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Preclinical assessment of transiently TCR redirected T cells for solid tumour immunotherapy. Cancer Immunol Immunother 2019; 68:1235-1243. [PMID: 31214732 PMCID: PMC6682583 DOI: 10.1007/s00262-019-02356-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 06/07/2019] [Indexed: 12/18/2022]
Abstract
Off-target toxicity due to the expression of target antigens in normal tissue or TCR cross-reactivity represents a major risk when using T cell receptor (TCR)-engineered T cells for treatment of solid tumours. Due to the inherent cross-reactivity of TCRs it is difficult to accurately predict their target recognition pre-clinically. It has become evident that direct testing in a human being represents the best evaluation of the risks. There is, therefore, a clear unmet need for assessing the safety of a therapeutic TCR in a more controllable manner than by the injection of permanently modified cellular products. Using transiently modified T cells combined with dose escalation has already been shown feasible for chimeric antigen receptor (CAR)-engineered T cells, but nothing is yet reported for TCR. We performed a preclinical evaluation of a therapeutic TCR transiently expressed in T cells by mRNA electroporation. We analyzed if the construct was active in vitro, how long it was detectable for and if this expression format was adapted to in vivo efficacy assessment. Our data demonstrate the potential of mRNA engineered T cells, although less powerful than permanent redirection, to induce a significant response. Thus, these findings support the development of mRNA based TCR-therapy strategies as a feasible and efficacious method for evaluating TCR safety and efficacy in first-in-man testing.
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Ewing E, Kular L, Fernandes SJ, Karathanasis N, Lagani V, Ruhrmann S, Tsamardinos I, Tegner J, Piehl F, Gomez-Cabrero D, Jagodic M. Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression. EBioMedicine 2019; 43:411-423. [PMID: 31053557 PMCID: PMC6558224 DOI: 10.1016/j.ebiom.2019.04.042] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 04/15/2019] [Accepted: 04/23/2019] [Indexed: 12/22/2022] Open
Abstract
Background Multiple Sclerosis (MS) is a chronic inflammatory disease and a leading cause of progressive neurological disability among young adults. DNA methylation, which intersects genes and environment to control cellular functions on a molecular level, may provide insights into MS pathogenesis. Methods We measured DNA methylation in CD4+ T cells (n = 31), CD8+ T cells (n = 28), CD14+ monocytes (n = 35) and CD19+ B cells (n = 27) from relapsing-remitting (RRMS), secondary progressive (SPMS) patients and healthy controls (HC) using Infinium HumanMethylation450 arrays. Monocyte (n = 25) and whole blood (n = 275) cohorts were used for validations. Findings B cells from MS patients displayed most significant differentially methylated positions (DMPs), followed by monocytes, while only few DMPs were detected in T cells. We implemented a non-parametric combination framework (omicsNPC) to increase discovery power by combining evidence from all four cell types. Identified shared DMPs co-localized at MS risk loci and clustered into distinct groups. Functional exploration of changes discriminating RRMS and SPMS from HC implicated lymphocyte signaling, T cell activation and migration. SPMS-specific changes, on the other hand, implicated myeloid cell functions and metabolism. Interestingly, neuronal and neurodegenerative genes and pathways were also specifically enriched in the SPMS cluster. Interpretation We utilized a statistical framework (omicsNPC) that combines multiple layers of evidence to identify DNA methylation changes that provide new insights into MS pathogenesis in general, and disease progression, in particular. Fund This work was supported by the Swedish Research Council, Stockholm County Council, AstraZeneca, European Research Council, Karolinska Institutet and Margaretha af Ugglas Foundation.
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Affiliation(s)
- Ewoud Ewing
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Lara Kular
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Sunjay J Fernandes
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, 17177, Sweden; Science for Life Laboratory, Solna, Sweden
| | - Nestoras Karathanasis
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece; Computational Medicine Center, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA 19107, USA
| | - Vincenzo Lagani
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia; Gnosis Data Analysis PC, Heraklion, Greece
| | - Sabrina Ruhrmann
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ioannis Tsamardinos
- Gnosis Data Analysis PC, Heraklion, Greece; Department of Computer Science, University of Crete, Heraklion, Greece
| | - Jesper Tegner
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, 17177, Sweden; Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Saudi Arabia; Science for Life Laboratory, Solna, Sweden
| | - Fredrik Piehl
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17177, Sweden; Center for Neurology, Academic Specialist Clinic, Stockholm Health Services, Stockholm, Sweden
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, 17177, Sweden; Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain; Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, UK
| | - Maja Jagodic
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17177, Sweden.
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