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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

          <p class="first" id="d4895712e478">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. </p><p id="d4895712e483">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 <sup>+</sup>CD27 <sup>+</sup>IgD <sup>−</sup> 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. </p><div class="section"> <a class="named-anchor" id="d4895712e494"> <!-- named anchor --> </a> <h5 class="section-title" id="d4895712e495">Significance:</h5> <p id="d4895712e497">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. </p> </div><p id="d4895712e502"> <div class="fig panel" id="fig0"> <a class="named-anchor" id="fig0"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/f8cb3562-9c7b-48fb-a841-9d3c0ba13026/PubMedCentral/image/overview_graphic_can-20-3773"/> </div> <div class="panel-content"/> </div> </p>

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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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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

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                Journal
                Cancer Research
                Cancer Res
                American Association for Cancer Research (AACR)
                0008-5472
                1538-7445
                August 16 2021
                August 15 2021
                August 15 2021
                June 15 2021
                : 81
                : 16
                : 4290-4304
                Article
                10.1158/0008-5472.CAN-20-3773
                e473e4d3-6823-4722-a396-403c350733b2
                © 2021
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