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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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Cai Y, Li D, Lv D, Yu J, Ma Y, Jiang T, Ding N, Liu Z, Li Y, Xu J. MHC-I-presented non-canonical antigens expand the cancer immunotherapy targets in acute myeloid leukemia. Sci Data 2024; 11:831. [PMID: 39090129 PMCID: PMC11294462 DOI: 10.1038/s41597-024-03660-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: 02/20/2024] [Accepted: 07/18/2024] [Indexed: 08/04/2024] Open
Abstract
Identification of tumor neoantigens is indispensable for the development of cancer immunotherapies. However, we are still lacking knowledge about the potential neoantigens derived from sequences outside protein-coding regions. Here, we comprehensively characterized the immunopeptidome landscape by integrating multi-omics data in acute myeloid leukemia (AML). Both canonical and non-canonical MHC-associated peptides (MAPs) in AML were identified. We found that the quality and characteristics of ncMAPs are comparable or superior to cMAPs, suggesting ncMAPs are indispensable sources for tumor neoantigens. We further proposed a computational framework to prioritize the neoantigens by integrating additional transcriptome and immunopeptidome in normal tissues. Notably, 6 of prioritized 13 neoantigens were derived from ncMAPs. The expressions of corresponding source genes are highly related to infiltrations of immune cells. Finally, a risk model was developed, which exhibited good performance for clinical prognosis in AML. Our findings expand potential cancer immunotherapy targets and provide in-depth insights into AML treatment, laying a new foundation for precision therapies in AML.
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Affiliation(s)
- Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Yingying Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Zhigang Liu
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Guangzhou, China.
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China.
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3
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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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4
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Iyengar BR, Grandchamp A, Bornberg-Bauer E. How antisense transcripts can evolve to encode novel proteins. Nat Commun 2024; 15:6187. [PMID: 39043684 PMCID: PMC11266595 DOI: 10.1038/s41467-024-50550-3] [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: 08/30/2023] [Accepted: 07/12/2024] [Indexed: 07/25/2024] Open
Abstract
Protein coding features can emerge de novo in non coding transcripts, resulting in emergence of new protein coding genes. Studies across many species show that a large fraction of evolutionarily novel non-coding RNAs have an antisense overlap with protein coding genes. The open reading frames (ORFs) in these antisense RNAs could also overlap with existing ORFs. In this study, we investigate how the evolution an ORF could be constrained by its overlap with an existing ORF in three different reading frames. Using a combination of mathematical modeling and genome/transcriptome data analysis in two different model organisms, we show that antisense overlap can increase the likelihood of ORF emergence and reduce the likelihood of ORF loss, especially in one of the three reading frames. In addition to rationalising the repeatedly reported prevalence of de novo emerged genes in antisense transcripts, our work also provides a generic modeling and an analytical framework that can be used to understand evolution of antisense genes.
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Affiliation(s)
- Bharat Ravi Iyengar
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, Germany.
| | - Anna Grandchamp
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, Germany
- Aix-Marseille Université, INSERM, TAGC, Marseille, France
| | - Erich Bornberg-Bauer
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, Germany
- Department of Protein Evolution, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, Tübingen, Germany
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5
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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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6
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Yang Q, Xu L, Dong W, Li X, Wang K, Dong S, Zhang X, Yang T, Jiang F, Zhang B, Luo G, Gao X, Wang G. HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses. Brief Bioinform 2024; 25:bbae302. [PMID: 38920343 PMCID: PMC11200192 DOI: 10.1093/bib/bbae302] [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: 03/19/2024] [Revised: 05/20/2024] [Accepted: 06/11/2024] [Indexed: 06/27/2024] Open
Abstract
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.
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Affiliation(s)
- Qiang Yang
- School of Medicine and Health, Harbin Institute of Technology, Yikuang Street, Harbin 150000, China
| | - Long Xu
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
| | - Weihe Dong
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
| | - Xiaokun Li
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, Harbin 150090, China
- Shandong Hengxun Technology Co., Ltd., Miaoling Road, Qingdao 266100, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
| | - Xianyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Haping Road, Harbin 150081, China
| | - Tiansong Yang
- Department of Rehabilitation, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, and Traditional Chinese Medicine Informatics Key Laboratory of Heilongjiang Province, Heping Road, Harbin 150040, China
| | - Feng Jiang
- School of Medicine and Health, Harbin Institute of Technology, Yikuang Street, Harbin 150000, China
| | - Bin Zhang
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
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7
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Wickland DP, McNinch C, Jessen E, Necela B, Shreeder B, Lin Y, Knutson KL, Asmann YW. Comprehensive profiling of cancer neoantigens from aberrant RNA splicing. J Immunother Cancer 2024; 12:e008988. [PMID: 38754917 PMCID: PMC11097882 DOI: 10.1136/jitc-2024-008988] [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] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Cancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a computational framework, REAL-neo, for identification, quality control, and prioritization of both class-I and class-II human leucocyte antigen (HLA)-presented neoantigens resulting from somatic single-nucleotide mutations, small insertions and deletions, and gene fusions. In this study, we developed a new module, SPLICE-neo, to identify neoantigens from aberrant RNA transcripts from two distinct sources: (1) DNA mutations within splice sites and (2) de novo RNA aberrant splicings. METHODS First, SPLICE-neo was used to profile all DNA splice-site mutations in 11,892 tumors from The Cancer Genome Atlas (TCGA) and identified 11 profiles of splicing donor or acceptor site gains or losses. Transcript isoforms resulting from the top seven most frequent profiles were computed using novel logic models. Second, SPLICE-neo identified de novo RNA splicing events using RNA sequencing reads mapped to novel exon junctions from either single, double, or multiple exon-skipping events. The aberrant transcripts from both sources were then ranked based on isoform expression levels and z-scores assuming that individual aberrant splicing events are rare. Finally, top-ranked novel isoforms were translated into protein, and the resulting neoepitopes were evaluated for neoantigen potential using REAL-neo. The top splicing neoantigen candidates binding to HLA-A*02:01 were validated using in vitro T2 binding assays. RESULTS We identified abundant splicing neoantigens in four representative TCGA cancers: BRCA, LUAD, LUSC, and LIHC. In addition to their substantial contribution to neoantigen load, several splicing neoantigens were potent tumor antigens with stronger bindings to HLA compared with the positive control of antigens from influenza virus. CONCLUSIONS SPLICE-neo is the first tool to comprehensively identify and prioritize splicing neoantigens from both DNA splice-site mutations and de novo RNA aberrant splicings. There are two major advances of SPLICE-neo. First, we developed novel logic models that assemble and prioritize full-length aberrant transcripts from DNA splice-site mutations. Second, SPLICE-neo can identify exon-skipping events involving more than two exons, which account for a quarter to one-third of all skipping events.
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Affiliation(s)
- Daniel P Wickland
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA
| | - Colton McNinch
- National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Erik Jessen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Brian Necela
- Department of Immunology, Mayo Clinic, Jacksonville, Florida, USA
| | - Barath Shreeder
- Department of Immunology, Mayo Clinic, Jacksonville, Florida, USA
| | - Yi Lin
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Keith L Knutson
- Department of Immunology, Mayo Clinic, Jacksonville, Florida, USA
| | - Yan W Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA
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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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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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11
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Feola S, Chiaro J, Fusciello M, Russo S, Kleino I, Ylösmäki L, Kekäläinen E, Hästbacka J, Pekkarinen PT, Ylösmäki E, Capone S, Folgori A, Raggioli A, Boni C, Tiezzi C, Vecchi A, Gelzo M, Kared H, Nardin A, Fehlings M, Barban V, Ahokas P, Viitala T, Castaldo G, Pastore L, Porter P, Pesonen S, Cerullo V. PeptiVAX: A new adaptable peptides-delivery platform for development of CTL-based, SARS-CoV-2 vaccines. Int J Biol Macromol 2024; 262:129926. [PMID: 38331062 DOI: 10.1016/j.ijbiomac.2024.129926] [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: 08/01/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/10/2024]
Abstract
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) posed a threat to public health and the global economy, necessitating the development of various vaccination strategies. Mutations in the SPIKE protein gene, a crucial component of mRNA and adenovirus-based vaccines, raised concerns about vaccine efficacy, prompting the need for rapid vaccine updates. To address this, we leveraged PeptiCRAd, an oncolytic vaccine based on tumor antigen decorated oncolytic adenoviruses, creating a vaccine platform called PeptiVAX. First, we identified multiple CD8 T-cell epitopes from highly conserved regions across coronaviruses, expanding the range of T-cell responses to non-SPIKE proteins. We designed short segments containing the predicted epitopes presented by common HLA-Is in the global population. Testing the immunogenicity, we characterized T-cell responses to candidate peptides in peripheral blood mononuclear cells (PBMCs) from pre-pandemic healthy donors and ICU patients. As a proof of concept in mice, we selected a peptide with epitopes predicted to bind to murine MHC-I haplotypes. Our technology successfully elicited peptide-specific T-cell responses, unaffected by the use of unarmed adenoviral vectors or adeno-based vaccines encoding SPIKE. In conclusion, PeptiVAX represents a fast and adaptable SARS-CoV-2 vaccine delivery system that broadens T-cell responses beyond the SPIKE protein, offering potential benefits for vaccine effectiveness.
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Affiliation(s)
- Sara Feola
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Viikinkaari 5E, University of Helsinki, 00790 Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Fabianinkatu 33, University of Helsinki, 00710 Helsinki, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, FI-00014 Helsinki, Finland
| | - Jacopo Chiaro
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Viikinkaari 5E, University of Helsinki, 00790 Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Fabianinkatu 33, University of Helsinki, 00710 Helsinki, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, FI-00014 Helsinki, Finland
| | - Manlio Fusciello
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Viikinkaari 5E, University of Helsinki, 00790 Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Fabianinkatu 33, University of Helsinki, 00710 Helsinki, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, FI-00014 Helsinki, Finland
| | - Salvatore Russo
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Viikinkaari 5E, University of Helsinki, 00790 Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Fabianinkatu 33, University of Helsinki, 00710 Helsinki, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, FI-00014 Helsinki, Finland
| | - Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | | | - Eliisa Kekäläinen
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; HUSLAB Clinical Microbiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Johanna Hästbacka
- HUSLAB Clinical Microbiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Pirkka T Pekkarinen
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Division of Intensive Care Medicine, Department of Anaesthesiology and Intensive Care, University of Helsinki and Helsinki University Hospital, Finland
| | - Erkko Ylösmäki
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Viikinkaari 5E, University of Helsinki, 00790 Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Fabianinkatu 33, University of Helsinki, 00710 Helsinki, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, FI-00014 Helsinki, Finland
| | | | | | | | - Carolina Boni
- Laboratory of Viral Immunopathology, Unit of Infectious Disease and Hepatology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Camilla Tiezzi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Andrea Vecchi
- Laboratory of Viral Immunopathology, Unit of Infectious Disease and Hepatology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Monica Gelzo
- CEINGE-Biotecnologie Avanzate, Naples, Italy; Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy
| | | | | | | | | | | | - Tapani Viitala
- Pharmaceutical Biophysics Research Group, Drug Research Program, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Giuseppe Castaldo
- CEINGE-Biotecnologie Avanzate, Naples, Italy; Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy
| | - Lucio Pastore
- CEINGE-Biotecnologie Avanzate, Naples, Italy; Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy; Department of Molecular Medicine and Medical Biotechnology, Naples University "Federico II", S. Pansini 5, Italy
| | - Paul Porter
- Valo Therapeutics Oy, Helsinki, Finland; School of Nursing, Curtin University, GPO Box U 1987, Perth, WA 6845, Australia
| | | | - Vincenzo Cerullo
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Viikinkaari 5E, University of Helsinki, 00790 Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Fabianinkatu 33, University of Helsinki, 00710 Helsinki, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, postal code Haartmaninkatu 8, University of Helsinki, 00290 Helsinki, Finland; Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, FI-00014 Helsinki, Finland; Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science (HiLIFE), University of Helsinki, FI-00014 Helsinki, Finland; Department of Molecular Medicine and Medical Biotechnology, Naples University "Federico II", S. Pansini 5, Italy.
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12
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Sueangoen N, Grove H, Chuangchot N, Prasopsiri J, Rungrotmongkol T, Sanachai K, Darai N, Thongchot S, Suriyaphol P, Sa-Nguanraksa D, Thuwajit P, Yenchitsomanus PT, Thuwajit C. Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells. Cancer Immunol Immunother 2024; 73:43. [PMID: 38349410 PMCID: PMC10864427 DOI: 10.1007/s00262-024-03627-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/06/2024] [Indexed: 02/15/2024]
Abstract
Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9-12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.
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Grants
- R016341038 The Research and Innovation Grant, the National Research Council of Thailand, Ministry of Higher Education, Science, Research and Innovation
- R016341038 The Research and Innovation Grant, the National Research Council of Thailand, Ministry of Higher Education, Science, Research and Innovation
- R016334002 Siriraj Research Grant, Faculty of Medicine Siriraj Hospital, Mahidol University
- R016334002 Siriraj Research Grant, Faculty of Medicine Siriraj Hospital, Mahidol University
- Mahidol University
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Affiliation(s)
- Natthaporn Sueangoen
- Graduate Program in Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Harald Grove
- Division of Bioinformatics and Data Management for Research, Research Group and Research Network Division, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nisa Chuangchot
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Jaturawitt Prasopsiri
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Thanyada Rungrotmongkol
- Center of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Kamonpan Sanachai
- Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Nitchakan Darai
- ASEAN Institute for Health Development, Mahidol University, Nakon Pathom, Thailand
| | - Suyanee Thongchot
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Prapat Suriyaphol
- Division of Bioinformatics and Data Management for Research, Research Group and Research Network Division, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Doonyapat Sa-Nguanraksa
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Peti Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pa-Thai Yenchitsomanus
- Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Chanitra Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
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13
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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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14
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Zhang J, Ma W, Yao H. Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method. Brief Bioinform 2023; 25:bbad436. [PMID: 38040492 PMCID: PMC10783865 DOI: 10.1093/bib/bbad436] [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: 08/04/2023] [Revised: 10/05/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023] Open
Abstract
Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, 'TCR Antigen Binding Recognition' based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.
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Affiliation(s)
- Jiawei Zhang
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Wang Ma
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Hui Yao
- Fresh Wind Biotechnologies USA Inc., Houston, TX, USA
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15
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He F, Bandyopadhyay AM, Klesse LJ, Rogojina A, Chun SH, Butler E, Hartshorne T, Holland T, Garcia D, Weldon K, Prado LNP, Langevin AM, Grimes AC, Sugalski A, Shah S, Assanasen C, Lai Z, Zou Y, Kurmashev D, Xu L, Xie Y, Chen Y, Wang X, Tomlinson GE, Skapek SX, Houghton PJ, Kurmasheva RT, Zheng S. Genomic profiling of subcutaneous patient-derived xenografts reveals immune constraints on tumor evolution in childhood solid cancer. Nat Commun 2023; 14:7600. [PMID: 37990009 PMCID: PMC10663468 DOI: 10.1038/s41467-023-43373-1] [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: 03/29/2023] [Accepted: 11/07/2023] [Indexed: 11/23/2023] Open
Abstract
Subcutaneous patient-derived xenografts (PDXs) are an important tool for childhood cancer research. Here, we describe a resource of 68 early passage PDXs established from 65 pediatric solid tumor patients. Through genomic profiling of paired PDXs and patient tumors (PTs), we observe low mutational similarity in about 30% of the PT/PDX pairs. Clonal analysis in these pairs show an aggressive PT minor subclone seeds the major clone in the PDX. We show evidence that this subclone is more immunogenic and is likely suppressed by immune responses in the PT. These results suggest interplay between intratumoral heterogeneity and antitumor immunity may underlie the genetic disparity between PTs and PDXs. We further show that PDXs generally recapitulate PTs in copy number and transcriptomic profiles. Finally, we report a gene fusion LRPAP1-PDGFRA. In summary, we report a childhood cancer PDX resource and our study highlights the role of immune constraints on tumor evolution.
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Affiliation(s)
- Funan He
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Abhik M Bandyopadhyay
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Laura J Klesse
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Gill Center for Cancer and Blood Disorders, Children's Health Children's Medical Center, Dallas, TX, USA
| | - Anna Rogojina
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Sang H Chun
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Erin Butler
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Gill Center for Cancer and Blood Disorders, Children's Health Children's Medical Center, Dallas, TX, USA
| | - Taylor Hartshorne
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Trevor Holland
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Dawn Garcia
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Korri Weldon
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Luz-Nereida Perez Prado
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Anne-Marie Langevin
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Allison C Grimes
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, TX, USA
| | - Aaron Sugalski
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, TX, USA
| | - Shafqat Shah
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, TX, USA
| | - Chatchawin Assanasen
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Zhao Lai
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Yi Zou
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Dias Kurmashev
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
| | - Lin Xu
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yang Xie
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yidong Chen
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Xiaojing Wang
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Gail E Tomlinson
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
| | - Stephen X Skapek
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Gill Center for Cancer and Blood Disorders, Children's Health Children's Medical Center, Dallas, TX, USA
| | - Peter J Houghton
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Raushan T Kurmasheva
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA.
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA.
- Department of Molecular Medicine, University of Texas Health Science Center, San Antonio, TX, USA.
| | - Siyuan Zheng
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, USA.
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA.
- Mays Cancer Center, University of Texas Health Science Center, San Antonio, TX, USA.
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16
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Gupta S, Nerli S, Kutti Kandy S, Mersky GL, Sgourakis NG. HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes. Nat Commun 2023; 14:6349. [PMID: 37816745 PMCID: PMC10564892 DOI: 10.1038/s41467-023-42163-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/29/2023] [Indexed: 10/12/2023] Open
Abstract
The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.
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Affiliation(s)
- Sagar Gupta
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Santrupti Nerli
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sreeja Kutti Kandy
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Glenn L Mersky
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nikolaos G Sgourakis
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Westcott PMK, Muyas F, Hauck H, Smith OC, Sacks NJ, Ely ZA, Jaeger AM, Rideout WM, Zhang D, Bhutkar A, Beytagh MC, Canner DA, Jaramillo GC, Bronson RT, Naranjo S, Jin A, Patten JJ, Cruz AM, Shanahan SL, Cortes-Ciriano I, Jacks T. Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity. Nat Genet 2023; 55:1686-1695. [PMID: 37709863 PMCID: PMC10562252 DOI: 10.1038/s41588-023-01499-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/07/2023] [Indexed: 09/16/2023]
Abstract
DNA mismatch repair deficiency (MMRd) is associated with a high tumor mutational burden (TMB) and sensitivity to immune checkpoint blockade (ICB) therapy. Nevertheless, most MMRd tumors do not durably respond to ICB and critical questions remain about immunosurveillance and TMB in these tumors. In the present study, we developed autochthonous mouse models of MMRd lung and colon cancer. Surprisingly, these models did not display increased T cell infiltration or ICB response, which we showed to be the result of substantial intratumor heterogeneity of mutations. Furthermore, we found that immunosurveillance shapes the clonal architecture but not the overall burden of neoantigens, and T cell responses against subclonal neoantigens are blunted. Finally, we showed that clonal, but not subclonal, neoantigen burden predicts ICB response in clinical trials of MMRd gastric and colorectal cancer. These results provide important context for understanding immune evasion in cancers with a high TMB and have major implications for therapies aimed at increasing TMB.
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Affiliation(s)
- Peter M K Westcott
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Haley Hauck
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olivia C Smith
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nathan J Sacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zackery A Ely
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex M Jaeger
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William M Rideout
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Daniel Zhang
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arjun Bhutkar
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mary C Beytagh
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David A Canner
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Grissel C Jaramillo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Santiago Naranjo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Abbey Jin
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J J Patten
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amanda M Cruz
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sean-Luc Shanahan
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Isidro Cortes-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Rodent Histopathology Core, Harvard Medical School, Boston, MA, USA.
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18
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Albert BA, Yang Y, Shao XM, Singh D, Smit KN, Anagnostou V, Karchin R. Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. NAT MACH INTELL 2023; 5:861-872. [PMID: 37829001 PMCID: PMC10569228 DOI: 10.1038/s42256-023-00694-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 06/23/2023] [Indexed: 10/14/2023]
Abstract
Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer vaccines. Experimental validation of candidate neoepitopes is extremely resource intensive and the vast majority of candidates are non-immunogenic, creating a needle-in-a-haystack problem. Here we address this challenge, presenting computational methods for predicting class I major histocompatibility complex (MHC-I) epitopes and identifying immunogenic neoepitopes with improved precision. The BigMHC method comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays and transfer learned on data from assays of antigen-specific immune response. Compared with four state-of-the-art classifiers, BigMHC significantly improves the prediction of epitope presentation on a test set of 45,409 MHC ligands among 900,592 random negatives (area under the receiver operating characteristic = 0.9733; area under the precision-recall curve = 0.8779). After transfer learning on immunogenicity data, BigMHC yields significantly higher precision than seven state-of-the-art models in identifying immunogenic neoepitopes, making BigMHC effective in clinical settings.
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Affiliation(s)
- Benjamin Alexander Albert
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yunxiao Yang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Xiaoshan M. Shao
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Dipika Singh
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kellie N. Smit
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Valsamo Anagnostou
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Rachel Karchin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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19
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Shahrear S, Islam ABMMK. Modeling of MT. P495, an mRNA-based vaccine against the phosphate-binding protein PstS1 of Mycobacterium tuberculosis. Mol Divers 2023; 27:1613-1632. [PMID: 36006502 PMCID: PMC9406248 DOI: 10.1007/s11030-022-10515-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/13/2022] [Indexed: 11/28/2022]
Abstract
Tuberculosis (TB) is a contagious disease that predominantly affects the lungs, but can also spread to other organs via the bloodstream. TB affects about one-fourth population of the world. With age, the effectiveness of Bacillus Calmette-Guérin (BCG), the only authorized TB vaccine, decreases. In the quest for a prophylactic and immunotherapeutic vaccine, in this study, a hypothetical mRNA vaccine is delineated, named MT. P495, implementing in silico and immunoinformatics approaches to evaluate key aspects and immunogenic epitopes across the PstS1, a highly conserved periplasmic protein of Mycobacterium tuberculosis (Mtb). PstS1 elicited the potential to generate 99.9% population coverage worldwide. The presence of T- and B-cell epitopes across the PstS1 protein were validated using several computational prediction tools. Molecular docking and dynamics simulation confirmed stable epitope-allele interaction. Immune cell response to the antigen clearance rate was verified by the in silico analysis of immune simulation. Codon optimization confirmed the efficient translation of the mRNA in the host cell. With Toll-like receptors, the vaccine exhibited stable and strong interactions. Findings suggest that the MT. P495 vaccine probably will elicit specific immune responses against Mtb. This mRNA vaccine model is a ready source for further wet-lab validation to confirm the efficacy of this proposed vaccine candidate.
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Affiliation(s)
- Sazzad Shahrear
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, 1000, Bangladesh
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20
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Bruno PM, Timms RT, Abdelfattah NS, Leng Y, Lelis FJN, Wesemann DR, Yu XG, Elledge SJ. High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform. Nat Biotechnol 2023; 41:980-992. [PMID: 36593401 PMCID: PMC10314971 DOI: 10.1038/s41587-022-01566-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 10/13/2022] [Indexed: 01/03/2023]
Abstract
Identification of CD8+ T cell epitopes is critical for the development of immunotherapeutics. Existing methods for major histocompatibility complex class I (MHC class I) ligand discovery are time intensive, specialized and unable to interrogate specific proteins on a large scale. Here, we present EpiScan, which uses surface MHC class I levels as a readout for whether a genetically encoded peptide is an MHC class I ligand. Predetermined starting pools composed of >100,000 peptides can be designed using oligonucleotide synthesis, permitting large-scale MHC class I screening. We exploit this programmability of EpiScan to uncover an unappreciated role for cysteine that increases the number of predicted ligands by 9-21%, reveal affinity hierarchies by analysis of biased anchor peptide libraries and screen viral proteomes for MHC class I ligands. Using these data, we generate and iteratively refine peptide binding predictions to create EpiScan Predictor. EpiScan Predictor performs comparably to other state-of-the-art MHC class I peptide binding prediction algorithms without suffering from underrepresentation of cysteine-containing peptides. Thus, targeted immunopeptidomics using EpiScan will accelerate CD8+ T cell epitope discovery toward the goal of individual-specific immunotherapeutics.
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Affiliation(s)
- Peter M Bruno
- Department of Genetics, Harvard Medical School and Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Richard T Timms
- Department of Genetics, Harvard Medical School and Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Nouran S Abdelfattah
- Department of Genetics, Harvard Medical School and Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Yumei Leng
- Department of Genetics, Harvard Medical School and Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Felipe J N Lelis
- Department of Medicine, Division of Allergy and Immunology, Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Duane R Wesemann
- Department of Medicine, Division of Allergy and Immunology, Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA
| | - Xu G Yu
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- Infectious Disease Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Stephen J Elledge
- Department of Genetics, Harvard Medical School and Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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21
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Stephan-Falkenau S, Streubel A, Mairinger T, Blum TG, Kollmeier J, Mairinger FD, Bauer T, Pfannschmidt J, Hollmann M, Wessolly M. Integrated Clinical, Molecular and Immunological Characterization of Pulmonary Sarcomatoid Carcinomas Reveals an Immune Escape Mechanism That May Influence Therapeutic Strategies. Int J Mol Sci 2023; 24:10558. [PMID: 37445733 DOI: 10.3390/ijms241310558] [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: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Pulmonary sarcomatoid carcinoma (PSC) has highly aggressive biological behaviour and poor clinical outcomes, raising expectations for new therapeutic strategies. We characterized 179 PSC by immunohistochemistry, next-generation sequencing and in silico analysis using a deep learning algorithm with respect to clinical, immunological and molecular features. PSC was more common in men, older ages and smokers. Surgery was an independent factor (p < 0.01) of overall survival (OS). PD-L1 expression was detected in 82.1% of all patients. PSC patients displaying altered epitopes due to processing mutations showed another PD-L1-independent immune escape mechanism, which also significantly influenced OS (p < 0.02). The effect was also maintained when only advanced tumour stages were considered (p < 0.01). These patients also showed improved survival with a significant correlation for immunotherapy (p < 0.05) when few or no processing mutations were detected, although this should be interpreted with caution due to the small number of patients studied. Genomic alterations for which there are already approved drugs were present in 35.4% of patients. Met exon 14 skipping was found more frequently (13.7%) and EGFR mutations less frequently (1.7%) than in other NSCLC. In summary, in addition to the divergent genomic landscape of PSC, the specific immunological features of this prognostically poor subtype should be considered in therapy stratification.
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Affiliation(s)
- Susann Stephan-Falkenau
- Institute for Tissue Diagnostics, MVZ at Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Anna Streubel
- Institute for Tissue Diagnostics, MVZ at Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Thomas Mairinger
- Institute for Tissue Diagnostics, MVZ at Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Torsten-Gerriet Blum
- Department of Pneumology, Heckeshorn Lung Clinic, Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Jens Kollmeier
- Department of Pneumology, Heckeshorn Lung Clinic, Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Fabian D Mairinger
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
- German Cancer Consortium (DKTK), Partner Site University Hospital Essen, 45147 Essen, Germany
| | - Torsten Bauer
- Department of Pneumology, Heckeshorn Lung Clinic, Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Joachim Pfannschmidt
- Department of Thoracic Surgery, Heckeshorn Lung Clinic, Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Manuel Hollmann
- Institute for Tissue Diagnostics, MVZ at Helios Klinikum Emil von Behring, 14165 Berlin, Germany
| | - Michael Wessolly
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
- German Cancer Consortium (DKTK), Partner Site University Hospital Essen, 45147 Essen, Germany
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22
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Xu AM, Chour W, DeLucia DC, Su Y, Pavlovitch-Bedzyk AJ, Ng R, Rasheed Y, Davis MM, Lee JK, Heath JR. Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities. Cell Syst 2023; 14:273-284.e5. [PMID: 37001518 PMCID: PMC10355346 DOI: 10.1016/j.cels.2023.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 09/01/2022] [Accepted: 03/01/2023] [Indexed: 04/22/2023]
Abstract
Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.
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Affiliation(s)
- Alexander M Xu
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - William Chour
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 91125, USA
| | - Diana C DeLucia
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yapeng Su
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Rachel Ng
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yusuf Rasheed
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Mark M Davis
- Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John K Lee
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA 98109, USA.
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23
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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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24
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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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25
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Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
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Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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26
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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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27
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Shen Y, Parks JM, Smith JC. HLA Class I Supertype Classification Based on Structural Similarity. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 210:103-114. [PMID: 36453976 DOI: 10.4049/jimmunol.2200685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/31/2022] [Indexed: 12/24/2022]
Abstract
HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA-disease association studies.
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Affiliation(s)
- Yue Shen
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN
| | - Jerry M Parks
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN; and
| | - Jeremy C Smith
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN; and.,Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN
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28
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Sun B, Zhang J, Li Z, Xie M, Luo C, Wang Y, Chen L, Wang Y, Jiang D, Yang K. Integration: Gospel for immune bioinformatician on epitope-based therapy. Front Immunol 2023; 14:1075419. [PMID: 36798136 PMCID: PMC9927647 DOI: 10.3389/fimmu.2023.1075419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Affiliation(s)
- Baozeng Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Junqi Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Zhikui Li
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Mingyang Xie
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Cheng Luo
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Yongkai Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Longyu Chen
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Yueyue Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Dongbo Jiang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,The Key Laboratory of Bio-hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,Department of Microbiology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Kun Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,The Key Laboratory of Bio-hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,Department of Rheumatology, Tangdu Hospital, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
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29
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Rahman MM, Masum MHU, Talukder A, Akter R. An in silico reverse vaccinology approach to design a novel multiepitope peptide vaccine for non-small cell lung cancers. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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30
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Calidonio JM, Hamad-Schifferli K. Biophysical and biochemical insights in the design of immunoassays. Biochim Biophys Acta Gen Subj 2023; 1867:130266. [PMID: 36309294 PMCID: PMC11193098 DOI: 10.1016/j.bbagen.2022.130266] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Rapid antigen assays have been attractive for decentralized, point of care diagnostics because of their low cost, robustness, and ease of use. The development of a diagnostic assay for a newly emerging infectious disease needs to take into account the progression of a disease, whether there is human to human transmission, and patient biomarker levels with time, and these all impact the choice of antigen targets and affinity agents. SCOPE OF REVIEW The factors involved in the biophysical design of rapid antigen immunoassays are discussed, focusing on antigen selection and designing for cross-reactivity. State of the art in the biophysical characterization of protein-ligand or antigen-antibody interactions, the different types of affinity agents used in immunoassays, and biochemical conjugation strategies are described. MAJOR CONCLUSIONS Antigen choice is a critical factor in immunoassay diagnostic development, and should account for the properties of the virion, virus, and disease progression. Biophysical and biochemical aspects of immunoassays are critical for performance. GENERAL SIGNIFICANCE This review can serve as an instructive guide to aid in diagnostic development for future emerging diseases.
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Affiliation(s)
| | - Kimberly Hamad-Schifferli
- Dept. of Engineering, University of Massachusetts Boston, Boston, MA, USA; School for the Environment, University of Massachusetts Boston, Boston, MA, USA.
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31
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Tiwari A, Trivedi R, Lin SY. Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci 2022; 29:83. [PMID: 36253762 PMCID: PMC9575280 DOI: 10.1186/s12929-022-00866-3] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 10/01/2022] [Indexed: 12/24/2022] Open
Abstract
Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.
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Affiliation(s)
- Aadhya Tiwari
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Rakesh Trivedi
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shiaw-Yih Lin
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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32
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Dhanda SK, Malviya J, Gupta S. Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes. Brief Bioinform 2022; 23:6692551. [PMID: 36070623 DOI: 10.1093/bib/bbac382] [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: 06/07/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Department of Oncology, St Jude Children's Research Hospital, Memphis, Tennessee, USA-38015.,Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India
| | - Jitendra Malviya
- Department of Life Sciences and Biological Science, IES University Bhopal, India
| | - Sudheer Gupta
- NGS & Bioinformatics Division, 3B BlackBio Biotech India Ltd., 7-C, Industrial Area, Govindpura, Bhopal, India
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33
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Kim GB, Fritsche J, Bunk S, Mahr A, Unverdorben F, Tosh K, Kong H, Maldini CR, Lau C, Srivatsa S, Jiang S, Glover J, Dopkin D, Zhang CX, Schuster H, Kowalewski DJ, Goldfinger V, Ott M, Fuhrmann D, Baues M, Boesmueller H, Schraeder C, Schimmack G, Song C, Hoffgaard F, Roemer M, Tsou CC, Hofmann M, Treiber T, Hutt M, Alten L, Jaworski M, Alpert A, Missel S, Reinhardt C, Singh H, Schoor O, Walter S, Wagner C, Maurer D, Weinschenk T, Riley JL. Quantitative immunopeptidomics reveals a tumor stroma-specific target for T cell therapy. Sci Transl Med 2022; 14:eabo6135. [PMID: 36044599 PMCID: PMC10130759 DOI: 10.1126/scitranslmed.abo6135] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
T cell receptor (TCR)-based immunotherapy has emerged as a promising therapeutic approach for the treatment of patients with solid cancers. Identifying peptide-human leukocyte antigen (pHLA) complexes highly presented on tumors and rarely expressed on healthy tissue in combination with high-affinity TCRs that when introduced into T cells can redirect T cells to eliminate tumor but not healthy tissue is a key requirement for safe and efficacious TCR-based therapies. To discover promising shared tumor antigens that could be targeted via TCR-based adoptive T cell therapy, we employed population-scale immunopeptidomics using quantitative mass spectrometry across ~1500 tumor and normal tissue samples. We identified an HLA-A*02:01-restricted pan-cancer epitope within the collagen type VI α-3 (COL6A3) gene that is highly presented on tumor stroma across multiple solid cancers due to a tumor-specific alternative splicing event that rarely occurs outside the tumor microenvironment. T cells expressing natural COL6A3-specific TCRs demonstrated only modest activity against cells presenting high copy numbers of COL6A3 pHLAs. One of these TCRs was affinity-enhanced, enabling transduced T cells to specifically eliminate tumors in vivo that expressed similar copy numbers of pHLAs as primary tumor specimens. The enhanced TCR variants exhibited a favorable safety profile with no detectable off-target reactivity, paving the way to initiate clinical trials using COL6A3-specific TCRs to target an array of solid tumors.
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Affiliation(s)
- Gloria B Kim
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jens Fritsche
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Sebastian Bunk
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Andrea Mahr
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Felix Unverdorben
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Kevin Tosh
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hong Kong
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Colby R Maldini
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chui Lau
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sriram Srivatsa
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shuguang Jiang
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joshua Glover
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Derek Dopkin
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carolyn X Zhang
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heiko Schuster
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Daniel J Kowalewski
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | | | - Martina Ott
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - David Fuhrmann
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Maike Baues
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Hans Boesmueller
- Institute of Pathology and Neuropathology, Eberhard Karls University, 72076 Tuebingen, Germany
| | - Christoph Schraeder
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Gisela Schimmack
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Colette Song
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Franziska Hoffgaard
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Michael Roemer
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Chih-Chiang Tsou
- Immatics US, 2201 W. Holcombe Blvd., Suite 205, Houston, TX 77030, USA
| | - Martin Hofmann
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Thomas Treiber
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Meike Hutt
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Leonie Alten
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Maike Jaworski
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Amir Alpert
- Immatics US, 2201 W. Holcombe Blvd., Suite 205, Houston, TX 77030, USA
| | - Sarah Missel
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Carsten Reinhardt
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Harpreet Singh
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany.,Immatics US, 2201 W. Holcombe Blvd., Suite 205, Houston, TX 77030, USA
| | - Oliver Schoor
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Steffen Walter
- Immatics US, 2201 W. Holcombe Blvd., Suite 205, Houston, TX 77030, USA
| | - Claudia Wagner
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Dominik Maurer
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany
| | - Toni Weinschenk
- Immatics Biotechnologies GmbH, Paul-Ehrlich-Str. 15, 72076 Tuebingen, Germany.,Immatics US, 2201 W. Holcombe Blvd., Suite 205, Houston, TX 77030, USA
| | - James L Riley
- Department of Microbiology, Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
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34
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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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35
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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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36
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Sun B, Zhang J, Wang J, Liu Y, Sun H, Lu Z, Chen L, Ding X, Pan J, Hu C, Yang S, Jiang D, Yang K. Comparative Immunoreactivity Analyses of Hantaan Virus Glycoprotein-Derived MHC-I Epitopes in Vaccination. Vaccines (Basel) 2022; 10:564. [PMID: 35455313 PMCID: PMC9030823 DOI: 10.3390/vaccines10040564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/02/2022] [Accepted: 03/07/2022] [Indexed: 11/25/2022] Open
Abstract
MHC-I antigen processes and presentation trigger host-specific anti-viral cellular responses during infection, in which epitope-recognizing cytotoxic T lymphocytes eliminate infected cells and contribute to viral clearance through a cytolytic killing effect. In this study, Hantaan virus (HTNV) GP-derived 9-mer dominant epitopes were obtained with high affinity to major HLA-I and H-2 superfamilies. Further immunogenicity and conservation analyses selected 11 promising candidates, and molecule docking (MD) was then simulated with the corresponding MHC-I alleles. Two-way hierarchical clustering revealed the interactions between GP peptides and MHC-I haplotypes. Briefly, epitope hotspots sharing good affinity to a wide spectrum of MHC-I molecules highlighted the biomedical practice for vaccination, and haplotype clusters represented the similarities among individuals during T-cell response establishment. Cross-validation proved the patterns observed through both MD simulation and public data integration. Lastly, 148 HTNV variants yielded six types of major amino acid residue replacements involving four in nine hotspots, which minimally influenced the general potential of MHC-I superfamily presentation. Altogether, our work comprehensively evaluates the pan-MHC-I immunoreactivity of HTNV GP through a state-of-the-art workflow in light of comparative immunology, acknowledges present discoveries, and offers guidance for ongoing HTNV vaccine pursuit.
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Affiliation(s)
- Baozeng Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Junqi Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Jiawei Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Yang Liu
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
- Shaanxi Provincial Center for Disease Control and Prevention, Xi’an 710054, China
| | - Hao Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
- Tangshan Sannvhe Airport, Tangshan 063000, China
| | - Zhenhua Lu
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
- Department of Epidemiology, Public Health School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China
| | - Longyu Chen
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Xushen Ding
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Jingyu Pan
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Chenchen Hu
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Shuya Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Dongbo Jiang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
| | - Kun Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi’an 710032, China; (B.S.); (J.Z.); (J.W.); (Y.L.); (H.S.); (Z.L.); (L.C.); (X.D.); (J.P.); (C.H.); (S.Y.)
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37
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [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: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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38
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Cheng R, Xu Z, Luo M, Wang P, Cao H, Jin X, Zhou W, Xiao L, Jiang Q. Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development. Brief Bioinform 2022; 23:bbab553. [PMID: 35279714 DOI: 10.1093/bib/bbab553] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/17/2023] Open
Abstract
Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.
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Affiliation(s)
- Rui Cheng
- Harbin Institute of Technology, China
| | | | - Meng Luo
- Harbin Institute of Technology, China
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39
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Iyengar BR, Wagner A. GroEL/S overexpression helps to purge deleterious mutations and reduce genetic diversity during adaptive protein evolution. Mol Biol Evol 2022; 39:6540901. [PMID: 35234895 PMCID: PMC9188349 DOI: 10.1093/molbev/msac047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Chaperones are proteins that help other proteins fold. They also affect the adaptive evolution of their client proteins by buffering the effect of deleterious mutations and increasing the genetic diversity of evolving proteins. We study how the bacterial chaperone GroE (GroEL + GroES) affects the evolution of green fluorescent protein (GFP). To this end we subjected GFP to multiple rounds of mutation and selection for its color phenotype in four replicate E. coli populations, and studied its evolutionary dynamics through high-throughput sequencing and mutant engineering. We evolved GFP both under stabilizing selection for its ancestral (green) phenotype, and to directional selection for a new (cyan) phenotype. We did so both under low and high expression of the chaperone GroE. In contrast to previous work, we observe that GroE does not just buffer but also helps purge deleterious (fluorescence reducing) mutations from evolving populations. In doing so, GroE helps reduce the genetic diversity of evolving populations. In addition, it causes phenotypic heterogeneity in mutants with the same genotype, helping to enhance their fluorescence in some cells, and reducing it in others. Our observations show that chaperones can affect adaptive evolution in more than one way.
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40
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An H, Eun M, Yi J, Park J. CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins. Brief Bioinform 2022; 23:6539139. [PMID: 35226074 DOI: 10.1093/bib/bbac056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/04/2022] [Accepted: 02/03/2022] [Indexed: 12/16/2022] Open
Abstract
The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.
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Affiliation(s)
- Hyunsu An
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Republic of Korea
| | - Minho Eun
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Republic of Korea
| | - Jawoon Yi
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Republic of Korea.,Anti-Virus Research Center, Gwangju Institute of Science and Technology (GIST), Republic of Korea.,Laboratory for cell mechanobiology, Gwangju Institute of Science and Technology (GIST), Republic of Korea
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41
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Okada M, Shimizu K, Fujii SI. Identification of Neoantigens in Cancer Cells as Targets for Immunotherapy. Int J Mol Sci 2022; 23:ijms23052594. [PMID: 35269735 PMCID: PMC8910406 DOI: 10.3390/ijms23052594] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/18/2022] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
Abstract
The clinical benefits of immune checkpoint blockage (ICB) therapy have been widely reported. In patients with cancer, researchers have demonstrated the clinical potential of antitumor cytotoxic T cells that can be reinvigorated or enhanced by ICB. Compared to self-antigens, neoantigens derived from tumor somatic mutations are believed to be ideal immune targets in tumors. Candidate tumor neoantigens can be identified through immunogenomic or immunopeptidomic approaches. Identification of neoantigens has revealed several points of the clinical relevance. For instance, tumor mutation burden (TMB) may be an indicator of immunotherapy. In various cancers, mutation rates accompanying neoantigen loads may be indicative of immunotherapy. Furthermore, mismatch repair-deficient tumors can be eradicated by T cells in ICB treatment. Hence, immunotherapies using vaccines or adoptive T-cell transfer targeting neoantigens are potential innovative strategies. However, significant efforts are required to identify the optimal epitopes. In this review, we summarize the recent progress in the identification of neoantigens and discussed preclinical and clinical studies based on neoantigens. We also discuss the issues remaining to be addressed before clinical applications of these new therapeutic strategies can be materialized.
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Affiliation(s)
- Masahiro Okada
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan; (M.O.); (K.S.)
| | - Kanako Shimizu
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan; (M.O.); (K.S.)
| | - Shin-ichiro Fujii
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan; (M.O.); (K.S.)
- Program for Drug Discovery and Medical Technology Platforms, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
- Correspondence: ; Tel.: +81-45-503-7062
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42
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Liu Y, Sun B, Wang J, Sun H, Lu Z, Chen L, Lan M, Xu J, Pan J, Shi J, Sun Y, Zhang X, Wang J, Jiang D, Yang K. In silico analyses and experimental validation of the MHC class-I restricted epitopes of Ebolavirus GP. Int Immunol 2022; 34:313-325. [PMID: 35192720 DOI: 10.1093/intimm/dxac006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 02/22/2022] [Indexed: 11/13/2022] Open
Abstract
Ebolavirus (EBOV) causes an extremely high mortality and prevalence disease called Ebola virus disease (EVD). There is only one glycoprotein (GP) on the virus particle surface, which mediates entry into the host cell. MHC class-I restricted CD8 + T cell responses are important antiviral immune responses. Therefore, it is of great importance to understand EBOV GP-specific MHC class-I restricted epitopes within immunogenicity. In this study, computational approaches were employed to predict the dominant MHC class-I molecule epitopes of EBOV GP for mouse H2 and major alleles of HLA class-I supertypes. Our results yielded 42 dominant epitopes in H2 haplotypes and 301 dominant epitopes in HLA class-I haplotypes. After validation by ELISpot assay, in-depth analyses to ascertain their nature of conservation, immunogenicity, and docking with the corresponding MHC class-I molecules were undertaken. Our study predicted MHC class-I restricted epitopes that may aid the advancement of anti-EBOV immune responses. And the integrated strategy of epitope prediction, validation, and comparative analyses were postulated, promising for epitope-based immunotherapy development and application to viral epidemics.
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Affiliation(s)
- Yang Liu
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China.,Shaanxi Provincial Center for Disease Control and Prevention, Xi'an, Shaanxi, P.R. China
| | - Baozeng Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Jiawei Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Hao Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China.,Tangshan Sannvhe Airport, Tangshan, Hebei, P.R. China
| | - Zhenhua Lu
- Department of Epidemiology, Public Health School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Longyu Chen
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Mingfu Lan
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Jiahao Xu
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Jingyu Pan
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Jingqi Shi
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Yuanjie Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Xiyang Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Jing Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Dongbo Jiang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
| | - Kun Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, P.R. China
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43
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Michalik M, Djahanschiri B, Leo JC, Linke D. An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2412:45-71. [PMID: 34918241 DOI: 10.1007/978-1-0716-1892-9_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles-mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.
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Affiliation(s)
| | - Bardya Djahanschiri
- Institute of Cell Biology and Neuroscience, Goethe University, Frankfurt, Germany
| | - Jack C Leo
- Department of Biosciences, Nottingham Trent University, Nottingham, UK
| | - Dirk Linke
- Department of Biosciences, University of Oslo, Oslo, Norway.
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44
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Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GPS. In silico tools and databases for designing cancer immunotherapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 129:1-50. [PMID: 35305716 DOI: 10.1016/bs.apcsb.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
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45
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Westcott PMK, Sacks NJ, Schenkel JM, Ely ZA, Smith O, Hauck H, Jaeger AM, Zhang D, Backlund CM, Beytagh MC, Patten JJ, Elbashir R, Eng G, Irvine DJ, Yilmaz OH, Jacks T. Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer. NATURE CANCER 2021; 2:1071-1085. [PMID: 34738089 PMCID: PMC8562866 DOI: 10.1038/s43018-021-00247-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 07/16/2021] [Indexed: 02/08/2023]
Abstract
Immune evasion is a hallmark of cancer, and therapies that restore immune surveillance have proven highly effective in cancers with high tumor mutation burden (TMB) (e.g., those with microsatellite instability (MSI)). Whether low TMB cancers, which are largely refractory to immunotherapy, harbor potentially immunogenic neoantigens remains unclear. Here, we show that tumors from all patients with microsatellite stable (MSS) colorectal cancer (CRC) express clonal predicted neoantigens despite low TMB. Unexpectedly, these neoantigens are broadly expressed at lower levels compared to those in MSI CRC. Using a versatile platform for modulating neoantigen expression in CRC organoids and transplantation into the distal colon of mice, we show that low expression precludes productive cross priming and drives immediate T cell dysfunction. Strikingly, experimental or therapeutic rescue of priming rendered T cells capable of controlling tumors with low neoantigen expression. These findings underscore a critical role of neoantigen expression level in immune evasion and therapy response.
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Affiliation(s)
- Peter M K Westcott
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nathan J Sacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jason M Schenkel
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Zackery A Ely
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olivia Smith
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haley Hauck
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex M Jaeger
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Daniel Zhang
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Coralie M Backlund
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mary C Beytagh
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J J Patten
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ryan Elbashir
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - George Eng
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Darrell J Irvine
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Omer H Yilmaz
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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46
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Cai X, Li JJ, Liu T, Brian O, Li J. Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design. Brief Funct Genomics 2021; 20:289-303. [PMID: 34089044 PMCID: PMC8194884 DOI: 10.1093/bfgp/elab027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 12/15/2022] Open
Abstract
Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.
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Affiliation(s)
- Xinhui Cai
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Jiao Jiao Li
- School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Tao Liu
- School of Life Sciences, Faculty of Science, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Oliver Brian
- Children’s Cancer Institute Australia, University of New South Wales Sydney, Children’s Cancer Institute Australia, Randwick, Sydney, 2031, New South Wales, Australia
| | - Jinyan Li
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
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47
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Arumugam S, Varamballi P. In-silico design of envelope based multi-epitope vaccine candidate against Kyasanur forest disease virus. Sci Rep 2021; 11:17118. [PMID: 34429443 PMCID: PMC8384868 DOI: 10.1038/s41598-021-94488-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/01/2021] [Indexed: 02/07/2023] Open
Abstract
Kyasanur forest disease virus (KFDV) causing tick-borne hemorrhagic fever which was earlier endemic to western Ghats, southern India, it is now encroaching into new geographic regions, but there is no approved medicine or effective vaccine against this deadly disease. In this study, we did in-silico design of multi-epitope subunit vaccine for KFDV. B-cell and T-cell epitopes were predicted from conserved regions of KFDV envelope protein and two vaccine candidates (VC1 and VC2) were constructed, those were found to be non-allergic and possess good antigenic properties, also gives cross-protection against Alkhurma hemorrhagic fever virus. The 3D structures of vaccine candidates were built and validated. Docking analysis of vaccine candidates with toll-like receptor-2 (TLR-2) by Cluspro and PatchDock revealed strong affinity between VC1 and TLR2. Ligplot tool was identified the intermolecular hydrogen bonds between vaccine candidates and TLR-2, iMOD server confirmed the stability of the docking complexes. JCAT sever ensured cloning efficiency of both vaccine constructs and in-silico cloning into pET30a (+) vector by SnapGene showed successful translation of epitope region. IMMSIM server was identified increased immunological responses. Finally, multi-epitope vaccine candidates were designed and validated their efficiency, it may pave the way for up-coming vaccine and diagnostic kit development.
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Affiliation(s)
- Sathishkumar Arumugam
- Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Prasad Varamballi
- Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
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48
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Koşaloğlu-Yalçın Z, Blazeska N, Carter H, Nielsen M, Cohen E, Kufe D, Conejo-Garcia J, Robbins P, Schoenberger SP, Peters B, Sette A. The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community. Front Immunol 2021; 12:735609. [PMID: 34504503 PMCID: PMC8421848 DOI: 10.3389/fimmu.2021.735609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/09/2021] [Indexed: 12/17/2022] Open
Abstract
Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.
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MESH Headings
- Antigens, Neoplasm/genetics
- Antigens, Neoplasm/immunology
- Computational Biology
- Databases, Genetic
- Epitopes, B-Lymphocyte
- Epitopes, T-Lymphocyte
- Humans
- Immunotherapy
- Mutation
- Neoplasms/genetics
- Neoplasms/immunology
- Neoplasms/therapy
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Tumor Microenvironment
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Affiliation(s)
- Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Nina Blazeska
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
| | - Ezra Cohen
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Donald Kufe
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Jose Conejo-Garcia
- Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Paul Robbins
- National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephen P. Schoenberger
- Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
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49
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Tang L, Zhang R, Zhang X, Yang L. Personalized Neoantigen-Pulsed DC Vaccines: Advances in Clinical Applications. Front Oncol 2021; 11:701777. [PMID: 34381724 PMCID: PMC8350509 DOI: 10.3389/fonc.2021.701777] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/12/2021] [Indexed: 02/05/2023] Open
Abstract
In the past few decades, great progress has been made in the clinical application of dendritic cell (DC) vaccines loaded with personalized neoantigens. Personalized neoantigens are antigens arising from somatic mutations in cancers, with specificity to each patient. DC vaccines work based on the fundamental characteristics of DCs, which are professional antigen-presenting cells (APCs), responsible for the uptake, processing, and presentation of antigens to T cells to activate immune responses. Neoantigens can exert their antitumor effects only after they are taken up by APCs and presented to T cells. In recent years, neoantigen-based personalized tumor therapeutic vaccines have proven to be safe, immunogenic and feasible treatment strategies in patients with melanoma and glioblastoma that provide new hope in the treatment of cancer patients and a new approach to cure cancer. In addition, according to ClinicalTrials.gov, hundreds of registered DC vaccine trials are either completed or ongoing worldwide, of which 9 are in early phase I, 191 in phase I, 166 in phase II and 8 in phase III. Hundreds of clinical studies on therapeutic tumor vaccines globally have proven that DC vaccines are stable, reliable and very safe. However, in this process, many other factors still limit the effectiveness of the vaccine. This review will focus on the current research progress on personalized neoantigen-pulsed DC vaccines, their limitations and future research directions of DC vaccines loaded with neoantigens. This review aims to provide a better understanding of DCs biology and manipulation of activated DCs for DCs researchers to produce the next generation of highly efficient cancer vaccines for patients.
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Affiliation(s)
- Lin Tang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Rui Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Xiaoyu Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Li Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, China
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50
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Jiang L, Yu H, Li J, Tang J, Guo Y, Guo F. Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution. Brief Bioinform 2021; 22:6299205. [PMID: 34131696 DOI: 10.1093/bib/bbab216] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 01/04/2023] Open
Abstract
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.
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Affiliation(s)
- Limin Jiang
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Hui Yu
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jiawei Li
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- Department of Computer Science, University of South Carolina, SC, USA.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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