1
|
Yadav S, Vora DS, Sundar D, Dhanjal JK. TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding. Comput Struct Biotechnol J 2024; 23:165-173. [PMID: 38146434 PMCID: PMC10749252 DOI: 10.1016/j.csbj.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 12/27/2023] Open
Abstract
Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.
Collapse
Affiliation(s)
- Shashank Yadav
- Department of Biomedical Engineering, University of Arizona, Tucson 85721, AZ, USA
| | - Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Jaspreet Kaur Dhanjal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| |
Collapse
|
2
|
Leary AY, Scott D, Gupta NT, Waite JC, Skokos D, Atwal GS, Hawkins PG. Designing meaningful continuous representations of T cell receptor sequences with deep generative models. Nat Commun 2024; 15:4271. [PMID: 38769289 PMCID: PMC11106309 DOI: 10.1038/s41467-024-48198-0] [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: 06/18/2023] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.
Collapse
Affiliation(s)
- Allen Y Leary
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
| | - Darius Scott
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Namita T Gupta
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Janelle C Waite
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Dimitris Skokos
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Gurinder S Atwal
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Peter G Hawkins
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
| |
Collapse
|
3
|
Jiang M, Yu Z, Lan X. VitTCR: A deep learning method for peptide recognition prediction. iScience 2024; 27:109770. [PMID: 38711451 PMCID: PMC11070698 DOI: 10.1016/j.isci.2024.109770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 01/21/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.
Collapse
Affiliation(s)
- Mengnan Jiang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zilan Yu
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| |
Collapse
|
4
|
Gupta T, Antanaviciute A, Hyun-Jung Lee C, Ottakandathil Babu R, Aulicino A, Christoforidou Z, Siejka-Zielinska P, O'Brien-Ball C, Chen H, Fawkner-Corbett D, Geros AS, Bridges E, McGregor C, Cianci N, Fryer E, Alham NK, Jagielowicz M, Santos AM, Fellermeyer M, Davis SJ, Parikh K, Cheung V, Al-Hillawi L, Sasson S, Slevin S, Brain O, Fernandes RA, Koohy H, Simmons A. Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitis. Cancer Cell 2024; 42:797-814.e15. [PMID: 38744246 DOI: 10.1016/j.ccell.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 02/09/2024] [Accepted: 04/17/2024] [Indexed: 05/16/2024]
Abstract
The success of checkpoint inhibitors (CPIs) for cancer has been tempered by immune-related adverse effects including colitis. CPI-induced colitis is hallmarked by expansion of resident mucosal IFNγ cytotoxic CD8+ T cells, but how these arise is unclear. Here, we track CPI-bound T cells in intestinal tissue using multimodal single-cell and subcellular spatial transcriptomics (ST). Target occupancy was increased in inflamed tissue, with drug-bound T cells located in distinct microdomains distinguished by specific intercellular signaling and transcriptional gradients. CPI-bound cells were largely CD4+ T cells, including enrichment in CPI-bound peripheral helper, follicular helper, and regulatory T cells. IFNγ CD8+ T cells emerged from both tissue-resident memory (TRM) and peripheral populations, displayed more restricted target occupancy profiles, and co-localized with damaged epithelial microdomains lacking effective regulatory cues. Our multimodal analysis identifies causal pathways and constitutes a resource to inform novel preventive strategies.
Collapse
Affiliation(s)
- Tarun Gupta
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Agne Antanaviciute
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
| | - Chloe Hyun-Jung Lee
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Rosana Ottakandathil Babu
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Anna Aulicino
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Zoe Christoforidou
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Paulina Siejka-Zielinska
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Caitlin O'Brien-Ball
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford OX3 7BN, UK
| | - Hannah Chen
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - David Fawkner-Corbett
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Academic Paediatric Surgery Unit (APSU), Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Ana Sousa Geros
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Esther Bridges
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Colleen McGregor
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Nicole Cianci
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Eve Fryer
- Pathology, Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Nasullah Khalid Alham
- Nuffield Department of Surgical Sciences and Oxford NIHR Biomedical Research Centre (BRC), University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Marta Jagielowicz
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Ana Mafalda Santos
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Martin Fellermeyer
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Simon J Davis
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Kaushal Parikh
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Vincent Cheung
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Lulia Al-Hillawi
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Sarah Sasson
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Stephanie Slevin
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Oliver Brain
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford OX3 7BN, UK
| | - Hashem Koohy
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
| | - Alison Simmons
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK.
| |
Collapse
|
5
|
Gao Y, Dong K, Gao Y, Jin X, Yang J, Yan G, Liu Q. Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning. CELL GENOMICS 2024; 4:100553. [PMID: 38688285 PMCID: PMC11099349 DOI: 10.1016/j.xgen.2024.100553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/09/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.
Collapse
Affiliation(s)
- Yicheng Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kejing Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuli Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingya Yang
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Gang Yan
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China; Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China.
| |
Collapse
|
6
|
Croce G, Bobisse S, Moreno DL, Schmidt J, Guillame P, Harari A, Gfeller D. Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Nat Commun 2024; 15:3211. [PMID: 38615042 PMCID: PMC11016097 DOI: 10.1038/s41467-024-47461-8] [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: 09/15/2023] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.
Collapse
Affiliation(s)
- Giancarlo Croce
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Sara Bobisse
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Dana Léa Moreno
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Schmidt
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Philippe Guillame
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
| |
Collapse
|
7
|
Shao W, Yao Y, Yang L, Li X, Ge T, Zheng Y, Zhu Q, Ge S, Gu X, Jia R, Song X, Zhuang A. Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy. Exp Hematol Oncol 2024; 13:37. [PMID: 38570883 PMCID: PMC10988985 DOI: 10.1186/s40164-024-00504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.
Collapse
Affiliation(s)
- Weihuan Shao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yiran Yao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Ludi Yang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiaoran Li
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Tongxin Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yue Zheng
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Qiuyi Zhu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Shengfang Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiang Gu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Renbing Jia
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Xin Song
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Ai Zhuang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| |
Collapse
|
8
|
He S, Liu SQ, Teng XY, He JY, Liu Y, Gao JH, Wu Y, Hu W, Dong ZJ, Bei JX, Xu JH. Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection. J Med Virol 2024; 96:e29577. [PMID: 38572977 DOI: 10.1002/jmv.29577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/02/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.
Collapse
Affiliation(s)
- Shuai He
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shu-Qiang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiang-Yun Teng
- Medical Laboratory Center, Maoming Hospital of Guangzhou University of Chinese Medicine, Maoming, China
| | - Jin-Yong He
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Yang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jia-Hui Gao
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Yue Wu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Wei Hu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Zhong-Jun Dong
- School of Medicine and Institute for Immunology, Tsinghua University, Beijing, China
| | - Jin-Xin Bei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian-Hua Xu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
- Medical Laboratory Center, Maoming Hospital of Guangzhou University of Chinese Medicine, Maoming, China
| |
Collapse
|
9
|
Ye X, Shih DJH, Ku Z, Hong J, Barrett DF, Rupp RE, Zhang N, Fu TM, Zheng WJ, An Z. Transcriptional signature of durable effector T cells elicited by a replication defective HCMV vaccine. NPJ Vaccines 2024; 9:70. [PMID: 38561339 PMCID: PMC10984989 DOI: 10.1038/s41541-024-00860-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Human cytomegalovirus (HCMV) is a leading infectious cause of birth defects and the most common opportunistic infection that causes life-threatening diseases post-transplantation; however, an effective vaccine remains elusive. V160 is a live-attenuated replication defective HCMV vaccine that showed a 42.4% efficacy against primary HCMV infection among seronegative women in a phase 2b clinical trial. Here, we integrated the multicolor flow cytometry, longitudinal T cell receptor (TCR) sequencing, and single-cell RNA/TCR sequencing approaches to characterize the magnitude, phenotype, and functional quality of human T cell responses to V160. We demonstrated that V160 de novo induces IE-1 and pp65 specific durable polyfunctional effector CD8 T cells that are comparable to those induced by natural HCMV infection. We identified a variety of V160-responsive T cell clones which exhibit distinctive "transient" and "durable" expansion kinetics, and revealed a transcriptional signature that marks durable CD8 T cells post-vaccination. Our study enhances the understanding of human T-cell immune responses to V160 vaccination.
Collapse
Affiliation(s)
- Xiaohua Ye
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Infectious Disease Research, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - David J H Shih
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Zhiqiang Ku
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Junping Hong
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Diane F Barrett
- Sealy Institute for Vaccine Sciences, University of Texas Medical Branch, Galveston, TX, USA
| | - Richard E Rupp
- Sealy Institute for Vaccine Sciences, University of Texas Medical Branch, Galveston, TX, USA
| | - Ningyan Zhang
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tong-Ming Fu
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Zhiqiang An
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA.
| |
Collapse
|
10
|
Izosimova AV, Shabalkina AV, Myshkin MY, Shurganova EV, Myalik DS, Ryzhichenko EO, Samitova AF, Barsova EV, Shagina IA, Britanova OV, Yuzhakova DV, Sharonov GV. Local Enrichment with Convergence of Enriched T-Cell Clones Are Hallmarks of Effective Peptide Vaccination against B16 Melanoma. Vaccines (Basel) 2024; 12:345. [PMID: 38675728 DOI: 10.3390/vaccines12040345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Some peptide anticancer vaccines elicit a strong T-cell memory response but fail to suppress tumor growth. To gain insight into tumor resistance, we compared two peptide vaccines, p20 and p30, against B16 melanoma, with both exhibiting good in vitro T-cell responses but different tumor suppression abilities. METHODS We compared activation markers and repertoires of T-lymphocytes from tumor-draining (dLN) and non-draining (ndLN) lymph nodes for the two peptide vaccines. RESULTS We showed that the p30 vaccine had better tumor control as opposed to p20. p20 vaccine induced better in vitro T-cell responsiveness but failed to suppress tumor growth. Efficient antitumor vaccination is associated with a higher clonality of cytotoxic T-cells (CTLs) in dLNs compared with ndLNs and the convergence of most of the enriched clones. With the inefficient p20 vaccine, the most expanded and converged were clones of the bystander T-cells without an LN preference. CONCLUSIONS Here, we show that the clonality and convergence of the T-cell response are the hallmarks of efficient antitumor vaccination. The high individual and methodological dependencies of these parameters can be avoided by comparing dLNs and ndLNs.
Collapse
Affiliation(s)
- Anna Vyacheslavovna Izosimova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - Alexandra Valerievna Shabalkina
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Mikhail Yurevich Myshkin
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Elizaveta Viktorovna Shurganova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - Daria Sergeevna Myalik
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
- Pathoanatomical Department, Nizhny Novgorod Regional Clinical Cancer Hospital, Nizhny Novgorod 603126, Russia
| | | | - Alina Faritovna Samitova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Ekaterina Vladimirovna Barsova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Irina Aleksandrovna Shagina
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Olga Vladimirovna Britanova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Diana Vladimirovna Yuzhakova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - George Vladimirovich Sharonov
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| |
Collapse
|
11
|
Jensen MF, Nielsen M. Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration. eLife 2024; 12:RP93934. [PMID: 38437160 PMCID: PMC10942633 DOI: 10.7554/elife.93934] [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] [Indexed: 03/06/2024] Open
Abstract
Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.
Collapse
Affiliation(s)
- Mathias Fynbo Jensen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
| |
Collapse
|
12
|
Hudson D, Lubbock A, Basham M, Koohy H. A comparison of clustering models for inference of T cell receptor antigen specificity. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:None. [PMID: 38525047 PMCID: PMC10955519 DOI: 10.1016/j.immuno.2024.100033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 03/26/2024]
Abstract
The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.
Collapse
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
| | | | | | - 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
- Alan Turning Fellow in Health and Medicine, UK
| |
Collapse
|
13
|
Hafler D, Lu B, Lucca L, Lewis W, Wang J, Nogeuira C, Heer S, Axisa PP, Buitrago-Pocasangre N, Pham G, Kojima M, Wei W, Aizenbud L, Bacchiocchi A, Zhang L, Walewski J, Chiang V, Olino K, Clune J, Halaban R, Kluger Y, Coyle A, Kisielow J, Obermair FJ, Kluger H. Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma. RESEARCH SQUARE 2024:rs.3.rs-3956671. [PMID: 38464315 PMCID: PMC10925449 DOI: 10.21203/rs.3.rs-3956671/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.
Collapse
|
14
|
Maurer K, Park CY, Mani S, Borji M, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Neuberg DS, Bachireddy P, Farhi SL, Li S, Livak KJ, Ritz J, Soiffer RJ, Wu CJ, Azizi E. Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579677. [PMID: 38405900 PMCID: PMC10888840 DOI: 10.1101/2024.02.09.579677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.
Collapse
|
15
|
Ravishankar S, Towlerton AM, Mooka P, Kafeero J, Coffey DG, Aicher LD, Mubiru KR, Okoche L, Atwinirembabazi P, Okonye J, Phipps WT, Warren EH. The signature of a T-cell response to KSHV persists across space and time in individuals with epidemic and endemic KS from Uganda. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579223. [PMID: 38370623 PMCID: PMC10871354 DOI: 10.1101/2024.02.06.579223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Inadequate T-cell control of Kaposi sarcoma-associated herpesvirus (KSHV) infection predisposes to development of Kaposi sarcoma (KS), but little is known about the T-cell response to KSHV. Postulating that KS tumors contain abundant KSHV-specific T-cells, we performed transcriptional profiling and T-cell receptor (TCR) repertoire analysis of tumor biopsies from 144 Ugandan adults with KS. We show that CD8+ T-cells and M2-polarized macrophages dominate the tumor micro-environment (TME). The TCR repertoire of KS tumor infiltrating lymphocytes (TIL) is shared across non-contiguous tumors and persists across time. Clusters of T-cells with predicted shared specificity for uncharacterized antigens, potentially encoded by KSHV, comprise ~25% of KS TIL, and are shared across tumors from different time points and individuals. Single-cell RNA-sequencing of blood identifies a non-proliferating effector memory phenotype and captured the TCRs in 14,698 putative KSHV-specific T-cells. These results suggest that a polyspecific KSHV-specific T-cell response inhibited by M2 macrophages exists within the KS TME, and provide a foundation for studies to define its specificity at a large scale.
Collapse
Affiliation(s)
- Shashidhar Ravishankar
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Andrea M.H. Towlerton
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - Peter Mooka
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - James Kafeero
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - David G. Coffey
- Division of Myeloma, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States
| | - Lauri D. Aicher
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | | | - Lazarus Okoche
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | | | - Joseph Okonye
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - Warren T. Phipps
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
| | - Edus H. Warren
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States
| |
Collapse
|
16
|
Banerjee A, Pattinson DJ, Wincek CL, Bunk P, Chapin SR, Navlakha S, Meyer HV. BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576714. [PMID: 38370810 PMCID: PMC10871174 DOI: 10.1101/2024.01.22.576714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.
Collapse
Affiliation(s)
- Amitava Banerjee
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - David J Pattinson
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53711, USA
| | - Cornelia L. Wincek
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Heidelberg University, 69117 Heidelberg, Germany
| | - Paul Bunk
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Sarah R. Chapin
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Saket Navlakha
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Hannah V. Meyer
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| |
Collapse
|
17
|
Rocco JM, Boswell KL, Laidlaw E, Epling B, Anderson M, Serebryannyy L, Narpala S, O'Connell S, Kalish H, Kelly S, Porche S, Oguz C, McDermott A, Manion M, Koup RA, Lisco A, Sereti I. Immune responses to SARS-CoV-2 mRNA vaccination in people with idiopathic CD4 lymphopenia. J Allergy Clin Immunol 2024; 153:503-512. [PMID: 38344971 PMCID: PMC10861932 DOI: 10.1016/j.jaci.2023.10.012] [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: 05/17/2023] [Revised: 10/02/2023] [Accepted: 10/10/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND The immunogenicity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines is variable in individuals with different inborn errors of immunity or acquired immune deficiencies and is yet unknown in people with idiopathic CD4 lymphopenia (ICL). OBJECTIVE We sought to determine the immunogenicity of mRNA vaccines in patients with ICL with a broad range of CD4 T-cell counts. METHODS Samples were collected from 25 patients with ICL and 23 age- and sex-matched healthy volunteers (HVs) after their second or third SARS-CoV-2 mRNA vaccine dose. Anti-spike and anti-receptor binding domain antibodies were measured. T-cell receptor sequencing and stimulation assays were performed to quantify SARS-CoV-2-specific T-cell responses. RESULTS The median age of ICL participants was 51 years, and their median CD4 count was 150 cells/μL; 11 participants had CD4 counts ≤100 cells/μL. Anti-spike IgG antibody levels were greater in HVs than in patients with ICL after 2 and 3 doses of mRNA vaccine. There was no detectable significant difference, however, in anti-S IgG between HVs and participants with ICL and CD4 counts >100 cells/μL. The depth of spike-specific T-cell responses by T-cell receptor sequencing was lower in individuals with ICL. Activation-induced markers and cytokine production of spike-specific CD4 T cells in participants with ICL did not differ significantly compared with HVs after 2 or 3 vaccine doses. CONCLUSIONS Patients with ICL and CD4 counts >100 cells/μL can mount vigorous humoral and cellular immune responses to SARS-CoV-2 vaccination; however, patients with more severe CD4 lymphopenia have blunted vaccine-induced immunity and may require additional vaccine doses and other risk mitigation strategies.
Collapse
Affiliation(s)
- Joseph M Rocco
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Kristin L Boswell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Elizabeth Laidlaw
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Brian Epling
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Megan Anderson
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Leonid Serebryannyy
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Sandeep Narpala
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Sarah O'Connell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Heather Kalish
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Md
| | - Sophie Kelly
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Md
| | - Sarah Porche
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Md
| | - Cihan Oguz
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Adrian McDermott
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Maura Manion
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Richard A Koup
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Andrea Lisco
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Irini Sereti
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md.
| |
Collapse
|
18
|
Zhang J, Wang Y, Huang Y, Tan X, Xu J, Yan Q, Tan J, Zhang Y, Zhang J, Ma Q, Zhu H, Ye J, Zhu Z, Lan W. Characterization of T cell receptor repertoire in penile cancer. Cancer Immunol Immunother 2024; 73:24. [PMID: 38280010 PMCID: PMC10822009 DOI: 10.1007/s00262-023-03615-z] [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: 09/26/2023] [Accepted: 12/04/2023] [Indexed: 01/29/2024]
Abstract
Tumor-infiltrating lymphocytes (TILs) play a key role in regulating the host immune response and shaping tumor microenvironment. It has been previously shown that T cell infiltration in penile tumors was associated with clinical outcomes. However, few studies have reported the T cell receptor (TCR) repertoire in patients with penile cancer. In the present study, we evaluated the TCR repertoires in tumor and adjacent normal tissues from 22 patients with penile squamous cell carcinoma (PSCC). Analysis of the T cell receptor beta-variable (TRBV) and joining (TRBJ) genes usage and analysis of complementarity determining region 3 (CDR3) length distribution did not show significant differences between tumor and matched normal tissues. Moreover, analysis of the median Jaccard index indicated a limited overlap of TCR repertoire between these groups. Compared with normal tissues, a significantly lower diversity and higher clonality of TCR repertoire was observed in tumor samples, which was associated with clinical characteristics. Further analysis of transcriptional profiles demonstrated that tumor samples with high clonality showed increased expression of genes associated with CD8 + T cells. In addition, we analyzed the TCR repertoire of CD4 + T cells and CD8 + T cells isolated from tumor tissues. We identified that expanded clonotypes were predominantly in the CD8 + T cell compartment, which presented with an exhausted phenotype. Overall, we comprehensively compared TCR repertoire between penile tumor and normal tissues and demonstrated the presence of distinct T cell immune microenvironments in patients with PSCC.
Collapse
Affiliation(s)
- Junying Zhang
- Chongqing Key Laboratory of High Active Traditional Chinese Drug Delivery System, Chongqing Engineering Research Center of Pharmaceutical Sciences, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, People's Republic of China
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Yapeng Wang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Yiqiang Huang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Xintao Tan
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jing Xu
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Qian Yan
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jiao Tan
- School of Pharmacy, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, People's Republic of China
| | - Yao Zhang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jun Zhang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Qiang Ma
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Hailin Zhu
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jin Ye
- Urinary Nephropathy Center, The Thirteenth People's Hospital of Chongqing, Chongqing, 400053, People's Republic of China.
| | - Zhaojing Zhu
- Chongqing Key Laboratory of High Active Traditional Chinese Drug Delivery System, Chongqing Engineering Research Center of Pharmaceutical Sciences, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, People's Republic of China.
| | - Weihua Lan
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China.
| |
Collapse
|
19
|
Li X, You J, Hong L, Liu W, Guo P, Hao X. Neoantigen cancer vaccines: a new star on the horizon. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0395. [PMID: 38164734 PMCID: PMC11033713 DOI: 10.20892/j.issn.2095-3941.2023.0395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.
Collapse
Affiliation(s)
- Xiaoling Li
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Jian You
- Department of Thoracic Oncology, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- Department of Thoracic Oncology Surgery, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Liping Hong
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Weijiang Liu
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Peng Guo
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Xishan Hao
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
- Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| |
Collapse
|
20
|
Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
Collapse
Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
21
|
Zhao M, Xu SX, Yang Y, Yuan M. GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response. J Chem Inf Model 2023; 63:7557-7567. [PMID: 37990917 DOI: 10.1021/acs.jcim.3c01293] [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: 11/23/2023]
Abstract
Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.
Collapse
Affiliation(s)
- Minghua Zhao
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | - Steven X Xu
- Genmab US, Inc., Princeton, New Jersey 08540, United States
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | - Min Yuan
- School of Public Health Administration, Anhui Medical University, Hefei 230032, China
| |
Collapse
|
22
|
Korpela D, Jokinen E, Dumitrescu A, Huuhtanen J, Mustjoki S, Lähdesmäki H. EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings. Bioinformatics 2023; 39:btad743. [PMID: 38070156 PMCID: PMC10963061 DOI: 10.1093/bioinformatics/btad743] [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: 06/30/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
MOTIVATION T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. RESULTS We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION https://github.com/DaniTheOrange/EPIC-TRACE.
Collapse
Affiliation(s)
- Dani Korpela
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
| | - Emmi Jokinen
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
| | | | - Jani Huuhtanen
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
| | - Satu Mustjoki
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
| |
Collapse
|
23
|
Louie RHY, Cai C, Samir J, Singh M, Deveson IW, Ferguson JM, Amos TG, McGuire HM, Gowrishankar K, Adikari T, Balderas R, Bonomi M, Ruella M, Bishop D, Gottlieb D, Blyth E, Micklethwaite K, Luciani F. CAR + and CAR - T cells share a differentiation trajectory into an NK-like subset after CD19 CAR T cell infusion in patients with B cell malignancies. Nat Commun 2023; 14:7767. [PMID: 38012187 PMCID: PMC10682404 DOI: 10.1038/s41467-023-43656-7] [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: 09/26/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
Chimeric antigen receptor (CAR) T cell therapy is effective in treating B cell malignancies, but factors influencing the persistence of functional CAR+ T cells, such as product composition, patients' lymphodepletion, and immune reconstitution, are not well understood. To shed light on this issue, here we conduct a single-cell multi-omics analysis of transcriptional, clonal, and phenotypic profiles from pre- to 1-month post-infusion of CAR+ and CAR- T cells from patients from a CARTELL study (ACTRN12617001579381) who received a donor-derived 4-1BB CAR product targeting CD19. Following infusion, CAR+ T cells and CAR- T cells shows similar differentiation profiles with clonally expanded populations across heterogeneous phenotypes, demonstrating clonal lineages and phenotypic plasticity. We validate these findings in 31 patients with large B cell lymphoma treated with CD19 CAR T therapy. For these patients, we identify using longitudinal mass-cytometry data an association between NK-like subsets and clinical outcomes at 6 months with both CAR+ and CAR- T cells. These results suggest that non-CAR-derived signals can provide information about patients' immune recovery and be used as correlate of clinically relevant parameters.
Collapse
Affiliation(s)
- Raymond Hall Yip Louie
- School of Computer Science and Engineering, UNSW Sydney, Sydney, NSW, Australia
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Curtis Cai
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Jerome Samir
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Mandeep Singh
- Garvan Institute for Medical Research, Sydney, NSW, Australia
| | - Ira W Deveson
- Garvan Institute for Medical Research, Sydney, NSW, Australia
| | | | - Timothy G Amos
- Garvan Institute for Medical Research, Sydney, NSW, Australia
| | - Helen Marie McGuire
- Ramaciotti Facility for Human Systems Biology, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- Infection, Immunity and Inflammation Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
| | - Kavitha Gowrishankar
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Thiruni Adikari
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | | | - Martina Bonomi
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- Department of Physics, University of Bologna, Bologna, Italy
| | - Marco Ruella
- Division of Hematology and Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - David Bishop
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - David Gottlieb
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - Emily Blyth
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - Kenneth Micklethwaite
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- NSW Health Pathology Blood Transplant and Cell Therapies Laboratory - ICPMR Westmead, Sydney, NSW, Australia
| | - Fabio Luciani
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia.
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.
- Garvan Institute for Medical Research, Sydney, NSW, Australia.
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
Kim T, Lim H, Jun S, Park J, Lee D, Lee JH, Lee JY, Bang D. Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer. Sci Rep 2023; 13:20485. [PMID: 37993659 PMCID: PMC10665396 DOI: 10.1038/s41598-023-47740-2] [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: 04/15/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Gynecologic cancer, including ovarian cancer and endometrial cancer, is characterized by morphological and molecular heterogeneity. Germline and somatic testing are available for patients to screen for pathogenic variants in genes such as BRCA1/2. Tissue expression levels of immunogenomic markers such as PD-L1 are also being used in clinical research. The basic therapeutic approach to gynecologic cancer combines surgery with chemotherapy. Immunotherapy, while not yet a mainstream treatment for gynecologic cancers, is advancing, with Dostarlimab recently receiving approval as a treatment for endometrial cancer. The goal remains to harness stimulated immune cells in the bloodstream to eradicate multiple metastases, a feat currently deemed challenging in a typical clinical setting. For the discovery of novel immunotherapy-based tumor targets, tumor-infiltrating lymphocytes (TILs) give a key insight on tumor-related immune activities by providing T cell receptor (TCR) sequences. Understanding the TCR repertoires of TILs in metastatic tissues and the circulation is important from an immunotherapy standpoint, as a subset of T cells in the blood have the potential to help kill tumor cells. To explore the relationship between distant tissue biopsy regions and blood circulation, we investigated the TCR beta chain (TCRβ) in bulk tumor and matched blood samples from 39 patients with gynecologic cancer. We found that the TCR clones of TILs at different tumor sites were globally shared within patients and had high overlap with the TCR clones in peripheral blood.
Collapse
Affiliation(s)
- Taehoon Kim
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Hyeonseob Lim
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Soyeong Jun
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Junsik Park
- Department of Obstetrics and Gynecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Dongin Lee
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Ji Hyun Lee
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
- Department of Biomedical Science and Technology, Kyung Hee Medical Science Research Institute, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
| | - Jung-Yun Lee
- Department of Obstetrics and Gynecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Duhee Bang
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| |
Collapse
|
26
|
Sponaugle A, Weideman AMK, Ranek J, Atassi G, Kuruc J, Adimora AA, Archin NM, Gay C, Kuritzkes DR, Margolis DM, Vincent BG, Stanley N, Hudgens MG, Eron JJ, Goonetilleke N. Dominant CD4 + T cell receptors remain stable throughout antiretroviral therapy-mediated immune restoration in people with HIV. Cell Rep Med 2023; 4:101268. [PMID: 37949070 PMCID: PMC10694675 DOI: 10.1016/j.xcrm.2023.101268] [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: 01/12/2023] [Revised: 06/05/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
In people with HIV (PWH), the post-antiretroviral therapy (ART) window is critical for immune restoration and HIV reservoir stabilization. We employ deep immune profiling and T cell receptor (TCR) sequencing and examine proliferation to assess how ART impacts T cell homeostasis. In PWH on long-term ART, lymphocyte frequencies and phenotypes are mostly stable. By contrast, broad phenotypic changes in natural killer (NK) cells, γδ T cells, B cells, and CD4+ and CD8+ T cells are observed in the post-ART window. Whereas CD8+ T cells mostly restore, memory CD4+ T subsets and cytolytic NK cells show incomplete restoration 1.4 years post ART. Surprisingly, the hierarchies and frequencies of dominant CD4 TCR clonotypes (0.1%-11% of all CD4+ T cells) remain stable post ART, suggesting that clonal homeostasis can be independent of homeostatic processes regulating CD4+ T cell absolute number, phenotypes, and function. The slow restoration of host immunity post ART also has implications for the design of ART interruption studies.
Collapse
Affiliation(s)
- Alexis Sponaugle
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Ann Marie K Weideman
- Department of Biostatistics, UNC Chapel Hill, Chapel Hill, NC, USA; Center for AIDS Research, School of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Jolene Ranek
- Computational Medicine Program, UNC Chapel Hill, Chapel Hill, NC, USA; Curriculum in Bioinformatics and Computational Biology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Gatphan Atassi
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill, Chapel Hill, NC, USA
| | - JoAnn Kuruc
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Adaora A Adimora
- Center for AIDS Research, School of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Nancie M Archin
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Cynthia Gay
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Daniel R Kuritzkes
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David M Margolis
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Benjamin G Vincent
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; Curriculum in Bioinformatics and Computational Biology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Michael G Hudgens
- Department of Biostatistics, UNC Chapel Hill, Chapel Hill, NC, USA; Center for AIDS Research, School of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Joseph J Eron
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Nilu Goonetilleke
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA.
| |
Collapse
|
27
|
Meng Z, Rodriguez Ehrenfried A, Tan CL, Steffens LK, Kehm H, Zens S, Lauenstein C, Paul A, Schwab M, Förster JD, Salek M, Riemer AB, Wu H, Eckert C, Leonhardt CS, Strobel O, Volkmar M, Poschke I, Offringa R. Transcriptome-based identification of tumor-reactive and bystander CD8 + T cell receptor clonotypes in human pancreatic cancer. Sci Transl Med 2023; 15:eadh9562. [PMID: 37967201 DOI: 10.1126/scitranslmed.adh9562] [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: 03/24/2023] [Accepted: 10/16/2023] [Indexed: 11/17/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is generally refractory to immune checkpoint blockade, although patients with genetically unstable tumors can show modest therapeutic benefit. We previously demonstrated the presence of tumor-reactive CD8+ T cells in PDAC samples. Here, we charted the tumor-infiltrating T cell repertoire in PDAC by combining single-cell transcriptomics with functional testing of T cell receptors (TCRs) for reactivity against autologous tumor cells. On the basis of a comprehensive dataset including 93 tumor-reactive and 65 bystander TCR clonotypes, we delineated a gene signature that effectively distinguishes between these T cell subsets in PDAC, as well as in other tumor indications. This revealed a high frequency of tumor-reactive TCR clonotypes in three genetically unstable samples. In contrast, the T cell repertoire in six genetically stable PDAC tumors was largely dominated by bystander T cells. Nevertheless, multiple tumor-reactive TCRs were successfully identified in each of these samples, thereby providing a perspective for personalized immunotherapy in this treatment-resistant indication.
Collapse
Affiliation(s)
- Zibo Meng
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
| | - Aaron Rodriguez Ehrenfried
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Helmholtz-Institute for Translational Oncology by DKFZ (HI-TRON), 55131 Mainz, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Chin Leng Tan
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Laura K Steffens
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Hannes Kehm
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Stefan Zens
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Claudia Lauenstein
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Alina Paul
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Marius Schwab
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Jonas D Förster
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
- Division of Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, 69120 Heidelberg, Germany
| | - Mogjiborahman Salek
- Division of Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, 69120 Heidelberg, Germany
| | - Angelika B Riemer
- Division of Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, 69120 Heidelberg, Germany
| | - Heshui Wu
- Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
| | - Christoph Eckert
- Pathology Institute, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Carl-Stephan Leonhardt
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Oliver Strobel
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Michael Volkmar
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Helmholtz-Institute for Translational Oncology by DKFZ (HI-TRON), 55131 Mainz, Germany
| | - Isabel Poschke
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center, 69120 Heidelberg, Germany
- Immune Monitoring Unit, National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Rienk Offringa
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
| |
Collapse
|
28
|
Seitz V, Gennermann K, Elezkurtaj S, Groth D, Schaper S, Dröge A, Lachmann N, Berg E, Lenze D, Kühl AA, Husemann C, Kleo K, Horst D, Lennerz V, Hennig S, Hummel M, Schumann M. Specific T-cell receptor beta-rearrangements of gluten-triggered CD8 + T-cells are enriched in celiac disease patients' duodenal mucosa. Clin Immunol 2023; 256:109795. [PMID: 37769786 DOI: 10.1016/j.clim.2023.109795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Celiac disease (CeD) is an autoimmune disorder affecting the small intestine with gluten as disease trigger. Infections including Influenza A, increase the CeD risk. While gluten-specific CD4+ T-cells, recognizing HLA-DQ2/DQ8 presented gluten-peptides, initiate and sustain the celiac immune response, CD8+ α/β intraepithelial T-cells elicit mucosal damage. Here, we subjected TCRs from a cohort of 56 CeD patients and 22 controls to an analysis employing 749 published CeD-related TCRβ-rearrangements derived from gluten-specific CD4+ T-cells and gluten-triggered peripheral blood CD8+ T-cells. We show, that in addition to TCRs from gluten-specific CD4+ T-cells, TCRs of gluten-triggered CD8+ T-cells are significantly enriched in CeD duodenal tissue samples. TCRβ-rearrangements of gluten-triggered CD8+ T-cells were even more expanded in patients than TCRs from gluten-specific CD4+ T-cells (p < 0.0002) and highest in refractory CeD. Sequence alignments with TCR-antigen databases suggest that a subgroup of these most likely indirectly gluten-triggered TCRs recognize microbial, viral, and autoantigens.
Collapse
Affiliation(s)
- V Seitz
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; HS Diagnomics GmbH, Berlin, Germany
| | | | - S Elezkurtaj
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Groth
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | | | - A Dröge
- HS Diagnomics GmbH, Berlin, Germany
| | - N Lachmann
- Centre for Tumor Medicine, Histocompatibility & Immunogenetics Laboratory, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - E Berg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Lenze
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - A A Kühl
- iPATH.Berlin - Core Unit of the Charité Universitätsmedizin Berlin, corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - C Husemann
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - K Kleo
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - S Hennig
- HS Diagnomics GmbH, Berlin, Germany
| | - M Hummel
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - M Schumann
- Medizinische Klinik m. S. Gastroenterologie, Infektiologie und Rheumatologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| |
Collapse
|
29
|
Boughter CT, Meier-Schellersheim M. An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator. PLoS Comput Biol 2023; 19:e1011577. [PMID: 37862356 PMCID: PMC10619816 DOI: 10.1371/journal.pcbi.1011577] [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: 12/19/2022] [Revised: 11/01/2023] [Accepted: 10/06/2023] [Indexed: 10/22/2023] Open
Abstract
The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.
Collapse
Affiliation(s)
- Christopher T. Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| |
Collapse
|
30
|
Kacsoh DB, Diaz MJ, Gozlan EC, Sahoo A, Song JJ, Yeagley M, Chobrutskiy A, Chobrutskiy BI, Blanck G. Blood-based T cell receptor anti-viral CDR3s are associated with worse overall survival for neuroblastoma. J Cancer Res Clin Oncol 2023; 149:12047-12056. [PMID: 37421457 DOI: 10.1007/s00432-023-05059-5] [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: 05/24/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023]
Abstract
With the advent of large collections of adaptive immune receptor recombination reads representing cancer, there is the opportunity to further investigate the adaptive immune response to viruses in the cancer setting. This is a particularly important goal due to longstanding but still not well-resolved questions about viral etiologies in cancer and viral infections as comorbidities. In this report, we assessed the T cell receptor complementarity determining region-3 (CDR3) amino acid (AA) sequences, for blood-sourced TCRs from neuroblastoma (NBL) cases, for exact AA sequence matches to previously identified anti-viral TCR CDR3 AA sequences. Results indicated the presence of anti-viral TCR CDR3 AA sequences in the NBL blood samples highly significantly correlated with worse overall survival. Furthermore, the TCR CDR3 AA sequences demonstrating chemical complementarity to many cytomegalovirus antigens represented cases with a worse outcome, including cases where such CDR3s were obtained from tumor samples. Overall, these results indicate a significant need for, and provide a novel strategy for assessing viral infection complications in NBL patients.
Collapse
Affiliation(s)
- Dorottya B Kacsoh
- College of Medicine, University of Central Florida, Orlando, FL, 32827, USA
| | - Michael J Diaz
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Etienne C Gozlan
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Arpan Sahoo
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Joanna J Song
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Michelle Yeagley
- University of Pittsburgh Medical Center, Pittsburgh, PA, 15261, USA
| | - Andrea Chobrutskiy
- Department of Pediatrics, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Boris I Chobrutskiy
- Department of Internal Medicine, Oregon Health and Science University Hospital, Portland, OR, 97239, USA
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA.
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
| |
Collapse
|
31
|
Montemurro A, Povlsen HR, Jessen LE, Nielsen M. Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data. Sci Rep 2023; 13:16147. [PMID: 37752190 PMCID: PMC10522655 DOI: 10.1038/s41598-023-43048-3] [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: 03/31/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.
Collapse
Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Helle Rus Povlsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark.
| |
Collapse
|
32
|
Flumens D, Gielis S, Bartholomeus E, Campillo-Davo D, van der Heijden S, Versteven M, De Reu H, Smits E, Ogunjimi B, Laukens K, Meysman P, Lion E. Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells. Methods Cell Biol 2023; 183:143-160. [PMID: 38548410 DOI: 10.1016/bs.mcb.2023.08.001] [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: 04/02/2024]
Abstract
Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in cancer research, prediction models have been developed to identify in silico epitope-specific TCRs. In this chapter, we provide a step-by-step protocol to train a prediction model using the user-friendly TCRex webtool for the nearly universal tumor-associated antigen Wilms' tumor 1 (WT1)-specific TCR repertoire. WT1 is a self-antigen overexpressed in numerous solid and hematological malignancies with a high clinical relevance. Training of computational models starts from a list of known epitope-specific TCRs which is often not available for new cancer epitopes. Therefore, we describe a workflow to assemble a training data set consisting of TCR sequences obtained from WT137-45-reactive CD8 T cell clones expanded and sorted from healthy donor peripheral blood mononuclear cells.
Collapse
Affiliation(s)
- Donovan Flumens
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Sofie Gielis
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Esther Bartholomeus
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), VAXINFECTIO, University of Antwerp, Antwerp, Belgium; Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Diana Campillo-Davo
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Sanne van der Heijden
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
| | - Maarten Versteven
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hans De Reu
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Evelien Smits
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), VAXINFECTIO, University of Antwerp, Antwerp, Belgium
| | - Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), VAXINFECTIO, University of Antwerp, Antwerp, Belgium; Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Eva Lion
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Center for Cell Therapy & Regenerative Medicine (CCRG), Antwerp University Hospital, Edegem, Belgium.
| |
Collapse
|
33
|
Bravi B, Di Gioacchino A, Fernandez-de-Cossio-Diaz J, Walczak AM, Mora T, Cocco S, Monasson R. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife 2023; 12:e85126. [PMID: 37681658 PMCID: PMC10522340 DOI: 10.7554/elife.85126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
Collapse
Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Andrea Di Gioacchino
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Aleksandra M Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| |
Collapse
|
34
|
Massey J, Artuz C, Dyer Z, Jackson K, Khoo M, Visweswaran M, Withers B, Moore J, Ma D, Sutton I. Diversification and expansion of the EBV-reactive cytotoxic T lymphocyte repertoire following autologous haematopoietic stem cell transplant for multiple sclerosis. Clin Immunol 2023; 254:109709. [PMID: 37495004 DOI: 10.1016/j.clim.2023.109709] [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: 06/05/2023] [Revised: 07/07/2023] [Accepted: 07/23/2023] [Indexed: 07/28/2023]
Abstract
Both genetic susceptibility and environmental exposures are thought to be involved in multiple sclerosis (MS) pathogenesis. Of all viruses potentially relevant to MS aetiology, Epstein-Barr virus (EBV) is the best-studied. EBV is a B cell lymphotropic virus which is able to evade the immune system by establishing latent infection in memory B cells, and EBV reactivation is restricted by CD8 cytotoxic T cell (CTL) responses in immune competent individuals. Autologous haematopoietic stem cell transplantation (AHSCT) is considered to be the most effective therapy in the treatment of relapsing MS even though chemotherapy-induced lymphopenia can associate with the re-emergence of latent viruses. Despite the increasing interest in EBV and MS pathogenesis the relationship between AHSCT, EBV and viral immunity in people with MS has not been investigated to date. This study analysed immune responses to EBV in a well characterised cohort of 13 individuals with MS by utilising pre-AHSCT, and 6-, 12- and 24-month post AHSCT bio-banked peripheral blood mononuclear cells and plasma samples. It is demonstrated that the infused stem cell product contains latently EBV-infected memory B cells, and that EBV viremia occurs in the immune-compromised recipient post-transplant. High throughput TCR analysis detected expansion and diversification of the CD8 CTL responses reactive with EBV lytic and latent antigens from 6 to 24 months following AHSCT. Increased levels of latent EBV infection found within the B cell pool following treatment, as measured by EBV genomic detection, did not associate with disease relapse. This is the first study of EBV immunity following application of AHSCT in the treatment of MS and not only raises important questions about the role of EBV infection in MS pathogenesis, but is of clinical importance given the expanding clinical trials of adoptive EBV-specific CTLs in MS.
Collapse
Affiliation(s)
- Jennifer Massey
- Department of Neurology, St Vincent's Hospital, Darlinghurst, NSW 2010, Australia; Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia.
| | - Crisbel Artuz
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia
| | - Zoe Dyer
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia
| | - Katherine Jackson
- Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Melissa Khoo
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia
| | - Malini Visweswaran
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia
| | - Barbara Withers
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Haematology, St Vincent's Hospital; Darlinghurst, NSW 2010, Australia
| | - John Moore
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Haematology, St Vincent's Hospital; Darlinghurst, NSW 2010, Australia
| | - David Ma
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Haematology, St Vincent's Hospital; Darlinghurst, NSW 2010, Australia
| | - Ian Sutton
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Neurology, St Vincent's Clinic; Darlinghurst, NSW 2010, Australia
| |
Collapse
|
35
|
John L, Poos AM, Brobeil A, Schinke C, Huhn S, Prokoph N, Lutz R, Wagner B, Zangari M, Tirier SM, Mallm JP, Schumacher S, Vonficht D, Solé-Boldo L, Quick S, Steiger S, Przybilla MJ, Bauer K, Baumann A, Hemmer S, Rehnitz C, Lückerath C, Sachpekidis C, Mechtersheimer G, Haberkorn U, Dimitrakopoulou-Strauss A, Reichert P, Barlogie B, Müller-Tidow C, Goldschmidt H, Hillengass J, Rasche L, Haas SF, van Rhee F, Rippe K, Raab MS, Sauer S, Weinhold N. Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level. Nat Commun 2023; 14:5011. [PMID: 37591845 PMCID: PMC10435504 DOI: 10.1038/s41467-023-40584-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 08/02/2023] [Indexed: 08/19/2023] Open
Abstract
In multiple myeloma spatial differences in the subclonal architecture, molecular signatures and composition of the microenvironment remain poorly characterized. To address this shortcoming, we perform multi-region sequencing on paired random bone marrow and focal lesion samples from 17 newly diagnosed patients. Using single-cell RNA- and ATAC-seq we find a median of 6 tumor subclones per patient and unique subclones in focal lesions. Genetically identical subclones display different levels of spatial transcriptional plasticity, including nearly identical profiles and pronounced heterogeneity at different sites, which can include differential expression of immunotherapy targets, such as CD20 and CD38. Macrophages are significantly depleted in the microenvironment of focal lesions. We observe proportional changes in the T-cell repertoire but no site-specific expansion of T-cell clones in intramedullary lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity in multiple myeloma with potential implications for models of cell-cell interactions and disease progression.
Collapse
Affiliation(s)
- Lukas John
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexandra M Poos
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Department of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Carolina Schinke
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Stefanie Huhn
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Nina Prokoph
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Raphael Lutz
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), Heidelberg, Germany
- Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Barbara Wagner
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Maurizio Zangari
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Stephan M Tirier
- Division of Chromatin Networks, German Cancer Research Center (DKFZ) and BioQuant, Heidelberg, Germany
| | - Jan-Philipp Mallm
- Single Cell Open Lab, German Cancer Research Center (DKFZ) and BioQuant, Heidelberg, Germany
| | - Sabrina Schumacher
- Division of Chromatin Networks, German Cancer Research Center (DKFZ) and BioQuant, Heidelberg, Germany
| | - Dominik Vonficht
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), Heidelberg, Germany
- Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Llorenç Solé-Boldo
- Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Department of Hematology, Oncology and Tumor Immunology, Charité University Medicine, Berlin, Germany
| | - Sabine Quick
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Simon Steiger
- Division of Chromatin Networks, German Cancer Research Center (DKFZ) and BioQuant, Heidelberg, Germany
| | - Moritz J Przybilla
- Division Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK
| | - Katharina Bauer
- Single Cell Open Lab, German Cancer Research Center (DKFZ) and BioQuant, Heidelberg, Germany
| | - Anja Baumann
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Hemmer
- Department of Orthopedic Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Christoph Rehnitz
- Department of Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Lückerath
- Department of Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christos Sachpekidis
- Department of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Uwe Haberkorn
- Department of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Antonia Dimitrakopoulou-Strauss
- Department of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp Reichert
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Bart Barlogie
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Carsten Müller-Tidow
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Hartmut Goldschmidt
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Jens Hillengass
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Leo Rasche
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
- Mildred Scheel Early Career Center (MSNZ), University Hospital of Würzburg, Würzburg, Germany
| | - Simon F Haas
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), Heidelberg, Germany
- Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Department of Hematology, Oncology and Tumor Immunology, Charité University Medicine, Berlin, Germany
| | - Frits van Rhee
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Karsten Rippe
- Division of Chromatin Networks, German Cancer Research Center (DKFZ) and BioQuant, Heidelberg, Germany
| | - Marc S Raab
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sandra Sauer
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Niels Weinhold
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany.
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| |
Collapse
|
36
|
Shcherbinin DS, Karnaukhov VK, Zvyagin IV, Chudakov DM, Shugay M. Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features. Front Immunol 2023; 14:1224969. [PMID: 37649481 PMCID: PMC10464843 DOI: 10.3389/fimmu.2023.1224969] [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: 05/18/2023] [Accepted: 07/27/2023] [Indexed: 09/01/2023] Open
Abstract
Introduction T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity. Methods Identification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R. Results Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Conclusion Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.
Collapse
Affiliation(s)
- Dmitrii S. Shcherbinin
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Laboratory of Structural Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Vadim K. Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Ivan V. Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Dmitriy M. Chudakov
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Center of Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Mikhail Shugay
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| |
Collapse
|
37
|
Yang M, Huang ZA, Zhou W, Ji J, Zhang J, He S, Zhu Z. MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations. Bioinformatics 2023; 39:btad475. [PMID: 37527015 PMCID: PMC10423027 DOI: 10.1093/bioinformatics/btad475] [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: 04/28/2023] [Revised: 07/05/2023] [Accepted: 07/29/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs. RESULTS To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset. AVAILABILITY AND IMPLEMENTATION The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.
Collapse
Affiliation(s)
- Minghao Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Zhi-An Huang
- Research Office, City University of Hong Kong (Dongguan), Dongguan 523000, China
| | - Wei Zhou
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Junkai Ji
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Jun Zhang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Shan He
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, China
| |
Collapse
|
38
|
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: 18] [Impact Index Per Article: 18.0] [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.
Collapse
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.
| |
Collapse
|
39
|
Assmann JL, Vlachonikola E, Kolijn PM, Agathangelidis A, Pechlivanis N, Papalexandri A, Stamatopoulos K, Chatzidimitriou A, Langerak AW. Context-dependent T-cell Receptor Gene Repertoire Profiles in Proliferations of T Large Granular Lymphocytes. Hemasphere 2023; 7:e929. [PMID: 37469801 PMCID: PMC10353713 DOI: 10.1097/hs9.0000000000000929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 06/12/2023] [Indexed: 07/21/2023] Open
Abstract
T cell large granular lymphocyte (T-LGL) lymphoproliferations constitute a disease spectrum ranging from poly/oligo to monoclonal. Boundaries within this spectrum of proliferations are not well established. T-LGL lymphoproliferations co-occur with a wide variety of other diseases ranging from autoimmune disorders, solid tumors, hematological malignancies, post solid organ, and hematopoietic stem cell transplantation, and can therefore arise as a consequence of a wide variety of antigenic triggers. Persistence of a dominant malignant T-LGL clone is established through continuous STAT3 activation. Using next-generation sequencing, we profiled a cohort of 27 well-established patients with T-LGL lymphoproliferations, aiming to identify the subclonal architecture of the T-cell receptor beta (TRB) chain gene repertoire. Moreover, we searched for associations between TRB gene repertoire patterns and clinical manifestations, with the ultimate objective of discriminating between T-LGL lymphoproliferations developing in different clinical contexts and/or displaying distinct clinical presentation. Altogether, our data demonstrates that the TRB gene repertoire of patients with T-LGL lymphoproliferations is context-dependent, displaying distinct clonal architectures in different settings. Our results also highlight that there are monoclonal T-LGL cells with or without STAT3 mutations that cause symptoms such as neutropenia on one end of a spectrum and reactive oligoclonal T-LGL lymphoproliferations on the other. Longitudinal analysis revealed temporal clonal dynamics and showed that T-LGL cells might arise as an epiphenomenon when co-occurring with other malignancies, possibly reactive toward tumor antigens.
Collapse
Affiliation(s)
- Jorn L.J.C. Assmann
- Laboratory for Medical Immunology, Department of Immunology, Erasmus MC, Rotterdam, Netherlands
| | | | - Pieter M. Kolijn
- Laboratory for Medical Immunology, Department of Immunology, Erasmus MC, Rotterdam, Netherlands
| | | | - Nikolaos Pechlivanis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Greece
| | | | - Kostas Stamatopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Greece
| | | | - Anton W. Langerak
- Laboratory for Medical Immunology, Department of Immunology, Erasmus MC, Rotterdam, Netherlands
| |
Collapse
|
40
|
Lee MH, Theodoropoulos J, Huuhtanen J, Bhattacharya D, Järvinen P, Tornberg S, Nísen H, Mirtti T, Uski I, Kumari A, Peltonen K, Draghi A, Donia M, Kreutzman A, Mustjoki S. Immunologic Characterization and T cell Receptor Repertoires of Expanded Tumor-infiltrating Lymphocytes in Patients with Renal Cell Carcinoma. CANCER RESEARCH COMMUNICATIONS 2023; 3:1260-1276. [PMID: 37484198 PMCID: PMC10361538 DOI: 10.1158/2767-9764.crc-22-0514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/27/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023]
Abstract
The successful use of expanded tumor-infiltrating lymphocytes (TIL) in adoptive TIL therapies has been reported, but the effects of the TIL expansion, immunophenotype, function, and T cell receptor (TCR) repertoire of the infused products relative to the tumor microenvironment (TME) are not well understood. In this study, we analyzed the tumor samples (n = 58) from treatment-naïve patients with renal cell carcinoma (RCC), "pre-rapidly expanded" TILs (pre-REP TIL, n = 15) and "rapidly expanded" TILs (REP TIL, n = 25) according to a clinical-grade TIL production protocol, with single-cell RNA (scRNA)+TCRαβ-seq (TCRαβ sequencing), TCRβ-sequencing (TCRβ-seq), and flow cytometry. REP TILs encompassed a greater abundance of CD4+ than CD8+ T cells, with increased LAG-3 and low PD-1 expressions in both CD4+ and CD8+ T cell compartments compared with the pre-REP TIL and tumor T cells. The REP protocol preferentially expanded small clones of the CD4+ phenotype (CD4, IL7R, KLRB1) in the TME, indicating that the largest exhausted T cell clones in the tumor do not expand during the expansion protocol. In addition, by generating a catalog of RCC-associated TCR motifs from >1,000 scRNA+TCRαβ-seq and TCRβ-seq RCC, healthy and other cancer sample cohorts, we quantified the RCC-associated TCRs from the expansion protocol. Unlike the low-remaining amount of anti-viral TCRs throughout the expansion, the quantity of the RCC-associated TCRs was high in the tumors and pre-REP TILs but decreased in the REP TILs. Our results provide an in-depth understanding of the origin, phenotype, and TCR specificity of RCC TIL products, paving the way for a more rationalized production of TILs. Significance TILs are a heterogenous group of immune cells that recognize and attack the tumor, thus are utilized in various clinical trials. In our study, we explored the TILs in patients with kidney cancer by expanding the TILs using a clinical-grade protocol, as well as observed their characteristics and ability to recognize the tumor using in-depth experimental and computational tools.
Collapse
Affiliation(s)
- Moon Hee Lee
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Jason Theodoropoulos
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Jani Huuhtanen
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Dipabarna Bhattacharya
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Petrus Järvinen
- Abdominal Center, Urology, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - Sara Tornberg
- Abdominal Center, Urology, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - Harry Nísen
- Abdominal Center, Urology, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas Mirtti
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- Department of Pathology, HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, Georgia
| | - Ilona Uski
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Anita Kumari
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Karita Peltonen
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Arianna Draghi
- National Center for Cancer Immune Therapy, Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Marco Donia
- National Center for Cancer Immune Therapy, Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Anna Kreutzman
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Satu Mustjoki
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| |
Collapse
|
41
|
Murad T, Ali S, Patterson M. Exploring the Potential of GANs in Biological Sequence Analysis. BIOLOGY 2023; 12:854. [PMID: 37372139 DOI: 10.3390/biology12060854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.
Collapse
Affiliation(s)
- Taslim Murad
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| |
Collapse
|
42
|
Mattila J, Sormunen S, Heikkilä N, Mattila IP, Saramäki J, Arstila TP. Analysis of thymic generation of shared T-cell receptor α repertoire associated with recognition of tumor antigens shows no preference for neoantigens over wild-type antigens. Cancer Med 2023; 12:13486-13496. [PMID: 37114587 PMCID: PMC10315763 DOI: 10.1002/cam4.6002] [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: 11/28/2022] [Revised: 04/06/2023] [Accepted: 04/15/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND The number of mutations in cancer cells is an important predictor of a positive response to cancer immunotherapy. It has been suggested that the neoantigens produced by these mutations are more immunogenic than nonmutated tumor antigens, which are likely to be protected by immunological tolerance. However, the mechanisms of tolerance as regards tumor antigens are incompletely understood. METHODS Here, we have analyzed the impact of thymic negative selection on shared T-cell receptor (TCR) repertoire associated with the recognition of either mutated or nonmutated tumor antigens by comparing previously known TCR-antigen-pairs to TCR repertoires of 21 immunologically healthy individuals. RESULTS Our results show that TCRα chains associated with either type of tumor antigens are readily generated in the thymus, at a frequency similar to TCRα chains associated with nonself. In the peripheral repertoire, the relative clone size of nonself-associated chains is higher than that of the tumor antigens, but importantly, there is no difference between TCRα chains associated with mutated or nonmutated tumor antigens. CONCLUSION This suggests that the tolerance mechanisms protecting nonmutated tumor antigens are non-deletional and therefore potentially reversible. As unmutated antigens are, unlike mutations, shared by a large number of patients, they may offer advantages in designing immunological approaches to cancer treatment.
Collapse
Affiliation(s)
- Joonatan Mattila
- Research Programs Unit, Translational Immunology, Haartmaninkatu 3 (PL 21) 00014, and MedicumUniversity of HelsinkiHelsinkiFinland
| | - Silja Sormunen
- Department of Computer ScienceAalto UniversityEspooFinland
| | - Nelli Heikkilä
- Research Programs Unit, Translational Immunology, Haartmaninkatu 3 (PL 21) 00014, and MedicumUniversity of HelsinkiHelsinkiFinland
- Faculty of Medicine, Center for Vaccinology, Department of Pathology and ImmunologyUniversity of GenevaGenevaSwitzerland
| | - Ilkka P. Mattila
- Department of Pediatric Cardiac and Transplantation SurgeryHospital for Children and Adolescents, Helsinki University Central HospitalHelsinkiFinland
| | - Jari Saramäki
- Department of Computer ScienceAalto UniversityEspooFinland
| | - T. Petteri Arstila
- Research Programs Unit, Translational Immunology, Haartmaninkatu 3 (PL 21) 00014, and MedicumUniversity of HelsinkiHelsinkiFinland
| |
Collapse
|
43
|
Luo J, Wang X, Zou Y, Chen L, Liu W, Zhang W, Li SC. Quantitative annotations of T-Cell repertoire specificity. Brief Bioinform 2023; 24:bbad175. [PMID: 37150761 DOI: 10.1093/bib/bbad175] [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: 02/15/2023] [Revised: 04/03/2023] [Accepted: 04/17/2023] [Indexed: 05/09/2023] Open
Abstract
The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.
Collapse
Affiliation(s)
- Jiaqi Luo
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| | - Xueying Wang
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| | - Yiping Zou
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| | - Lingxi Chen
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| | - Wei Liu
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| | - Wei Zhang
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Tree Ave, Kowloon Tong, Hong Kong, China
| |
Collapse
|
44
|
Zhang Y, Jian X, Xu L, Zhao J, Lu M, Lin Y, Xie L. iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. Front Genet 2023; 14:1141535. [PMID: 37229205 PMCID: PMC10203616 DOI: 10.3389/fgene.2023.1141535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.
Collapse
Affiliation(s)
- Yu Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Bio-Diversity Science, School of Life Sciences, Fudan University, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Manman Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yong Lin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| |
Collapse
|
45
|
Yin R, Ribeiro-Filho HV, Lin V, Gowthaman R, Cheung M, Pierce BG. TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning. Nucleic Acids Res 2023:7151345. [PMID: 37140040 DOI: 10.1093/nar/gkad356] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/08/2023] [Accepted: 04/25/2023] [Indexed: 05/05/2023] Open
Abstract
The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.
Collapse
Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Helder V Ribeiro-Filho
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
| | - Valerie Lin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Thomas S. Wootton High School, Rockville, MD 20850, USA
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Melyssa Cheung
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
| |
Collapse
|
46
|
Povlsen HR, Bentzen AK, Kadivar M, Jessen LE, Hadrup SR, Nielsen M. Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells. eLife 2023; 12:e81810. [PMID: 37133356 PMCID: PMC10156162 DOI: 10.7554/elife.81810] [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: 07/12/2022] [Accepted: 03/13/2023] [Indexed: 05/04/2023] Open
Abstract
Novel single-cell-based technologies hold the promise of matching T cell receptor (TCR) sequences with their cognate peptide-MHC recognition motif in a high-throughput manner. Parallel capture of TCR transcripts and peptide-MHC is enabled through the use of reagents labeled with DNA barcodes. However, analysis and annotation of such single-cell sequencing (SCseq) data are challenged by dropout, random noise, and other technical artifacts that must be carefully handled in the downstream processing steps. We here propose a rational, data-driven method termed ITRAP (improved T cell Receptor Antigen Paring) to deal with these challenges, filtering away likely artifacts, and enable the generation of large sets of TCR-pMHC sequence data with a high degree of specificity and sensitivity, thus outputting the most likely pMHC target per T cell. We have validated this approach across 10 different virus-specific T cell responses in 16 healthy donors. Across these samples, we have identified up to 1494 high-confident TCR-pMHC pairs derived from 4135 single cells.
Collapse
Affiliation(s)
- Helle Rus Povlsen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Amalie Kai Bentzen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Mohammad Kadivar
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Leon Eyrich Jessen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Sine Reker Hadrup
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Morten Nielsen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| |
Collapse
|
47
|
Wu J, Qi M, Zhang F, Zheng Y. TPBTE: A model based on convolutional Transformer for predicting the binding of TCR to epitope. Mol Immunol 2023; 157:30-41. [PMID: 36966551 DOI: 10.1016/j.molimm.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/29/2023]
Abstract
T cell receptors (TCRs) selectively bind to antigens to fight pathogens with specific immunity. Current tools focus on the nature of amino acids within sequences and take less into account the nature of amino acids far apart and the relationship between sequences, leading to significant differences in the results from different datasets. We propose TPBTE, a model based on convolutional Transformer for Predicting the Binding of TCR to Epitope. It takes epitope sequences and the complementary decision region 3 (CDR3) sequences of TCRβ chain as inputs. And it uses a convolutional attention mechanism to learn amino acid representations between different positions of the sequences based on learning local features of the sequences. At the same time, it uses cross attention to learn the interaction information between TCR sequences and epitope sequences. A comprehensive evaluation of the TCR-epitope data shows that the average area under the curve of TPBTE outperforms the baseline model, and demonstrate an intentional performance. In addition, TPBTE can give the probability of binding TCR to epitopes, which can be used as the first step of epitope screening, narrowing the scope of epitope search and reducing the time of epitope search.
Collapse
|
48
|
Edahiro R, Shirai Y, Takeshima Y, Sakakibara S, Yamaguchi Y, Murakami T, Morita T, Kato Y, Liu YC, Motooka D, Naito Y, Takuwa A, Sugihara F, Tanaka K, Wing JB, Sonehara K, Tomofuji Y, Namkoong H, Tanaka H, Lee H, Fukunaga K, Hirata H, Takeda Y, Okuzaki D, Kumanogoh A, Okada Y. Single-cell analyses and host genetics highlight the role of innate immune cells in COVID-19 severity. Nat Genet 2023; 55:753-767. [PMID: 37095364 PMCID: PMC10181941 DOI: 10.1038/s41588-023-01375-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/15/2023] [Indexed: 04/26/2023]
Abstract
Mechanisms underpinning the dysfunctional immune response in severe acute respiratory syndrome coronavirus 2 infection are elusive. We analyzed single-cell transcriptomes and T and B cell receptors (BCR) of >895,000 peripheral blood mononuclear cells from 73 coronavirus disease 2019 (COVID-19) patients and 75 healthy controls of Japanese ancestry with host genetic data. COVID-19 patients showed a low fraction of nonclassical monocytes (ncMono). We report downregulated cell transitions from classical monocytes to ncMono in COVID-19 with reduced CXCL10 expression in ncMono in severe disease. Cell-cell communication analysis inferred decreased cellular interactions involving ncMono in severe COVID-19. Clonal expansions of BCR were evident in the plasmablasts of patients. Putative disease genes identified by COVID-19 genome-wide association study showed cell type-specific expressions in monocytes and dendritic cells. A COVID-19-associated risk variant at the IFNAR2 locus (rs13050728) had context-specific and monocyte-specific expression quantitative trait loci effects. Our study highlights biological and host genetic involvement of innate immune cells in COVID-19 severity.
Collapse
Affiliation(s)
- Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Yusuke Takeshima
- Laboratory of Experimental Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Shuhei Sakakibara
- Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Yuta Yamaguchi
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Teruaki Murakami
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Takayoshi Morita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Yasuhiro Kato
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Yu-Chen Liu
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Daisuke Motooka
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Yoko Naito
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Ayako Takuwa
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Fuminori Sugihara
- Core Instrumentation Facility, Immunology Frontier Research Center and Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Kentaro Tanaka
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - James B Wing
- Laboratory of Human Immunology (Single Cell Immunology), Immunology Frontier Research Center, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshihiko Tomofuji
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Haruhiko Hirata
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshito Takeda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Daisuke Okuzaki
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Japan
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology (AMED-CREST), Osaka University, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan.
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology (AMED-CREST), Osaka University, Osaka, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| |
Collapse
|
49
|
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.
Collapse
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.
| |
Collapse
|
50
|
Li Q, Wang F, Shi Y, Zhong L, Duan S, Kuang W, Liu N, Luo E, Zhou Y, Jiang L, Dan H, Luo X, Zhang D, Chen Q, Zeng X, Li T. Single-cell immune profiling reveals immune responses in oral lichen planus. Front Immunol 2023; 14:1182732. [PMID: 37090715 PMCID: PMC10116058 DOI: 10.3389/fimmu.2023.1182732] [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: 03/09/2023] [Accepted: 03/27/2023] [Indexed: 04/08/2023] Open
Abstract
IntroductionOral lichen planus (OLP) is a common chronic inflammatory disorder of the oral mucosa with an unclear etiology. Several types of immune cells are involved in the pathogenesis of OLP.MethodsWe used single-cell RNA sequencing and immune repertoire sequencing to characterize the mucosal immune microenvironment of OLP. The presence of tissue-resident memory CD8+ T cells are validated by multiplex immunofluorescence.ResultsWe generated a transcriptome atlas from four OLP biopsy samples and their paired peripheral blood mononuclear cells (PBMCs), and compared them with two healthy tissues and three healthy PBMCs samples. Our analysis revealed activated tissue-resident memory CD8+ T cells in OLP tissues. T cell receptor repertoires displayed apperant clonal expansion and preferrential gene pairing in OLP patients. Additionally, obvious BCR clonal expansion was observed in OLP lesions. Plasmacytoid dendritic cells, a subtype that can promote dendritic cell maturation and enhance lymphocyte cytotoxicity, were identified in OLP. Conventional dendritic cells and macrophages are also found to exhibit pro-inflammatory activity in OLP. Cell-cell communication analysis reveals that fibroblasts might promote the recruitment and extravasation of immune cells into connective tissue.DiscussionOur study provides insights into the immune ecosystem of OLP, serving as a valuable resource for precision diagnosis and therapy of OLP.
Collapse
Affiliation(s)
- Qionghua Li
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Fei Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yujie Shi
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Liang Zhong
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Shumin Duan
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Wenjing Kuang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Na Liu
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - En Luo
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yu Zhou
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Lu Jiang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Hongxia Dan
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Xiaobo Luo
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Dunfang Zhang
- Department of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qianming Chen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Xin Zeng
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- *Correspondence: Taiwen Li, ; Xin Zeng,
| | - Taiwen Li
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, China
- *Correspondence: Taiwen Li, ; Xin Zeng,
| |
Collapse
|