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      Tumor-Infiltrating B Lymphocyte Profiling Identifies IgG-Biased, Clonally Expanded Prognostic Phenotypes in Triple-Negative Breast Cancer

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          Abstract

          Tumor-infiltrating B lymphocytes assemble in clusters, undergoing B-cell receptor–driven activation, proliferation, and isotype switching. Clonally expanded, IgG isotype-biased humoral immunity associates with favorable prognosis primarily in triple-negative breast cancers.

          Abstract

          In breast cancer, humoral immune responses may contribute to clinical outcomes, especially in more immunogenic subtypes. Here, we investigated B lymphocyte subsets, immunoglobulin expression, and clonal features in breast tumors, focusing on aggressive triple-negative breast cancers (TNBC). In samples from patients with TNBC and healthy volunteers, circulating and tumor-infiltrating B lymphocytes (TIL-B) were evaluated. CD20 +CD27 +IgD isotype-switched B lymphocytes were increased in tumors, compared with matched blood. TIL-B frequently formed stromal clusters with T lymphocytes and engaged in bidirectional functional cross-talk, consistent with gene signatures associated with lymphoid assembly, costimulation, cytokine–cytokine receptor interactions, cytotoxic T-cell activation, and T-cell–dependent B-cell activation. TIL-B–upregulated B-cell receptor (BCR) pathway molecules FOS and JUN, germinal center chemokine regulator RGS1, activation marker CD69, and TNFα signal transduction via NFκB, suggesting BCR–immune complex formation. Expression of genes associated with B lymphocyte recruitment and lymphoid assembly, including CXCL13, CXCR4, and DC-LAMP, was elevated in TNBC compared with other subtypes and normal breast. TIL-B–rich tumors showed expansion of IgG but not IgA isotypes, and IgG isotype switching positively associated with survival outcomes in TNBC. Clonal expansion was biased toward IgG, showing expansive clonal families with specific variable region gene combinations and narrow repertoires. Stronger positive selection pressure was present in the complementarity determining regions of IgG compared with their clonally related IgA in tumor samples. Overall, class-switched B lymphocyte lineage traits were conspicuous in TNBC, associated with improved clinical outcomes, and conferred IgG-biased, clonally expanded, and likely antigen-driven humoral responses.

          Significance:

          Tumor-infiltrating B lymphocytes assemble in clusters, undergoing B-cell receptor–driven activation, proliferation, and isotype switching. Clonally expanded, IgG isotype-biased humoral immunity associates with favorable prognosis primarily in triple-negative breast cancers.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Robust enumeration of cell subsets from tissue expression profiles

            We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Journal
                Cancer Res
                Cancer Res
                Cancer Research
                American Association for Cancer Research
                0008-5472
                1538-7445
                15 August 2021
                15 June 2021
                : 81
                : 16
                : 4290-4304
                Affiliations
                [1 ]St. John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, United Kingdom.
                [2 ]NIHR Biomedical Research Center at Guy's and St. Thomas' Hospitals and King's College London, Guy's Hospital, King's College London, London, United Kingdom.
                [3 ]King's Health Partners Cancer Research UK Cancer Center, King's College London, London, United Kingdom.
                [4 ]Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, King's College London, Guy's Cancer Center, London, United Kingdom.
                [5 ]Randall Center for Cell and Molecular Biophysics, King's College London, London, United Kingdom.
                [6 ]Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London, United Kingdom.
                [7 ]Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
                [8 ]School of Cancer and Pharmaceutical Studies, Translational Oncology and Urology Research (TOUR), King's College London, London, United Kingdom.
                [9 ]School of Cancer and Pharmaceutical Sciences, King's College London, Comprehensive Cancer Center, Guy's Hospital, London, United Kingdom.
                [10 ]King's Health Partners Cancer Biobank, King's College London, London, United Kingdom.
                [11 ]Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
                [12 ]Breast Cancer Now Toby Robins Research Center, Institute of Cancer Research, London, United Kingdom.
                Author notes
                [#]

                R.J. Harris and A. Cheung contributed equally as co-authors of this article.

                [* ] Corresponding Author: Sophia N. Karagiannis, St. John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, Guy's Hospital, Tower Wing, 9th Floor, London, SE1 9RT, UK. Phone: 44-207-188-6355; E-mail: sophia.karagiannis@ 123456kcl.ac.uk
                Author information
                https://orcid.org/0000-0002-2988-2786
                https://orcid.org/0000-0002-3617-5211
                https://orcid.org/0000-0002-0118-4548
                https://orcid.org/0000-0002-1909-5957
                https://orcid.org/0000-0002-5412-3677
                https://orcid.org/0000-0003-3643-4057
                https://orcid.org/0000-0002-4419-7162
                https://orcid.org/0000-0003-3732-8491
                https://orcid.org/0000-0001-8403-1282
                https://orcid.org/0000-0003-3434-201X
                https://orcid.org/0000-0001-8715-2901
                https://orcid.org/0000-0002-4100-7810
                Article
                CAN-20-3773
                10.1158/0008-5472.CAN-20-3773
                7611538
                34224371
                e473e4d3-6823-4722-a396-403c350733b2
                ©2021 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 19 November 2020
                : 23 March 2021
                : 14 June 2021
                Page count
                Pages: 15
                Funding
                Funded by: Breast Cancer Now, https://doi.org/10.13039/501100007913;
                Award ID: 147
                Award ID: KCL-BCN-Q3
                Award Recipient :
                Funded by: Cancer Research UK (CRUK), https://doi.org/10.13039/501100000289;
                Award ID: C604/A25135
                Award Recipient :
                Funded by: Cancer Research UK (CRUK), https://doi.org/10.13039/501100000289;
                Award ID: C30122/A11527
                Award Recipient :
                Funded by: Cancer Research UK (CRUK), https://doi.org/10.13039/501100000289;
                Award ID: C30122/A15774
                Award Recipient :
                Funded by: Medical Research Council (MRC), https://doi.org/10.13039/501100000265;
                Award ID: MR/L023091/1
                Award Recipient :
                Funded by: CR UK//NIHR in England/DoH for Scotland, Wales and Northern Ireland Experimental Cancer Medicine Centre, https://doi.org/10.13039/;
                Award ID: C10355/A15587
                Award Recipient :
                Funded by: National Institute for Health Research (NIHR), https://doi.org/10.13039/501100000272;
                Award ID: S-BRC-1215-20006
                Award Recipient :
                Categories
                Tumor Biology and Immunology

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