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Yang X, Wu C, Liu W, Fu K, Tian Y, Wei X, Zhang W, Sun P, Luo H, Huang J. A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model. Comput Struct Biotechnol J 2024; 24:404-411. [PMID: 38813092 PMCID: PMC11134880 DOI: 10.1016/j.csbj.2024.05.010] [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: 11/09/2023] [Revised: 04/15/2024] [Accepted: 05/07/2024] [Indexed: 05/31/2024] Open
Abstract
Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named "Lung Cancer Prediction" (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.
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Affiliation(s)
- Xin Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Changchun Wu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wenwen Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Kaiyu Fu
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu 610041, China
| | - Yuke Tian
- Department of medical oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Xing Wei
- Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Wei Zhang
- Department of medical oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Ping Sun
- Department of Health Management Center & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, China
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T. RR, Demerdash ONA, Smith JC. TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets. Front Immunol 2024; 15:1426173. [PMID: 39221256 PMCID: PMC11361934 DOI: 10.3389/fimmu.2024.1426173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are 'unseen' during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope 'hard splitting' (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for 'strict splitting' in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction.
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Affiliation(s)
- Rajitha Rajeshwar T.
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Omar N. A. Demerdash
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Jeremy C. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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3
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Sharma S, Gerber AN, Kraft M, Wenzel SE. Asthma Pathogenesis: Phenotypes, Therapies, and Gaps: Summary of the Aspen Lung Conference 2023. Am J Respir Cell Mol Biol 2024; 71:154-168. [PMID: 38635858 PMCID: PMC11299090 DOI: 10.1165/rcmb.2024-0082ws] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/17/2024] [Indexed: 04/20/2024] Open
Abstract
Although substantial progress has been made in our understanding of asthma pathogenesis and phenotypes over the nearly 60-year history of the Aspen Lung Conferences on asthma, many ongoing challenges exist in our understanding of the clinical and molecular heterogeneity of the disease and an individual patient's response to therapy. This report summarizes the proceedings of the 2023 Aspen Lung Conference, which was organized to review the clinical and molecular heterogeneity of asthma and to better understand the impact of genetic, environmental, cellular, and molecular influences on disease susceptibility, heterogeneity, and severity. The goals of the conference were to review new information about asthma phenotypes, cellular processes, and cellular signatures underlying disease heterogeneity and treatment response. The report concludes with ongoing gaps in our understanding of asthma pathobiology and provides some recommendations for future research to better understand the clinical and basic mechanisms underlying disease heterogeneity in asthma and to advance the development of new treatments for this growing public health problem.
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Affiliation(s)
- Sunita Sharma
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Anthony N. Gerber
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
- Department of Medicine, National Jewish Health, Denver, Colorado
| | - Monica Kraft
- Department of Medicine, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, New York; and
| | - Sally E. Wenzel
- Department of Environmental and Occupational Health, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
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4
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Ehrlich R, Glynn E, Singh M, Ghersi D. Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity. Annu Rev Biomed Data Sci 2024; 7:295-316. [PMID: 38748864 DOI: 10.1146/annurev-biodatasci-102423-122741] [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: 08/25/2024]
Abstract
The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.
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Affiliation(s)
- Ryan Ehrlich
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
| | - Eric Glynn
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA;
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Dario Ghersi
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
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Burtis AEC, DeNicola DMC, Ferguson ME, Santos RG, Pinilla C, Kriss MS, Orlicky DJ, Tamburini BAJ, Gillen AE, Burchill MA. Ag-driven CD8+ T cell clonal expansion is a prominent feature of MASH in humans and mice. Hepatology 2024:01515467-990000000-00978. [PMID: 39047085 DOI: 10.1097/hep.0000000000000971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND AIMS Chronic liver disease due to metabolic dysfunction-associated steatohepatitis (MASH) is a rapidly increasing global epidemic. MASH progression is a consequence of the complex interplay between inflammatory insults and dysregulated hepatic immune responses. T lymphocytes have been shown to accumulate in the liver during MASH, but the cause and consequence of T cell accumulation in the liver remain unclear. Our study aimed to define the phenotype and T cell receptor diversity of T cells from human cirrhotic livers and an animal model of MASH to begin resolving their function in disease. APPROACH AND RESULTS In these studies, we evaluated differences in T cell phenotype in the context of liver disease. Accordingly, we isolated liver resident T cell populations from humans with cirrhosis and from mice with diet-induced MASH. Using both 5' single-cell sequencing and flow cytometry, we defined the phenotype and T cell receptor repertoire of liver resident T cells during health and disease. CONCLUSIONS MASH-induced human cirrhosis and diet-induced MASH in mice resulted in the accumulation of activated and clonally expanded T cells in the liver. The clonally expanded T cells in the liver expressed markers of chronic antigenic stimulation, including PD1, TIGIT, and TOX. Overall, this study establishes for the first time that T cells undergo Ag-dependent clonal expansion and functional differentiation during the progression of MASH. These studies could lead to the identification of antigenic targets that drive T cell activation, clonal expansion, and recruitment to the liver during MASH.
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Affiliation(s)
- Abbigayl E C Burtis
- Division of Gastroenterology and Hepatology, Department of Medicine, Aurora, Colorado, USA
- Molecular Biology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Destiny M C DeNicola
- Division of Gastroenterology and Hepatology, Department of Medicine, Aurora, Colorado, USA
- Molecular Biology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Megan E Ferguson
- Division of Gastroenterology and Hepatology, Department of Medicine, Aurora, Colorado, USA
| | - Radleigh G Santos
- Department of Mathematics, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Clemencia Pinilla
- Department of Medicinal Chemistry, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael S Kriss
- Division of Gastroenterology and Hepatology, Department of Medicine, Aurora, Colorado, USA
| | - David J Orlicky
- Department of Pathology, University of Colorado Anschutz Medical Campus. Aurora, Colorado, USA
| | - Beth A Jirón Tamburini
- Division of Gastroenterology and Hepatology, Department of Medicine, Aurora, Colorado, USA
- Molecular Biology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Austin E Gillen
- Division of Hematology, Department of Medicine, University of Colorado Anschutz Medical Campus. Aurora, Colorado, USA
| | - Matthew A Burchill
- Division of Gastroenterology and Hepatology, Department of Medicine, Aurora, Colorado, USA
- Molecular Biology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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6
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Moore E, Bharrhan S, Rao DA, Macian F, Putterman C. Characterisation of choroid plexus-infiltrating T cells reveals novel therapeutic targets in murine neuropsychiatric lupus. Ann Rheum Dis 2024; 83:1006-1017. [PMID: 38531610 DOI: 10.1136/ard-2023-224689] [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: 07/06/2023] [Accepted: 03/17/2024] [Indexed: 03/28/2024]
Abstract
OBJECTIVE Diffuse central nervous system manifestations, referred to as neuropsychiatric lupus (NPSLE), are observed in 20-40% of lupus patients and involve complex mechanisms that have not yet been adequately elucidated. In murine NPSLE models, choroid plexus (ChP)-infiltrating T cells have not been fully evaluated as drivers of neuropsychiatric disease. METHOD Droplet-based single-cell transcriptomic analysis (single-cell RNA sequencing) and immune T-cell receptor profiling were performed on ChP tissue from MRL/lpr mice, an NPSLE mouse model, at an 'early' and 'late' disease state, to investigate the infiltrating immune cells that accumulate with NPSLE disease progression. RESULTS We found 19 unique clusters of stromal and infiltrating cells present in the ChP of NPSLE mice. Higher resolution of the T-cell clusters uncovered multiple T-cell subsets, with increased exhaustion and hypoxia expression profiles. Clonal analysis revealed that the clonal CD8+T cell CDR3 sequence, ASGDALGGYEQY, matched that of a published T-cell receptor sequence with specificity for myelin basic protein. Stromal fibroblasts are likely drivers of T-cell recruitment by upregulating the VCAM signalling pathway. Systemic blockade of VLA-4, the cognate ligand of VCAM, resulted in significant resolution of the ChP immune cell infiltration and attenuation of the depressive phenotype. CONCLUSION Our analysis details the dynamic transcriptomic changes associated with murine NPSLE disease progression, and highlights its potential use in identifying prospective lupus brain therapeutic targets.
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Affiliation(s)
- Erica Moore
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Sushma Bharrhan
- Department of Microbiology and Immunology, Louisiana State University Shreveport, Shreveport, Louisiana, USA
| | - Deepak A Rao
- Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Fernando Macian
- Department of Pathology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Chaim Putterman
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA
- Azrieli Faculty of Medicine of Bar-Ilan University, Safed, Israel
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7
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Kaminski J, Fleming RA, Alvarez-Calderon F, Winschel MB, McGuckin C, Ho EE, Eng F, Rui X, Keskula P, Cagnin L, Charles J, Zavistaski J, Margossian SP, Kapadia MA, Rottman JB, Lane J, Baumeister SHC, Tkachev V, Shalek AK, Kean LS, Gerdemann U. B-cell-directed CAR T-cell therapy activates CD8+ cytotoxic CARneg bystander T cells in patients and nonhuman primates. Blood 2024; 144:46-60. [PMID: 38558106 DOI: 10.1182/blood.2023022717] [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: 10/19/2023] [Revised: 02/23/2024] [Accepted: 03/16/2024] [Indexed: 04/04/2024] Open
Abstract
ABSTRACT Chimeric antigen receptor (CAR) T cells hold promise as a therapy for B-cell-derived malignancies, and despite their impressive initial response rates, a significant proportion of patients ultimately experience relapse. Although recent studies have explored the mechanisms of in vivo CAR T-cell function, little is understood about the activation of surrounding CARneg bystander T cells and their potential to enhance tumor responses. We performed single-cell RNA sequencing on nonhuman primate (NHP) and patient-derived T cells to identify the phenotypic and transcriptomic hallmarks of bystander activation of CARneg T cells following B-cell-targeted CAR T-cell therapy. Using a highly translatable CD20 CAR NHP model, we observed a distinct population of activated CD8+ CARneg T cells emerging during CAR T-cell expansion. These bystander CD8+ CARneg T cells exhibited a unique transcriptional signature with upregulation of natural killer-cell markers (KIR3DL2, CD160, and KLRD1), chemokines, and chemokine receptors (CCL5, XCL1, and CCR9), and downregulation of naïve T-cell-associated genes (SELL and CD28). A transcriptionally similar population was identified in patients after a tisagenlecleucel infusion. Mechanistic studies revealed that interleukin-2 (IL-2) and IL-15 exposure induced bystander-like CD8+ T cells in a dose-dependent manner. In vitro activated and patient-derived T cells with a bystander phenotype efficiently killed leukemic cells through a T-cell receptor-independent mechanism. Collectively, to our knowledge, these data provide the first comprehensive identification and profiling of CARneg bystander CD8+ T cells following B-cell-targeting CAR T-cell therapy and suggest a novel mechanism through which CAR T-cell infusion might trigger enhanced antileukemic responses. Patient samples were obtained from the trial #NCT03369353, registered at www.ClinicalTrials.gov.
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Affiliation(s)
- James Kaminski
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Ryan A Fleming
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Francesca Alvarez-Calderon
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Marlana B Winschel
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Connor McGuckin
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | | | | | - Xianliang Rui
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Paula Keskula
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Lorenzo Cagnin
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Joanne Charles
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Jillian Zavistaski
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
| | - Steven P Margossian
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Malika A Kapadia
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | | | - Jennifer Lane
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA
| | - Susanne H C Baumeister
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Victor Tkachev
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA
| | - Alex K Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
- Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA
| | - Leslie S Kean
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Ulrike Gerdemann
- Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
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8
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Johanns TM, Garfinkle EA, Miller KE, Livingstone AJ, Roberts KF, Rao Venkata LP, Dowling JL, Chicoine MR, Dacey RG, Zipfel GJ, Kim AH, Mardis ER, Dunn GP. Integrating Multisector Molecular Characterization into Personalized Peptide Vaccine Design for Patients with Newly Diagnosed Glioblastoma. Clin Cancer Res 2024; 30:2729-2742. [PMID: 38639919 PMCID: PMC11215407 DOI: 10.1158/1078-0432.ccr-23-3077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/18/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Outcomes for patients with glioblastoma (GBM) remain poor despite multimodality treatment with surgery, radiation, and chemotherapy. There are few immunotherapy options due to the lack of tumor immunogenicity. Several clinical trials have reported promising results with cancer vaccines. To date, studies have used data from a single tumor site to identify targetable antigens, but this approach limits the antigen pool and is antithetical to the heterogeneity of GBM. We have implemented multisector sequencing to increase the pool of neoantigens across the GBM genomic landscape that can be incorporated into personalized peptide vaccines called NeoVax. PATIENTS AND METHODS In this study, we report the findings of four patients enrolled onto the NeoVax clinical trial (NCT0342209). RESULTS Immune reactivity to NeoVax neoantigens was assessed in peripheral blood mononuclear cells pre- and post-NeoVax for patients 1 to 3 using IFNγ-ELISPOT assay. A statistically significant increase in IFNγ producing T cells at the post-NeoVax time point for several neoantigens was observed. Furthermore, a post-NeoVax tumor biopsy was obtained from patient 3 and, upon evaluation, revealed evidence of infiltrating, clonally expanded T cells. CONCLUSIONS Collectively, our findings suggest that NeoVax stimulated the expansion of neoantigen-specific effector T cells and provide encouraging results to aid in the development of future neoantigen vaccine-based clinical trials in patients with GBM. Herein, we demonstrate the feasibility of incorporating multisector sampling in cancer vaccine design and provide information on the clinical applicability of clonality, distribution, and immunogenicity of the neoantigen landscape in patients with GBM.
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Affiliation(s)
- Tanner M. Johanns
- Division of Medical Oncology, Washington University School of Medicine, St. Louis, Missouri.
- Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St. Louis, Missouri.
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
| | - Elizabeth A.R. Garfinkle
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
| | - Katherine E. Miller
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
| | | | - Kaleigh F. Roberts
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Lakshmi P. Rao Venkata
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
| | - Joshua L. Dowling
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Michael R. Chicoine
- Department of Neurosurgery, University of Missouri in Columbia, Columbia, Missouri.
| | - Ralph G. Dacey
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Gregory J. Zipfel
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Albert H. Kim
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Elaine R. Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
- Department of Pediatrics, Ohio State University College of Medicine, Columbus, Ohio.
| | - Gavin P. Dunn
- Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.
- Brain Tumor Immunology and Immunotherapy Program, Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.
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9
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Cooper RS, Sutherland C, Smith LM, Cowan G, Barnett M, Mitchell D, McLean C, Imlach S, Hayes A, Zahra S, Manchanayake C, Vickers MA, Graham G, McGowan NWA, Turner ML, Campbell JDM, Fraser AR. EBV T-cell immunotherapy generated by peptide selection has enhanced effector functionality compared to LCL stimulation. Front Immunol 2024; 15:1412211. [PMID: 39011042 PMCID: PMC11246990 DOI: 10.3389/fimmu.2024.1412211] [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: 04/04/2024] [Accepted: 06/05/2024] [Indexed: 07/17/2024] Open
Abstract
Adoptive immunotherapy with Epstein-Barr virus (EBV)-specific T cells is an effective treatment for relapsed or refractory EBV-induced post-transplant lymphoproliferative disorders (PTLD) with overall survival rates of up to 69%. EBV-specific T cells have been conventionally made by repeated stimulation with EBV-transformed lymphoblastoid cell lines (LCL), which act as antigen-presenting cells. However, this process is expensive, takes many months, and has practical risks associated with live virus. We have developed a peptide-based, virus-free, serum-free closed system to manufacture a bank of virus-specific T cells (VST) for clinical use. We compared these with standard LCL-derived VST using comprehensive characterization and potency assays to determine differences that might influence clinical benefits. Multi-parameter flow cytometry revealed that peptide-derived VST had an expanded central memory population and less exhaustion marker expression than LCL-derived VST. A quantitative HLA-matched allogeneic cytotoxicity assay demonstrated similar specific killing of EBV-infected targets, though peptide-derived EBV T cells had a significantly higher expression of antiviral cytokines and degranulation markers after antigen recall. High-throughput T cell receptor-beta (TCRβ) sequencing demonstrated oligoclonal repertoires, with more matches to known EBV-binding complementary determining region 3 (CDR3) sequences in peptide-derived EBV T cells. Peptide-derived products showed broader and enhanced specificities to EBV nuclear antigens (EBNAs) in both CD8 and CD4 compartments, which may improve the targeting of highly expressed latency antigens in PTLD. Importantly, peptide-based isolation and expansion allows rapid manufacture and significantly increased product yield over conventional LCL-based approaches.
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Affiliation(s)
- Rachel S. Cooper
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
- Chemokine Research Group, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Catherine Sutherland
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Linda M. Smith
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Graeme Cowan
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark Barnett
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Donna Mitchell
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Colin McLean
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Stuart Imlach
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Alan Hayes
- Chemokine Research Group, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Sharon Zahra
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Champa Manchanayake
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Mark A. Vickers
- Blood Transfusion Centre, Scottish National Blood Transfusion Service, Aberdeen, United Kingdom
- Microbiology and Immunity, School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, Aberdeen, United Kingdom
| | - Gerry Graham
- Chemokine Research Group, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Neil W. A. McGowan
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - Marc L. Turner
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
| | - John D. M. Campbell
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
- Chemokine Research Group, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Alasdair R. Fraser
- Tissues, Cells and Advanced Therapeutics, Scottish National Blood Transfusion Service, Jack Copland Centre, Heriot Watt Research Park, Edinburgh, United Kingdom
- Chemokine Research Group, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
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10
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Roush SM, Coelho J, Xu AM, Puranam K, Mponda M, Kasonkanji E, Mulenga M, Tomoka T, Galeotti J, Brownlee A, Ghadially H, Chagomerana M, Damania B, Painschab M, Merchant A, Gopal S, Fedoriw Y. HIV infection and ART exposure affect tumor TCR repertoire of diffuse large B cell lymphoma. JCI Insight 2024; 9:e180771. [PMID: 38781015 DOI: 10.1172/jci.insight.180771] [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: 03/05/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024] Open
Abstract
The most common subtype of lymphoma globally, diffuse large B cell lymphoma (DLBCL), is a leading cause of cancer death in people with HIV. The restructuring of the T cell compartment because of HIV infection and antiretroviral therapy (ART) may have implications for modern treatment selection, but current understanding of these dynamic interactions is limited. Here, we investigated the T cell response to DLBCL by sequencing the T cell receptor (TCR) repertoire in a cohort of HIV-negative (HIV-), HIV+/ART-experienced, and HIV+/ART-naive patients with DLBCL. HIV+/ART-naive tumor TCR repertoires were more clonal and more distinct from each other than HIV- and HIV+/ART-experienced ones. Further, increased overlap between tumor and blood TCR repertoires was associated with improved survival and HIV/ART status. Our study describes TCR repertoire characteristics for the first time to our knowledge in an African DLBCL cohort and demonstrates contributions of HIV infection and ART exposure to the DLBCL TCR repertoire.
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Affiliation(s)
- Sophia M Roush
- Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jenny Coelho
- Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Alexander M Xu
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Kaushik Puranam
- University of North Carolina Project Malawi, Lilongwe, Malawi
| | - Marriam Mponda
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
| | - Edwards Kasonkanji
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
| | - Maurice Mulenga
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
| | - Tamiwe Tomoka
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
| | - Jonathan Galeotti
- Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina, USA
| | - Amy Brownlee
- Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Hormas Ghadially
- Department of Pathology, School of Medicine and Oral Health, Kamuzu University of Health Sciences, Lilongwe, Malawi
| | - Maganizo Chagomerana
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
| | - Blossom Damania
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina, USA
- Department of Microbiology and Immunology and
| | - Matthew Painschab
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina, USA
- Division of Hematology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Akil Merchant
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Division of Hematology and Oncology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Satish Gopal
- National Cancer Institute Center for Global Health, Rockville, Maryland, USA
| | - Yuri Fedoriw
- Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
- University of North Carolina Project Malawi, Lilongwe, Malawi
- University of Malawi College of Medicine, Lilongwe, Malawi
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina, USA
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11
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Liu Y, Zhou Y, Hu X, Le-Ge W, Wang H, Jiang T, Li J, Hu Y, Wang Y. DIRMC: a database of immunotherapy-related molecular characteristics. Database (Oxford) 2024; 2024:baae032. [PMID: 38713861 PMCID: PMC11184449 DOI: 10.1093/database/baae032] [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: 11/09/2023] [Revised: 03/02/2024] [Accepted: 03/29/2024] [Indexed: 05/09/2024]
Abstract
Cancer immunotherapy has brought about a revolutionary breakthrough in the field of cancer treatment. Immunotherapy has changed the treatment landscape for a variety of solid and hematologic malignancies. To assist researchers in efficiently uncovering valuable information related to cancer immunotherapy, we have presented a manually curated comprehensive database called DIRMC, which focuses on molecular features involved in cancer immunotherapy. All the content was collected manually from published literature, authoritative clinical trial data submitted by clinicians, some databases for drug target prediction such as DrugBank, and some experimentally confirmed high-throughput data sets for the characterization of immune-related molecular interactions in cancer, such as a curated database of T-cell receptor sequences with known antigen specificity (VDJdb), a pathology-associated TCR database (McPAS-TCR) et al. By constructing a fully connected functional network, ranging from cancer-related gene mutations to target genes to translated target proteins to protein regions or sites that may specifically affect protein function, we aim to comprehensively characterize molecular features related to cancer immunotherapy. We have developed the scoring criteria to assess the reliability of each MHC-peptide-T-cell receptor (TCR) interaction item to provide a reference for users. The database provides a user-friendly interface to browse and retrieve data by genes, target proteins, diseases and more. DIRMC also provides a download and submission page for researchers to access data of interest for further investigation or submit new interactions related to cancer immunotherapy targets. Furthermore, DIRMC provides a graphical interface to help users predict the binding affinity between their own peptide of interest and MHC or TCR. This database will provide researchers with a one-stop resource to understand cancer immunotherapy-related targets as well as data on MHC-peptide-TCR interactions. It aims to offer reliable molecular characteristics support for both the analysis of the current status of cancer immunotherapy and the development of new immunotherapy. DIRMC is available at http://www.dirmc.tech/. Database URL: http://www.dirmc.tech/.
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Affiliation(s)
- Yue Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Yuhuan Zhou
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Xiumei Hu
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Wuri Le-Ge
- Department of Pain, Tongliao City Hospital, Tongliao 028000, China
| | - Haoyan Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Tao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Hu
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Yadong Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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12
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Truex N, Mohapatra S, Melo M, Rodriguez J, Li N, Abraham W, Sementa D, Touti F, Keskin DB, Wu CJ, Irvine DJ, Gómez-Bombarelli R, Pentelute BL. Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons. ACS CENTRAL SCIENCE 2024; 10:793-802. [PMID: 38680558 PMCID: PMC11046456 DOI: 10.1021/acscentsci.3c01544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/13/2024] [Accepted: 02/16/2024] [Indexed: 05/01/2024]
Abstract
Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins. Epitopes with a low score show pronounced immunogenicity due to antigen processing, but epitopes with a high score show limited immunogenicity. This work sheds light on the sequence-activity relationships between proteasomal degradation and epitope immunogenicity. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by these vaccines in clinical settings.
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Affiliation(s)
- Nicholas
L. Truex
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
- Department
of Chemistry and Biochemistry, University
of South Carolina, Columbia, South Carolina 29208, United States
| | - Somesh Mohapatra
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Machine
Intelligence and Manufacturing Operations Group, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Mariane Melo
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Ragon Institute
of Massachusetts General Hospital, Massachusetts
Institute of Technology, and Harvard University, Cambridge, Massachusetts 02139, United States
| | - Jacob Rodriguez
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - Na Li
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Wuhbet Abraham
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Deborah Sementa
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - Faycal Touti
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - Derin B. Keskin
- Department
of Medical Oncology, Dana-Farber Cancer
Institute, Boston, Massachusetts 02215, United States
- Harvard
Medical School, Boston, Massachusetts 02115, United States
- Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Translational
Immunogenomics Laboratory (TIGL), Dana-Farber
Cancer Institute, Boston, Massachusetts 02215, United States
- Department
of Computer Science, Metropolitan College, Boston University, Boston, Massachusetts 02215, United States
- Section
for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby DK-2800, Denmark
| | - Catherine J. Wu
- Department
of Medical Oncology, Dana-Farber Cancer
Institute, Boston, Massachusetts 02215, United States
- Harvard
Medical School, Boston, Massachusetts 02115, United States
- Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115, United States
| | - Darrell J. Irvine
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Ragon Institute
of Massachusetts General Hospital, Massachusetts
Institute of Technology, and Harvard University, Cambridge, Massachusetts 02139, United States
- Department
of Biological Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, United States
| | - Rafael Gómez-Bombarelli
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bradley L. Pentelute
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
- The
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Broad
Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Center
for Environmental Health Sciences, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
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13
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Marrer-Berger E, Nicastri A, Augustin A, Kramar V, Liao H, Hanisch LJ, Carpy A, Weinzierl T, Durr E, Schaub N, Nudischer R, Ortiz-Franyuti D, Breous-Nystrom E, Stucki J, Hobi N, Raggi G, Cabon L, Lezan E, Umaña P, Woodhouse I, Bujotzek A, Klein C, Ternette N. The physiological interactome of TCR-like antibody therapeutics in human tissues. Nat Commun 2024; 15:3271. [PMID: 38627373 PMCID: PMC11021511 DOI: 10.1038/s41467-024-47062-5] [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: 07/05/2022] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
Selective binding of TCR-like antibodies that target a single tumour-specific peptide antigen presented by human leukocyte antigens (HLA) is the absolute prerequisite for their therapeutic suitability and patient safety. To date, selectivity assessment has been limited to peptide library screening and predictive modeling. We developed an experimental platform to de novo identify interactomes of TCR-like antibodies directly in human tissues using mass spectrometry. As proof of concept, we confirm the target epitope of a MAGE-A4-specific TCR-like antibody. We further determine cross-reactive peptide sequences for ESK1, a TCR-like antibody with known off-target activity, in human liver tissue. We confirm off-target-induced T cell activation and ESK1-mediated liver spheroid killing. Off-target sequences feature an amino acid motif that allows a structural groove-coordination mimicking that of the target peptide, therefore allowing the interaction with the engager molecule. We conclude that our strategy offers an accurate, scalable route for evaluating the non-clinical safety profile of TCR-like antibody therapeutics prior to first-in-human clinical application.
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Affiliation(s)
- Estelle Marrer-Berger
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Annalisa Nicastri
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK
| | - Angelique Augustin
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Vesna Kramar
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland
| | - Hanqing Liao
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK
| | | | - Alejandro Carpy
- Roche Pharma Research & Early Development, Roche Innovation Center Munich, 82377, Penzberg, Germany
| | - Tina Weinzierl
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland
| | - Evelyne Durr
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Nathalie Schaub
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Ramona Nudischer
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Daniela Ortiz-Franyuti
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Ekaterina Breous-Nystrom
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Janick Stucki
- Alveolix AG, Swiss Organs-on-Chip Innovation, 3010, Bern, Switzerland
| | - Nina Hobi
- Alveolix AG, Swiss Organs-on-Chip Innovation, 3010, Bern, Switzerland
| | - Giulia Raggi
- Alveolix AG, Swiss Organs-on-Chip Innovation, 3010, Bern, Switzerland
| | - Lauriane Cabon
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Emmanuelle Lezan
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Pablo Umaña
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland
| | - Isaac Woodhouse
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK
| | - Alexander Bujotzek
- Roche Pharma Research & Early Development, Roche Innovation Center Munich, 82377, Penzberg, Germany
| | - Christian Klein
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland.
| | - Nicola Ternette
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK.
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK.
- Department of Pharmaceutical Sciences, University of Utrecht, 3584, CH, Utrecht, The Netherlands.
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14
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Eskandari A, Leow TC, Rahman MBA, Oslan SN. Advances in Therapeutic Cancer Vaccines, Their Obstacles, and Prospects Toward Tumor Immunotherapy. Mol Biotechnol 2024:10.1007/s12033-024-01144-3. [PMID: 38625508 DOI: 10.1007/s12033-024-01144-3] [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: 01/26/2024] [Accepted: 03/15/2024] [Indexed: 04/17/2024]
Abstract
Over the past few decades, cancer immunotherapy has experienced a significant revolution due to the advancements in immune checkpoint inhibitors (ICIs) and adoptive cell therapies (ACTs), along with their regulatory approvals. In recent times, there has been hope in the effectiveness of cancer vaccines for therapy as they have been able to stimulate de novo T-cell reactions against tumor antigens. These tumor antigens include both tumor-associated antigen (TAA) and tumor-specific antigen (TSA). Nevertheless, the constant quest to fully achieve these abilities persists. Therefore, this review offers a broad perspective on the existing status of cancer immunizations. Cancer vaccine design has been revolutionized due to the advancements made in antigen selection, the development of antigen delivery systems, and a deeper understanding of the strategic intricacies involved in effective antigen presentation. In addition, this review addresses the present condition of clinical tests and deliberates on their approaches, with a particular emphasis on the immunogenicity specific to tumors and the evaluation of effectiveness against tumors. Nevertheless, the ongoing clinical endeavors to create cancer vaccines have failed to produce remarkable clinical results as a result of substantial obstacles, such as the suppression of the tumor immune microenvironment, the identification of suitable candidates, the assessment of immune responses, and the acceleration of vaccine production. Hence, there are possibilities for the industry to overcome challenges and enhance patient results in the coming years. This can be achieved by recognizing the intricate nature of clinical issues and continuously working toward surpassing existing limitations.
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Affiliation(s)
- Azadeh Eskandari
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
| | - Thean Chor Leow
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Enzyme Technology and X-ray Crystallography Laboratory, VacBio 5, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
| | | | - Siti Nurbaya Oslan
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Enzyme Technology and X-ray Crystallography Laboratory, VacBio 5, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
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15
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Ji H, Wang XX, Zhang Q, Zhang C, Zhang HM. Predicting TCR sequences for unseen antigen epitopes using structural and sequence features. Brief Bioinform 2024; 25:bbae210. [PMID: 38711371 PMCID: PMC11074592 DOI: 10.1093/bib/bbae210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/04/2024] [Accepted: 04/22/2024] [Indexed: 05/08/2024] Open
Abstract
T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-β regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-β sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-β sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.
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MESH Headings
- Receptors, Antigen, T-Cell/chemistry
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/metabolism
- Receptors, Antigen, T-Cell/genetics
- Humans
- Epitopes/chemistry
- Epitopes/immunology
- Computational Biology/methods
- Neural Networks, Computer
- Epitopes, T-Lymphocyte/immunology
- Epitopes, T-Lymphocyte/chemistry
- Antigens/chemistry
- Antigens/immunology
- Amino Acid Sequence
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Affiliation(s)
- Hongchen Ji
- Department of Oncology of Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
| | - Xiang-Xu Wang
- Department of Oncology of Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
| | - Qiong Zhang
- Department of Oncology of Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
| | - Chengkai Zhang
- Department of Oncology of Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
| | - Hong-Mei Zhang
- Department of Oncology of Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
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16
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Burtis AEC, DeNicola DMC, Ferguson ME, Santos RG, Pinilla C, Kriss MS, Orlicky DJ, Tamburini BAJ, Gillen AE, Burchill MA. Antigen-driven CD8 + T cell clonal expansion is a prominent feature of MASH in humans and mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.583964. [PMID: 38562766 PMCID: PMC10983976 DOI: 10.1101/2024.03.20.583964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background and Aims Chronic liver disease due to metabolic dysfunction-associated steatohepatitis (MASH) is a rapidly increasing global epidemic. MASH progression is a consequence of the complex interplay between inflammatory insults and dysregulated hepatic immune responses. T lymphocytes have been shown to accumulate in the liver during MASH, but the cause and consequence of T cell accumulation in the liver remain unclear. Our study aimed to define the phenotype and T cell receptor diversity of T cells from human cirrhotic livers and an animal model of MASH to begin resolving their function in disease. Approach and Results In these studies, we evaluated differences in T cell phenotype in the context of liver disease we isolated liver resident T cell populations from individuals with cirrhosis and a murine model of MASH. Using both 5' single cell sequencing and flow cytometry we defined the phenotype and T cell receptor repertoire of liver resident T cells during health and disease. Conclusions MASH-induced cirrhosis and diet-induced MASH in mice resulted in the accumulation of activated and clonally expanded T cells in the liver. The clonally expanded T cells in the liver expressed markers of chronic antigenic stimulation, including PD1 , TIGIT and TOX . Overall, this study establishes for the first time that T cells undergo antigen-dependent clonal expansion and functional differentiation during the progression of MASH. These studies could lead to the identification of potential antigenic targets that drive T cell activation, clonal expansion, and recruitment to the liver during MASH.
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17
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Pothuri VS, Hogg GD, Conant L, Borcherding N, James CA, Mudd J, Williams G, Seo YD, Hawkins WG, Pillarisetty VG, DeNardo DG, Fields RC. Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma. Oncoimmunology 2024; 13:2320411. [PMID: 38504847 PMCID: PMC10950267 DOI: 10.1080/2162402x.2024.2320411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.
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Affiliation(s)
- Vikram S. Pothuri
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Graham D. Hogg
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Leah Conant
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas Borcherding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - C. Alston James
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline Mudd
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Greg Williams
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Yongwoo David Seo
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Department of Surgical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - William G. Hawkins
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Venu G. Pillarisetty
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WAUSA
| | - David G. DeNardo
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Ryan C. Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
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18
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Porcheddu V, Lhomme G, Giraudet R, Correia E, Maillère B. The self-reactive FVIII T cell repertoire in healthy individuals relies on a short set of epitopes and public clonotypes. Front Immunol 2024; 15:1345195. [PMID: 38510258 PMCID: PMC10951066 DOI: 10.3389/fimmu.2024.1345195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/31/2024] [Indexed: 03/22/2024] Open
Abstract
Non-mutated FVIII-specific CD4 T cell epitopes have been recently found to contribute to the development of inhibitors in patients with hemophilia A (HA), while auto-reactive CD4 T cells specific to FVIII circulate in the blood of healthy individuals at a frequency close to the foreign protein ovalbumin. Thus, although FVIII is a self-protein, the central tolerance raised against FVIII appears to be low. In this study, we conducted a comprehensive analysis of the FVIII CD4 T cell repertoire in 29 healthy donors. Sequencing of the CDR3β TCR region from isolated FVIII-specific CD4 T cells revealed a limited usage and pairing of TRBV and TRBJ genes as well as a mostly hydrophobic composition of the CDR3β region according to their auto-reactivity. The FVIII repertoire is dominated by a few clonotypes, with only 13 clonotypes accounting for half of the FVIII response. Through a large-scale epitope mapping of the full-length FVIII sequence, we identified 18 immunodominant epitopes located in the A1, A3, C1, and C2 domains and covering half of the T cell response. These epitopes exhibited a broad specificity for HLA-DR or DP molecules or both. T cell priming with this reduced set of peptides revealed that highly expanded clonotypes specific to these epitopes were responsible individually for up to 32% of the total FVIII repertoire. These FVIII T cell epitopes and clonotypes were shared among HLA-unrelated donors tested and previously reported HA patients. Our study highlights the role of the auto-reactive T cell response against FVIII in HA and its similarity to the response observed in healthy individuals. Thus, it provides valuable insights for the development of new tolerance induction and deimmunization strategies.
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Affiliation(s)
- Valeria Porcheddu
- Université de Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Département Médicaments et Technologies pour la Santé, Service d’Ingénierie Moléculaire pour la Santé (SIMoS), Gif-sur-Yvette, France
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19
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Chen J, Zhao B, Lin S, Sun H, Mao X, Wang M, Chu Y, Hong L, Wei D, Li M, Xiong Y. TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning. Protein Sci 2024; 33:e4841. [PMID: 37983648 PMCID: PMC10731497 DOI: 10.1002/pro.4841] [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: 07/31/2023] [Revised: 10/11/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023]
Abstract
The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for developing immunotherapies, including neoantigen vaccine and drugs. Accurate prediction of TCR-epitope binding specificity via deep learning remains challenging, especially in test cases which are unseen in the training set. Here, we propose TEPCAM (TCR-EPitope identification based on Cross-Attention and Multi-channel convolution), a deep learning model that incorporates self-attention, cross-attention mechanism, and multi-channel convolution to improve the generalizability and enhance the model interpretability. Experimental results demonstrate that our model outperformed several state-of-the-art models on two challenging tasks including a strictly split dataset and an external dataset. Furthermore, the model can learn some interaction patterns between TCR and epitope by extracting the interpretable matrix from cross-attention layer and mapping them to the three-dimensional structures. The source code and data are freely available at https://github.com/Chenjw99/TEPCAM.
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Affiliation(s)
- Junwei Chen
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Bowen Zhao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Heqi Sun
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Meng Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Yanyi Chu
- Department of PathologyStanford University School of MedicineStandfordCaliforniaUSA
| | - Liang Hong
- Institute of Natural Sciences, Shanghai Jiao Tong UniversityShanghaiChina
- Artificial Intelligence Biomedical Center, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong UniversityShanghaiChina
| | - Dong‐Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
- Artificial Intelligence Biomedical Center, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong UniversityShanghaiChina
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20
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Fan T, Zhang M, Yang J, Zhu Z, Cao W, Dong C. Therapeutic cancer vaccines: advancements, challenges, and prospects. Signal Transduct Target Ther 2023; 8:450. [PMID: 38086815 PMCID: PMC10716479 DOI: 10.1038/s41392-023-01674-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 12/18/2023] Open
Abstract
With the development and regulatory approval of immune checkpoint inhibitors and adoptive cell therapies, cancer immunotherapy has undergone a profound transformation over the past decades. Recently, therapeutic cancer vaccines have shown promise by eliciting de novo T cell responses targeting tumor antigens, including tumor-associated antigens and tumor-specific antigens. The objective was to amplify and diversify the intrinsic repertoire of tumor-specific T cells. However, the complete realization of these capabilities remains an ongoing pursuit. Therefore, we provide an overview of the current landscape of cancer vaccines in this review. The range of antigen selection, antigen delivery systems development the strategic nuances underlying effective antigen presentation have pioneered cancer vaccine design. Furthermore, this review addresses the current status of clinical trials and discusses their strategies, focusing on tumor-specific immunogenicity and anti-tumor efficacy assessment. However, current clinical attempts toward developing cancer vaccines have not yielded breakthrough clinical outcomes due to significant challenges, including tumor immune microenvironment suppression, optimal candidate identification, immune response evaluation, and vaccine manufacturing acceleration. Therefore, the field is poised to overcome hurdles and improve patient outcomes in the future by acknowledging these clinical complexities and persistently striving to surmount inherent constraints.
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Affiliation(s)
- Ting Fan
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Mingna Zhang
- Postgraduate Training Base, Shanghai East Hospital, Jinzhou Medical University, Shanghai, 200120, China
| | - Jingxian Yang
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Zhounan Zhu
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Wanlu Cao
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
| | - Chunyan Dong
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
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21
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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.
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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
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22
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VanOudenhove J, Liu Y, Nelakanti R, Kim D, Busarello E, Ovalle NT, Qi Z, Mamillapalli P, Siddon A, Bai Z, Axtmayer A, Corso C, Kothari S, Foss F, Isufi I, Tebaldi T, Gowda L, Fan R, Seropian S, Halene S. Impact of Memory T Cells on SARS-COV-2 Vaccine Response in Hematopoietic Stem Cell Transplant. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564259. [PMID: 37961434 PMCID: PMC10634862 DOI: 10.1101/2023.10.26.564259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
During the COVID-19 pandemic, hematopoietic stem cell transplant (HSCT) recipients faced an elevated mortality rate from SARS-CoV-2 infection, ranging between 10-40%. The SARS-CoV-2 mRNA vaccines are important tools in preventing severe disease, yet their efficacy in the post-transplant setting remains unclear, especially in patients subjected to myeloablative chemotherapy and immunosuppression. We evaluated the humoral and adaptive immune responses to the SARS-CoV-2 mRNA vaccination series in 42 HSCT recipients and 5 healthy controls. Peripheral blood mononuclear nuclear cells and serum were prospectively collected before and after each dose of the SARS-CoV-2 vaccine. Post-vaccination responses were assessed by measuring anti-spike IgG and nucleocapsid titers, and antigen specific T cell activity, before and after vaccination. In order to examine mechanisms behind a lack of response, pre-and post-vaccine samples were selected based on humoral and cellular responses for single-cell RNA sequencing with TCR and BCR sequencing. Our observations revealed that while all participants eventually mounted a humoral response, transplant recipients had defects in memory T cell populations that were associated with an absence of T cell response, some of which could be detected pre-vaccination.
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23
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Su W, Saravia J, Risch I, Rankin S, Guy C, Chapman NM, Shi H, Sun Y, Kc A, Li W, Huang H, Lim SA, Hu H, Wang Y, Liu D, Jiao Y, Chen PC, Soliman H, Yan KK, Zhang J, Vogel P, Liu X, Serrano GE, Beach TG, Yu J, Peng J, Chi H. CXCR6 orchestrates brain CD8 + T cell residency and limits mouse Alzheimer's disease pathology. Nat Immunol 2023; 24:1735-1747. [PMID: 37679549 PMCID: PMC11102766 DOI: 10.1038/s41590-023-01604-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/26/2023] [Indexed: 09/09/2023]
Abstract
Neurodegenerative diseases, including Alzheimer's disease (AD), are characterized by innate immune-mediated inflammation, but functional and mechanistic effects of the adaptive immune system remain unclear. Here we identify brain-resident CD8+ T cells that coexpress CXCR6 and PD-1 and are in proximity to plaque-associated microglia in human and mouse AD brains. We also establish that CD8+ T cells restrict AD pathologies, including β-amyloid deposition and cognitive decline. Ligand-receptor interaction analysis identifies CXCL16-CXCR6 intercellular communication between microglia and CD8+ T cells. Further, Cxcr6 deficiency impairs accumulation, tissue residency programming and clonal expansion of brain PD-1+CD8+ T cells. Ablation of Cxcr6 or CD8+ T cells ultimately increases proinflammatory cytokine production from microglia, with CXCR6 orchestrating brain CD8+ T cell-microglia colocalization. Collectively, our study reveals protective roles for brain CD8+ T cells and CXCR6 in mouse AD pathogenesis and highlights that microenvironment-specific, intercellular communication orchestrates tissue homeostasis and protection from neuroinflammation.
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Affiliation(s)
- Wei Su
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jordy Saravia
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Isabel Risch
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Sherri Rankin
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Cliff Guy
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Nicole M Chapman
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Hao Shi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yu Sun
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Anil Kc
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Wei Li
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Hongling Huang
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Seon Ah Lim
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Haoran Hu
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yan Wang
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Danting Liu
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yun Jiao
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ping-Chung Chen
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Hadeer Soliman
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Koon-Kiu Yan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jonathan Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Peter Vogel
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Xueyan Liu
- Department of Mathematics, University of New Orleans, New Orleans, LA, USA
| | | | | | - Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Junmin Peng
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Hongbo Chi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA.
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24
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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.
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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.
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25
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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.
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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.
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26
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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.
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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
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Miao Y, Shi Z, Zhang W, Zhu L, Tang S, Chen H, Wang X, Du Q, Li S, Zhang Y, Luo W, Jin X, Fang M, Zhou H. Immune Repertoire Profiling Reveals Its Clinical Application Potential and Triggers for Neuromyelitis Optica Spectrum Disorders. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:e200134. [PMID: 37414573 DOI: 10.1212/nxi.0000000000200134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/27/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Neuromyelitis optica spectrum disorders (NMOSD) is widely recognized as a CNS demyelinating disease associated with AQP4-IgG (T cell-dependent antibody), and its trigger is still unclear. In addition, although the treatment of NMOSD currently can rely on traditional immunosuppressive and modulating agents, effective methods to predict the efficacy of these therapeutics are lacking. METHODS In this study, high-throughput T-cell receptor (TCR) sequencing was performed on peripheral blood from 151 pretreatment patients with AQP4-IgG+ NMOSD and 151 healthy individuals. We compared the TCR repertoire of those with NMOSD with that of healthy individuals and identified TCR clones that were significantly enriched in NMOSD. In addition, we treated 28 patients with AQP4-IgG+ NMOSD with immunosuppressants and followed up for 6 months to compare changes in NMOSD-specific TCRs (NMOSD-TCRs) before and after treatment. Moreover, we analyzed transcriptome and single-cell B-cell receptor (BCR) data from public databases and performed T-cell activation experiments using antigenic epitopes of cytomegalovirus (CMV) to further explore the triggers of AQP4-IgG+ NMOSD. RESULTS Compared with healthy controls, patients with AQP4-IgG+ NMOSD had significantly reduced diversity and shorter CDR3 length of TCRβ repertoire. Furthermore, we identified 597 NMOSD-TCRs with a high sequence similarity that have the potential to be used in the diagnosis and prognosis of NMOSD. The characterization of NMOSD-TCRs and pathology-associated clonotype annotation indicated that the occurrence of AQP4-IgG+ NMOSD may be associated with CMV infection, which was further corroborated by transcriptome and single-cell BCR analysis results from public databases and T-cell activation experiments. DISCUSSION Our findings suggest that the occurrence of AQP4-IgG+ NMOSD may be associated with CMV infection. In conclusion, our study provides new clues to uncover the causative factors of AQP4-IgG+ NMOSD and provides a theoretical foundation for treating and monitoring the disease.
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Affiliation(s)
- Yu Miao
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Ziyan Shi
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Wei Zhang
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Lin Zhu
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Shanshan Tang
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Hongxi Chen
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Xiaofei Wang
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Qin Du
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China.
| | - Shuaicheng Li
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China.
| | - Ying Zhang
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Wenqin Luo
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China
| | - Xin Jin
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China.
| | - Mingyan Fang
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China.
| | - Hongyu Zhou
- From the College of Life Sciences (M., X.J.), University of Chinese Academy of Sciences, Beijing; Department of Neurology (Z.S., L.Z., S.T., H.C., X.W., Q.D., Y.Z., W.L., M.F., H.Z.), West China Hospital, Sichuan University, Chengdu; and City University of Hong Kong (W.Z., S.L.), Shenzhen Research Institute, China.
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Liu ZQ, Dai H, Yao L, Chen WF, Wang Y, Ma LY, Li XQ, Lin SL, He MJ, Gao PT, Liu XY, Xu JX, Xu XY, Wang KH, Wang L, Chen L, Zhou PH, Li QL. A single-cell transcriptional landscape of immune cells shows disease-specific changes of T cell and macrophage populations in human achalasia. Nat Commun 2023; 14:4685. [PMID: 37542039 PMCID: PMC10403544 DOI: 10.1038/s41467-023-39750-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 06/28/2023] [Indexed: 08/06/2023] Open
Abstract
Achalasia is a rare motility disorder of the esophagus caused by the gradual degeneration of myenteric neurons. Immune-mediated ganglionitis has been proposed to underlie the loss of myenteric neurons. Here, we measure the immune cell transcriptional profile of paired lower esophageal sphincter (LES) tissue and blood samples in achalasia and controls using single-cell RNA sequencing (scRNA-seq). In achalasia, we identify a pattern of expanded immune cells and a specific transcriptional phenotype, especially in LES tissue. We show C1QC+ macrophages and tissue-resident memory T cells (TRM), especially ZNF683+ CD8+ TRM and XCL1+ CD4+ TRM, are significantly expanded and localized surrounding the myenteric plexus in the LES tissue of achalasia. C1QC+ macrophages are transcriptionally similar to microglia of the central nervous system and have a neurodegenerative dysfunctional phenotype in achalasia. TRM also expresses transcripts of dysregulated immune responses in achalasia. Moreover, inflammation increases with disease progression since immune cells are more activated in type I compared with type II achalasia. Thus, we profile the immune cell transcriptional landscape and identify C1QC+ macrophages and TRM as disease-associated immune cell subsets in achalasia.
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Affiliation(s)
- Zu-Qiang Liu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Hao Dai
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Lu Yao
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Wei-Feng Chen
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Yun Wang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Li-Yun Ma
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Xiao-Qing Li
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Sheng-Li Lin
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Meng-Jiang He
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Ping-Ting Gao
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Xin-Yang Liu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Jia-Xin Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Xiao-Yue Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Ke-Hao Wang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Li Wang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Ping-Hong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China.
| | - Quan-Lin Li
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China.
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29
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Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H. Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 2023; 23:511-521. [PMID: 36755161 PMCID: PMC9908307 DOI: 10.1038/s41577-023-00835-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 02/10/2023]
Abstract
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Graham Ogg
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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30
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 168] [Impact Index Per Article: 168.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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31
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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.
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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
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32
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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.
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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
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33
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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.
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Xu AM, Chour W, DeLucia DC, Su Y, Pavlovitch-Bedzyk AJ, Ng R, Rasheed Y, Davis MM, Lee JK, Heath JR. Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities. Cell Syst 2023; 14:273-284.e5. [PMID: 37001518 PMCID: PMC10355346 DOI: 10.1016/j.cels.2023.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 09/01/2022] [Accepted: 03/01/2023] [Indexed: 04/22/2023]
Abstract
Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.
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Affiliation(s)
- Alexander M Xu
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - William Chour
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 91125, USA
| | - Diana C DeLucia
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yapeng Su
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Rachel Ng
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yusuf Rasheed
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Mark M Davis
- Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John K Lee
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA 98109, USA.
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Tippalagama R, Chihab LY, Kearns K, Lewis S, Panda S, Willemsen L, Burel JG, Lindestam Arlehamn CS. Antigen-specificity measurements are the key to understanding T cell responses. Front Immunol 2023; 14:1127470. [PMID: 37122719 PMCID: PMC10140422 DOI: 10.3389/fimmu.2023.1127470] [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: 12/19/2022] [Accepted: 03/30/2023] [Indexed: 05/02/2023] Open
Abstract
Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.
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36
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Jiang Y, Li SC. Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires. Brief Bioinform 2023; 24:7031156. [PMID: 36752378 DOI: 10.1093/bib/bbad038] [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: 09/14/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/09/2023] Open
Abstract
T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.
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Affiliation(s)
- Yuepeng Jiang
- Department of Computer science, City University of Hong Kong, Kowloon Tong, Hong Kong
| | - Shuai Cheng Li
- Department of Computer science, City University of Hong Kong, Kowloon Tong, Hong Kong
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37
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Frank ML, Lu K, Erdogan C, Han Y, Hu J, Wang T, Heymach JV, Zhang J, Reuben A. T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy. Clin Cancer Res 2023; 29:994-1008. [PMID: 36413126 PMCID: PMC10011887 DOI: 10.1158/1078-0432.ccr-22-2469] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.
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Affiliation(s)
- Meredith L Frank
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Kaylene Lu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Can Erdogan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Rice University, Houston, Texas
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jian Hu
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas.,Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
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Sorosina M, Santoro S, Ferrè L, Mascia E, Clarelli F, Giordano A, Cannizzaro M, Lucia M, Martinelli V, Filippi M, Esposito F. Risk HLA Variants Affect the T-Cell Repertoire in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:10/3/e200093. [PMID: 36792371 PMCID: PMC9931183 DOI: 10.1212/nxi.0000000000200093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/14/2022] [Indexed: 02/17/2023]
Abstract
BACKGROUND AND OBJECTIVES The major histocompatibility complex (MHC) locus has a predominant role in the genetic predisposition to multiple sclerosis (MS), with 32 associations found to be involved. We aimed to investigate the impact of MHC MS-risk alleles on T-cell repertoire in patients with MS. METHODS We studied 161 untreated patients with relapsing-remitting MS for whom Class I and II human leukocyte antigen (HLA) alleles were inferred from whole-genome genotyping data, and T-cell receptor (TCR) CDR3 sequences were obtained through next-generation sequencing. T-cell repertoire features including diversity, public clones, and architecture were evaluated. RESULTS We identified 5 MS-risk loci associated with TCR diversity: HLA-DRB1*15:01 (7.65 × 10-3), rs9271366 (1.96 × 10-3), rs766848979 A (1.89 × 10-2), rs9277626 (2.95 × 10-2), and rs11751659 (1.92 × 10-2), with evidence of expanded clonotypes in carriers of risk alleles. Moreover, HLA-DRB1*15:01 (4.99 × 10-3), rs9271366 (6.54 × 10-3), rs1049079 C (4.37 × 10-2), AA DQΒ1 position -5 L (1.05 × 10-3), and AA DQΒ1 position 221 Q (9.39 × 10-4) showed an association with the CDR3 aminoacidic sequence architecture, suggesting an impact on the antigen recognition breadth as well. Evaluating the sharing of clones across MS-risk allele carrier individuals revealed the presence of highly shared clonotypes predicted to target viral antigens, including Epstein-Barr virus. DISCUSSION Our study supports the association between MHC-risk alleles and macrofeatures of the T-cell repertoire in the context of MS. Further studies are needed to understand the underlying molecular mechanisms.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Federica Esposito
- From the Laboratory of Neurological Complex Disorders (M.S., S.S., L.F., E.M., F.C., A.G., M.C., F.E.), Division of Neuroscience, Institute of Experimental Neurology (INSPE), IRCCS San Raffaele Scientific Institute; Neurology and Neurorehabilitation Unit (L.F., A.G., M.C., M.L., V.M., M.F., F.E.), IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University (M.F.); Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit, Division of Neuroscience, Institute of Experimental Neurology (INSPE) (P.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Kim H, Park S, Han KY, Lee N, Kim H, Jung HA, Sun JM, Ahn JS, Ahn MJ, Lee SH, Park WY. Clonal expansion of resident memory T cells in peripheral blood of patients with non-small cell lung cancer during immune checkpoint inhibitor treatment. J Immunother Cancer 2023; 11:jitc-2022-005509. [PMID: 36787939 PMCID: PMC9930609 DOI: 10.1136/jitc-2022-005509] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are an essential treatment for non-small cell lung cancer (NSCLC). Currently, the tumor-related intrinsic factors in response to ICIs have mostly been elucidated in tissue samples. However, tissue immune status and changes in the immune microenvironment can also be reflected and monitored through peripheral blood. METHODS Single-cell RNA and T cell receptor (scTCR) sequencing were conducted using peripheral blood mononuclear cells (PBMCs) from 60 patients with stage IV NSCLC. Those samples were prospectively acquired from patients treated with anti-PD(L)-1 therapy for advanced lung cancer. Based on the clinical outcomes, samples were classified as durable clinical benefit (DCB) and non-durable clinical benefit (NCB). The samples constituted paired longitudinal samples, consisting of pre-treatment and on-treatment. Additionally, PBMC samples from 60 healthy donors from the Asian Immune Diversity Atlas project were used as a control. RESULTS The dynamic changes in major cell types between pre-treatment and on-treatment PBMCs were associated with an increase in proliferating T cells and NK cells in both DCB and NCB groups. Among T cell subtypes, effector memory CD8+ T cells (CD8+ TEM_GZMK_PDCD1) were increased after ICI treatment in both DCB and NCB. From the lineage trajectory analysis, effector memory CD8+ T cells resided at the bifurcation point, which has the potential to differentiate into lineages with precursor exhausted CD8+ T cells (CD8+ TCM cells) assumed to be related to the ICI response. From the scTCR-seq, effector memory CD8+ T cells along with T cells recognizing unknown antigen expanded and composed of novel clones skewed toward dysfunctional status, especially in on-treatment samples of the DCB group. The extent of immunophenotype conversion capabilities of the TCR with effector memory CD8+ T cells showed remarkable variation in the on-treatment sample in the DCB group. CONCLUSION A transitioning T cell subtype identified in PBMCs might be related to the prolonged ICI response. From our study, expansion of effector memory CD8+ T cells with novel TCRs in PBMCs after ICI treatment could contribute to a better clinical outcome in patients with NSCLC. This proof-of-concept research strengthens the use of non-invasive PBMCs in studying systemic changes of immune reactions related to the ICI treatment.
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Affiliation(s)
- Hyunsu Kim
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, The Republic of Korea,Samsung Genome Institute, Samsung Medical Center, Seoul, The Republic of Korea
| | - Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Kyoung-Yeon Han
- Samsung Genome Institute, Samsung Medical Center, Seoul, The Republic of Korea
| | - Naeun Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, The Republic of Korea
| | - Hyemin Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Hyun Ae Jung
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Jong-Mu Sun
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Jin Seok Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Se-Hoon Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, The Republic of Korea .,Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
| | - Woong-Yang Park
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, The Republic of Korea .,Samsung Genome Institute, Samsung Medical Center, Seoul, The Republic of Korea.,Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Seoul, The Republic of Korea
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40
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Jian X, Zhang Y, Zhao J, Zhao Z, Lu M, Xie L. CoV2-TCR: A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes. Comput Struct Biotechnol J 2023; 21:1362-1371. [PMID: 36741787 PMCID: PMC9882952 DOI: 10.1016/j.csbj.2023.01.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/08/2023] [Accepted: 01/26/2023] [Indexed: 01/30/2023] Open
Abstract
Although multiple vaccines have been developed and widely administered, several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported to evade immune responses and spread diffusely. Here, 108 RNA-seq files from coronavirus disease 2019 (COVID-19) patients and healthy donors (HD) were downloaded to extract their TCR immune repertoire by MiXCR. Those extracted TCR repertoire were compared and it was found that disease progression was related negatively with diversity and positively with clonality. Specifically, greater proportions of high-abundance clonotypes were observed in active and severe COVID-19 samples, probably resulting from strong stimulation of SARS-CoV-2 epitopes and a continued immune response in host. To investigate the specific recognition between TCR CDR3 and SARS-CoV-2 epitopes, we constructed an accurate classifier CoV2-TCR with an AUC of 0.967 in an independent dataset, which outperformed several similar tools. Based on this model, we observed a huge range in the number of those TCR CDR3 recognizing those different peptides, including 28 MHC-I epitopes from SARS-CoV-2 and 22 immunogenic peptides from SARS-CoV-2 variants. Interestingly, their proportions of high-abundance, low-abundance and rare clonotypes were close for each peptide. To expand the potential application of this model, we established the webserver, CoV2-TCR, in which users can obtain those recognizing CDR3 sequences from the TCR repertoire of COVID-19 patients based on the 9-mer peptides containing mutation site(s) on the four main proteins of SARS-CoV-2 variants. Overall, this study provides preliminary screening for candidate antigen epitopes and the TCR CDR3 that recognizes them, and should be helpful for vaccine design on SARS-CoV-2 variants.
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Affiliation(s)
- Xingxing Jian
- Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China,Corresponding author.
| | - Yu Zhang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Zhuoming Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Manman Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Lu Xie
- Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China,Shanghai-MOST Key Laboratory of Health and Disease Genomics (Chinese National Human Genome Center at Shanghai), Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China,Corresponding author at: Bioinformatics Center & National Clinical Research Centre for Geriatric Disorders & Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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Dhanda SK, Mahajan S, Manoharan M. Neoepitopes prediction strategies: an integration of cancer genomics and immunoinformatics approaches. Brief Funct Genomics 2023; 22:1-8. [PMID: 36398967 DOI: 10.1093/bfgp/elac041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/28/2022] [Accepted: 10/14/2022] [Indexed: 11/19/2022] Open
Abstract
A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Swapnil Mahajan
- DeepKnomics Labs Private Limited, 7014 Prestige Garden Bay, IVRI Road, Avalahalli, Behind CRPF Campus, Yelahanka, Bangalore 560064, India
| | - Malini Manoharan
- DeepKnomics Labs Private Limited, 7014 Prestige Garden Bay, IVRI Road, Avalahalli, Behind CRPF Campus, Yelahanka, Bangalore 560064, India
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42
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Wong P, Cina DP, Sherwood KR, Fenninger F, Sapir-Pichhadze R, Polychronakos C, Lan J, Keown PA. Clinical application of immune repertoire sequencing in solid organ transplant. Front Immunol 2023; 14:1100479. [PMID: 36865546 PMCID: PMC9971933 DOI: 10.3389/fimmu.2023.1100479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/25/2023] [Indexed: 02/16/2023] Open
Abstract
Background Measurement of T cell receptor (TCR) or B cell receptor (BCR) gene utilization may be valuable in monitoring the dynamic changes in donor-reactive clonal populations following transplantation and enabling adjustment in therapy to avoid the consequences of excess immune suppression or to prevent rejection with contingent graft damage and to indicate the development of tolerance. Objective We performed a review of current literature to examine research in immune repertoire sequencing in organ transplantation and to assess the feasibility of this technology for clinical application in immune monitoring. Methods We searched MEDLINE and PubMed Central for English-language studies published between 2010 and 2021 that examined T cell/B cell repertoire dynamics upon immune activation. Manual filtering of the search results was performed based on relevancy and predefined inclusion criteria. Data were extracted based on study and methodology characteristics. Results Our initial search yielded 1933 articles of which 37 met the inclusion criteria; 16 of these were kidney transplant studies (43%) and 21 were other or general transplantation studies (57%). The predominant method for repertoire characterization was sequencing the CDR3 region of the TCR β chain. Repertoires of transplant recipients were found to have decreased diversity in both rejectors and non-rejectors when compared to healthy controls. Rejectors and those with opportunistic infections were more likely to have clonal expansion in T or B cell populations. Mixed lymphocyte culture followed by TCR sequencing was used in 6 studies to define an alloreactive repertoire and in specialized transplant settings to track tolerance. Conclusion Methodological approaches to immune repertoire sequencing are becoming established and offer considerable potential as a novel clinical tool for pre- and post-transplant immune monitoring.
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Affiliation(s)
- Paaksum Wong
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Davide P Cina
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Karen R Sherwood
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Franz Fenninger
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ruth Sapir-Pichhadze
- Department of Medicine, Division of Nephrology, McGill University, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Constantin Polychronakos
- Department of Pediatrics, The Research Institute of the McGill University Health Centre and the Montreal Children's Hospital, Montreal, QC, Canada
| | - James Lan
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Paul A Keown
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
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43
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Grazioli F, Machart P, Mösch A, Li K, Castorina LV, Pfeifer N, Min MR. Attentive Variational Information Bottleneck for TCR-peptide interaction prediction. Bioinformatics 2022; 39:6960920. [PMID: 36571499 PMCID: PMC9825246 DOI: 10.1093/bioinformatics/btac820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/18/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides. RESULTS Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences. AVAILABILITY AND IMPLEMENTATION The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany
| | - Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany
| | - Kai Li
- Machine Learning Department, NEC Laboratories America, Princeton, NJ 08540, USA
| | | | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
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Montemurro A, Jessen LE, Nielsen M. NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Front Immunol 2022; 13:1055151. [PMID: 36561755 PMCID: PMC9763291 DOI: 10.3389/fimmu.2022.1055151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.
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Affiliation(s)
- Alessandro Montemurro
- 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,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina,*Correspondence: Morten Nielsen,
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Armistead B, Jiang Y, Carlson M, Ford ES, Jani S, Houck J, Wu X, Jing L, Pecor T, Kachikis A, Yeung W, Nguyen T, Minkah N, Larsen SE, Coler RN, Koelle DM, Harrington WE. Spike-specific T cells are enriched in breastmilk following SARS-CoV-2 mRNA vaccination. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2021.12.03.21267036. [PMID: 36203549 PMCID: PMC9536058 DOI: 10.1101/2021.12.03.21267036] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Human breastmilk is rich in T cells; however, their specificity and function are largely unknown. We compared the phenotype, diversity, and antigen specificity of T cells in the breastmilk and peripheral blood of lactating individuals who received SARS-CoV-2 mRNA vaccination. Relative to blood, breastmilk contained higher frequencies of T effector and central memory populations that expressed mucosal-homing markers. T cell receptor (TCR) sequence overlap was limited between blood and breastmilk. Overabundan t breastmilk clones were observed in all individuals, were diverse, and contained CDR3 sequences with known epitope specificity including to SARS-CoV-2 Spike. Spike-specific TCRs were more frequent in breastmilk compared to blood and expanded in breastmilk following a third mRNA vaccine dose. Our observations indicate that the lactating breast contains a distinct T cell population that can be modulated by maternal vaccination with potential implications for infant passive protection. One-Sentence Summary The breastmilk T cell repertoire is distinct and enriched for SARS-CoV-2 Spike-specificity after maternal mRNA vaccination.
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Affiliation(s)
- Blair Armistead
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Yonghou Jiang
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Marc Carlson
- Research Scientific Computing, Enterprise Analytics, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Emily S Ford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; Seattle, WA, USA
- Department of Medicine, University of Washington; Seattle, WA, USA
| | - Saumya Jani
- Department of Medicine, University of Washington; Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington; Seattle, WA, USA
| | - John Houck
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Xia Wu
- Department of Medicine, University of Washington; Seattle, WA, USA
| | - Lichen Jing
- Department of Medicine, University of Washington; Seattle, WA, USA
| | - Tiffany Pecor
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Alisa Kachikis
- Department of Obstetrics & Gynecology, University of Washington; Seattle, WA, USA
| | - Winnie Yeung
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Tina Nguyen
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Nana Minkah
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
- Department of Pediatrics, University of Washington; Seattle, WA, USA
| | - Sasha E Larsen
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
| | - Rhea N Coler
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
- Department of Global Health, University of Washington; Seattle, WA, USA
- Department of Pediatrics, University of Washington; Seattle, WA, USA
| | - David M Koelle
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; Seattle, WA, USA
- Department of Medicine, University of Washington; Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington; Seattle, WA, USA
- Department of Global Health, University of Washington; Seattle, WA, USA
- Benaroya Research Institute; Seattle, WA, USA
| | - Whitney E Harrington
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute; Seattle, WA, USA
- Department of Global Health, University of Washington; Seattle, WA, USA
- Department of Pediatrics, University of Washington; Seattle, WA, USA
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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.
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Neoantigens in precision cancer immunotherapy: from identification to clinical applications. Chin Med J (Engl) 2022; 135:1285-1298. [PMID: 35838545 PMCID: PMC9433083 DOI: 10.1097/cm9.0000000000002181] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Immunotherapies targeting cancer neoantigens are safe, effective, and precise. Neoantigens can be identified mainly by genomic techniques such as next-generation sequencing and high-throughput single-cell sequencing; proteomic techniques such as mass spectrometry; and bioinformatics tools based on high-throughput sequencing data, mass spectrometry data, and biological databases. Neoantigen-related therapies are widely used in clinical practice and include neoantigen vaccines, neoantigen-specific CD8+ and CD4+ T cells, and neoantigen-pulsed dendritic cells. In addition, neoantigens can be used as biomarkers to assess immunotherapy response, resistance, and prognosis. Therapies based on neoantigens are an important and promising branch of cancer immunotherapy. Unremitting efforts are needed to unravel the comprehensive role of neoantigens in anti-tumor immunity and to extend their clinical application. This review aimed to summarize the progress in neoantigen research and to discuss its opportunities and challenges in precision cancer immunotherapy.
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Deshpande P, Li Y, Thorne M, Palubinsky AM, Phillips EJ, Gibson A. Practical Implementation of Genetics: New Concepts in Immunogenomics to Predict, Prevent, and Diagnose Drug Hypersensitivity. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2022; 10:1689-1700. [PMID: 35526777 PMCID: PMC9948495 DOI: 10.1016/j.jaip.2022.04.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 02/05/2023]
Abstract
Delayed drug hypersensitivities are CD8+ T cell-mediated reactions associated with up to 50% mortality. Human leukocyte antigen (HLA) alleles are known to predispose disease and are specific to drug, reaction, and patient ethnicity. Pretreatment screening is recommended for a handful of the strongest associations to identify and prevent drug use in high-risk patients. However, an incomplete predictive value implicates other HLA-imposed risk factors, and low carriage of many identified HLA-risk alleles combined with the high cost of sequence-based typing has limited economic viability for similar recommendation of screening across drugs and health care systems. For mitigation, an expanding armory of low-cost polymerase chain reaction-based screens is being developed, and HLA-imposed risk factors are being discovered. These include (1) polymorphic variants of metabolic and endoplasmic reticulum aminopeptidase enzymes toward multiallelic screening with increased predictivity; (2) regulation by immune checkpoint inhibitors, enabling detolerized animal models of human disease; and (3) immunodominant T cell receptors (TCR) on clonally expanded CD8+ T cells. For the latter, HLA risk-restricted TCR provides immunogenomic strategies and samples from a single patient to identify novel HLA-risk associations in underserved minority populations, tissue-relevant effector biomarkers toward earlier diagnosis and treatment, and HLA-TCR-presented immunogenic structures to aid future drug development.
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Affiliation(s)
- Pooja Deshpande
- Institute for Immunology and Infectious Disease (IIID), Murdoch University, Perth, WA, Australia
| | - Yueran Li
- Institute for Immunology and Infectious Disease (IIID), Murdoch University, Perth, WA, Australia
| | - Michael Thorne
- Institute for Immunology and Infectious Disease (IIID), Murdoch University, Perth, WA, Australia
| | | | - Elizabeth J Phillips
- Institute for Immunology and Infectious Disease (IIID), Murdoch University, Perth, WA, Australia,Vanderbilt University Medical Centre (VUMC), Nashville, TN, USA
| | - Andrew Gibson
- Institute for Immunology and Infectious Disease, Murdoch University, Perth, Western Australia, Australia.
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Liu M, Goo J, Liu Y, Sun W, Wu MC, Hsu L, He Q. TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes. BMC Bioinformatics 2022; 23:152. [PMID: 35484495 PMCID: PMC9052542 DOI: 10.1186/s12859-022-04690-2] [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: 11/12/2021] [Accepted: 04/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed. Methods We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined. Results Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients. Conclusion The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.
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Affiliation(s)
- Meiling Liu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Juna Goo
- Department of Mathematics, Boise State University, Boise, USA
| | - Yang Liu
- Department of Mathematics and Statistics, Wright State University, Dayton, USA
| | - Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Michael C Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
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Mining the Proteome of Toxoplasma Parasites Seeking Vaccine and Diagnostic Candidates. Animals (Basel) 2022; 12:ani12091098. [PMID: 35565525 PMCID: PMC9099775 DOI: 10.3390/ani12091098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/16/2022] [Accepted: 04/20/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary The One Health concept to toxoplasmosis highlights that the health of humans is closely related to the health of animals and our common environment. Toxoplasmosis outcomes might be severe and fatal in patients with immunodeficiency, diabetes, and pregnant women and infants. Consequently, the development of effective vaccine and diagnostic strategies is urgent for the elimination of this disease. Proteomics analysis has allowed the identification of key proteins that can be utilized in the development of novel disease diagnostics and vaccines. This work presents relevant proteins found in the proteome of the life cycle-specific stages of Toxoplasma parasites. In fact, it brings together the main functionality key proteins from Toxoplasma parasites coming from proteomic approaches that are most likely to be useful in improving the disease management, and critically proposes innovative directions to finally develop promising vaccines and diagnostics tools. Abstract Toxoplasma gondii is a pathogenic protozoan parasite that infects the nucleated cells of warm-blooded hosts leading to an infectious zoonotic disease known as toxoplasmosis. The infection outcomes might be severe and fatal in patients with immunodeficiency, diabetes, and pregnant women and infants. The One Health approach to toxoplasmosis highlights that the health of humans is closely related to the health of animals and our common environment. The presence of drug resistance and side effects, the further improvement of sensitivity and specificity of serodiagnostic tools and the potentiality of vaccine candidates to induce the host immune response are considered as justifiable reasons for the identification of novel targets for the better management of toxoplasmosis. Thus, the identification of new critical proteins in the proteome of Toxoplasma parasites can also be helpful in designing and test more effective drugs, vaccines, and diagnostic tools. Accordingly, in this study we present important proteins found in the proteome of the life cycle-specific stages of Toxoplasma parasites that are potential diagnostic or vaccine candidates. The current study might help to understand the complexity of these parasites and provide a possible source of strategies and biomolecules that can be further evaluated in the pathobiology of Toxoplasma parasites and for diagnostics and vaccine trials against this disease.
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