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      Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19 + B cell tumors: a phase 1/2 trial

      research-article
      1 , 1 , 1 , 1 , 1 , 2 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 1 , 4 , 4 , 1 , 1 , 5 , 1 , 1 , 1 , 1 , 6 , 6 , 1 , 1 , 1 , 1 , 1 , 1 , 2 , 1 , 1 ,
      Nature Medicine
      Nature Publishing Group US
      NK cells, Cancer immunotherapy, Leukaemia, Lymphoma

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          Abstract

          There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19 + B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 10 7 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339.

          Abstract

          In the final report of a phase 1/2 trial evaluating allogeneic CD19-specific CAR-NK cells armored with IL-15 in patients with CD19 + hematologic malignancies, the therapy was shown to be safe and efficacious with distinct cord blood features associated with response.

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          Most cited references74

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Contributors
                krezvani@mdanderson.org
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                18 January 2024
                18 January 2024
                2024
                : 30
                : 3
                : 772-784
                Affiliations
                [1 ]Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA
                [2 ]Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA
                [3 ]Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA
                [4 ]Department of Biostatistics, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA
                [5 ]Department of Laboratory Medicine, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA
                [6 ]Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA
                Author information
                http://orcid.org/0000-0001-7727-0875
                http://orcid.org/0000-0002-5724-4183
                http://orcid.org/0000-0002-6026-8397
                http://orcid.org/0000-0002-5536-6750
                http://orcid.org/0000-0002-0868-5863
                http://orcid.org/0000-0002-7293-529X
                http://orcid.org/0000-0002-4534-2042
                http://orcid.org/0000-0001-5039-021X
                http://orcid.org/0000-0003-3739-0287
                http://orcid.org/0000-0003-0079-8617
                http://orcid.org/0000-0001-6071-8610
                http://orcid.org/0000-0002-0524-267X
                http://orcid.org/0000-0003-4013-5279
                http://orcid.org/0000-0002-9599-2246
                Article
                2785
                10.1038/s41591-023-02785-8
                10957466
                38238616
                24b6d676-ea26-4988-9e5f-41e5d6ee363b
                © The Author(s) 2024

                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
                : 17 July 2023
                : 20 December 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: P01CA148600-03
                Award ID: P50CA100632
                Award ID: CA016672
                Award ID: 1 R01CA211044-01
                Award Recipient :
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                © Springer Nature America, Inc. 2024

                Medicine
                nk cells,cancer immunotherapy,leukaemia,lymphoma
                Medicine
                nk cells, cancer immunotherapy, leukaemia, lymphoma

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