1
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Yu Z, Jiang M, Lan X. HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction. Commun Biol 2024; 7:684. [PMID: 38834836 DOI: 10.1038/s42003-024-06380-6] [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: 10/06/2023] [Accepted: 05/23/2024] [Indexed: 06/06/2024] Open
Abstract
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.
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Affiliation(s)
- Zilan Yu
- School of Medicine, Tsinghua University, 100084, Beijing, China
- Centre for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Mengnan Jiang
- School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Xun Lan
- School of Medicine, Tsinghua University, 100084, Beijing, China.
- Centre for Life Sciences, Tsinghua University, 100084, Beijing, China.
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, Tsinghua University, 100084, Beijing, China.
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2
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Mösch A, Grazioli F, Machart P, Malone B. NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population. Bioinformatics 2024; 40:btae205. [PMID: 38614133 PMCID: PMC11076149 DOI: 10.1093/bioinformatics/btae205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
MOTIVATION Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity. RESULTS Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients. AVAILABILITY AND IMPLEMENTATION The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.
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Affiliation(s)
- Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Filippo Grazioli
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Brandon Malone
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
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3
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Pang Z, Lu MM, Zhang Y, Gao Y, Bai JJ, Gu JY, Xie L, Wu WZ. Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges. Biomark Res 2023; 11:104. [PMID: 38037114 PMCID: PMC10690996 DOI: 10.1186/s40364-023-00534-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/22/2023] [Indexed: 12/02/2023] Open
Abstract
Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.
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Affiliation(s)
- Zhi Pang
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Man-Man Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Yu Zhang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Yuan Gao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China
| | - Jin-Jin Bai
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jian-Ying Gu
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200237, China.
| | - Wei-Zhong Wu
- Liver Cancer Institute, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Clinical Center for Biotherapy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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4
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Zhang Z, Chen X, Tang R, Zhu Y, Guo H, Qu Y, Xie P, Lian IY, Wang Y, Lo YH. Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry. Sci Rep 2023; 13:20533. [PMID: 37996496 PMCID: PMC10667244 DOI: 10.1038/s41598-023-46782-w] [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: 01/26/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023] Open
Abstract
A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.
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Affiliation(s)
- Zunming Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Xinyu Chen
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rui Tang
- NanoCellect Biomedical, Inc., San Diego, CA, 92121, USA
| | - Yuxuan Zhu
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Han Guo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Yunjia Qu
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0435, USA
| | - Pengtao Xie
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ian Y Lian
- Department of Biology, Lamar University, Beaumont, TX, 77710, USA
| | - Yingxiao Wang
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0435, USA
| | - Yu-Hwa Lo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
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5
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Bäckel N, Hort S, Kis T, Nettleton DF, Egan JR, Jacobs JJL, Grunert D, Schmitt RH. Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing. FRONTIERS IN MOLECULAR MEDICINE 2023; 3:1250508. [PMID: 39086671 PMCID: PMC11285580 DOI: 10.3389/fmmed.2023.1250508] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/28/2023] [Indexed: 08/02/2024]
Abstract
This paper discusses the challenges of producing CAR-T cells for cancer treatment and the potential for Artificial Intelligence (AI) for its improvement. CAR-T cell therapy was approved in 2018 as the first Advanced Therapy Medicinal Product (ATMP) for treating acute leukemia and lymphoma. ATMPs are cell- and gene-based therapies that show great promise for treating various cancers and hereditary diseases. While some new ATMPs have been approved, ongoing clinical trials are expected to lead to the approval of many more. However, the production of CAR-T cells presents a significant challenge due to the high costs associated with the manufacturing process, making the therapy very expensive (approx. $400,000). Furthermore, autologous CAR-T therapy is limited to a make-to-order approach, which makes scaling economical production difficult. First attempts are being made to automate this multi-step manufacturing process, which will not only directly reduce the high manufacturing costs but will also enable comprehensive data collection. AI technologies have the ability to analyze this data and convert it into knowledge and insights. In order to exploit these opportunities, this paper analyses the data potential in the automated CAR-T production process and creates a mapping to the capabilities of AI applications. The paper explores the possible use of AI in analyzing the data generated during the automated process and its capabilities to further improve the efficiency and cost-effectiveness of CAR-T cell production.
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Affiliation(s)
- Niklas Bäckel
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Simon Hort
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Tamás Kis
- Institute for Computer Science and Control, Hungarian Research Network, Budapest, Hungary
| | | | - Joseph R. Egan
- Department of Biochemical Engineering, Mathematical Modelling of Cell and Gene Therapies, University College London, London, United Kingdom
| | | | - Dennis Grunert
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Robert H. Schmitt
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
- Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
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6
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Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H. Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 2023; 23:511-521. [PMID: 36755161 PMCID: PMC9908307 DOI: 10.1038/s41577-023-00835-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 02/10/2023]
Abstract
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Graham Ogg
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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7
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Jiang N, Malone M, Chizari S. Antigen-specific and cross-reactive T cells in protection and disease. Immunol Rev 2023; 316:120-135. [PMID: 37209375 PMCID: PMC10524458 DOI: 10.1111/imr.13217] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/22/2023]
Abstract
Human T cells have a diverse T-cell receptor (TCR) repertoire that endows them with the ability to identify and defend against a broad spectrum of antigens. The universe of possible antigens that T cells may encounter, however, is even larger. To effectively surveil such a vast universe, the T-cell repertoire must adopt a high degree of cross-reactivity. Likewise, antigen-specific and cross-reactive T-cell responses play pivotal roles in both protective and pathological immune responses in numerous diseases. In this review, we explore the implications of these antigen-driven T-cell responses, with a particular focus on CD8+ T cells, using infection, neurodegeneration, and cancer as examples. We also summarize recent technological advances that facilitate high-throughput profiling of antigen-specific and cross-reactive T-cell responses experimentally, as well as computational biology approaches that predict these interactions.
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Affiliation(s)
- Ning Jiang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
- Institute for Immunology, University of Pennsylvania, Philadelphia, PA, 19104
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA, 19104
- Institute for RNA Innovation, University of Pennsylvania, Philadelphia, PA, 19104
| | - Michael Malone
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
| | - Shahab Chizari
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
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8
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Cai Y, Chen R, Gao S, Li W, Liu Y, Su G, Song M, Jiang M, Jiang C, Zhang X. Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol 2023; 12:1054231. [PMID: 36698417 PMCID: PMC9868469 DOI: 10.3389/fonc.2022.1054231] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023] Open
Abstract
The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.
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Affiliation(s)
- Yu Cai
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Rui Chen
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Shenghan Gao
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Wenqing Li
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Yuru Liu
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Guodong Su
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mingming Song
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mengju Jiang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Chao Jiang
- Department of Neurology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
| | - Xi Zhang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
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9
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Fang Y, Liu X, Liu H. Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity. Brief Bioinform 2022; 23:6696141. [PMID: 36094087 DOI: 10.1093/bib/bbac378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/06/2022] [Accepted: 08/09/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION It has been proven that only a small fraction of the neoantigens presented by major histocompatibility complex (MHC) class I molecules on the cell surface can elicit T cells. This restriction can be attributed to the binding specificity of T cell receptor (TCR) and peptide-MHC complex (pMHC). Computational prediction of T cells binding to neoantigens is a challenging and unresolved task. RESULTS In this paper, we proposed an attention-aware contrastive learning model, ATMTCR, to infer the TCR-pMHC binding specificity. For each TCR sequence, we used a transformer encoder to transform it to latent representation, and then masked a percentage of amino acids guided by attention weights to generate its contrastive view. Compared to fully-supervised baseline model, we verified that contrastive learning-based pretraining on large-scale TCR sequences significantly improved the prediction performance of downstream tasks. Interestingly, masking a percentage of amino acids with low attention weights yielded best performance compared to other masking strategies. Comparison experiments on two independent datasets demonstrated our method achieved better performance than other existing algorithms. Moreover, we identified important amino acids and their positional preference through attention weights, which indicated the potential interpretability of our proposed model.
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Affiliation(s)
- Yiming Fang
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
| | - Xuejun Liu
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
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10
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Antonio EC, Meireles MR, Bragatte MADS, Vieira GF. Viral immunogenic footprints conferring T cell cross-protection to SARS-CoV-2 and its variants. Front Immunol 2022; 13:931372. [PMID: 35967415 PMCID: PMC9366040 DOI: 10.3389/fimmu.2022.931372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
COVID-19 brought scenes from sci-fi movies into real life. Infected individuals include asymptomatic cases to severe disease leading to death, suggesting the involvement of the genetic constitution of populations and pathogens contributing to differential individuals' outcomes. To investigate shared immunogenic features between SARS-CoV-2 targets and other coronaviruses, we modeled their peptides in 3D structures of HLA-A*02:01 (pMHC), comparing their molecular surfaces These structures were also compared with a panel of epitopes from unrelated viruses, looking for potential triggers conferring cross-protection in uninfected individuals. As expected, SARS-CoV 1 and 2 peptides share molecular and physicochemical features, providing an explanation for the verified experimental immunogenicity among them. Surprisingly, even discordant sequences from human coronaviruses 229E, OC43 and epitopes from unrelated viruses involved in endemic human infections exhibit similar fingerprints of immunogenicity with SARS-CoV-2 peptides. The same approach indicates a conserved CD8+ T cell recognition between Wuhan SARS-CoV-2 sequences and altered peptides from Variants of Concern. Examination of structural data over epitope sequence analysis here could explain how previous infections may produce a heterologous immunity response in a global scale against emergent diseases such as Covid-19, mitigating its full lethal potential, and paves the way for the development of wide spectrum vaccine development.
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Affiliation(s)
- Eduardo Cheuiche Antonio
- Post Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Mariana Rost Meireles
- Post Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Gustavo Fioravanti Vieira
- Post Graduation Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Post Graduation Program in Health and Human Development, Universidade La Salle Canoas, Canoas, Brazil
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11
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Cai M, Bang S, Zhang P, Lee H. ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model. Front Immunol 2022; 13:893247. [PMID: 35874725 PMCID: PMC9299376 DOI: 10.3389/fimmu.2022.893247] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/27/2022] [Indexed: 11/29/2022] Open
Abstract
TCR-epitope pair binding is the key component for T cell regulation. The ability to predict whether a given pair binds is fundamental to understanding the underlying biology of the binding mechanism as well as developing T-cell mediated immunotherapy approaches. The advent of large-scale public databases containing TCR-epitope binding pairs enabled the recent development of computational prediction methods for TCR-epitope binding. However, the number of epitopes reported along with binding TCRs is far too small, resulting in poor out-of-sample performance for unseen epitopes. In order to address this issue, we present our model ATM-TCR which uses a multi-head self-attention mechanism to capture biological contextual information and improve generalization performance. Additionally, we present a novel application of the attention map from our model to improve out-of-sample performance by demonstrating on recent SARS-CoV-2 data.
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Affiliation(s)
- Michael Cai
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.,Biodesign Institute, Arizona State University, Tempe, AZ, United States
| | - Seojin Bang
- Biodesign Institute, Arizona State University, Tempe, AZ, United States
| | - Pengfei Zhang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.,Biodesign Institute, Arizona State University, Tempe, AZ, United States
| | - Heewook Lee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.,Biodesign Institute, Arizona State University, Tempe, AZ, United States
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12
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Katayama Y, Yokota R, Akiyama T, Kobayashi TJ. Machine Learning Approaches to TCR Repertoire Analysis. Front Immunol 2022; 13:858057. [PMID: 35911778 PMCID: PMC9334875 DOI: 10.3389/fimmu.2022.858057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.
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Affiliation(s)
- Yotaro Katayama
- Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- *Correspondence: Yotaro Katayama,
| | - Ryo Yokota
- National Research Institute of Police Science, Kashiwa, Chiba, Japan
| | - Taishin Akiyama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Tetsuya J. Kobayashi
- Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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13
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Papadopoulou I, Nguyen AP, Weber A, Martínez MR. DECODE: a computational pipeline to discover T cell receptor binding rules. Bioinformatics 2022; 38:i246-i254. [PMID: 35758821 PMCID: PMC9235487 DOI: 10.1093/bioinformatics/btac257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Motivation Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-based therapies. Advances in repertoire sequencing technologies have made available millions of TCR sequences. Data abundance has, in turn, fueled the development of many computational models to predict the binding properties of TCRs from their sequences. Unfortunately, while many of these works have made great strides toward predicting TCR specificity using machine learning, the black-box nature of these models has resulted in a limited understanding of the rules that govern the binding of a TCR and an epitope. Results We present an easy-to-use and customizable computational pipeline, DECODE, to extract the binding rules from any black-box model designed to predict the TCR-epitope binding. DECODE offers a range of analytical and visualization tools to guide the user in the extraction of such rules. We demonstrate our pipeline on a recently published TCR-binding prediction model, TITAN, and show how to use the provided metrics to assess the quality of the computed rules. In conclusion, DECODE can lead to a better understanding of the sequence motifs that underlie TCR binding. Our pipeline can facilitate the investigation of current immunotherapeutic challenges, such as cross-reactive events due to off-target TCR binding. Availability and implementation Code is available publicly at https://github.com/phineasng/DECODE. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iliana Papadopoulou
- IBM Research Europe, 8803 Rüschlikon, Switzerland.,ETH Zurich, Department of Biosystems Science and Engineering (D-BSSE), 4058 Basel, Switzerland
| | - An-Phi Nguyen
- IBM Research Europe, 8803 Rüschlikon, Switzerland.,ETH Zurich, Department of Mathematics (D-Math), 8092 Zurich, Switzerland
| | - Anna Weber
- IBM Research Europe, 8803 Rüschlikon, Switzerland.,ETH Zurich, Department of Biosystems Science and Engineering (D-BSSE), 4058 Basel, Switzerland
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14
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Perez MAS, Cuendet MA, Röhrig UF, Michielin O, Zoete V. Structural Prediction of Peptide-MHC Binding Modes. Methods Mol Biol 2022; 2405:245-282. [PMID: 35298818 DOI: 10.1007/978-1-0716-1855-4_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.
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Affiliation(s)
- Marta A S Perez
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne, Switzerland
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michel A Cuendet
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland
| | - Ute F Röhrig
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olivier Michielin
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland.
| | - Vincent Zoete
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, Lausanne, Switzerland.
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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15
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Bugada LF, Smith MR, Wen F. Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS). Methods Mol Biol 2022; 2574:233-250. [PMID: 36087205 DOI: 10.1007/978-1-0716-2712-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
CD4+ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII) molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.
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Affiliation(s)
- Luke F Bugada
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Mason R Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Fei Wen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
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16
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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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17
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Barbosa CRR, Barton J, Shepherd AJ, Mishto M. Mechanistic diversity in MHC class I antigen recognition. Biochem J 2021; 478:4187-4202. [PMID: 34940832 PMCID: PMC8786304 DOI: 10.1042/bcj20200910] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/16/2021] [Accepted: 11/18/2021] [Indexed: 12/20/2022]
Abstract
Throughout its evolution, the human immune system has developed a plethora of strategies to diversify the antigenic peptide sequences that can be targeted by the CD8+ T cell response against pathogens and aberrations of self. Here we provide a general overview of the mechanisms that lead to the diversity of antigens presented by MHC class I complexes and their recognition by CD8+ T cells, together with a more detailed analysis of recent progress in two important areas that are highly controversial: the prevalence and immunological relevance of unconventional antigen peptides; and cross-recognition of antigenic peptides by the T cell receptors of CD8+ T cells.
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Affiliation(s)
- Camila R. R. Barbosa
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL London, U.K
- Francis Crick Institute, NW1 1AT London, U.K
| | - Justin Barton
- Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, WC1E 7HX London, U.K
| | - Adrian J. Shepherd
- Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, WC1E 7HX London, U.K
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL London, U.K
- Francis Crick Institute, NW1 1AT London, U.K
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18
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Baulu E, Dougé A, Chuvin N, Bay JO, Depil S. [T cell-based immunotherapies in solid tumors]. Bull Cancer 2021; 108:S96-S108. [PMID: 34920813 DOI: 10.1016/j.bulcan.2021.06.004] [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: 03/17/2021] [Revised: 06/07/2021] [Accepted: 06/11/2021] [Indexed: 12/08/2022]
Abstract
In solid tumors, adoptive T cell therapies based on ex vivo amplification of antitumor T cell are represented by three main complementary approaches : (i) tumor infiltrating lymphocytes (TILs) which are amplified in vitro before reinjection to the patient, (ii) chimeric antigen receptor (CAR) engineered T cells and (iii) T cell receptor (TCR) engineered T cells. Despite encouraging results, some obstacles remain, such as optimal target selection and tumor microenvironment. In this Review, we discuss pros and cons of these different therapeutic strategies that may open new perspectives in the treatment of solid tumors.
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Affiliation(s)
- Estelle Baulu
- Centre de recherche en cancérologie de Lyon, 28, rue Laennec, 69008 Lyon, France; ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France
| | - Aurore Dougé
- CHU Estaing, service d'hématologie, 1, rue Lucie et Raymond Aubrac, 63100 Clermont-Ferrand, France
| | - Nicolas Chuvin
- ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France
| | - Jacques-Olivier Bay
- CHU Estaing, service d'hématologie, 1, rue Lucie et Raymond Aubrac, 63100 Clermont-Ferrand, France; Faculté de médecine, 28, place Henri Dunant, 63000 Clermont-Ferrand, France
| | - Stéphane Depil
- Centre de recherche en cancérologie de Lyon, 28, rue Laennec, 69008 Lyon, France; ErVaccine Technologies, 28, rue Laennec, 69008 Lyon, France; Centre Léon Bérard, 28, Prom. Léa et Napoléon Bullukian, 69008 Lyon, France; Université Claude-Bernard Lyon 1, 43, boulevard du 11 novembre 1918, 69100 Villeurbanne, France.
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19
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Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharm Sin B 2021; 11:3393-3405. [PMID: 34900525 PMCID: PMC8642413 DOI: 10.1016/j.apsb.2021.02.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/07/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.
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Key Words
- AI, artificial intelligence
- Artificial intelligence
- CT, computed tomography
- CTLA-4, cytotoxic T lymphocyte-associated antigen 4
- Cancer immunotherapy
- DL, deep learning
- Diagnostics
- ICB, immune checkpoint blockade
- MHC-I, major histocompatibility complex class I
- ML, machine learning
- MMR, mismatch repair
- MRI, magnetic resonance imaging
- Machine learning
- PD-1, programmed cell death protein 1
- PD-L1, PD-1 ligand1
- TNBC, triple-negative breast cancer
- US, ultrasonography
- irAEs, immune-related adverse events
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Affiliation(s)
- Zhijie Xu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xiang Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shuangshuang Zeng
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xinxin Ren
- Center for Molecular Medicine, Xiangya Hospital, Key Laboratory of Molecular Radiation Oncology of Hunan Province, Central South University, Changsha 410008, China
| | - Yuanliang Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhicheng Gong
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
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20
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Zhao X, Pan X, Wang Y, Zhang Y. Targeting neoantigens for cancer immunotherapy. Biomark Res 2021; 9:61. [PMID: 34321091 PMCID: PMC8317330 DOI: 10.1186/s40364-021-00315-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/16/2021] [Indexed: 12/13/2022] Open
Abstract
Neoantigens, a type of tumor-specific antigens derived from non-synonymous mutations, have recently been characterized as attractive targets for cancer immunotherapy. Owing to the development of next-generation sequencing and utilization of machine-learning algorithms, it has become feasible to computationally predict neoantigens by depicting genetic alterations, aberrant post-transcriptional mRNA processing and abnormal mRNA translation events within tumor tissues. Consequently, neoantigen-based therapies such as cancer vaccines have been widely tested in clinical trials and have demonstrated promising safety and efficacy, opening a new era for cancer immunotherapy. We systematically summarize recent advances in the identification of both personalized and public neoantigens, neoantigen formulations and neoantigen-based clinical trials in this review. Moreover, we discuss future techniques and strategies for neoantigen-based cancer treatment either as a monotherapy or as a combination therapy with radiotherapy, chemotherapy or immune checkpoint inhibitors.
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Affiliation(s)
- Xuan Zhao
- Biotherapy Center & Cancer Center, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, China.,State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, 450052, Zhengzhou, China
| | - Xiaoxin Pan
- Shenzhen NeoCura Biotechnology Corporation, 518055, Shenzhen, China
| | - Yi Wang
- Shenzhen NeoCura Biotechnology Corporation, 518055, Shenzhen, China
| | - Yi Zhang
- Biotherapy Center & Cancer Center, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, China. .,State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, 450052, Zhengzhou, China. .,School of Life Sciences, Zhengzhou University, 450052, Zhengzhou, China. .,Henan Key Laboratory for Tumor Immunology and Biotherapy, 450052, Zhengzhou, China.
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21
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Weber A, Born J, Rodriguez Martínez M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics 2021; 37:i237-i244. [PMID: 34252922 PMCID: PMC8275323 DOI: 10.1093/bioinformatics/btab294] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The activity of the adaptive immune system is governed by T-cells and their specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent advances in experimental techniques have enabled sequencing of TCRs and their antigenic targets (epitopes), allowing to research the missing link between TCR sequence and epitope binding specificity. Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance. Here, we establish a k-nearest-neighbor (K-NN) classifier as a strong baseline and then propose Tcr epITope bimodal Attention Networks (TITAN), a bimodal neural network that explicitly encodes both TCR sequences and epitopes to enable the independent study of generalization capabilities to unseen TCRs and/or epitopes. RESULTS By encoding epitopes at the atomic level with SMILES sequences, we leverage transfer learning and data augmentation to enrich the input data space and boost performance. TITAN achieves high performance in the prediction of specificity of unseen TCRs (ROC-AUC 0.87 in 10-fold CV) and surpasses the results of the current state-of-the-art (ImRex) by a large margin. Notably, our Levenshtein-based K-NN classifier also exhibits competitive performance on unseen TCRs. While the generalization to unseen epitopes remains challenging, we report two major breakthroughs. First, by dissecting the attention heatmaps, we demonstrate that the sparsity of available epitope data favors an implicit treatment of epitopes as classes. This may be a general problem that limits unseen epitope performance for sufficiently complex models. Second, we show that TITAN nevertheless exhibits significantly improved performance on unseen epitopes and is capable of focusing attention on chemically meaningful molecular structures. AVAILABILITY AND IMPLEMENTATION The code as well as the dataset used in this study is publicly available at https://github.com/PaccMann/TITAN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anna Weber
- IBM Research Europe, 8803 Rüschlikon, Switzerland.,ETH Zurich, Department of Biosystems Science and Engineering (D-BSSE), 4058 Basel, Switzerland
| | - Jannis Born
- IBM Research Europe, 8803 Rüschlikon, Switzerland.,ETH Zurich, Department of Biosystems Science and Engineering (D-BSSE), 4058 Basel, Switzerland
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22
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Banerjee R, Shah N, Dicker AP. Next-Generation Implementation of Chimeric Antigen Receptor T-Cell Therapy Using Digital Health. JCO Clin Cancer Inform 2021; 5:668-678. [PMID: 34110929 DOI: 10.1200/cci.21.00023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Chimeric antigen receptor T-cell (CAR-T) therapy is a paradigm-shifting immunotherapy modality in oncology; however, unique toxicities such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome limit its ability to be implemented more widely in the outpatient setting or at smaller-volume centers. Three operational challenges with CAR-T therapy include the following: (1) the logistics of toxicity monitoring, ie, with frequent vital sign checks and neurologic assessments; (2) the specialized knowledge required for toxicity management, particularly with regard to CRS and immune effector cell-associated neurotoxicity syndrome; and (3) the need for high-quality symptomatic and supportive care during this intensive period. In this review, we explore potential niches for digital innovations that can improve the implementation of CAR-T therapy in each of these domains. These tools include patient-facing technologies and provider-facing platforms: for example, wearable devices and mobile health apps to screen for fevers and encephalopathy, electronic patient-reported outcome assessments-based workflows to assist with symptom management, machine learning algorithms to predict emerging CRS in real time, clinical decision support systems to assist with toxicity management, and digital coaching to help maintain wellness. Televisits, which have grown in prominence since the novel coronavirus pandemic, will continue to play a key role in the monitoring and management of CAR-T-related toxicities as well. Limitations of these strategies include the need to ensure care equity and stakeholder buy-in, both operationally and financially. Nevertheless, once developed and validated, the next-generation implementation of CAR-T therapy using these digital tools may improve both its safety and accessibility.
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Affiliation(s)
- Rahul Banerjee
- Division of Hematology/Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Nina Shah
- Division of Hematology/Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Adam P Dicker
- Department of Radiation Oncology, Jefferson University, Philadelphia, PA.,Jefferson Center for Digital Health, Jefferson University, Philadelphia, PA
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23
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Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool. Sci Rep 2021; 11:10780. [PMID: 34031450 PMCID: PMC8144223 DOI: 10.1038/s41598-021-89927-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/27/2021] [Indexed: 12/05/2022] Open
Abstract
Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system’s predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.
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24
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Morotti M, Albukhari A, Alsaadi A, Artibani M, Brenton JD, Curbishley SM, Dong T, Dustin ML, Hu Z, McGranahan N, Miller ML, Santana-Gonzalez L, Seymour LW, Shi T, Van Loo P, Yau C, White H, Wietek N, Church DN, Wedge DC, Ahmed AA. Promises and challenges of adoptive T-cell therapies for solid tumours. Br J Cancer 2021; 124:1759-1776. [PMID: 33782566 PMCID: PMC8144577 DOI: 10.1038/s41416-021-01353-6] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/22/2021] [Accepted: 03/04/2021] [Indexed: 12/11/2022] Open
Abstract
Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas-cancer genomics, cancer immunology and cell-therapy manufacturing-that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours.
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Affiliation(s)
- Matteo Morotti
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Ashwag Albukhari
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulkhaliq Alsaadi
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Mara Artibani
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - James D Brenton
- Functional Genomics of Ovarian Cancer Laboratory, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Stuart M Curbishley
- Advanced Therapies Facility and National Institute for Health Research (NIHR) Biomedical Research Centre, University of Birmingham, Birmingham, UK
| | - Tao Dong
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute, University of Oxford, Oxford, UK
| | - Michael L Dustin
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Zhiyuan Hu
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Nicholas McGranahan
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
| | - Martin L Miller
- Cancer System Biology Group, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Laura Santana-Gonzalez
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Leonard W Seymour
- Gene Therapy Group, Department of Oncology, University of Oxford, Oxford, UK
| | - Tingyan Shi
- Department of Gynecological Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peter Van Loo
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Christopher Yau
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
- The Alan Turing Institute, London, UK
| | - Helen White
- Patient Representative, Endometrial Cancer Genomics England Clinical Interpretation Partnership (GeCIP) Domain, London, UK
| | - Nina Wietek
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - David N Church
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Oxford, UK.
| | - David C Wedge
- Oxford NIHR Biomedical Research Centre, Oxford, UK.
- Manchester Cancer Research Centre, University of Manchester, Manchester, UK.
| | - Ahmed A Ahmed
- Ovarian Cancer Cell Laboratory, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Oxford, UK.
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
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25
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Kuksin M, Morel D, Aglave M, Danlos FX, Marabelle A, Zinovyev A, Gautheret D, Verlingue L. Applications of single-cell and bulk RNA sequencing in onco-immunology. Eur J Cancer 2021; 149:193-210. [PMID: 33866228 DOI: 10.1016/j.ejca.2021.03.005] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/26/2021] [Accepted: 03/04/2021] [Indexed: 02/08/2023]
Abstract
The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.
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Affiliation(s)
- Maria Kuksin
- ENS de Lyon, 15 Parvis René Descartes, 69007, Lyon, France; Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France
| | - Daphné Morel
- Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France; Département de Radiothérapie, Gustave Roussy Cancer Campus, Gustave Roussy, 114 rue Edouard Vaillant, 94800, Villejuif, France; INSERM UMR1030, Molecular Radiotherapy and Therapeutic Innovations, Gustave Roussy, 114 rue Edouard Vaillant, 94800, Villejuif, France
| | - Marine Aglave
- INSERM US23, CNRS UMS 3655, Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France
| | | | - Aurélien Marabelle
- Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France; INSERM U1015, Gustave Roussy, Université Paris Saclay, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005, Paris, France; INSERM, U900, F-75005, Paris, France; MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006, Paris, France; Laboratory of Advanced Methods for High-dimensional Data Analysis, Lobachevsky University, 603000, Nizhny Novgorod, Russia
| | - Daniel Gautheret
- Institute for Integrative Biology of the Cell, UMR 9198, CEA, CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France; IHU PRISM, Gustave Roussy Cancer Campus, Gustave Roussy, 114 Rue Edouard Vaillant, 94800, Villejuif, France; Université Paris-Saclay, France
| | - Loïc Verlingue
- Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France; INSERM UMR1030, Molecular Radiotherapy and Therapeutic Innovations, Gustave Roussy, 114 rue Edouard Vaillant, 94800, Villejuif, France; Institut Curie, PSL Research University, F-75005, Paris, France; Université Paris-Saclay, France.
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26
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Jokinen E, Huuhtanen J, Mustjoki S, Heinonen M, Lähdesmäki H. Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Comput Biol 2021; 17:e1008814. [PMID: 33764977 PMCID: PMC8023491 DOI: 10.1371/journal.pcbi.1008814] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 04/06/2021] [Accepted: 02/17/2021] [Indexed: 12/31/2022] Open
Abstract
Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals' immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.
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MESH Headings
- Amino Acid Sequence
- Complementarity Determining Regions
- Computational Biology/methods
- Epitopes, T-Lymphocyte/chemistry
- Epitopes, T-Lymphocyte/genetics
- Epitopes, T-Lymphocyte/metabolism
- Humans
- Normal Distribution
- Receptors, Antigen, T-Cell/chemistry
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/metabolism
- Sequence Analysis, Protein/methods
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Affiliation(s)
- Emmi Jokinen
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Jani Huuhtanen
- Translational Immunology Research program and Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Satu Mustjoki
- Translational Immunology Research program and Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Markus Heinonen
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology, Espoo, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, Espoo, Finland
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27
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Abstract
Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.
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Affiliation(s)
- Damijan Valentinuzzi
- Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1111 Ljubljana, Slovenia
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28
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Hoffmann MM, Slansky JE. T-cell receptor affinity in the age of cancer immunotherapy. Mol Carcinog 2020; 59:862-870. [PMID: 32386086 DOI: 10.1002/mc.23212] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 12/13/2022]
Abstract
The strength of the interaction between T-cell receptors (TCRs) and their ligands, peptide/major histocompatibility complex complexes (pMHCs), is one of the most frequently discussed and investigated features of T cells in immuno-oncology today. Although there are many molecules on the surface of T cells that interact with ligands on other cells, the TCR/pMHC is the only receptor-ligand pair that offers antigen specificity and dictates the functional response of the T cell. The strength of the TCR/pMHC interaction, along with the environment in which this interaction takes place, is key to how the T cell will respond. The TCR repertoire of T cells that interact with tumor-associated antigens is vast, although typically of low affinity. Here, we focus on the low-affinity interactions between TCRs from CD8+ T cells and different models used in immuno-oncology.
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Affiliation(s)
- Michele M Hoffmann
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jill E Slansky
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado
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