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      CAR-T cell therapy-related cytokine release syndrome and therapeutic response is modulated by the gut microbiome in hematologic malignancies

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          Abstract

          Immunotherapy utilizing chimeric antigen receptor T cell (CAR-T) therapy holds promise for hematologic malignancies, however, response rates and associated immune-related adverse effects widely vary among patients. Here we show, by comparing diversity and composition of the gut microbiome during different CAR-T therapeutic phases in the clinical trial ChiCTR1800017404, that the gut flora characteristically differs among patients and according to treatment stages, and might also reflect patient response to therapy in relapsed/refractory multiple myeloma (MM; n = 43), acute lympholastic leukemia (ALL; n = 23) and non-Hodgkin lymphoma (NHL; n = 12). We observe significant temporal differences in diversity and abundance of Bifidobacterium, Prevotella, Sutterella, and Collinsella between MM patients in complete remission ( n = 24) and those in partial remission ( n = 11). Furthermore, we find that patients with severe cytokine release syndrome present with higher abundance of Bifidobacterium, Leuconostoc, Stenotrophomonas, and Staphylococcus, which is reproducible in an independent cohort of 38 MM patients. This study has important implications for understanding the biological role of the microbiome in CAR-T treatment responsiveness of hematologic malignancy patients, and may guide therapeutic intervention to increase efficacy. The success rate of CAR-T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here we show that therapeutic response in MM, ALL and NHL, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

          Abstract

          The success rate of chimeric antigen receptor T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here authors show that therapeutic response in multiple myeloma, acute lymphoblastic leukemia and non-Hodgkin lymphoma, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

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          Most cited references53

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                alexhchang@yahoo.com
                vandenbm@mskcc.org
                ml2km@zju.edu.cn
                huanghe@zju.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 September 2022
                9 September 2022
                2022
                : 13
                : 5313
                Affiliations
                [1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Bone Marrow Transplantation Center, The First Affiliated Hospital, School of Medicine, , Zhejiang University, ; Hangzhou, China
                [2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, ; Hangzhou, China
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Institute of Hematology, , Zhejiang University, ; Hangzhou, China
                [4 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Zhejiang Laboratory for Systems & Precision Medicine, , Zhejiang University Medical Center, ; Hangzhou, China
                [5 ]GRID grid.452661.2, ISNI 0000 0004 1803 6319, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, , The First Affiliated Hospital, Zhejiang University School of Medicine, ; Hangzhou, China
                [6 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Research Center for Air Pollution and Health, , Zhejiang University, ; Hangzhou, China
                [7 ]GRID grid.412370.3, ISNI 0000 0004 1937 1100, Department of Hematology, Sorbonne University, , Hospital Saint Antoine, ; Paris, France
                [8 ]GRID grid.7429.8, ISNI 0000000121866389, INSERM UMRs 938, and EBMT Paris Study office/CEREST-TC, ; Paris, France
                [9 ]GRID grid.413795.d, ISNI 0000 0001 2107 2845, Hematology and Bone Marrow Transplantation Division, , Chaim Sheba Medical Center, ; Tel-Hashomer, Israel
                [10 ]GRID grid.24516.34, ISNI 0000000123704535, Clinical Translational Research Center, Shanghai Pulmonary Hospital, , Tongji University School of Medicine, ; Shanghai, China
                [11 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Immunology, Sloan Kettering Institute, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                Author information
                http://orcid.org/0000-0002-9882-7377
                http://orcid.org/0000-0002-7572-309X
                http://orcid.org/0000-0003-0696-4401
                http://orcid.org/0000-0002-2723-1621
                Article
                32960
                10.1038/s41467-022-32960-3
                9461447
                36085303
                0420fd6a-0f7d-4a21-bad9-b09f612546c3
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 July 2021
                : 24 August 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81730008,81770201
                Award Recipient :
                Funded by: Key Project of Science and Technology Department of Zhejiang Province (grant No. 2019C03016)
                Categories
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                Custom metadata
                © The Author(s) 2022

                Uncategorized
                acute lymphocytic leukaemia,myeloma,immunization,predictive markers
                Uncategorized
                acute lymphocytic leukaemia, myeloma, immunization, predictive markers

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