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Kheirkhah AH, Habibi S, Yousefi MH, Mehri S, Ma B, Saleh M, Kavianpour M. Finding potential targets in cell-based immunotherapy for handling the challenges of acute myeloid leukemia. Front Immunol 2024; 15:1460437. [PMID: 39411712 PMCID: PMC11474923 DOI: 10.3389/fimmu.2024.1460437] [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: 07/06/2024] [Accepted: 08/29/2024] [Indexed: 10/19/2024] Open
Abstract
Acute myeloid leukemia (AML) is a hostile hematological malignancy under great danger of relapse and poor long-term survival rates, despite recent therapeutic advancements. To deal with this unfulfilled clinical necessity, innovative cell-based immunotherapies have surfaced as promising approaches to improve anti-tumor immunity and enhance patient outcomes. In this comprehensive review, we provide a detailed examination of the latest developments in cell-based immunotherapies for AML, including chimeric antigen receptor (CAR) T-cell therapy, T-cell receptor (TCR)-engineered T-cell therapy, and natural killer (NK) cell-based therapies. We critically evaluate the unique mechanisms of action, current challenges, and evolving strategies to improve the efficacy and safety of these modalities. The review emphasizes how promising these cutting-edge immune-based strategies are in overcoming the inherent complexities and heterogeneity of AML. We discuss the identification of optimal target antigens, the importance of mitigating on-target/off-tumor toxicity, and the need to enhance the persistence and functionality of engineered immune effector cells. All things considered, this review offers a thorough overview of the rapidly evolving field of cell-based immunotherapy for AML, underscoring the significant progress made and the ongoing efforts to translate these innovative approaches into more effective and durable treatments for this devastating disease.
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MESH Headings
- Humans
- Leukemia, Myeloid, Acute/therapy
- Leukemia, Myeloid, Acute/immunology
- Immunotherapy, Adoptive/methods
- Immunotherapy, Adoptive/adverse effects
- Receptors, Chimeric Antigen/immunology
- Receptors, Chimeric Antigen/genetics
- Animals
- Killer Cells, Natural/immunology
- Immunotherapy/methods
- Antigens, Neoplasm/immunology
- T-Lymphocytes/immunology
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Affiliation(s)
- Amir Hossein Kheirkhah
- Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Sina Habibi
- Department of Hematology and Blood Banking, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hasan Yousefi
- Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Sara Mehri
- Department of Biotechnology, School of Paramedical Sciences, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Bin Ma
- School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
- Clinical Stem Cell Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mahshid Saleh
- Wisconsin National Primate Research Center, University of Wisconsin Graduate School, Madison, WI, United States
| | - Maria Kavianpour
- Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Qom University of Medical Sciences, Qom, Iran
- Cellular and Molecular Research Center, Qom University of Medical Sciences, Qom, Iran
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2
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Gielis S, Flumens D, van der Heijden S, Versteven M, De Reu H, Bartholomeus E, Schippers J, Campillo-Davo D, Berneman ZN, Anguille S, Smits E, Ogunjimi B, Lion E, Laukens K, Meysman P. Analysis of Wilms' tumor protein 1 specific TCR repertoire in AML patients uncovers higher diversity in patients in remission than in relapsed. Ann Hematol 2024:10.1007/s00277-024-05919-1. [PMID: 39259326 DOI: 10.1007/s00277-024-05919-1] [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: 03/14/2024] [Accepted: 07/26/2024] [Indexed: 09/13/2024]
Abstract
The Wilms' tumor protein 1 (WT1) is a well-known and prioritized tumor-associated antigen expressed in numerous solid and blood tumors. Its abundance and immunogenicity have led to the development of different WT1-specific immune therapies. The driving player in these therapies, the WT1-specific T-cell receptor (TCR) repertoire, has received much less attention. Importantly, T cells with high affinity against the WT1 self-antigen are normally eliminated after negative selection in the thymus and are thus rare in peripheral blood. Here, we developed computational models for the robust and fast identification of WT1-specific TCRs from TCR repertoire data. To this end, WT137-45 (WT1-37) and WT1126-134 (WT1-126)-specific T cells were isolated from WT1 peptide-stimulated blood of healthy individuals. The TCR repertoire from these WT1-specific T cells was sequenced and used to train a pattern recognition model for the identification of WT1-specific TCR patterns for the WT1-37 or WT1-126 epitopes. The resulting computational models were applied on an independent published dataset from acute myeloid leukemia (AML) patients, treated with hematopoietic stem cell transplantation, to track WT1-specific TCRs in silico. Several WT1-specific TCRs were found in AML patients. Subsequent clustering analysis of all repertoires indicated the presence of more diverse TCR patterns within the WT1-specific TCR repertoires of AML patients in complete remission in contrast to relapsing patients. We demonstrate the possibility of tracking WT1-37 and WT1-126-specific TCRs directly from TCR repertoire data using computational methods, eliminating the need for additional blood samples and experiments for the two studied WT1 epitopes.
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Affiliation(s)
- 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
| | - Donovan Flumens
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
| | - Sanne van der Heijden
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium
| | - Maarten Versteven
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
| | - Hans De Reu
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, 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 and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
- Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
| | - Jolien Schippers
- Genetics, Pharmacology and Physiopathology of Heart, Blood Vessels and Skeleton (GENCOR) department, University of Antwerp, Edegem, Belgium
| | - Diana Campillo-Davo
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
| | - Zwi N Berneman
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
- Center for Cell Therapy & Regenerative Medicine (CCRG), Antwerp University Hospital, Edegem, Belgium
| | - Sébastien Anguille
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
- Center for Cell Therapy & Regenerative Medicine (CCRG), Antwerp University Hospital, Edegem, Belgium
- Division of Hematology, Antwerp University Hospital, Edegem, Belgium
| | - Evelien Smits
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium
- Center for Cell Therapy & Regenerative Medicine (CCRG), Antwerp University Hospital, Edegem, Belgium
| | - Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Department of Paediatrics, Antwerp University Hospital, Edegem, Belgium
- Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
- Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
| | - Eva Lion
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium
- Center for Cell Therapy & Regenerative Medicine (CCRG), Antwerp University Hospital, Edegem, 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.
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Tanzi M, Montini E, Rumolo A, Moretta A, Comoli P, Acquafredda G, Rotella J, Taurino G, Compagno F, Cave FD, Perotti C, Marseglia GL, Zecca M, Montagna D. Production of donor-derived cytotoxic T lymphocytes with potent anti-leukemia activity for adoptive immunotherapy in high-risk pediatric patients given haploidentical hematopoietic stem cell transplantation. Cytotherapy 2024; 26:878-889. [PMID: 38703155 DOI: 10.1016/j.jcyt.2024.04.005] [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: 09/20/2023] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND AIMS Somatic cell therapy based on the infusion of donor-derived cytotoxic T lymphocytes (CTL) able to recognize patients' leukemia blasts (LB) is a promising approach to control leukemia relapse after allogeneic HSCT. The success of this approach strongly depends on the ex vivo generation of high-quality donor-derived anti-leukemia CTL in compliance with Good Manufacturing Practices (GMP). We previously described a procedure for generating large numbers of donor-derived anti-leukemia CTL through stimulation of CD8-enriched lymphocytes with dendritic cells (DCs) pulsed with apoptotic LB in the presence of interleukin (IL)-12, IL-7 and IL-15. Here we report that the use of IFN-DC and the addition of IFNα2b during the priming phase significantly improve the generation of an efficient anti-leukemia T cells response in vitro. METHODS Using this approach, 20 high-risk pediatric patients given haploidentical HSCT for high-risk acute leukemia were enrolled and 51 batches of advanced therapy medical products (ATMP), anti-leukemia CTL, were produced. RESULTS Quality controls demonstrated that all batches were sterile, free of mycoplasma and conformed to acceptable endotoxin levels. Genotype analysis confirmed the molecular identity of the ATMP based on the starting biological material used for their production. The majority of ATMP were CD3+/CD8+ cells, with a memory/terminal activated phenotype, including T-central memory populations. ATMP were viable after thawing, and most ATMP batches displayed efficient capacity to lyse patients' LB and to secrete interferon-γ and tumor necrosis factor-α. CONCLUSIONS These results demonstrated that our protocol is highly reproducible and allows the generation of large numbers of immunologically safe and functional anti-leukemia CTL with a high level of standardization.
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Affiliation(s)
- Matteo Tanzi
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Enrica Montini
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Agnese Rumolo
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Antonia Moretta
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Patrizia Comoli
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Gloria Acquafredda
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Jessica Rotella
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Gloria Taurino
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Francesca Compagno
- Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Francesco Delle Cave
- Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Cesare Perotti
- Immunohaematology and Transfusion Medicine Service (SIMT), Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Gian Luigi Marseglia
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Sciences Clinic-Surgical, Diagnostic and Pediatric, University of Pavia, Pavia, Italy
| | - Marco Zecca
- Pediatric Hematology/Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Daniela Montagna
- Cell Factory, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Sciences Clinic-Surgical, Diagnostic and Pediatric, University of Pavia, Pavia, Italy.
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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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Bajwa G, Arber C. Rapid Generation of TCR and CD8αβ Transgenic Virus Specific T Cells for Immunotherapy of Leukemia. Front Immunol 2022; 13:830021. [PMID: 35572604 PMCID: PMC9100812 DOI: 10.3389/fimmu.2022.830021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Virus-specific T cells (VSTs) are an attractive cell therapy platform for the delivery of tumor-targeted transgenic receptors. However, manufacturing with conventional methods may require several weeks and intensive handling. Here we evaluated the feasibility and timelines when combining IFN-γ cytokine capture (CC) with retroviral transduction for the generation of T cell receptor (TCR) and CD8αβ (TCR8) transgenic VSTs to simultaneously target several viral and tumor antigens in a single product. Methods Healthy donor peripheral blood mononuclear cells were stimulated with cytomegalovirus (CMV) and Epstein-Barr-Virus (EBV) peptide mixtures derived from immunogenic viral proteins, followed by CC bead selection. After 3 days in culture, cells were transduced with a retroviral vector encoding four genes (a survivin-specific αβTCR and CD8αβ). TCR8-transgenic or control VSTs were expanded and characterized for their phenotype, specificity and anti-viral and anti-tumor functions. Results CC selected cells were efficiently transduced with TCR8. Average fold expansion was 269-fold in 10 days, and cells contained a high proportion of CD8+ T central memory cells. TCR8+ VSTs simultaneously expressed native anti-viral and transgenic anti-survivin TCRs on their cell surface. Both control and TCR8+ VSTs produced cytokines to and killed viral targets, while tumor targets were only recognized and killed by TCR8+ VSTs. Conclusions IFN-γ cytokine capture selects and activates CMV and EBV-specific memory precursor CD8+ T cells that can be efficiently gene-modified by retroviral transduction and rapidly ex vivo expanded. Our multi-specific T cells are polyfunctional and recognize and kill viral and leukemic targets expressing the cognate antigens.
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Affiliation(s)
- Gagan Bajwa
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, United States
| | - Caroline Arber
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, United States
- Department of Oncology UNIL CHUV, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), and Ludwig Institute for Cancer Research Lausanne Branch, Lausanne, Switzerland
- *Correspondence: Caroline Arber,
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