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Deng Q, Wang Z, Xiang S, Wang Q, Liu Y, Hou T, Sun H. RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning. J Chem Inf Model 2024; 64:6432-6449. [PMID: 39118363 DOI: 10.1021/acs.jcim.4c01153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.
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Affiliation(s)
- Qirui Deng
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, P. R. China
| | - Sutong Xiang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
| | - Qinghua Wang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
| | - Yifei Liu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
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2
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Su Z, Wu Y, Cao K, Du J, Cao L, Wu Z, Wu X, Wang X, Song Y, Wang X, Duan H. APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules. Methods 2024; 228:38-47. [PMID: 38772499 DOI: 10.1016/j.ymeth.2024.05.013] [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: 01/27/2024] [Revised: 04/28/2024] [Accepted: 05/18/2024] [Indexed: 05/23/2024] Open
Abstract
Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.
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Affiliation(s)
- Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Yejian Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Kaiqiang Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Jie Du
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Zhipeng Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinqiao Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Xudong Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
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3
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Su L, Yan Y, Ma B, Zhao S, Cui Z. GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction. Front Genet 2024; 15:1405032. [PMID: 39050251 PMCID: PMC11266168 DOI: 10.3389/fgene.2024.1405032] [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: 03/22/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024] Open
Abstract
Accurately predicting the binding affinities between Human Leukocyte Antigen (HLA) molecules and peptides is a crucial step in understanding the adaptive immune response. This knowledge can have important implications for the development of effective vaccines and the design of targeted immunotherapies. Existing sequence-based methods are insufficient to capture the structure information. Besides, the current methods lack model interpretability, which hinder revealing the key binding amino acids between the two molecules. To address these limitations, we proposed an interpretable graph convolutional neural network (GCNN) based prediction method named GIHP. Considering the size differences between HLA and short peptides, GIHP represent HLA structure as amino acid-level graph while represent peptide SMILE string as atom-level graph. For interpretation, we design a novel visual explanation method, gradient weighted activation mapping (Grad-WAM), for identifying key binding residues. GIHP achieved better prediction accuracy than state-of-the-art methods across various datasets. According to current research findings, key HLA-peptide binding residues mutations directly impact immunotherapy efficacy. Therefore, we verified those highlighted key residues to see whether they can significantly distinguish immunotherapy patient groups. We have verified that the identified functional residues can successfully separate patient survival groups across breast, bladder, and pan-cancer datasets. Results demonstrate that GIHP improves the accuracy and interpretation capabilities of HLA-peptide prediction, and the findings of this study can be used to guide personalized cancer immunotherapy treatment. Codes and datasets are publicly accessible at: https://github.com/sdustSu/GIHP.
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Affiliation(s)
- Lingtao Su
- Shandong University of Science and Technology, Qingdao, China
| | - Yan Yan
- Shandong Guohe Industrial Technology Research Institute Co. Ltd., Jinan, China
| | - Bo Ma
- Qingdao UNIC Information Technology Co. Ltd., Qingdao, China
| | - Shiwei Zhao
- Shandong University of Science and Technology, Qingdao, China
| | - Zhenyu Cui
- Shandong University of Science and Technology, Qingdao, China
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4
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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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Mugunthan SP, Venkatesan D, Govindasamy C, Selvaraj D, Harish MC. Systems approach to design multi-epitopic peptide vaccine candidate against fowl adenovirus structural proteins for Gallus gallus domesticus. Front Cell Infect Microbiol 2024; 14:1351303. [PMID: 38881736 PMCID: PMC11177691 DOI: 10.3389/fcimb.2024.1351303] [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: 12/06/2023] [Accepted: 05/06/2024] [Indexed: 06/18/2024] Open
Abstract
Introduction Fowl adenovirus (FAdV) is a significant pathogen in poultry, causing various diseases such as hepatitis-hydropericardium, inclusion body hepatitis, and gizzard erosion. Different serotypes of FAdV are associated with specific conditions, highlighting the need for targeted prevention strategies. Given the rising prevalence of FAdV-related diseases globally, effective vaccination and biosecurity measures are crucial. In this study, we explore the potential of structural proteins to design a multi-epitope vaccine targeting FAdV. Methods We employed an in silico approach to design the multi-epitope vaccine. Essential viral structural proteins, including hexon, penton, and fiber protein, were selected as vaccine targets. T-cell and B-cell epitopes binding to MHC-I and MHC-II molecules were predicted using computational methods. Molecular docking studies were conducted to validate the interaction of the multi-epitope vaccine candidate with chicken Toll-like receptors 2 and 5. Results Our in silico methodology successfully identified potential T-cell and B-cell epitopes within the selected viral structural proteins. Molecular docking studies revealed strong interactions between the multi-epitope vaccine candidate and chicken Toll-like receptors 2 and 5, indicating the structural integrity and immunogenic potential of the designed vaccine. Discussion The designed multi-epitope vaccine presents a promising approach for combating FAdV infections in chickens. By targeting essential viral structural proteins, the vaccine is expected to induce a robust immunological response. The in silico methodology utilized in this study provides a rapid and cost-effective means of vaccine design, offering insights into potential vaccine candidates before experimental validation. Future studies should focus on in vitro and in vivo evaluations to further assess the efficacy and safety of the proposed vaccine.
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Affiliation(s)
| | | | - Chandramohan Govindasamy
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dhivya Selvaraj
- Artificial Intelligence Laboratory, School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
| | - Mani Chandra Harish
- Department of Biotechnology, Thiruvalluvar University, Vellore, Tamil Nadu, India
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6
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Mösch A, Grazioli F, Machart P, Malone B. NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population. Bioinformatics 2024; 40:btae205. [PMID: 38614133 PMCID: PMC11076149 DOI: 10.1093/bioinformatics/btae205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
MOTIVATION Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity. RESULTS Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients. AVAILABILITY AND IMPLEMENTATION The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.
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Affiliation(s)
- Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Filippo Grazioli
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Brandon Malone
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
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7
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Sugio T, Uchida N, Miyawaki K, Ohno Y, Eto T, Mori Y, Yoshimoto G, Kikushige Y, Kunisaki Y, Mizuno S, Nagafuji K, Iwasaki H, Kamimura T, Ogawa R, Miyamoto T, Taniguchi S, Akashi K, Kato K. Prognostic impact of HLA supertype mismatch in single-unit cord blood transplantation. Bone Marrow Transplant 2024; 59:466-472. [PMID: 38238452 DOI: 10.1038/s41409-023-02183-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 11/26/2023] [Accepted: 12/12/2023] [Indexed: 04/06/2024]
Abstract
The "human leukocyte antigen (HLA) supertype" is a functional classification of HLA alleles, which was defined by structural features and peptide specificities, and has been reportedly associated with the clinical outcomes of viral infections and autoimmune diseases. Although the disparity in each HLA locus was reported to have no clinical significance in single-unit cord blood transplantation (sCBT), the clinical significance of the HLA supertype in sCBT remains unknown. Therefore, we retrospectively analyzed clinical data of 1603 patients who received sCBT in eight institutes in Japan between 2000 and 2017. Each HLA allele was categorized into 19 supertypes, and the prognostic effect of disparities was then assessed. An HLA-B supertype mismatch was identified as a poor prognostic factor (PFS: hazard ratio [HR] = 1.23, p = 0.00044) and was associated with a higher cumulative incidence (CI) of relapse (HR = 1.24, p = 0.013). However, an HLA-B supertype mismatch was not associated with the CI of acute and chronic graft-versus-host-disease. The multivariate analysis for relapse and PFS showed the significance of an HLA-B supertype mismatch independent of allelic mismatches, and other previously reported prognostic factors. HLA-B supertype-matched grafts should be selected in sCBT.
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Affiliation(s)
- Takeshi Sugio
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
- Divisions of Oncology and Hematology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Naoyuki Uchida
- Department of Hematology, Toranomon Hospital, Tokyo, Japan
| | - Kohta Miyawaki
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Yuju Ohno
- Department of Hematology, Kitakyushu Municipal Medical Center, Fukuoka, Japan
| | - Tetsuya Eto
- Department of Hematology, Hamanomachi Hospital, Fukuoka, Japan
| | - Yasuo Mori
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Goichi Yoshimoto
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Yoshikane Kikushige
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Yuya Kunisaki
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Shinichi Mizuno
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koji Nagafuji
- Department of Medicine, Kurume University School of Medicine, Fukuoka, Japan
| | - Hiromi Iwasaki
- Department of Hematology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | | | - Ryosuke Ogawa
- Department of Hematology, JCHO Kyushu Hospital, Fukuoka, Japan
| | - Toshihiro Miyamoto
- Department of Hematology, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa, Japan
| | - Shuichi Taniguchi
- Department of Hematology, Toranomon Hospital, Tokyo, Japan
- Department of Hematology, Hamanomachi Hospital, Fukuoka, Japan
| | - Koichi Akashi
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Koji Kato
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan.
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8
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Yazdani Z, Rafiei A, Ghoreyshi M, Abediankenari S. In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis. Mol Biotechnol 2024; 66:769-783. [PMID: 36940016 PMCID: PMC10026239 DOI: 10.1007/s12033-023-00698-y] [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: 06/21/2022] [Accepted: 02/13/2023] [Indexed: 03/21/2023]
Abstract
Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.
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Affiliation(s)
- Zahra Yazdani
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
- Students Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Alireza Rafiei
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran.
| | - Mehrafarin Ghoreyshi
- Students Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Saeid Abediankenari
- Immunogenetics Research Center, Department of Immunology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
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9
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Wang M, Lei C, Wang J, Li Y, Li M. TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. Brief Bioinform 2024; 25:bbae154. [PMID: 38600667 PMCID: PMC11006794 DOI: 10.1093/bib/bbae154] [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: 12/26/2023] [Revised: 02/16/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024] Open
Abstract
Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.
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Affiliation(s)
- Meng Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Chuqi Lei
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Min Li
- School of Computer Science and engineering, Central South University, Changsha 410083, China
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10
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Kirk AM, Crawford JC, Chou CH, Guy C, Pandey K, Kozlik T, Shah RK, Chung S, Nguyen P, Zhang X, Wang J, Bell M, Mettelman RC, Allen EK, Pogorelyy MV, Kim H, Minervina AA, Awad W, Bajracharya R, White T, Long D, Gordon B, Morrison M, Glazer ES, Murphy AJ, Jiang Y, Fitzpatrick EA, Yarchoan M, Sethupathy P, Croft NP, Purcell AW, Federico SM, Stewart E, Gottschalk S, Zamora AE, DeRenzo C, Strome SE, Thomas PG. DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma. Cell Rep Med 2024; 5:101469. [PMID: 38508137 PMCID: PMC10983114 DOI: 10.1016/j.xcrm.2024.101469] [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: 08/23/2023] [Revised: 11/29/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Fibrolamellar carcinoma (FLC) is a liver tumor with a high mortality burden and few treatment options. A promising therapeutic vulnerability in FLC is its driver mutation, a conserved DNAJB1-PRKACA gene fusion that could be an ideal target neoantigen for immunotherapy. In this study, we aim to define endogenous CD8 T cell responses to this fusion in FLC patients and evaluate fusion-specific T cell receptors (TCRs) for use in cellular immunotherapies. We observe that fusion-specific CD8 T cells are rare and that FLC patient TCR repertoires lack large clusters of related TCR sequences characteristic of potent antigen-specific responses, potentially explaining why endogenous immune responses are insufficient to clear FLC tumors. Nevertheless, we define two functional fusion-specific TCRs, one of which has strong anti-tumor activity in vivo. Together, our results provide insights into the fragmented nature of neoantigen-specific repertoires in humans and indicate routes for clinical development of successful immunotherapies for FLC.
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Affiliation(s)
- Allison M Kirk
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Jeremy Chase Crawford
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Ching-Heng Chou
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Cliff Guy
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Kirti Pandey
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Tanya Kozlik
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ravi K Shah
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shanzou Chung
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Phuong Nguyen
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Xiaoyu Zhang
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Jin Wang
- Department of Microbiology, Immunology, and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Matthew Bell
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Robert C Mettelman
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - E Kaitlynn Allen
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Mikhail V Pogorelyy
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hyunjin Kim
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Anastasia A Minervina
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Walid Awad
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Resha Bajracharya
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Toni White
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Donald Long
- Department of Biomedical Sciences, Cornell University, Ithaca, NY 14850, USA
| | - Brittney Gordon
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Michelle Morrison
- Center for Cancer Research, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan S Glazer
- Center for Cancer Research, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA; Department of Surgery, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Andrew J Murphy
- Department of Surgery, The University of Tennessee Health Science Center, Memphis, TN 38163, USA; Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yixing Jiang
- Department of Medical Oncology, Marlene and Stewart Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Elizabeth A Fitzpatrick
- Department of Microbiology, Immunology, and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Mark Yarchoan
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Praveen Sethupathy
- Department of Biomedical Sciences, Cornell University, Ithaca, NY 14850, USA
| | - Nathan P Croft
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Sara M Federico
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Elizabeth Stewart
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Stephen Gottschalk
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Anthony E Zamora
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Christopher DeRenzo
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Scott E Strome
- College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Paul G Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
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11
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Fasoulis R, Rigo MM, Lizée G, Antunes DA, Kavraki LE. APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries. J Chem Inf Model 2024; 64:1730-1750. [PMID: 38415656 PMCID: PMC10936522 DOI: 10.1021/acs.jcim.3c01667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/29/2024]
Abstract
The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.
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Affiliation(s)
- Romanos Fasoulis
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Mauricio M. Rigo
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Gregory Lizée
- Department
of Melanoma Medical Oncology—Research, The University of Texas MD Anderson Cancer Center, Houston, Texas 77054, United States
| | - Dinler A. Antunes
- Department
of Biology and Biochemistry, University
of Houston, Houston, Texas 77004, United States
| | - Lydia E. Kavraki
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
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12
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Wadapurkar R, Singh S, Singh A. Leveraging the immunoinformatics approach for designing the SARS-CoV-2 omicron-specific antigenic cassette of mRNA vaccine. Vaccine 2024; 42:1630-1647. [PMID: 38336561 DOI: 10.1016/j.vaccine.2024.01.087] [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: 09/02/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
Emergence of SARS-CoV-2 Omicron variant has presented a significant challenge to global health, demanding rapid development of mRNA-based vaccines. The mRNA-guided vaccine platforms offer various advantages over traditional vaccine platforms. The mRNA by nature is a short-lived molecule that guides the cells to manufacture antigenic proteins. In the present work, we have created an omicron spike antigenic protein sequence characterized by base composition analysis, modeling, and docking with the ACE-2 receptor. Further, we predicted the B-cell and T-cell epitopes followed by antigenicity, toxicity, and allergenicity. Finally, the protein was reverse translated, codon-optimized, and encoding mRNA sequence was checked for its stability by predicting the secondary structures. A comprehensive examination of in-silico data revealed 628.2 as a potent antigenic candidate that was finally used in Gemcovac®-OM, a heterologous booster mRNA vaccine for COVID-19.
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Affiliation(s)
- Rucha Wadapurkar
- Gennova Biopharmaceuticals Ltd., ITBT Park, Hinjewadi Phase 2 Rd, Hinjewadi Rajiv Gandhi Infotech Park, Hinjewadi, Pune, Maharashtra 411057, India
| | - Sanjay Singh
- Gennova Biopharmaceuticals Ltd., ITBT Park, Hinjewadi Phase 2 Rd, Hinjewadi Rajiv Gandhi Infotech Park, Hinjewadi, Pune, Maharashtra 411057, India
| | - Ajay Singh
- Gennova Biopharmaceuticals Ltd., ITBT Park, Hinjewadi Phase 2 Rd, Hinjewadi Rajiv Gandhi Infotech Park, Hinjewadi, Pune, Maharashtra 411057, India.
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13
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Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:100030. [PMID: 38577265 PMCID: PMC10994007 DOI: 10.1016/j.immuno.2023.100030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Mauricio Menegatti Rigo
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Rd, Houston, 77004, TX, United States
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E and 27 Neapoleos St, Athens, 15341, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
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14
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Plata-Pineda SE, Cárdenas-Munévar LX, Castro-Cavadía CJ, Buitrago SP, Garzón-Ospina D. Evaluating the genetic diversity of the Plasmodium vivax siap2 locus: A promising candidate for an effective malaria vaccine? Acta Trop 2024; 251:107111. [PMID: 38151069 DOI: 10.1016/j.actatropica.2023.107111] [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: 07/26/2023] [Revised: 11/27/2023] [Accepted: 12/24/2023] [Indexed: 12/29/2023]
Abstract
Malaria is the deadliest parasitic disease in the world. Traditional control measures have become less effective; hence, there is a need to explore alternative strategies, such as antimalarial vaccines. However, designing an anti-Plasmodium vivax vaccine is considered a challenge due to the complex parasite biology and the antigens' high genetic diversity. Recently, the sporozoite invasion-associated protein 2 (SIAP2) has been suggested as a potential antigen to be considered in vaccine design due to its significance during hepatocyte invasion. However, its use may be limited by the incomplete understanding of gene/protein diversity. Here, the genetic diversity of pvsiap2 using P. vivax DNA samples from Colombia was assessed. Through PCR amplification and sequencing, we compared the Colombian sequences with available worldwide sequences, revealing that pvsiap2 displays low genetic diversity. Molecular evolutionary analyses showed that pvsiap2 appears to be influenced by directional selection. Moreover, the haplotypes found differ by a few mutational steps and several of them were shared between different geographical areas. On the other hand, several conserved regions within PvSIAP2 were predicted as potential B-cell or T-cell epitopes. Considering these characteristics and its role in hepatocyte invasion, the PvSIAP2 protein emerges as a promising antigen to be considered in a multi-antigen-multi-stage (multivalent) fully effective vaccine against P. vivax malaria.
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Affiliation(s)
- Sergio E Plata-Pineda
- School of Biological Sciences, Grupo de Estudios en Genética y Biología Molecular (GEBIMOL), Universidad Pedagógica y Tecnológica de Colombia - UPTC, Tunja, Boyacá, Colombia
| | - Laura X Cárdenas-Munévar
- School of Biological Sciences, Grupo de Estudios en Genética y Biología Molecular (GEBIMOL), Universidad Pedagógica y Tecnológica de Colombia - UPTC, Tunja, Boyacá, Colombia
| | - Carlos J Castro-Cavadía
- Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba (GIMBIC), School of Health Sciences, Universidad de Córdoba, Montería, Córdoba, Colombia
| | - Sindy P Buitrago
- School of Biological Sciences, Grupo de Estudios en Genética y Biología Molecular (GEBIMOL), Universidad Pedagógica y Tecnológica de Colombia - UPTC, Tunja, Boyacá, Colombia; Population Genetics And Molecular Evolution (PGAME), Fundación Scient, Tunja, Boyacá, Colombia
| | - Diego Garzón-Ospina
- School of Biological Sciences, Grupo de Estudios en Genética y Biología Molecular (GEBIMOL), Universidad Pedagógica y Tecnológica de Colombia - UPTC, Tunja, Boyacá, Colombia; Population Genetics And Molecular Evolution (PGAME), Fundación Scient, Tunja, Boyacá, Colombia.
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15
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Zhou M, Zhao F, Yu L, Liu J, Wang J, Zhang JZH. An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1. Molecules 2024; 29:881. [PMID: 38398632 PMCID: PMC10892774 DOI: 10.3390/molecules29040881] [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: 12/25/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.
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Affiliation(s)
- Mengchen Zhou
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
| | - Fanyu Zhao
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
| | - Lan Yu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jian Wang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
- Department of Chemistry, New York University, New York, NY 10003, USA
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
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16
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Conev A, Fasoulis R, Hall-Swan S, Ferreira R, Kavraki LE. HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors. iScience 2024; 27:108613. [PMID: 38188519 PMCID: PMC10770483 DOI: 10.1016/j.isci.2023.108613] [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: 11/08/2023] [Revised: 11/13/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.
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Affiliation(s)
- Anja Conev
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Rodrigo Ferreira
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, USA
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17
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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18
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Huang X, Gan Z, Cui H, Lan T, Liu Y, Caron E, Shao W. The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics. Nucleic Acids Res 2024; 52:D1062-D1071. [PMID: 38000392 PMCID: PMC10767952 DOI: 10.1093/nar/gkad1068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/16/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.
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Affiliation(s)
- Xiaoxiang Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziao Gan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haowei Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tian Lan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Etienne Caron
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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19
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Wu J, Li J, Chen S, Zhou Z. DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity. Methods Mol Biol 2024; 2809:237-244. [PMID: 38907901 DOI: 10.1007/978-1-0716-3874-3_15] [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] [Indexed: 06/24/2024]
Abstract
Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformatics prediction for tumor neoantigens has rapidly developed, focusing on the prediction of peptide-HLA binding affinity. In this chapter, we introduce a user-friendly tool named DeepHLApan, which utilizes deep learning techniques to predict neoantigens by considering both peptide-HLA binding affinity and immunogenicity. We provide the application of DeepHLApan, along with the source code, docker version, and web-server. These resources are freely available at https://github.com/zjupgx/deephlapan and http://pgx.zju.edu.cn/deephlapan/ .
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Affiliation(s)
- Jingcheng Wu
- Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jiaoyang Li
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Shuqing Chen
- Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Zhan Zhou
- Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
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20
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Wang L, Diao M, Zhang Z, Jiang M, Chen S, Zhao D, Liu Z, Zhou C. Comparison of the somatic genomic landscape between central- and peripheral-type non-small cell lung cancer. Lung Cancer 2024; 187:107439. [PMID: 38113653 DOI: 10.1016/j.lungcan.2023.107439] [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: 08/31/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023]
Abstract
OBJECTIVE Lung cancer is classified into central and peripheral types based on the anatomic location. The present study aimed to explore the distinct patterns of genomic alterations between central- and peripheral-type non-small cell lung cancers (NSCLCs) with negative driver genes and identify potential driver genes and biomarkers to improve therapy strategies for NSCLC. METHODS Whole-exome sequencing (WES) was performed with 182 tumor/control pairs of samples from 145 Chinese NSCLC patients without EGFR, ALK, or ROS1 alterations. Significantly mutated genes (SMGs) and somatic copy number alterations (SCNAs) were identified. Subsequently, tumor mutation burden (TMB), weighted genome integrity index (wGII), copy number alteration (CNA) burden, Shannon diversity index (SDI), intratumor heterogeneity (ITH), neoantigen load (NAL), and clonal variations were evaluated in central- and peripheral-type NSCLCs. Furthermore, mutational signature analysis and survival analysis were performed. RESULTS TP53 was the most frequently mutated gene in NSCLC and more frequently mutated in central-type NSCLC. Higher wGII, ITH, and SDI were found in central-type lung adenocarcinoma (LUAD) than in peripheral-type LUAD. The NAL of central-type lung squamous cell carcinoma (LUSC) with stage III/IV was significantly higher than that of peripheral-type LUSC. Mutational signature analysis revealed that SBS10b, SBS24, and ID7 were significantly different in central- and peripheral-type NSCLCs. Furthermore, central-type NSCLC was found to evolve at a higher level with fewer clones and more subclones, particularly in central-type LUSC. Survival analysis revealed that TMB, CNA burden, NAL, subclonal driver mutations, and subclonal mutations were negatively related to the overall survival (OS) and the progression-free survival (PFS) of central-type LUAD. CONCLUSIONS Central-type NSCLC tended to evolve at a higher level and might suggest a favorable response to immunotherapy. Our study also identified several potential driver genes and promising biomarkers for the prognosis and prediction of chemotherapy responses in NSCLC.
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Affiliation(s)
- Lei Wang
- Department of Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, PR China
| | - Meng Diao
- Department of Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, PR China
| | - Zheng Zhang
- Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, PR China
| | - Minlin Jiang
- Department of Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, PR China
| | - Shifu Chen
- HaploX Biotechnology Co., Shenzhen, PR China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, PR China.
| | - Zhenguo Liu
- Department of Anesthesiology, Weifang People's Hospital, Weifang, Shandong Province, PR China.
| | - Caicun Zhou
- Department of Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, PR China.
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21
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Zhang G, Han L, Zhao Y, Li Q, Wang S, Shi H. Development and evaluation of a multi-epitope subunit vaccine against Mycoplasma synoviae infection. Int J Biol Macromol 2023; 253:126685. [PMID: 37666406 DOI: 10.1016/j.ijbiomac.2023.126685] [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: 07/10/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023]
Abstract
Mycoplasma synoviae is an extremely significant avian pathogen, causing substantial financial harm to poultry farmers worldwide, and impacting both chicken and turkey production. Multi-epitope vaccines offer higher immunity and lower allergenicity compared to conventional vaccines. In this study, our objective is to develop a multi-epitope vaccine for M. synoviae (MSMV) and to evaluate the immune responses and protective efficacy of MSMV in chickens. We successfully identified a total of 14 B-cell, 5 MHC-I, and 16 MHC-II binding epitopes from the immunodominant proteins RS01790, BMP, GrpE, RS00900, and RS00275. Subsequently, we synthesized the multi-epitope vaccine by connecting all conserved epitopes using appropriate linkers. The resulting MSMV demonstrated notable antigenicity, non-allergenic properties, and stability. Notably, the MSMV effectively stimulated high levels of antibody production in chickens. Furthermore, MSMV the vaccine elicited a robust cellular immune response in chickens, characterized by a well-balanced Th1/Th2-type cytokine profile and enhanced lymphocyte proliferation. In immune protection experiments, the vaccinated chickens exhibited reduced air sac lesion scores and tracheal mucosal thickness compared to their non-vaccinated chickens. Additionally, vaccinated chickens displayed lower M. synoviae loads in throat swabs. These findings collectively suggested that the MSMV holds significant potential as a promising vaccine candidate for managing M. synoviae infections.
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Affiliation(s)
- Guihua Zhang
- College of Veterinary Medicine, Yangzhou University, Yangzhou, 225009, Jiangsu, China; Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou, China
| | - Lejiabao Han
- College of Veterinary Medicine, Yangzhou University, Yangzhou, 225009, Jiangsu, China; Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou, China
| | - Yuying Zhao
- College of Veterinary Medicine, Yangzhou University, Yangzhou, 225009, Jiangsu, China; Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou, China
| | - Quan Li
- College of Veterinary Medicine, Yangzhou University, Yangzhou, 225009, Jiangsu, China; Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou, China.
| | - Shifeng Wang
- Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL 32611-0880, USA.
| | - Huoying Shi
- College of Veterinary Medicine, Yangzhou University, Yangzhou, 225009, Jiangsu, China; Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou, China; Joint International Research Laboratory of Agriculture and Agri-Product Safety, Yangzhou University (JIRLAAPS), Yangzhou, China.
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22
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Mistry S, Gouripeddi R, Facelli JC. Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology. J Autoimmun 2023; 140:103115. [PMID: 37774556 PMCID: PMC10965504 DOI: 10.1016/j.jaut.2023.103115] [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: 06/01/2023] [Revised: 07/28/2023] [Accepted: 09/14/2023] [Indexed: 10/01/2023]
Abstract
Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.
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Affiliation(s)
- Sejal Mistry
- Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA
| | - Ramkiran Gouripeddi
- Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, USA.
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23
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García-Mulero S, Fornelino R, Punta M, Lise S, Varela M, del Carpio LP, Moreno R, Costa-García M, Rieder D, Trajanoski Z, Gros A, Alemany R, Piulats JM, Sanz-Pamplona R. Driver mutations in GNAQ and GNA11 genes as potential targets for precision immunotherapy in uveal melanoma patients. Oncoimmunology 2023; 12:2261278. [PMID: 38126027 PMCID: PMC10732647 DOI: 10.1080/2162402x.2023.2261278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/17/2023] [Indexed: 12/23/2023] Open
Abstract
Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (amino acid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and does not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data from primary uveal tumors were collected from the TCGA-UM dataset (n = 80) and used to assess the immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations using a variety of tools and HLA type information. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA than GNAQ/11 Q209P. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000 G databases whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed a higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Finally, samples carrying Q209L had a higher immune-reactive phenotype. Regarding cancer risk, seven HLA genotypes with low Q209L affinity show higher frequency in uveal melanoma patients than in the general population. However, no clear association was found between any HLA genotype and survival. Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.
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Affiliation(s)
- Sandra García-Mulero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Anatomy Unit, Department of Pathology and Experimental Therapy, and Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Roberto Fornelino
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
| | - Marco Punta
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Stefano Lise
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Mar Varela
- Department of Pathology, Bellvitge University Hospital, Barcelona, Spain
| | - Luis P. del Carpio
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Rafael Moreno
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Marcel Costa-García
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Dietmar Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Zlatko Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Alena Gros
- Tumor Immunology and Immunotherapy, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Ramón Alemany
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | | | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Institute for Health Research Aragon (IISA), ARAID Foundation, Aragon Government, University Hospital Lozano Blesa, Zaragoza, Spain
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24
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Zhang C, Li M, Yu T. Bioinformatics analysis of Muscovy duck parvovirus REP and VP1 proteins. J Biomol Struct Dyn 2023; 41:7174-7189. [PMID: 36065642 DOI: 10.1080/07391102.2022.2118170] [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: 05/09/2022] [Accepted: 08/20/2022] [Indexed: 10/14/2022]
Abstract
This article was aimed at analyzing the sequence, structure, and function of the two Muscovy duck parvovirus proteins, including REP and VP1. The antigenicity, physical and chemical properties, transmembrane regions, phosphorylation sites, glycosylation sites, three-dimensional structure, and linear epitope of VP1 and REP were predicted and analyzed through bioinformatics methods. A multi-epitope vaccine was also constructed based on the screened epitopes, and the vaccine was characterized, modeled, molecularly docked and molecularly cloned. The epitopes were screened according to the criteria of antigenicity, non-allergenicity and non-toxicity, and 12 epitope fragments were obtained. The B cell epitopes were analyzed according to four scales: β-turn, hydrophilicity, surface accessibility and antigenicity. Combined with the epitope prediction results based on structure, the final epitope prediction results were obtained. The multi-epitope vaccine used an EAAAK-linked adjuvant, a GPGPG-linked T-cell epitope, and a KK-linked B-cell epitope. The analysis showed that the vaccine was stable hydrophilic, antigenic, conserved and non-allergenic. Based on molecular docking it was shown that good interactions between the vaccine and the immune receptor were generated and were essential to generate an immune response. The final vaccine was reverse translated into cDNA and the DNA vaccine was designed by codon optimization and molecular cloning. Further trials are still needed to demonstrate the immunogenicity and other aspects of vaccine efficacy.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Chi Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Ming Li
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Tianfei Yu
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
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25
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Jiang M, Yang J, Yang L, Wang L, Wang T, Han S, Cheng Y, Chen Z, Su Y, Zhang L, Yang F, Chen SA, Zhang J, Xiong H, Wang L, Zhang Z, Ma L, Luo X, Xing Q. An association study of HLA with levofloxacin-induced severe cutaneous adverse drug reactions in Han Chinese. iScience 2023; 26:107391. [PMID: 37554438 PMCID: PMC10404721 DOI: 10.1016/j.isci.2023.107391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/21/2023] [Accepted: 07/10/2023] [Indexed: 08/10/2023] Open
Abstract
Levofloxacin-induced severe cutaneous adverse drug reactions (LEV-SCARs) remain unexplored. An association study of human leukocyte antigen (HLA) alleles with LEV-SCARs among 12 patients, 806 healthy subjects, and 100 levofloxacin-tolerant individuals was performed. The carrier frequencies of HLA-B∗13:01 (odds ratio [OR]: 4.50; 95% confidence interval [CI]: 1.15-17.65; p = 0.043), HLA-B∗13:02 (OR: 6.14; 95% CI: 1.73-21.76; p = 0.0072), and serotype B13 (OR: 17.73; 95% CI: 3.61-86.95; p = 4.85 × 10-5) in patients with LEV-SCARs were significantly higher than those of levofloxacin-tolerant individuals. Molecular docking analysis suggested that levofloxacin formed more stable binding models with HLA-B∗13:01 and HLA-B∗13:02 than with non-risk HLA-B∗46:01. Mass spectrometry revealed that nonapeptides bound to HLA-B∗13:02 shifted at several positions after exposure to levofloxacin. Prospective screening for serotype B13 (sensitivity: 83%, specificity: 78%) and alternative drug treatment for carriers may significantly decrease the incidence of LEV-SCARs.
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Affiliation(s)
- Menglin Jiang
- Children’s Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai 200032, China
| | - Jin Yang
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Linlin Yang
- Department of Pharmacology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Lina Wang
- Department of Pharmacology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Ting Wang
- Children’s Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai 200032, China
| | - Shengna Han
- Department of Pharmacology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Ye Cheng
- Children’s Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai 200032, China
| | - Zihua Chen
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yu Su
- Children’s Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai 200032, China
| | - Lirong Zhang
- Department of Pharmacology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Fanping Yang
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Sheng-an Chen
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jin Zhang
- Children’s Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai 200032, China
| | - Hao Xiong
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Lanting Wang
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zhen Zhang
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Li Ma
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xiaoqun Luo
- Department of Allergy and Immunology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Qinghe Xing
- Children’s Hospital of Fudan University and Institutes of Biomedical Sciences of Fudan University, Shanghai 200032, China
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26
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Liu L, Yu W, Cai K, Ma S, Wang Y, Ma Y, Zhao H. Identification of vaccine candidates against rhodococcus equi by combining pangenome analysis with a reverse vaccinology approach. Heliyon 2023; 9:e18623. [PMID: 37576287 PMCID: PMC10413060 DOI: 10.1016/j.heliyon.2023.e18623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/12/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023] Open
Abstract
Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that can cause life-threatening infections. The rapid evolution of multidrug-resistant R. equi and the fact that there is no currently licensed effective vaccine against R. equi warrant the need for vaccine development. Reverse vaccinology (RV), which involves screening a pathogen's entire genome and proteome using various web-based prediction tools, is considered one of the most effective approaches for identifying vaccine candidates. Here, we performed a pangenome analysis to determine the core proteins of R. equi. We then used the RV approach to examine the subcellular localization, host and gut flora homology, antigenicity, transmembrane helices, physicochemical properties, and immunogenicity of the core proteins to select potential vaccine candidates. The vaccine candidates were then subjected to epitope mapping to predict the exposed antigenic epitopes that possess the ability to bind with major histocompatibility complex I/II (MHC I/II) molecules. These vaccine candidates and epitopes will form a library of elements for the development of a polyvalent or universal vaccine against R. equi. Sixteen R. equi complete proteomes were found to contain 6,238 protein families, and the core proteins consisted of 3,969 protein families (∼63.63% of the pangenome), reflecting a low degree of intraspecies genomic variability. From the pool of core proteins, 483 nonhost homologous membrane and extracellular proteins were screened, and 12 vaccine candidates were finally identified according to their antigenicity, physicochemical properties and other factors. These included four cell wall/membrane/envelope biogenesis proteins; four amino acid transport and metabolism proteins; one cell cycle control, cell division and chromosome partitioning protein; one carbohydrate transport and metabolism protein; one secondary metabolite biosynthesis, transport and catabolism protein; and one defense mechanism protein. All 12 vaccine candidates have an experimentally validated 3D structure available in the protein data bank (PDB). Epitope mapping of the candidates showed that 16 MHC I epitopes and 13 MHC II epitopes with the strongest immunogenicity were exposed on the protein surface, indicating that they could be used to develop a polypeptide vaccine. Thus, we utilized an analytical strategy that combines pangenome analysis and RV to generate a peptide antigen library that simplifies the development of multivalent or universal vaccines against R. equi and can be applied to the development of other vaccines.
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Affiliation(s)
- Lu Liu
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Wanli Yu
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Kuojun Cai
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Siyuan Ma
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Yanfeng Wang
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Yuhui Ma
- Zhaosu Xiyu Horse Industry Co., Ltd. Zhaosu County 835699, Yili Prefecture, Xinjiang, China
| | - Hongqiong Zhao
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
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27
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Zhuang L, Ye Z, Li L, Yang L, Gong W. Next-Generation TB Vaccines: Progress, Challenges, and Prospects. Vaccines (Basel) 2023; 11:1304. [PMID: 37631874 PMCID: PMC10457792 DOI: 10.3390/vaccines11081304] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.
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Affiliation(s)
- Li Zhuang
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
- Hebei North University, Zhangjiakou 075000, China
| | - Zhaoyang Ye
- Hebei North University, Zhangjiakou 075000, China
| | - Linsheng Li
- Hebei North University, Zhangjiakou 075000, China
| | - Ling Yang
- Hebei North University, Zhangjiakou 075000, China
| | - Wenping Gong
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
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28
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Kalemati M, Darvishi S, Koohi S. CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks. Commun Biol 2023; 6:492. [PMID: 37147498 PMCID: PMC10162658 DOI: 10.1038/s42003-023-04867-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/24/2023] [Indexed: 05/07/2023] Open
Abstract
The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.
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Affiliation(s)
- Mahmood Kalemati
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Saeid Darvishi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
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29
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Xia H, McMichael J, Becker-Hapak M, Onyeador OC, Buchli R, McClain E, Pence P, Supabphol S, Richters MM, Basu A, Ramirez CA, Puig-Saus C, Cotto KC, Freshour SL, Hundal J, Kiwala S, Goedegebuure SP, Johanns TM, Dunn GP, Ribas A, Miller CA, Gillanders WE, Fehniger TA, Griffith OL, Griffith M. Computational prediction of MHC anchor locations guides neoantigen identification and prioritization. Sci Immunol 2023; 8:eabg2200. [PMID: 37027480 PMCID: PMC10450883 DOI: 10.1126/sciimmunol.abg2200] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/16/2023] [Indexed: 04/09/2023]
Abstract
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
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Affiliation(s)
- Huiming Xia
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Michelle Becker-Hapak
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Onyinyechi C. Onyeador
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Rico Buchli
- Pure Protein LLC, Oklahoma City, OK 73104, USA
| | - Ethan McClain
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Patrick Pence
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Suangson Supabphol
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- The Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Megan M. Richters
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Anamika Basu
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Cody A. Ramirez
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Cristina Puig-Saus
- Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Kelsy C. Cotto
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Sharon L. Freshour
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jasreet Hundal
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - S. Peter Goedegebuure
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tanner M. Johanns
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gavin P. Dunn
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Antoni Ribas
- Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Christopher A. Miller
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - William E. Gillanders
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Todd A. Fehniger
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Obi L. Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
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30
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Mayoh C, Gifford AJ, Terry R, Lau LMS, Wong M, Rao P, Shai-Hee T, Saletta F, Khuong-Quang DA, Qin V, Mateos MK, Meyran D, Miller KE, Yuksel A, Mould EVA, Bowen-James R, Govender D, Senapati A, Zhukova N, Omer N, Dholaria H, Alvaro F, Tapp H, Diamond Y, Pozza LD, Moore AS, Nicholls W, Gottardo NG, McCowage G, Hansford JR, Khaw SL, Wood PJ, Catchpoole D, Cottrell CE, Mardis ER, Marshall GM, Tyrrell V, Haber M, Ziegler DS, Vittorio O, Trapani JA, Cowley MJ, Neeson PJ, Ekert PG. A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer. Genome Med 2023; 15:20. [PMID: 37013636 PMCID: PMC10071693 DOI: 10.1186/s13073-023-01170-x] [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: 10/13/2022] [Accepted: 03/08/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. METHODS We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8+ and CD4+ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB. RESULTS A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells. CONCLUSIONS Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.
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Affiliation(s)
- Chelsea Mayoh
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- University of New South Wales Centre for Childhood Cancer Research, UNSW, Kensington, NSW, Australia
| | - Andrew J Gifford
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Anatomical Pathology, NSW Health Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Rachael Terry
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Loretta M S Lau
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Marie Wong
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Padmashree Rao
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Tyler Shai-Hee
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Federica Saletta
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Dong-Anh Khuong-Quang
- Children's Cancer Centre, Royal Children's Hospital, Parkville, VIC, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
| | - Vicky Qin
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Marion K Mateos
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Deborah Meyran
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Katherine E Miller
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Aysen Yuksel
- Tumour Bank, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Emily V A Mould
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Rachel Bowen-James
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, Australia
- School of Biomedical Engineering, UNSW Sydney, Kensington, NSW, Australia
| | - Dinisha Govender
- Cancer Centre for Children, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Akanksha Senapati
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Nataliya Zhukova
- Monash Children's Hospital, Melbourne, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Department of Paediatrics, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Natacha Omer
- Oncology Service, Children's Health Queensland Hospital & Health Service, Brisbane, QLD, Australia
- The University of Queensland Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Hetal Dholaria
- Department of Paediatric and Adolescent Oncology and Haematology, Perth Children's Hospital, Nedlands, WA, Australia
- Brain Tumour Research Program, Telethon Kids Institute, Nedlands, WA, Australia
| | - Frank Alvaro
- John Hunter Children's Hospital, New Lambton Heights, NSW, Australia
| | - Heather Tapp
- Michael Rice Cancer Centre, Women's and Children's Hospital, South Australia Health and Medical Research Institute, Adelaide, SA, Australia
| | - Yonatan Diamond
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
- Children's Cancer Centre, Royal Children's Hospital, Parkville, VIC, Australia
| | - Luciano Dalla Pozza
- Cancer Centre for Children, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Andrew S Moore
- Oncology Service, Children's Health Queensland Hospital & Health Service, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Wayne Nicholls
- Oncology Service, Children's Health Queensland Hospital & Health Service, Brisbane, QLD, Australia
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Gottardo
- Department of Paediatric and Adolescent Oncology and Haematology, Perth Children's Hospital, Nedlands, WA, Australia
- Brain Tumour Research Program, Telethon Kids Institute, Nedlands, WA, Australia
| | - Geoffrey McCowage
- Cancer Centre for Children, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Jordan R Hansford
- Michael Rice Cancer Centre, Women's and Children's Hospital, South Australia Health and Medical Research Institute, Adelaide, SA, Australia
- South Australia ImmunoGENomics Cancer Institute, University of Adelaide, Adelaide, SA, Australia
| | - Seong-Lin Khaw
- Children's Cancer Centre, Royal Children's Hospital, Parkville, VIC, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
| | - Paul J Wood
- Monash Children's Hospital, Melbourne, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Department of Paediatrics, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | | | - Catherine E Cottrell
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Elaine R Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Glenn M Marshall
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Vanessa Tyrrell
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Michelle Haber
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - David S Ziegler
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Orazio Vittorio
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Joseph A Trapani
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Mark J Cowley
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Paul J Neeson
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Paul G Ekert
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia.
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia.
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia.
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Lim CP, Kok BH, Lim HT, Chuah C, Abdul Rahman B, Abdul Majeed AB, Wykes M, Leow CH, Leow CY. Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design. Pathog Glob Health 2023; 117:134-151. [PMID: 35550001 PMCID: PMC9970233 DOI: 10.1080/20477724.2022.2072456] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.
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Affiliation(s)
- Chin Peng Lim
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia.,Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Boon Hui Kok
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Hui Ting Lim
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Candy Chuah
- Faculty of Health Sciences, Universiti Teknologi MARA, Penang, Malaysia
| | | | | | - Michelle Wykes
- Molecular Immunology Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Chiuan Herng Leow
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Chiuan Yee Leow
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
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Filip I, Wang A, Kravets O, Orenbuch R, Zhao J, Perea-Chamblee TE, Manji GA, López de Maturana E, Malats N, Olive KP, Rabadan R. Pervasiveness of HLA allele-specific expression loss across tumor types. Genome Med 2023; 15:8. [PMID: 36759885 PMCID: PMC9912643 DOI: 10.1186/s13073-023-01154-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 01/12/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Efficient presentation of mutant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune-mediated killing of cancer cells. According to recent reports, patient HLA-I genotypes can impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has also been reported in different tumor types, the role of HLA-I allele-specific expression loss - that is, the preferential RNA expression loss of specific HLA-I alleles - has not been fully characterized in cancer. METHODS Here, we use RNA and whole-exome sequencing data to quantify HLA-I allele-specific expression (ASE) in cancer using our novel method arcasHLA-quant. RESULTS We show that HLA-I ASE loss in at least one of the three HLA-I genes is a pervasive phenomenon across TCGA tumor types. In pancreatic adenocarcinoma, tumor-specific HLA-I ASE loss is associated with decreased overall survival specifically in the basal-like subtype, a finding that we validated in an independent cohort through laser-capture microdissection. Additionally, we show that HLA-I ASE loss is associated with poor immunotherapy outcomes in metastatic melanoma through retrospective analyses. CONCLUSIONS Together, our results highlight the prevalence of HLA-I ASE loss and provide initial evidence of its clinical significance in cancer prognosis and immunotherapy treatment.
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Affiliation(s)
- Ioan Filip
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Anqi Wang
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Oleksandr Kravets
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Rose Orenbuch
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA.,Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Junfei Zhao
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Tomin E Perea-Chamblee
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA
| | - Gulam A Manji
- Department of Medicine, Division of Hematology and Oncology, Columbia University, New York, NY, USA
| | - Evangelina López de Maturana
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Madrid, Spain
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Madrid, Spain
| | - Kenneth P Olive
- Department of Medicine, Division of Digestive and Liver Diseases, Columbia University, New York, NY, USA
| | - Raul Rabadan
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA. .,Department of Biomedical Informatics, Columbia University, New York, NY, USA.
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Biswas N, Chakrabarti S, Padul V, Jones LD, Ashili S. Designing neoantigen cancer vaccines, trials, and outcomes. Front Immunol 2023; 14:1105420. [PMID: 36845151 PMCID: PMC9947792 DOI: 10.3389/fimmu.2023.1105420] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/30/2023] [Indexed: 02/11/2023] Open
Abstract
Neoantigen vaccines are based on epitopes of antigenic parts of mutant proteins expressed in cancer cells. These highly immunogenic antigens may trigger the immune system to combat cancer cells. Improvements in sequencing technology and computational tools have resulted in several clinical trials of neoantigen vaccines on cancer patients. In this review, we have looked into the design of the vaccines which are undergoing several clinical trials. We have discussed the criteria, processes, and challenges associated with the design of neoantigens. We searched different databases to track the ongoing clinical trials and their reported outcomes. We observed, in several trials, the vaccines boost the immune system to combat the cancer cells while maintaining a reasonable margin of safety. Detection of neoantigens has led to the development of several databases. Adjuvants also play a catalytic role in improving the efficacy of the vaccine. Through this review, we can conclude that the efficacy of vaccines can make it a potential treatment across different types of cancers.
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Affiliation(s)
- Nupur Biswas
- Rhenix Lifesciences, Hyderabad, India,*Correspondence: Nupur Biswas, ;
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34
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Cireli E, Çavaş L. A Sample Guideline for Reverse Vaccinology Approach for the Development of Subunit Vaccine Using Varicella Zoster as a Model Disease. Methods Mol Biol 2023; 2673:453-474. [PMID: 37258932 DOI: 10.1007/978-1-0716-3239-0_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
For the development of multi-peptide vaccine, identification of antigenic epitopes is crucial. If it is done using wet lab techniques, the identification process can be time-consuming, laborious, and cost-intensive. In silico tools, on the other hand, enable researchers to predict potential epitopes with little to no cost for further in vivo and in vitro testing. The rapid identification process using in silico tools helps in responding to health emergencies faster. Developing an efficient and high coverage vaccine is one of the ways to reduce morbidity and mortality rates of the diseases and protect the affected populations. In this chapter, we introduce the necessary tools and methodology for the identification and characterization of antigenic epitopes to design a multi-epitope vaccine using varicella-zoster virus as an example vector model.
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Affiliation(s)
- Elif Cireli
- Department of Life Sciences and Chemistry, Constructor University Bremen, Bremen, Germany
| | - Levent Çavaş
- Dokuz Eylül University, Faculty of Science, Department of Chemistry (Biochemistry Division), İzmir, Turkey.
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Chen J, Tan S, Avadhanula V, Moise L, Piedra PA, De Groot AS, Bahl J. Diversity and evolution of computationally predicted T cell epitopes against human respiratory syncytial virus. PLoS Comput Biol 2023; 19:e1010360. [PMID: 36626370 PMCID: PMC9870173 DOI: 10.1371/journal.pcbi.1010360] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/23/2023] [Accepted: 12/07/2022] [Indexed: 01/11/2023] Open
Abstract
Human respiratory syncytial virus (RSV) is a major cause of lower respiratory infection. Despite more than 60 years of research, there is no licensed vaccine. While B cell response is a major focus for vaccine design, the T cell epitope profile of RSV is also important for vaccine development. Here, we computationally predicted putative T cell epitopes in the Fusion protein (F) and Glycoprotein (G) of RSV wild circulating strains by predicting Major Histocompatibility Complex (MHC) class I and class II binding affinity. We limited our inferences to conserved epitopes in both F and G proteins that have been experimentally validated. We applied multidimensional scaling (MDS) to construct T cell epitope landscapes to investigate the diversity and evolution of T cell profiles across different RSV strains. We find the RSV strains are clustered into three RSV-A groups and two RSV-B groups on this T epitope landscape. These clusters represent divergent RSV strains with potentially different immunogenic profiles. In addition, our results show a greater proportion of F protein T cell epitope content conservation among recent epidemic strains, whereas the G protein T cell epitope content was decreased. Importantly, our results suggest that RSV-A and RSV-B have different patterns of epitope drift and replacement and that RSV-B vaccines may need more frequent updates. Our study provides a novel framework to study RSV T cell epitope evolution. Understanding the patterns of T cell epitope conservation and change may be valuable for vaccine design and assessment.
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Affiliation(s)
- Jiani Chen
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease and Emergence Response, University of Georgia, Athens, Georgia, United States of America
| | - Swan Tan
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease and Emergence Response, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Vasanthi Avadhanula
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Leonard Moise
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- EpiVax Inc., Providence, Rhode Island, United States of America
| | - Pedro A. Piedra
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Anne S. De Groot
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- EpiVax Inc., Providence, Rhode Island, United States of America
| | - Justin Bahl
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease and Emergence Response, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, United States of America
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36
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Zhang J, Liu M, Chen Y, Zhou Z, Wang P, Yu Y, Jiao S. Epitope identification for p53R273C mutant. Immun Inflamm Dis 2022; 11:e752. [PMID: 36705409 PMCID: PMC9761341 DOI: 10.1002/iid3.752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/08/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND With the rise of immunotherapy based on cancer neoantigen, identification of neoepitopes has become an urgent problem to be solved. The TP53 R273C mutation is one of the hotspot mutations of TP53, however, the immunogenicity of this mutation is not yet clear. The aim of this study is to identify potential epitopes for p53R273C mutant. METHODS In this study, bioinformatic methods, peptide exchange assay, and peptide-immunized human leukocyte antigen (HLA) transgenic mouse model were used to explore the immunogenicity of this mutation. RESULTS Peptides with higher affinity to common HLA-A alleles (A*11:01, A*02:01) were discovered by computational prediction. All the 8-11 mer peptides contain the mutation site were synthesized and soluble peptides were used in the peptide exchange assay. However, the exchange efficiencies of these predicted peptides to HLAs were lower. Fortunately, other peptides with higher exchange efficiency were discovered. Then, the immunogenicity of these peptides was validated with the HLA-A2 transgenic mice model. CONCLUSION We identified three potential neoepitopes of p53R273C for HLA-A*02:01, one potential neoepitope for HLA-A*11:01 and no neoepitope for HLA-A*24:02.
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Affiliation(s)
- Jian Zhang
- School of MedicineNankai UniversityTianjinChina,Department of Oncology, Oncology LaboratoryChinese PLA General HospitalBeijingChina,Research and Development DepartmentBeijing DCTY Biotech Co., Ltd.BeijingPeople's Republic of China
| | - Minglu Liu
- Department of Oncology, Oncology LaboratoryChinese PLA General HospitalBeijingChina
| | - Yin Chen
- Research and Development DepartmentBeijing DCTY Biotech Co., Ltd.BeijingPeople's Republic of China
| | - Zishan Zhou
- Research and Development DepartmentBeijing DCTY Biotech Co., Ltd.BeijingPeople's Republic of China
| | - Ping Wang
- Research and Development DepartmentBeijing DCTY Biotech Co., Ltd.BeijingPeople's Republic of China
| | - Yang Yu
- Research and Development DepartmentBeijing DCTY Biotech Co., Ltd.BeijingPeople's Republic of China
| | - Shunchang Jiao
- School of MedicineNankai UniversityTianjinChina,Department of Oncology, Oncology LaboratoryChinese PLA General HospitalBeijingChina
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Shared 6mer Peptides of Human and Omicron (21K and 21L) at SARS-CoV-2 Mutation Sites. Antibodies (Basel) 2022; 11:antib11040068. [PMID: 36412834 PMCID: PMC9680445 DOI: 10.3390/antib11040068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 12/14/2022] Open
Abstract
We investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.
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Diao K, Chen J, Wu T, Wang X, Wang G, Sun X, Zhao X, Wu C, Wang J, Yao H, Gerarduzzi C, Liu XS. Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction. Int J Mol Sci 2022; 23:ijms231911624. [PMID: 36232923 PMCID: PMC9569519 DOI: 10.3390/ijms231911624] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/18/2022] [Accepted: 09/21/2022] [Indexed: 11/09/2022] Open
Abstract
Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.
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Affiliation(s)
- Kaixuan Diao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
- Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Chen
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
- Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Wu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Xuan Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Guangshuai Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Xiaoqin Sun
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Xiangyu Zhao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Chenxu Wu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Jinyu Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Huizi Yao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
| | - Casimiro Gerarduzzi
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC H4T 1G2, Canada
| | - Xue-Song Liu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China
- Correspondence:
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Fehlings M, Kim L, Guan X, Yuen K, Tafazzol A, Sanjabi S, Zill OA, Rishipathak D, Wallace A, Nardin A, Ma S, Milojkovic A, Newell EW, Mariathasan S, Yadav M. Single-cell analysis reveals clonally expanded tumor-associated CD57 + CD8 T cells are enriched in the periphery of patients with metastatic urothelial cancer responding to PD-L1 blockade. J Immunother Cancer 2022; 10:jitc-2022-004759. [PMID: 35981786 PMCID: PMC9394212 DOI: 10.1136/jitc-2022-004759] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2022] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND A growing body of evidence suggests that T-cell responses against neoantigens are critical regulators of response to immune checkpoint blockade. We previously showed that circulating neoantigen-specific CD8 T cells in patients with lung cancer responding to anti-Programmed death-ligand 1 (PD-L1) (atezolizumab) exhibit a unique phenotype with high expression of CD57, CD244, and KLRG1. Here, we extended our analysis on neoantigen-specific CD8 T cells to patients with metastatic urothelial cancer (mUC) and further profiled total CD8 T cells to identify blood-based predictive biomarkers of response to atezolizumab. METHODS We identified tumor neoantigens from 20 patients with mUC and profiled their peripheral CD8 T cells using highly multiplexed combinatorial tetramer staining. Another set of patients with mUC treated with atezolizumab (n=30) or chemotherapy (n=40) were selected to profile peripheral CD8 T cells by mass cytometry. Using single-cell transcriptional analysis (single-cell RNA sequencing (scRNA-seq)), together with CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and paired T-cell receptor (TCR) sequencing, we further characterized peripheral CD8 T cells in a subset of patients (n=16). RESULTS High frequency of CD57 was observed in neoantigen-specific CD8 T cells in patients with mUC responding to atezolizumab. Extending these findings to bulk CD8 T cells, we found higher frequency of CD57 expressing CD8 T cells before treatment in patients responding to atezolizumab (n=20, p<0.01) but not to chemotherapy. These findings were corroborated in a validation cohort (n=30, p<0.01) and notably were independent of known biomarkers of response. scRNA-seq analysis identified a clonally expanded cluster enriched within CD57+ CD8 T cells in responding patients characterized by higher expression of genes associated with activation, cytotoxicity, and tissue-resident memory markers. Furthermore, compared with CD57- CD8 T cells, TCRs of CD57+ CD8 T cells showed increased overlap with the TCR repertoire of tumor-infiltrating T cells. CONCLUSIONS Collectively, we show high frequencies of CD57 among neoantigen-specific and bulk CD8 T cells in patients responding to atezolizumab. The TCR repertoire overlap between peripheral CD57+ CD8 T cells and tumor-infiltrating lymphocytes suggest that accumulation of peripheral CD57+ CD8 T cells is reflective of an ongoing antitumor T-cell response. Our findings provide evidence and rationale for using circulating CD8 T cells expressing CD57 as a readily accessible blood-based biomarker for selecting patients with mUC for atezolizumab therapy.
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Affiliation(s)
| | - Leesun Kim
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Xiangnan Guan
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Kobe Yuen
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Alireza Tafazzol
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Shomyseh Sanjabi
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Oliver A Zill
- Department of Oncology Bioinformatics, Genentech Inc, South San Francisco, California, USA
| | - Deepali Rishipathak
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Andrew Wallace
- Department of Oncology Bioinformatics, Genentech Inc, South San Francisco, California, USA
| | | | | | | | - Evan W Newell
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Sanjeev Mariathasan
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
| | - Mahesh Yadav
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, California, USA
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Li V, Lee C, Yoo D, Cho S, Kim H. In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology. Genes Genomics 2022; 44:937-944. [PMID: 35665465 PMCID: PMC9166176 DOI: 10.1007/s13258-022-01255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/31/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began in 2019 but it remains as a serious threat today. To reduce and prevent spread of the virus, multiple vaccines have been developed. Despite the efforts in developing vaccines, Omicron strain of the virus has recently been designated as a variant of concern (VOC) by the World Health Organization (WHO). OBJECTIVE To develop a vaccine candidate against Omicron strain (B.1.1.529, BA.1) of the SARS-CoV-19. METHODS We applied reverse vaccinology methods for BA.1 and BA.2 as the vaccine target and a control, respectively. First, we predicted MHC I, MHC II and B cell epitopes based on their viral genome sequences. Second, after estimation of antigenicity, allergenicity and toxicity, a vaccine construct was assembled and tested for physicochemical properties and solubility. Third, AlphaFold2, RaptorX and RoseTTAfold servers were used to predict secondary structures and 3D structures of the vaccine construct. Fourth, molecular docking analysis was performed to test binding of our construct with angiotensin converting enzyme 2 (ACE2). Lastly, we compared mutation profiles on the epitopes between BA.1, BA.2, and wild type to estimate the efficacy of the vaccine. RESULTS We collected a total of 10 MHC I, 9 MHC II and 5 B cell epitopes for the final vaccine construct for Omicron strain. All epitopes were predicted to be antigenic, non-allergenic and non-toxic. The construct was estimated to have proper stability and solubility. The best modelled tertiary structures were selected for molecular docking analysis with ACE2 receptor. CONCLUSIONS These results suggest the potential efficacy of our newly developed vaccine construct as a novel vaccine candidate against Omicron strain of the coronavirus.
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Affiliation(s)
- Vladimir Li
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Chul Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - DongAhn Yoo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | | | - Heebal Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
- eGnome, Seoul, Republic of Korea.
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Antonio EC, Meireles MR, Bragatte MADS, Vieira GF. Viral immunogenic footprints conferring T cell cross-protection to SARS-CoV-2 and its variants. Front Immunol 2022; 13:931372. [PMID: 35967415 PMCID: PMC9366040 DOI: 10.3389/fimmu.2022.931372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
COVID-19 brought scenes from sci-fi movies into real life. Infected individuals include asymptomatic cases to severe disease leading to death, suggesting the involvement of the genetic constitution of populations and pathogens contributing to differential individuals' outcomes. To investigate shared immunogenic features between SARS-CoV-2 targets and other coronaviruses, we modeled their peptides in 3D structures of HLA-A*02:01 (pMHC), comparing their molecular surfaces These structures were also compared with a panel of epitopes from unrelated viruses, looking for potential triggers conferring cross-protection in uninfected individuals. As expected, SARS-CoV 1 and 2 peptides share molecular and physicochemical features, providing an explanation for the verified experimental immunogenicity among them. Surprisingly, even discordant sequences from human coronaviruses 229E, OC43 and epitopes from unrelated viruses involved in endemic human infections exhibit similar fingerprints of immunogenicity with SARS-CoV-2 peptides. The same approach indicates a conserved CD8+ T cell recognition between Wuhan SARS-CoV-2 sequences and altered peptides from Variants of Concern. Examination of structural data over epitope sequence analysis here could explain how previous infections may produce a heterologous immunity response in a global scale against emergent diseases such as Covid-19, mitigating its full lethal potential, and paves the way for the development of wide spectrum vaccine development.
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Affiliation(s)
- Eduardo Cheuiche Antonio
- Post Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Mariana Rost Meireles
- Post Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Gustavo Fioravanti Vieira
- Post Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Post Graduation Program in Health and Human Development, Universidade La Salle Canoas, Canoas, Brazil
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Neoantigens in precision cancer immunotherapy: from identification to clinical applications. Chin Med J (Engl) 2022; 135:1285-1298. [PMID: 35838545 PMCID: PMC9433083 DOI: 10.1097/cm9.0000000000002181] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Immunotherapies targeting cancer neoantigens are safe, effective, and precise. Neoantigens can be identified mainly by genomic techniques such as next-generation sequencing and high-throughput single-cell sequencing; proteomic techniques such as mass spectrometry; and bioinformatics tools based on high-throughput sequencing data, mass spectrometry data, and biological databases. Neoantigen-related therapies are widely used in clinical practice and include neoantigen vaccines, neoantigen-specific CD8+ and CD4+ T cells, and neoantigen-pulsed dendritic cells. In addition, neoantigens can be used as biomarkers to assess immunotherapy response, resistance, and prognosis. Therapies based on neoantigens are an important and promising branch of cancer immunotherapy. Unremitting efforts are needed to unravel the comprehensive role of neoantigens in anti-tumor immunity and to extend their clinical application. This review aimed to summarize the progress in neoantigen research and to discuss its opportunities and challenges in precision cancer immunotherapy.
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Trevizani R, Yan Z, Greenbaum JA, Sette A, Nielsen M, Peters B. A comprehensive analysis of the IEDB MHC class-I automated benchmark. Brief Bioinform 2022; 23:6632617. [PMID: 35794711 DOI: 10.1093/bib/bbac259] [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: 03/23/2022] [Revised: 05/27/2022] [Accepted: 06/05/2022] [Indexed: 11/12/2022] Open
Abstract
In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.
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Affiliation(s)
- Raphael Trevizani
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA.,Fiocruz Ceará, Fundação Oswaldo Cruz, Rua São José s/n, Precabura, Eusébio/CE, Brazil
| | - Zhen Yan
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, California 92037, USA
| | - Jason A Greenbaum
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, California 92037, USA
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA.,Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B1650 Buenos Aires, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA.,Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
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Wang H, Wu G, Yang Y, Lian F, Xue S. Sequence similarity suggests molecular mimicry-induced cardiovascular symptoms in multisystem inflammatory syndrome in children (MIS-C). Immunol Lett 2022; 250:7-14. [PMID: 35809666 PMCID: PMC9259193 DOI: 10.1016/j.imlet.2022.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 12/27/2022]
Affiliation(s)
- Heng Wang
- Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital.
| | - Gangning Wu
- Shanghai Jiao Tong University College of Basic Medicine.
| | - Yan Yang
- Shanghai Jiao Tong University School of Medicine Affiliated Ninth People's Hospital.
| | - Feng Lian
- Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital.
| | - Song Xue
- Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital.
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Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat Med 2022; 28:1599-1611. [PMID: 35739268 DOI: 10.1038/s41591-022-01868-2] [Citation(s) in RCA: 202] [Impact Index Per Article: 101.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 05/11/2022] [Indexed: 02/08/2023]
Abstract
Atezolizumab (anti-programmed death-ligand 1 (PD-L1)) and bevacizumab (anti-vascular endothelial growth factor (VEGF)) combination therapy has become the new standard of care in patients with unresectable hepatocellular carcinoma. However, potential predictive biomarkers and mechanisms of response and resistance remain less well understood. We report integrated molecular analyses of tumor samples from 358 patients with hepatocellular carcinoma (HCC) enrolled in the GO30140 phase 1b or IMbrave150 phase 3 trial and treated with atezolizumab combined with bevacizumab, atezolizumab alone or sorafenib (multikinase inhibitor). Pre-existing immunity (high expression of CD274, T-effector signature and intratumoral CD8+ T cell density) was associated with better clinical outcomes with the combination. Reduced clinical benefit was associated with high regulatory T cell (Treg) to effector T cell (Teff) ratio and expression of oncofetal genes (GPC3, AFP). Improved outcomes from the combination versus atezolizumab alone were associated with high expression of VEGF Receptor 2 (KDR), Tregs and myeloid inflammation signatures. These findings were further validated by analyses of paired pre- and post-treatment biopsies, in situ analyses and in vivo mouse models. Our study identified key molecular correlates of the combination therapy and highlighted that anti-VEGF might synergize with anti-PD-L1 by targeting angiogenesis, Treg proliferation and myeloid cell inflammation.
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46
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Jiang L, Tang J, Guo F, Guo Y. Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks. BIOLOGY 2022; 11:biology11060848. [PMID: 35741369 PMCID: PMC9220200 DOI: 10.3390/biology11060848] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022]
Abstract
Simple Summary Major histocompatibility complex molecules are of significant biological and clinical importance due to their utility in immunotherapy. The prediction of potential MHC binding peptides can estimate a T-cell immune response. The variable length of existing MHC binding peptides creates difficulty for MHC binding prediction algorithms. Thus, we utilized a bilateral and variable long-short term memory neural network to address this specific problem and developed a novel MHC binding prediction tool. Abstract As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use.
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Affiliation(s)
- Limin Jiang
- Comprehensive Cancer Center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA;
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
- Correspondence: (F.G.); (Y.G.)
| | - Yan Guo
- Comprehensive Cancer Center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA;
- Correspondence: (F.G.); (Y.G.)
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Zhang Y, Zhu G, Li K, Li F, Huang L, Duan M, Zhou F. HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction. Brief Bioinform 2022; 23:6581432. [PMID: 35514183 PMCID: PMC9487590 DOI: 10.1093/bib/bbac173] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/29/2022] [Accepted: 04/18/2022] [Indexed: 12/11/2022] Open
Abstract
Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.
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Affiliation(s)
- Yaqi Zhang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
| | - Gancheng Zhu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
| | - Kewei Li
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
| | - Fei Li
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
| | - Lan Huang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
| | - Meiyu Duan
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China
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48
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Wang F, Wang H, Wang L, Lu H, Qiu S, Zang T, Zhang X, Hu Y. MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences. Brief Bioinform 2022; 23:6571528. [PMID: 35443027 DOI: 10.1093/bib/bbab595] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/14/2021] [Accepted: 12/23/2021] [Indexed: 11/14/2022] Open
Abstract
Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide-MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide-MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).
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Affiliation(s)
- Fuxu Wang
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Haoyan Wang
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Lizhuang Wang
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Haoyu Lu
- Center for Bioinformatics, school of life science and technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Shizheng Qiu
- Center for Bioinformatics, school of life science and technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Tianyi Zang
- Cisco Research, NLP team, California, United States
| | - Xinjun Zhang
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Yang Hu
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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Isolation of Neoantigen-Specific Human T Cell Receptors from Different Human and Murine Repertoires. Cancers (Basel) 2022; 14:cancers14071842. [PMID: 35406613 PMCID: PMC8998067 DOI: 10.3390/cancers14071842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 01/24/2023] Open
Abstract
Simple Summary T cell-based immunotherapy has achieved remarkable clinical responses in patients with cancer. Neoepitope-specific T cells can specifically recognize mutated tumor cells and have led to tumor regression in mouse models and clinical studies. However, isolating neoepitope-specific T cell receptors (TCRs) from the patients’ own repertoire has shown limited success. Sourcing T cell repertoires, other than the patients’ own, has certain advantages: the availability of larger amounts of blood from healthy donors, circumventing tumor-related immunosuppression in patients, and including different donors to broaden the pool of specific T cells. Here, for the first time, a side-by-side comparison of three different TCR donor repertoires, including patients and HLA-matched allogenic healthy human repertoires, as well as repertoires of transgenic mice, is performed. Our results support recent studies that using not only healthy donor T cell repertoires, but also transgenic mice might be a viable strategy for isolating TCRs with known specificity directed against neoantigens for adoptive T cell therapy. Abstract (1) Background: Mutation-specific T cell receptor (TCR)-based adoptive T cell therapy represents a truly tumor-specific immunotherapeutic strategy. However, isolating neoepitope-specific TCRs remains a challenge. (2) Methods: We investigated, side by side, different TCR repertoires—patients’ peripheral lymphocytes (PBLs) and tumor-infiltrating lymphocytes (TILs), PBLs of healthy donors, and a humanized mouse model—to isolate neoepitope-specific TCRs against eight neoepitope candidates from a colon cancer and an ovarian cancer patient. Neoepitope candidates were used to stimulate T cells from different repertoires in vitro to generate neoepitope-specific T cells and isolate the specific TCRs. (3) Results: We isolated six TCRs from healthy donors, directed against four neoepitope candidates and one TCR from the murine T cell repertoire. Endogenous processing of one neoepitope, for which we isolated one TCR from both human and mouse-derived repertoires, could be shown. No neoepitope-specific TCR could be generated from the patients’ own repertoire. (4) Conclusion: Our data indicate that successful isolation of neoepitope-specific TCRs depends on various factors such as the heathy donor’s TCR repertoire or the presence of a tumor microenvironment allowing neoepitope-specific immune responses of the host. We show the advantage and feasibility of using healthy donor repertoires and humanized mouse TCR repertoires to generate mutation-specific TCRs with different specificities, especially in a setting when the availability of patient material is limited.
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50
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A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00459-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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