.20892/j.issn.2095-3941.2023.0395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.
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Affiliation(s)
- Xiaoling Li
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Jian You
- Department of Thoracic Oncology, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- Department of Thoracic Oncology Surgery, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Liping Hong
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Weijiang Liu
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Peng Guo
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Xishan Hao
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
- Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
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Pang Z, Lu MM, Zhang Y, Gao Y, Bai JJ, Gu JY, Xie L, Wu WZ. Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges. Biomark Res 2023; 11:104. [PMID: 38037114 PMCID: PMC10690996 DOI: 10.1186/s40364-023-00534-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/22/2023] [Indexed: 12/02/2023] Open
Abstract
Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.
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Affiliation(s)
- Zhi Pang
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Man-Man Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Yu Zhang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Yuan Gao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Jin-Jin Bai
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jian-Ying Gu
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China.
| | - Wei-Zhong Wu
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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5
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Nguyen BQT, Tran TPD, Nguyen HT, Nguyen TN, Pham TMQ, Nguyen HTP, Tran DH, Nguyen V, Tran TS, Pham TVN, Le MT, Phan MD, Giang H, Nguyen HN, Tran LS. Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling. Front Immunol 2023; 14:1251603. [PMID: 37731488 PMCID: PMC10507271 DOI: 10.3389/fimmu.2023.1251603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 08/17/2023] [Indexed: 09/22/2023] Open
Abstract
Introduction Neoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow. Methods In this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP). Results We detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data. Discussion This integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients.
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Affiliation(s)
| | | | - Huu Thinh Nguyen
- University Medical Center Ho Chi Minh City, Ho Chi Minh, Vietnam
| | | | | | | | - Duc Huy Tran
- University Medical Center Ho Chi Minh City, Ho Chi Minh, Vietnam
| | - Vy Nguyen
- Medical Genetics Institute, Ho Chi Minh, Vietnam
| | - Thanh Sang Tran
- University Medical Center Ho Chi Minh City, Ho Chi Minh, Vietnam
| | | | - Minh-Triet Le
- University Medical Center Ho Chi Minh City, Ho Chi Minh, Vietnam
| | | | - Hoa Giang
- Medical Genetics Institute, Ho Chi Minh, Vietnam
| | | | - Le Son Tran
- Medical Genetics Institute, Ho Chi Minh, Vietnam
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6
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Tian J, Ma J. The Value of Microbes in Cancer Neoantigen Immunotherapy. Pharmaceutics 2023; 15:2138. [PMID: 37631352 PMCID: PMC10459105 DOI: 10.3390/pharmaceutics15082138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/06/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.
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Affiliation(s)
- Junrui Tian
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
| | - Jian Ma
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
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Nibeyro G, Baronetto V, Folco JI, Pastore P, Girotti MR, Prato L, Morón G, Luján HD, Fernández EA. Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis. Front Immunol 2023; 14:1094236. [PMID: 37564650 PMCID: PMC10411733 DOI: 10.3389/fimmu.2023.1094236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/10/2023] [Indexed: 08/12/2023] Open
Abstract
Introduction Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Methods Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers. Results Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers. Conclusion Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.
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Affiliation(s)
- Guadalupe Nibeyro
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
| | - Veronica Baronetto
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
| | - Juan I. Folco
- Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
| | - Pablo Pastore
- Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
| | - Maria Romina Girotti
- Universidad Argentina de la Empresa (UADE), Instituto de Tecnología (INTEC), Buenos Aires, Argentina
| | - Laura Prato
- Instituto Académico Pedagógico de Ciencias Básicas y Aplicadas, Universidad Nacional de Villa María, Villa María, Córdoba, Argentina
| | - Gabriel Morón
- Departamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Córdoba, Argentina
- Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Hugo D. Luján
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
- Facultad de Ciencias de la Salud, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
| | - Elmer A. Fernández
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
- Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
- Facultad de Ciencias Exactas, Físicas y Naturales (FCEFyN), Universidad Nacional de Córdoba (UNC), Córdoba, Argentina
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Zhang Y, Jian X, Xu L, Zhao J, Lu M, Lin Y, Xie L. iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. Front Genet 2023; 14:1141535. [PMID: 37229205 PMCID: PMC10203616 DOI: 10.3389/fgene.2023.1141535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.
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Affiliation(s)
- Yu Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Bio-Diversity Science, School of Life Sciences, Fudan University, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Manman Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yong Lin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
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Tan X, Xu L, Jian X, Ouyang J, Hu B, Yang X, Wang T, Xie L. PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions. Cells 2023; 12:cells12050782. [PMID: 36899918 PMCID: PMC10000440 DOI: 10.3390/cells12050782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.
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Affiliation(s)
- Xiaoxiu Tan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Jian Ouyang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Bo Hu
- Liver Cancer Institute, Fudan University, Shanghai 200032, China
| | - Xinrong Yang
- Liver Cancer Institute, Fudan University, Shanghai 200032, China
| | - Tao Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Correspondence: (T.W.); (L.X.)
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
- Correspondence: (T.W.); (L.X.)
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10
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Jian X, Xu L, Zhao J, Wang Y, Zhou W, Xie L. NAIRscore as a biomarker for the quality of immune response to neoantigens is related with an increased overall survival in multiple myeloma. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 29:285-295. [PMID: 35950215 PMCID: PMC9352810 DOI: 10.1016/j.omtn.2022.07.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022]
Abstract
Neoantigen provides a promising breakthrough in tumor immunotherapy, although only a subset of patients responds well due to the quality of their immune response. However, few biomarkers have been reported to measure the quality of immune response to neoantigens and to predict prognosis of patients with multiple myeloma (MM). Here, we first developed a neoantigen-prediction pipeline starting from outcomes of somatic mutations and gene-expression profiles. Given the expression of some specific marker genes, the human leukocyte antigen (HLA)-I score and the cytolytic score were evaluated respectively to reflect HLA-I molecular expression and CD8+ T/natural killer (NK) cell abundance. According to the process of the immune response to neoantigens, we comprehensively took neoantigen load, cytolytic score, and HLA-I score to construct a neoantigen immune response score (NAIRscore), in which the HLA-I score presented a hazard ratio (HR) of less than 1, while the cytolytic score and neoantigen load presented a HR of greater than 1. Meanwhile, NAIRscore presented a competitive advantage to stratify MM samples. Especially, those exhibiting high NAIRscore correlated with an increased overall survival (OS), echoing the underlying molecular signatures of lower driver-gene mutations and down-regulated immune response. Notably, an online tool based on this study is provided to identify neoantigens and predict OS.
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Affiliation(s)
- Xingxing Jian
- Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Corresponding author Xingxing Jian, Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Yanhui Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Wen Zhou
- Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Sciences, Central South University, Changsha, Hunan, China
| | - Lu Xie
- Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Corresponding author Lu Xie, Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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