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      Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma

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          Abstract

          Cutaneous T cell lymphomas (CTCL) are rare but aggressive cancers without effective treatments. While a subset of patients derive benefit from PD-1 blockade, there is a critically unmet need for predictive biomarkers of response. Herein, we perform CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions from 14 advanced CTCL patients enrolled in a pembrolizumab clinical trial (NCT02243579). We find no differences in the frequencies of immune or tumor cells between responders and non-responders. Instead, we identify topographical differences between effector PD-1 + CD4 + T cells, tumor cells, and immunosuppressive Tregs, from which we derive a spatial biomarker, termed the SpatialScore, that correlates strongly with pembrolizumab response in CTCL. The SpatialScore coincides with differences in the functional immune state of the tumor microenvironment, T cell function, and tumor cell-specific chemokine recruitment and is validated using a simplified, clinically accessible tissue imaging platform. Collectively, these results provide a paradigm for investigating the spatial balance of effector and suppressive T cell activity and broadly leveraging this biomarker approach to inform the clinical use of immunotherapies.

          Abstract

          PD-1 blockade is effective for only a subset of patients with cutaneous T cell lymphomas. Here, the authors report a spatial biomarker that uses immune and cancer cell topography to predict response to PD-1 blockade in this disease.

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          Most cited references106

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Fitting Linear Mixed-Effects Models Usinglme4

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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                christian.schuerch@med.uni-tuebingen.de
                gnolan@stanford.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 November 2021
                18 November 2021
                2021
                : 12
                : 6726
                Affiliations
                [1 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Microbiology & Immunology, , Stanford University School of Medicine, ; Stanford, CA 94305 USA
                [2 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Dermatology, , Stanford University School of Medicine, ; Stanford, CA 94305 USA
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Pathology, , Stanford University School of Medicine, ; Stanford, CA 94305 USA
                [4 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Bioengineering, , Stanford University Schools of Engineering and Medicine, ; Stanford, CA 94305 USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, ; Boston, MA 02215 USA
                [6 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Cancer Immunotherapy Trials Network, Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                [7 ]GRID grid.168010.e, ISNI 0000000419368956, Division of Oncology, , Stanford University School of Medicine, ; Stanford, CA 94305 USA
                [8 ]GRID grid.411544.1, ISNI 0000 0001 0196 8249, Department of Pathology and Neuropathology, , University Hospital and Comprehensive Cancer Center Tübingen, ; Tübingen, Germany
                Author information
                http://orcid.org/0000-0002-0804-3342
                http://orcid.org/0000-0001-9335-5414
                http://orcid.org/0000-0001-6149-3142
                http://orcid.org/0000-0002-7301-8055
                http://orcid.org/0000-0002-2329-8298
                http://orcid.org/0000-0001-6263-0153
                http://orcid.org/0000-0002-6613-9342
                http://orcid.org/0000-0003-4079-7969
                http://orcid.org/0000-0002-1792-1768
                http://orcid.org/0000-0002-8862-9043
                Article
                26974
                10.1038/s41467-021-26974-6
                8602403
                34795254
                e1c4af6b-3563-4349-b624-9e65d7664034
                © The Author(s) 2021

                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
                : 26 July 2021
                : 26 October 2021
                Funding
                Funded by: This work was supported by the National Institutes of Health (NIH) 2U19AI057229-16, 5P01HL10879707, 5R01GM10983604, 5R33CA18365403, 5U01AI101984-07, 5UH2AR06767604, 5R01CA19665703, 5U54CA20997103, 5F99CA212231-02, 1F32CA233203-01, 5U01AI140498-02, 1U54HG010426-01, 5U19AI100627-07, 1R01HL120724-01A1, R33CA183692, R01HL128173-04, 5P01AI131374-02, 5UG3DK114937-02, 1U19AI135976-01, IDIQ17X149, 1U2CCA233238-01, 1U2CCA233195-01 (G.P.N.), F32CA233203 (D.P.), T32AR007422 (D.P.), T32AI007290 (G.L.B.); The National Cancer Institute: U01CA154967 and UM1CA154967 (M.A.C., S.P.F.); Cancer Center Support Grant P30 CA015704 (M.A.C., S.P.F). The Department of Defense (W81XWH-14-1-0180 and W81XWH-12-1-0591) (G.P.N.); The Food and Drug Administration (HHSF223201610018C and DSTL/AGR/00980/01) (G.P.N.); Cancer Research UK (C27165/A29073) (G.P.N.); The Bill and Melinda Gates Foundation (OPP1113682) (S.J., G.P.N.); The Cancer Research Institute (G.P.N.); The Parker Institute for Cancer Immunotherapy (G.P.N.); The Kenneth Rainin Foundation (2018-575) (G.P.N.); Celgene, Inc. (133826 and 134073) (G.P.N.); the Rachford & Carlotta A. Harris Endowed Chair (G.P.N.); and The Beckman Center for Molecular and Genetic Medicine (D.P., C.M.S., Y.H.K, G.P.N.).
                Categories
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                © The Author(s) 2021

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                cancer immunotherapy,t-cell lymphoma,imaging the immune system,predictive markers,cancer microenvironment

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