1
|
Linsley PS, Nakayama M, Balmas E, Chen J, Barahmand-Pour-Whitman F, Bansal S, Bottorff T, Serti E, Speake C, Pugliese A, Cerosaletti K. Germline-like TCR-α chains shared between autoreactive T cells in blood and pancreas. Nat Commun 2024; 15:4971. [PMID: 38871688 PMCID: PMC11176301 DOI: 10.1038/s41467-024-48833-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 05/13/2024] [Indexed: 06/15/2024] Open
Abstract
Human type 1 diabetes (T1D) is caused by autoimmune attack on the insulin-producing pancreatic beta cells by islet antigen-reactive T cells. How human islet antigen-reactive (IAR) CD4+ memory T cells from peripheral blood affect T1D progression in the pancreas is poorly understood. Here, we aim to determine if IAR T cells in blood could be detected in pancreas. We identify paired αβ (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new-onset, and established T1D donors, and measured sequence overlap with TCRs in pancreata from healthy, at risk and T1D organ donors. We report extensive TRA junction sharing between IAR T cells and pancreas-infiltrating T cells (PIT), with perfect-match or single-mismatch TRA junction amino acid sequences comprising ~29% total unique IAR TRA junctions (942/3,264). PIT-matched TRA junctions were largely public and enriched for TRAV41 usage, showing significant nucleotide sequence convergence, increased use of germline-encoded versus non-templated residues in epitope engagement, and a potential for cross-reactivity. Our findings thus link T cells with distinctive germline-like TRA chains in the peripheral blood with T cells in the pancreas.
Collapse
Affiliation(s)
- Peter S Linsley
- Benaroya Research Institute at Virginia Mason, Seattle, WA, USA.
| | - Maki Nakayama
- Barbara Davis Center for Childhood Diabetes, Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Elisa Balmas
- Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | - Janice Chen
- Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | | | - Shubham Bansal
- Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | - Ty Bottorff
- Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | | | - Cate Speake
- Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | - Alberto Pugliese
- Department of Diabetes Immunology & The Wanek Family Project for Type 1 Diabetes, Arthur Riggs Diabetes & Metabolism Research Institute, City of Hope, Duarte, CA, USA
| | | |
Collapse
|
2
|
Schnell A. Stem-like T cells in cancer and autoimmunity. Immunol Rev 2024. [PMID: 38804499 DOI: 10.1111/imr.13356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Stem-like T cells are characterized by their ability to self-renew, survive long-term, and give rise to a heterogeneous pool of effector and memory T cells. Recent advances in single-cell RNA-sequencing (scRNA-seq) and lineage tracing technologies revealed an important role for stem-like T cells in both autoimmunity and cancer. In cancer, stem-like T cells constitute an important arm of the anti-tumor immune response by giving rise to effector T cells that mediate tumor control. In contrast, in autoimmunity stem-like T cells perform an unfavorable role by forming a reservoir of long-lived autoreactive cells that replenish the pathogenic, effector T-cell pool and thereby driving disease pathology. This review provides background on the discovery of stem-like T cells and their function in cancer and autoimmunity. Moreover, the influence of the microbiota and metabolism on the stem-like T-cell pool is summarized. Lastly, the implications of our knowledge about stem-like T cells for clinical treatment strategies for cancer and autoimmunity will be discussed.
Collapse
Affiliation(s)
- Alexandra Schnell
- Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
3
|
Cui J, Xu H, Yu J, Ran S, Zhang X, Li Y, Chen Z, Niu Y, Wang S, Ye W, Chen W, Wu J, Xia J. Targeted depletion of PD-1-expressing cells induces immune tolerance through peripheral clonal deletion. Sci Immunol 2024; 9:eadh0085. [PMID: 38669317 DOI: 10.1126/sciimmunol.adh0085] [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: 02/03/2023] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
Thymic negative selection of the T cell receptor (TCR) repertoire is essential for establishing self-tolerance and acquired allograft tolerance following organ transplantation. However, it is unclear whether and how peripheral clonal deletion of alloreactive T cells induces transplantation tolerance. Here, we establish that programmed cell death protein 1 (PD-1) is a hallmark of alloreactive T cells and is associated with clonal expansion after alloantigen encounter. Moreover, we found that diphtheria toxin receptor (DTR)-mediated ablation of PD-1+ cells reshaped the TCR repertoire through peripheral clonal deletion of alloreactive T cells and promoted tolerance in mouse transplantation models. In addition, by using PD-1-specific depleting antibodies, we found that antibody-mediated depletion of PD-1+ cells prevented heart transplant rejection and the development of experimental autoimmune encephalomyelitis (EAE) in humanized PD-1 mice. Thus, these data suggest that PD-1 is an attractive target for peripheral clonal deletion and induction of immune tolerance.
Collapse
Affiliation(s)
- Jikai Cui
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Heng Xu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jizhang Yu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuan Ran
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Xi Zhang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Yuan Li
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Zhang Chen
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Yuqing Niu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Song Wang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Weicong Ye
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Wenhao Chen
- Immunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute and Institute for Academic Medicine, Houston Methodist Hospital, Houston, TX, USA
| | - Jie Wu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Translational Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiahong Xia
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Translational Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
4
|
Tayebi Z, Ali S, Murad T, Khan I, Patterson M. PseAAC2Vec protein encoding for TCR protein sequence classification. Comput Biol Med 2024; 170:107956. [PMID: 38217977 DOI: 10.1016/j.compbiomed.2024.107956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/07/2023] [Accepted: 01/01/2024] [Indexed: 01/15/2024]
Abstract
The classification and prediction of T-cell receptors (TCRs) protein sequences are of significant interest in understanding the immune system and developing personalized immunotherapies. In this study, we propose a novel approach using Pseudo Amino Acid Composition (PseAAC) protein encoding for accurate TCR protein sequence classification. The PseAAC2Vec encoding method captures the physicochemical properties of amino acids and their local sequence information, enabling the representation of protein sequences as fixed-length feature vectors. By incorporating physicochemical properties such as hydrophobicity, polarity, charge, molecular weight, and solvent accessibility, PseAAC2Vec provides a comprehensive and informative characterization of TCR protein sequences. To evaluate the effectiveness of the proposed PseAAC2Vec encoding approach, we assembled a large dataset of TCR protein sequences with annotated classes. We applied the PseAAC2Vec encoding scheme to each sequence and generated feature vectors based on a specified window size. Subsequently, we employed state-of-the-art machine learning algorithms, such as support vector machines (SVM) and random forests (RF), to classify the TCR protein sequences. Experimental results on the benchmark dataset demonstrated the superior performance of the PseAAC2Vec-based approach compared to existing methods. The PseAAC2Vec encoding effectively captures the discriminative patterns in TCR protein sequences, leading to improved classification accuracy and robustness. Furthermore, the encoding scheme showed promising results across different window sizes, indicating its adaptability to varying sequence contexts.
Collapse
Affiliation(s)
- Zahra Tayebi
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| | - Taslim Murad
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| | - Imdadullah Khan
- Department of Computer Science, Lahore University of Management Sciences, Lahore, Punjab, Pakistan.
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| |
Collapse
|
5
|
Chen E, Ling AL, Reardon DA, Chiocca EA. Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling? Neuro Oncol 2024; 26:211-225. [PMID: 37995317 PMCID: PMC10836778 DOI: 10.1093/neuonc/noad211] [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: 11/25/2023] Open
Abstract
Glioblastoma (GBM)'s median overall survival is almost 21 months. Six phase 3 immunotherapy clinical trials have recently been published, yet 5/6 did not meet approval by regulatory bodies. For the sixth, approval is uncertain. Trial failures result from multiple factors, ranging from intrinsic tumor biology to clinical trial design. Understanding the clinical and basic science of these 6 trials is compelled by other immunotherapies reaching the point of advanced phase 3 clinical trial testing. We need to understand more of the science in human GBMs in early trials: the "window of opportunity" design may not be best to understand complex changes brought about by immunotherapeutic perturbations of the GBM microenvironment. The convergence of increased safety of image-guided biopsies with "multi-omics" of small cell numbers now permits longitudinal sampling of tumor and biofluids to dissect the complex temporal changes in the GBM microenvironment as a function of the immunotherapy.
Collapse
Affiliation(s)
- Ethan Chen
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Alexander L Ling
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - E Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| |
Collapse
|
6
|
Perrotta S, Carnevale D. TCR-Sequencing Provides a Barcode of Immune Activation and Target Organ Damage in Human Hypertension. Hypertension 2023; 80:2330-2332. [PMID: 37851766 DOI: 10.1161/hypertensionaha.123.21830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Affiliation(s)
- Sara Perrotta
- Department of Angiocardioneurology and Translational Medicine, Unit of Neuro and Cardiovascular Physiology, Italy (S.P., D.C.)
| | - Daniela Carnevale
- Department of Angiocardioneurology and Translational Medicine, Unit of Neuro and Cardiovascular Physiology, Italy (S.P., D.C.)
- Department of Molecular Medicine, "Sapienza" University of Rome, Italy (D.C.)
| |
Collapse
|
7
|
Nagano Y, Chain B. tidytcells: standardizer for TR/MH nomenclature. Front Immunol 2023; 14:1276106. [PMID: 37954585 PMCID: PMC10634431 DOI: 10.3389/fimmu.2023.1276106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/09/2023] [Indexed: 11/14/2023] Open
Abstract
T cell receptors (TR) underpin the diversity and specificity of T cell activity. As such, TR repertoire data is valuable both as an adaptive immune biomarker, and as a way to identify candidate therapeutic TR. Analysis of TR repertoires relies heavily on computational analysis, and therefore it is of vital importance that the data is standardized and computer-readable. However in practice, the usage of different abbreviations and non-standard nomenclature in different datasets makes this data pre-processing non-trivial. tidytcells is a lightweight, platform-independent Python package that provides easy-to-use standardization tools specifically designed for TR nomenclature. The software is open-sourced under the MIT license and is available to install from the Python Package Index (PyPI). At the time of publishing, tidytcells is on version 2.0.0.
Collapse
Affiliation(s)
- Yuta Nagano
- Division of Medicine, Faculty of Medical Scienecs, University College London (UCL), London, United Kingdom
| | - Benjamin Chain
- Division of Infection and Immunity, Faculty of Medical Sciences, University College London (UCL), London, United Kingdom
| |
Collapse
|
8
|
Cao LL, Kagan JC. Targeting innate immune pathways for cancer immunotherapy. Immunity 2023; 56:2206-2217. [PMID: 37703879 PMCID: PMC10591974 DOI: 10.1016/j.immuni.2023.07.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/28/2023] [Accepted: 07/26/2023] [Indexed: 09/15/2023]
Abstract
The innate immune system is critical for inducing durable and protective T cell responses to infection and has been increasingly recognized as a target for cancer immunotherapy. In this review, we present a framework wherein distinct innate immune signaling pathways activate five key dendritic cell activities that are important for T cell-mediated immunity. We discuss molecular pathways that can agonize these activities and highlight that no single pathway can agonize all activities needed for durable immunity. The immunological distinctions between innate immunotherapy administration to the tumor microenvironment versus administration via vaccination are examined, with particular focus on the strategies that enhance dendritic cell migration, interferon expression, and interleukin-1 family cytokine production. In this context, we argue for the importance of appreciating necessity vs. sufficiency when considering the impact of innate immune signaling in inflammation and protective immunity and offer a conceptual guideline for the development of efficacious cancer immunotherapies.
Collapse
Affiliation(s)
- Longyue L Cao
- Harvard Medical School and Division of Gastroenterology, Boston Children's Hospital, Boston, MA, USA
| | - Jonathan C Kagan
- Harvard Medical School and Division of Gastroenterology, Boston Children's Hospital, Boston, MA, USA.
| |
Collapse
|
9
|
Collier JL, Pauken KE, Lee CA, Patterson DG, Markson SC, Conway TS, Fung ME, France JA, Mucciarone KN, Lian CG, Murphy GF, Sharpe AH. Single-cell profiling reveals unique features of diabetogenic T cells in anti-PD-1-induced type 1 diabetes mice. J Exp Med 2023; 220:e20221920. [PMID: 37432393 PMCID: PMC10336233 DOI: 10.1084/jem.20221920] [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: 11/09/2022] [Revised: 04/28/2023] [Accepted: 06/23/2023] [Indexed: 07/12/2023] Open
Abstract
Immune-related adverse events (irAEs) are a notable complication of PD-1 cancer immunotherapy. A better understanding of how these iatrogenic diseases compare with naturally arising autoimmune diseases is needed for treatment and monitoring of irAEs. We identified differences in anti-PD-1-induced type 1 diabetes (T1D) and spontaneous T1D in non-obese diabetic (NOD) mice by performing single-cell RNA-seq and TCR-seq on T cells from the pancreas, pancreas-draining lymph node (pLN), and blood of mice with PD-1-induced T1D or spontaneous T1D. In the pancreas, anti-PD-1 resulted in expansion of terminally exhausted/effector-like CD8+ T cells, an increase in T-bethi CD4+FoxP3- T cells, and a decrease in memory CD4+FoxP3- and CD8+ T cells in contrast to spontaneous T1D. Notably, anti-PD-1 caused increased TCR sharing between the pancreas and the periphery. Moreover, T cells in the blood of anti-PD-1-treated mice expressed markers that differed from spontaneous T1D, suggesting that the blood may provide a window to monitor irAEs rather than relying exclusively on the autoimmune target organ.
Collapse
Affiliation(s)
- Jenna L. Collier
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | - Kristen E. Pauken
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | | | - Dillon G. Patterson
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | - Samuel C. Markson
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | - Thomas S. Conway
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | - Megan E. Fung
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | - Joshua A. France
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
| | | | - Christine G. Lian
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - George F. Murphy
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Arlene H. Sharpe
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| |
Collapse
|
10
|
Lagattuta KA, Nathan A, Rumker L, Birnbaum ME, Raychaudhuri S. The T cell receptor sequence influences the likelihood of T cell memory formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549939. [PMID: 37502994 PMCID: PMC10370203 DOI: 10.1101/2023.07.20.549939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.
Collapse
Affiliation(s)
- Kaitlyn A. Lagattuta
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael E. Birnbaum
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biomedical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| |
Collapse
|
11
|
Piper M, Kluger H, Ruppin E, Hu-Lieskovan S. Immune Resistance Mechanisms and the Road to Personalized Immunotherapy. Am Soc Clin Oncol Educ Book 2023; 43:e390290. [PMID: 37459578 DOI: 10.1200/edbk_390290] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
What does the future of cancer immunotherapy look like and how do we get there? Find out where we've been and where we're headed in A Report on Resistance: The Road to personalized immunotherapy.
Collapse
Affiliation(s)
- Miles Piper
- School of Medicine, University of Utah, Salt Lake City, UT
| | | | - Eytan Ruppin
- Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - Siwen Hu-Lieskovan
- School of Medicine, University of Utah, Salt Lake City, UT
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| |
Collapse
|
12
|
Wang G, Yao Y, Huang H, Zhou J, Ni C. Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy. Front Oncol 2023; 13:1131259. [PMID: 37284197 PMCID: PMC10239824 DOI: 10.3389/fonc.2023.1131259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/08/2023] [Indexed: 06/08/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.
Collapse
Affiliation(s)
- Gang Wang
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Yao Yao
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Huanhuan Huang
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Jun Zhou
- Department of Breast Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chao Ni
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| |
Collapse
|
13
|
Abstract
Recent advances in cancer immunotherapy - ranging from immune-checkpoint blockade therapy to adoptive cellular therapy and vaccines - have revolutionized cancer treatment paradigms, yet the variability in clinical responses to these agents has motivated intense interest in understanding how the T cell landscape evolves with respect to response to immune intervention. Over the past decade, the advent of multidimensional single-cell technologies has provided the unprecedented ability to dissect the constellation of cell states of lymphocytes within a tumour microenvironment. In particular, the rapidly expanding capacity to definitively link intratumoural phenotypes with the antigen specificity of T cells provided by T cell receptors (TCRs) has now made it possible to focus on investigating the properties of T cells with tumour-specific reactivity. Moreover, the assessment of TCR clonality has enabled a molecular approach to track the trajectories, clonal dynamics and phenotypic changes of antitumour T cells over the course of immunotherapeutic intervention. Here, we review the current knowledge on the cellular states and antigen specificities of antitumour T cells and examine how fine characterization of T cell dynamics in patients has provided meaningful insights into the mechanisms underlying effective cancer immunotherapy. We highlight those T cell subsets associated with productive T cell responses and discuss how diverse immunotherapies might leverage the pre-existing tumour-reactive T cell pool or instruct de novo generation of antitumour specificities. Future studies aimed at elucidating the factors associated with the elicitation of productive antitumour T cell immunity are anticipated to instruct the design of more efficacious treatment strategies.
Collapse
Affiliation(s)
- Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| |
Collapse
|
14
|
Sanromán ÁF, Joshi K, Au L, Chain B, Turajlic S. TCR sequencing: applications in immuno-oncology research. IMMUNO-ONCOLOGY TECHNOLOGY 2023; 17:100373. [PMID: 36908996 PMCID: PMC9996383 DOI: 10.1016/j.iotech.2023.100373] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
•T-cell receptor (TCR) interaction with major histocompatibility complex-antigen complexes leads to antitumour responses.•TCR sequencing analysis allows characterisation of T cells that recognise tumour neoantigens.•T-cell clonal revival and clonal replacement potentially underpin immunotherapy responses.
Collapse
Affiliation(s)
- Á F Sanromán
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - K Joshi
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London, UK.,Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - L Au
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia.,Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Australia
| | - B Chain
- Division of Infection and Immunity, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - S Turajlic
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK.,Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, UK
| |
Collapse
|
15
|
Hudson WH, Wieland A. Technology meets TILs: Deciphering T cell function in the -omics era. Cancer Cell 2023; 41:41-57. [PMID: 36206755 PMCID: PMC9839604 DOI: 10.1016/j.ccell.2022.09.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/15/2022] [Accepted: 09/15/2022] [Indexed: 01/17/2023]
Abstract
T cells are at the center of cancer immunology because of their ability to recognize mutations in tumor cells and directly mediate cancer cell killing. Immunotherapies to rejuvenate exhausted T cell responses have transformed the clinical management of several malignancies. In parallel, the development of novel multidimensional analysis platforms, such as single-cell RNA sequencing and high-dimensional flow cytometry, has yielded unprecedented insights into immune cell biology. This convergence has revealed substantial heterogeneity of tumor-infiltrating immune cells in single tumors, across tumor types, and among individuals with cancer. Here we discuss the opportunities and challenges of studying the complex tumor microenvironment with -omics technologies that generate vast amounts of data, highlighting the opportunities and limitations of these technologies with a particular focus on interpreting high-dimensional studies of CD8+ T cells in the tumor microenvironment.
Collapse
Affiliation(s)
- William H Hudson
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA; Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Andreas Wieland
- Department of Otolaryngology, The Ohio State University, Columbus, OH 43210, USA; Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH 43210, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH 43210, USA.
| |
Collapse
|
16
|
Chopp L, Redmond C, O'Shea JJ, Schwartz DM. From thymus to tissues and tumors: A review of T-cell biology. J Allergy Clin Immunol 2023; 151:81-97. [PMID: 36272581 PMCID: PMC9825672 DOI: 10.1016/j.jaci.2022.10.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022]
Abstract
T cells are critical orchestrators of the adaptive immune response that optimally eliminate a specific pathogen. Aberrant T-cell development and function are implicated in a broad range of human disease including immunodeficiencies, autoimmune diseases, and allergic diseases. Accordingly, therapies targeting T cells and their effector cytokines have markedly improved the care of patients with immune dysregulatory diseases. Newer discoveries concerning T-cell-mediated antitumor immunity and T-cell exhaustion have further prompted development of highly effective and novel treatment modalities for malignancies, including checkpoint inhibitors and antigen-reactive T cells. Recent discoveries are also uncovering the depth and variability of T-cell phenotypes: while T cells have long been described using a subset-based classification system, next-generation sequencing technologies suggest an astounding degree of complexity and heterogeneity at the single-cell level.
Collapse
Affiliation(s)
- Laura Chopp
- Laboratory of Immune Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda
| | - Christopher Redmond
- Clinical Fellowship Program, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda
| | - John J O'Shea
- Molecular Immunology and Inflammation Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda
| | - Daniella M Schwartz
- Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda; Division of Rheumatology and Clinical Immunology, University of Pittsburgh, Pittsburgh.
| |
Collapse
|
17
|
Han F, Chen Y, Zhu Y, Huang Z. Antigen receptor structure and signaling. Adv Immunol 2023; 157:1-28. [PMID: 37061286 DOI: 10.1016/bs.ai.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
The key to mounting an immune response is that the host cells must be coordinated to generate an appropriate immune response against the pathogenic invaders. Antigen receptors recognize specific molecular structures and recruit adaptors through their effector domains, triggering trans-membrane transduction signaling pathway to exert immune response. The T cell antigen receptor (TCR) and B cell antigen receptor (BCR) are the primary determinant of immune responses to antigens. Their structure determines the mode of signaling and signal transduction determines cell fate, leading to changes at the molecular and cellular level. Studies of antigen receptor structure and signaling revealed the basis of immune response triggering, providing clues to antigen receptor priming and a foundation for the rational design of immunotherapies. In recent years, the increased research on the structure of antigen receptors has greatly contributed to the understanding of immune response, different immune-related diseases and even tumors. In this review, we describe in detail the current view and advances of the antigen structure and signaling.
Collapse
Affiliation(s)
- Fang Han
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yan Chen
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yuwei Zhu
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhiwei Huang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
| |
Collapse
|
18
|
Clauze A, Enose-Akahata Y, Jacobson S. T cell receptor repertoire analysis in HTLV-1-associated diseases. Front Immunol 2022; 13:984274. [PMID: 36189294 PMCID: PMC9520328 DOI: 10.3389/fimmu.2022.984274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Human T lymphotropic virus 1 (HTLV-1) is a human retrovirus identified as the causative agent in adult T-cell leukemia/lymphoma (ATL) and chronic-progressive neuroinflammatory disorder HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). HTLV-1 is estimated to infect between 5-20 million people worldwide, although most infected individuals remain asymptomatic. HTLV-1 infected persons carry an estimated lifetime risk of approximately 5% of developing ATL, and between 0.25% and 1.8% of developing HAM/TSP. Most HTLV-1 infection is detected in CD4+ T cells in vivo which causes the aggressive malignancy in ATL. In HAM/TSP, the increase of HTLV-1 provirus induces immune dysregulation to alter inflammatory milieu, such as expansion of HTLV-1-specific CD8+ T cells, in the central nervous system of the infected subjects, which have been suggested to underlie the pathogenesis of HAM/TSP. Factors contributing to the conversion from asymptomatic carrier to disease state remain poorly understood. As such, the identification and tracking of HTLV-1-specific T cell biomarkers that may be used to monitor the progression from primary infection to immune dysfunction and disease are of great interest. T cell receptor (TCR) repertoires have been extensively investigated as a mechanism of monitoring adaptive T cell immune response to viruses and tumors. Breakthrough technologies such as single-cell RNA sequencing have increased the specificity with which T cell clones may be characterized and continue to improve our understanding of TCR signatures in viral infection, cancer, and associated treatments. In HTLV-1-associated disease, sequencing of TCR repertoires has been used to reveal repertoire patterns, diversity, and clonal expansions of HTLV-1-specific T cells capable of immune evasion and dysregulation in ATL as well as in HAM/TSP. Conserved sequence analysis has further been used to identify CDR3 motif sequences and exploit disease- or patient-specificity and commonality in HTLV-1-associated disease. In this article we review current research on TCR repertoires and HTLV-1-specific clonotypes in HTLV-1-associated diseases ATL and HAM/TSP and discuss the implications of TCR clonal expansions on HTLV-1-associated disease course and treatments.
Collapse
|
19
|
T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer. Int J Mol Sci 2022; 23:ijms23158590. [PMID: 35955721 PMCID: PMC9369427 DOI: 10.3390/ijms23158590] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/17/2022] Open
Abstract
The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.
Collapse
|
20
|
Trofimov A, Brouillard P, Larouche JD, Séguin J, Laverdure JP, Brasey A, Ehx G, Roy DC, Busque L, Lachance S, Lemieux S, Perreault C. Two types of human TCR differentially regulate reactivity to self and non-self antigens. iScience 2022; 25:104968. [PMID: 36111255 PMCID: PMC9468382 DOI: 10.1016/j.isci.2022.104968] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/24/2022] [Accepted: 08/12/2022] [Indexed: 11/30/2022] Open
Abstract
Based on analyses of TCR sequences from over 1,000 individuals, we report that the TCR repertoire is composed of two ontogenically and functionally distinct types of TCRs. Their production is regulated by variations in thymic output and terminal deoxynucleotidyl transferase (TDT) activity. Neonatal TCRs derived from TDT-negative progenitors persist throughout life, are highly shared among subjects, and are reported as disease-associated. Thus, 10%–30% of most frequent cord blood TCRs are associated with common pathogens and autoantigens. TDT-dependent TCRs present distinct structural features and are less shared among subjects. TDT-dependent TCRs are produced in maximal numbers during infancy when thymic output and TDT activity reach a summit, are more abundant in subjects with AIRE mutations, and seem to play a dominant role in graft-versus-host disease. Factors decreasing thymic output (age, male sex) negatively impact TCR diversity. Males compensate for their lower repertoire diversity via hyperexpansion of selected TCR clonotypes. Over 108 TCR CDR3 sequences from ∼103 individuals and 7 cohorts were analyzed The TCR repertoire is composed of two layers: neonatal and TDT-dependent layer ∼70% of frequent cord blood TCRs are associated with common pathogens Acute graft-vs-host disease correlates with a high proportion of TDT-dependent TCRs
Collapse
Affiliation(s)
- Assya Trofimov
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Department of Computer Science and Research Operations, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Quebec Institute for Learning Algorithms (Mila), Montreal, Quebec H2S 3H1, Canada
- Currently Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Currently Department of Physics, University of Washington, Seattle, WA 98195-1560, USA
| | - Philippe Brouillard
- Department of Computer Science and Research Operations, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Quebec Institute for Learning Algorithms (Mila), Montreal, Quebec H2S 3H1, Canada
| | - Jean-David Larouche
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Jonathan Séguin
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Jean-Philippe Laverdure
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Ann Brasey
- Maisonneuve-Rosemont Hospital, Montreal, Quebec H1T 2M4, Canada
| | - Gregory Ehx
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Currently Interdisciplinary Cluster for Applied Geno-Proteomics (GIGA-I3), University of Liege, Liege 4000, Belgium
| | | | - Lambert Busque
- Maisonneuve-Rosemont Hospital, Montreal, Quebec H1T 2M4, Canada
| | - Silvy Lachance
- Department of Medicine, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Maisonneuve-Rosemont Hospital, Montreal, Quebec H1T 2M4, Canada
| | - Sébastien Lemieux
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Department of Computer Science and Research Operations, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Department of Biochemistry at University of Montreal, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Corresponding author
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
- Maisonneuve-Rosemont Hospital, Montreal, Quebec H1T 2M4, Canada
- Corresponding author
| |
Collapse
|