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Parhiz H, Atochina-Vasserman EN, Weissman D. mRNA-based therapeutics: looking beyond COVID-19 vaccines. Lancet 2024; 403:1192-1204. [PMID: 38461842 DOI: 10.1016/s0140-6736(23)02444-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/06/2023] [Accepted: 10/30/2023] [Indexed: 03/12/2024]
Abstract
Recent advances in mRNA technology and its delivery have enabled mRNA-based therapeutics to enter a new era in medicine. The rapid, potent, and transient nature of mRNA-encoded proteins, without the need to enter the nucleus or the risk of genomic integration, makes them desirable tools for treatment of a range of diseases, from infectious diseases to cancer and monogenic disorders. The rapid pace and ease of mass-scale manufacturability of mRNA-based therapeutics supported the global response to the COVID-19 pandemic. Nonetheless, challenges remain with regards to mRNA stability, duration of expression, delivery efficiency, and targetability, to broaden the applicability of mRNA therapeutics beyond COVID-19 vaccines. By learning from the rapidly expanding preclinical and clinical studies, we can optimise the mRNA platform to meet the clinical needs of each disease. Here, we will summarise the recent advances in mRNA technology; its use in vaccines, immunotherapeutics, protein replacement therapy, and genomic editing; and its delivery to desired specific cell types and organs for development of a new generation of targeted mRNA-based therapeutics.
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Affiliation(s)
- Hamideh Parhiz
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Drew Weissman
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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3
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Lang F, Sorn P, Schrörs B, Weber D, Kramer S, Sahin U, Löwer M. Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates. iScience 2023; 26:108014. [PMID: 37965155 PMCID: PMC10641489 DOI: 10.1016/j.isci.2023.108014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/28/2022] [Accepted: 09/18/2023] [Indexed: 11/16/2023] Open
Abstract
Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.
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Affiliation(s)
- Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Patrick Sorn
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Barbara Schrörs
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - David Weber
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Stefan Kramer
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - Ugur Sahin
- BioNTech SE, 55131 Mainz, Germany
- University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
| | - Martin Löwer
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
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Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [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] [Indexed: 04/02/2024]
Abstract
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
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Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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Glazer N, Akerman O, Louzoun Y. Naive and memory T cells TCR-HLA-binding prediction. OXFORD OPEN IMMUNOLOGY 2022; 3:iqac001. [PMID: 36846560 PMCID: PMC9914496 DOI: 10.1093/oxfimm/iqac001] [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: 01/05/2022] [Revised: 05/01/2022] [Accepted: 05/17/2022] [Indexed: 11/12/2022] Open
Abstract
T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cells in TCR repertoires, we developed Natural Language Processing-based methods to predict TCR-MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR-pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted TCR HLA-binding predictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4-CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac.il/Home.
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Affiliation(s)
- Neta Glazer
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Ofek Akerman
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Yoram Louzoun
- Correspondence address. Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel. E-mail:
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Ladak RJ, He AJ, Huang YH, Ding Y. The Current Landscape of mRNA Vaccines Against Viruses and Cancer-A Mini Review. Front Immunol 2022; 13:885371. [PMID: 35603213 PMCID: PMC9120423 DOI: 10.3389/fimmu.2022.885371] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/14/2022] [Indexed: 12/11/2022] Open
Abstract
Both infectious viral diseases and cancer have historically been some of the most common causes of death worldwide. The COVID-19 pandemic is a decidedly relevant example of the former. Despite progress having been made over past decades, new and improved techniques are still needed to address the limitations faced by current treatment standards, with mRNA-based therapy emerging as a promising solution. Highly flexible, scalable and cost-effective, mRNA therapy is proving to be a compelling vaccine platform against viruses. Likewise, mRNA vaccines show similar promise against cancer as a platform capable of encoding multiple antigens for a diverse array of cancers, including those that are patient specific as a novel form of personalized medicine. In this review, the molecular mechanisms, biotechnological aspects, and clinical developments of mRNA vaccines against viral infections and cancer are discussed to provide an informative update on the current state of mRNA therapy research.
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Affiliation(s)
- Reese Jalal Ladak
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, QC, Canada
- Department of Biochemistry, McGill University, Montreal, QC, Canada
| | - Alexander J. He
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, United Kingdom
| | - Yu-Hsun Huang
- Department of Anatomy and Cell Biology, McGill University, Montreal, QC, Canada
| | - Yu Ding
- Department of Biochemistry, McGill University, Montreal, QC, Canada
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Lang F, Schrörs B, Löwer M, Türeci Ö, Sahin U. Identification of neoantigens for individualized therapeutic cancer vaccines. Nat Rev Drug Discov 2022; 21:261-282. [PMID: 35105974 PMCID: PMC7612664 DOI: 10.1038/s41573-021-00387-y] [Citation(s) in RCA: 172] [Impact Index Per Article: 86.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2021] [Indexed: 02/07/2023]
Abstract
Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.
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Affiliation(s)
- Franziska Lang
- TRON Translational Oncology, Mainz, Germany
- Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | | | | | - Ugur Sahin
- BioNTech, Mainz, Germany.
- University Medical Center, Johannes Gutenberg University, Mainz, Germany.
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Zur RT, Adler G, Shamalov K, Tal Y, Ankri C, Cohen CJ. Adoptive T-cell Immunotherapy: Perfecting Self-Defenses. EXPERIENTIA SUPPLEMENTUM (2012) 2022; 113:253-294. [PMID: 35165867 DOI: 10.1007/978-3-030-91311-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
As an important part of the immune system, T lymphocytes exhibit undoubtedly an important role in targeting and eradicating cancer. However, despite these characteristics, their natural antitumor response may be insufficient. Numerous clinical trials in terminally ill cancer patients testing the design of novel and efficient immunotherapeutic approaches based on the adoptive transfer of autologous tumor-specific T lymphocytes have shown encouraging results. Moreover, this also led to the approval of engineered T-cell therapies in patients. Herein, we will expand on the development and the use of such strategies using tumor-infiltrating lymphocytes or genetically engineered T-cells. We will also comment on the requirements and potential hurdles encountered when elaborating and implementing such treatments as well as the exciting prospects for this kind of emerging personalized medicine therapy.
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Affiliation(s)
- Raphaëlle Toledano Zur
- Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Galit Adler
- Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Katerina Shamalov
- Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Yair Tal
- Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Chen Ankri
- Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Cyrille J Cohen
- Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.
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Cancer Vaccines: Antigen Selection Strategy. Vaccines (Basel) 2021; 9:vaccines9020085. [PMID: 33503926 PMCID: PMC7911511 DOI: 10.3390/vaccines9020085] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 02/06/2023] Open
Abstract
Unlike traditional cancer therapies, cancer vaccines (CVs) harness a high specificity of the host’s immunity to kill tumor cells. CVs can train and bolster the patient’s immune system to recognize and eliminate malignant cells by enhancing immune cells’ identification of antigens expressed on cancer cells. Various features of antigens like immunogenicity and avidity influence the efficacy of CVs. Therefore, the choice and application of antigens play a critical role in establishing and developing CVs. Tumor-associated antigens (TAAs), a group of proteins expressed at elevated levels in tumor cells but lower levels in healthy normal cells, have been well-studied and developed in CVs. However, immunological tolerance, HLA restriction, and adverse events are major obstacles that threaten TAA-based CVs’ efficacy due to the “self-protein” characteristic of TAAs. As “abnormal proteins” that are completely absent from normal cells, tumor-specific antigens (TSAs) can trigger a robust immune response against tumor cells with high specificity and without going through central tolerance, contributing to cancer vaccine development feasibility. In this review, we focus on the unique features of TAAs and TSAs and their application in vaccines, summarizing their performance in preclinical and clinical trials.
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Pourseif MM, Parvizpour S, Jafari B, Dehghani J, Naghili B, Omidi Y. A domain-based vaccine construct against SARS-CoV-2, the causative agent of COVID-19 pandemic: development of self-amplifying mRNA and peptide vaccines. BIOIMPACTS : BI 2020; 11:65-84. [PMID: 33469510 PMCID: PMC7803919 DOI: 10.34172/bi.2021.11] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/15/2022]
Abstract
Introduction: Coronavirus disease 2019 (COVID-19) is undoubtedly the most challenging pandemic in the current century with more than 293,241 deaths worldwide since its emergence in late 2019 (updated May 13, 2020). COVID-19 is caused by a novel emerged coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Today, the world needs crucially to develop a prophylactic vaccine scheme for such emerged and emerging infectious pathogens. Methods: In this study, we have targeted spike (S) glycoprotein, as an important surface antigen to identify its B- and T-cell immunodominant regions. We have conducted a multi-method B-cell epitope (BCE) prediction approach using different predictor algorithms to discover the most potential BCEs. Besides, we sought among a pool of MHC class I and II-associated peptide binders provided by the IEDB server through the strict cut-off values. To design a broad-coverage vaccine, we carried out a population coverage analysis for a set of candidate T-cell epitopes and based on the HLA allele frequency in the top most-affected countries by COVID-19 (update 02 April 2020). Results: The final determined B- and T-cell epitopes were mapped on the S glycoprotein sequence, and three potential hub regions covering the largest number of overlapping epitopes were identified for the vaccine designing (I531-N711; T717-C877; and V883-E973). Here, we have designed two domain-based constructs to be produced and delivered through the recombinant protein- and gene-based approaches, including (i) an adjuvanted domain-based protein vaccine construct (DPVC), and (ii) a self-amplifying mRNA vaccine (SAMV) construct. The safety, stability, and immunogenicity of the DPVC were validated using the integrated sequential (i.e. allergenicity, autoimmunity, and physicochemical features) and structural (i.e. molecular docking between the vaccine and human Toll-like receptors (TLRs) 4 and 5) analysis. The stability of the docked complexes was evaluated using the molecular dynamics (MD) simulations. Conclusion: These rigorous in silico validations supported the potential of the DPVC and SAMV to promote both innate and specific immune responses in preclinical studies.
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Affiliation(s)
- Mohammad Mostafa Pourseif
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Jafari
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medicinal Chemistry, Faculty of Pharmacy, Urmia University of Medical Sciences, Urmia, Iran
| | - Jaber Dehghani
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behrouz Naghili
- Research Center for Infectious and Tropical Diseases, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Nova Southeastern University, College of Pharmacy, Florida, USA
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11
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Xu P, Luo H, Kong Y, Lai WF, Cui L, Zhu X. Cancer neoantigen: Boosting immunotherapy. Biomed Pharmacother 2020; 131:110640. [PMID: 32836075 DOI: 10.1016/j.biopha.2020.110640] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/13/2020] [Accepted: 08/16/2020] [Indexed: 12/21/2022] Open
Abstract
Tumor neoantigen has a high degree of immunogenicity. As one of the emerging methods of tumor immunotherapy, the vaccine developed against it has served to clinical trials of various solid tumors, especially in the treatment of melanoma. Currently, a variety of immunotherapy methods have been applied to the treatment of the tumor. However, other therapeutic methods have the disadvantages of low specificity and prominent side effects. Treatments require tumor antigen with higher immunogenicity as the target of immune attack. This review will recommend the identification of neoantigen, the influencing factors of neoantigen, and the application of personalized vaccines for neoantigen in metastatic tumors such as malignant melanoma.
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Affiliation(s)
- Peijia Xu
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, 524023, China; The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, 524023, China
| | - Haiqing Luo
- Cancer Center, Affiliated Hospital, Guangdong Medical University, Zhanjiang, 524023, China
| | - Ying Kong
- Department of Clinical Laboratory, Hubei No. 3 People's Hospital of Jianghan University, Wuhan, 430033, China
| | - Wing-Fu Lai
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong, China; School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China.
| | - Liao Cui
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, 524023, China.
| | - Xiao Zhu
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, 524023, China; The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, 524023, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, 524023, China.
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12
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Moey MYY, Gougis P, Goldschmidt V, Johnson DB, Lebrun-Vignes B, Moslehi J, Cadranel J, Salem JE. Increased reporting of fatal pneumonitis associated with immune checkpoint inhibitors: a WHO pharmacovigilance database analysis. Eur Respir J 2020; 55:13993003.00038-2020. [PMID: 32079644 DOI: 10.1183/13993003.00038-2020] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 02/09/2020] [Indexed: 11/05/2022]
Affiliation(s)
- Melissa Y Y Moey
- Dept of Cardiovascular Sciences, Vidant Medical Center/East Carolina University, Greenville, NC, USA
| | - Paul Gougis
- Dept of Pharmacology, INSERM CIC-1901, UNICO-GRECO Cardio-oncology Program, CLIP2 Galilée, APHP.Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Vincent Goldschmidt
- Dept of Pharmacology, INSERM CIC-1901, UNICO-GRECO Cardio-oncology Program, CLIP2 Galilée, APHP.Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Douglas B Johnson
- Pitié and Saint Antoine Pharmacovigilance Centres, APHP, Sorbonne Université, UPEC EA EpiDermE 7379, Paris, France
| | - Bénédicte Lebrun-Vignes
- Dept of Pharmacology, INSERM CIC-1901, UNICO-GRECO Cardio-oncology Program, CLIP2 Galilée, APHP.Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.,Pitié and Saint Antoine Pharmacovigilance Centres, APHP, Sorbonne Université, UPEC EA EpiDermE 7379, Paris, France
| | - Javid Moslehi
- Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacques Cadranel
- Chest Dept, Thoracic Oncology Expert Center, Rare Pulmonary Diseases Constitutive Center, GRC #4, Theranoscan Sorbonne Université and APHP, Sorbonne Université, Hôpital Tenon, Paris, France
| | - Joe-Elie Salem
- Dept of Pharmacology, INSERM CIC-1901, UNICO-GRECO Cardio-oncology Program, CLIP2 Galilée, APHP.Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.,Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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Meril S, Harush O, Reboh Y, Matikhina T, Barliya T, Cohen CJ. Targeting glycosylated antigens on cancer cells using siglec‐7/9‐based CAR T‐cells. Mol Carcinog 2020; 59:713-723. [DOI: 10.1002/mc.23213] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 01/05/2023]
Affiliation(s)
- Sara Meril
- Division of Molecular, Cellular and Medical BiologyThe Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University Ramat Gan Israel
| | - Ortal Harush
- Division of Molecular, Cellular and Medical BiologyThe Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University Ramat Gan Israel
| | - Yishai Reboh
- Division of Molecular, Cellular and Medical BiologyThe Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University Ramat Gan Israel
| | - Tatyana Matikhina
- Division of Molecular, Cellular and Medical BiologyThe Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University Ramat Gan Israel
| | - Tilda Barliya
- Division of Molecular, Cellular and Medical BiologyThe Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University Ramat Gan Israel
| | - Cyrille J. Cohen
- Division of Molecular, Cellular and Medical BiologyThe Laboratory of Tumor Immunology and Immunotherapy, The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University Ramat Gan Israel
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