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      Dynamic changes in B cell subpopulations in response to triple-negative breast cancer development

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          Abstract

          Despite presenting a worse prognosis and being associated with highly aggressive tumors, triple-negative breast cancer (TNBC) is characterized by the higher frequency of tumor-infiltrating lymphocytes, which have been implicated in better overall survival and response to therapy. Though recent studies have reported the capacity of B lymphocytes to recognize overly-expressed normal proteins, and tumor-associated antigens, how tumor development potentially modifies B cell response is yet to be elucidated. Our findings reveal distinct effects of 4T1 and E0771 murine tumor development on B cells in secondary lymphoid organs. Notably, we observe a significant expansion of total B cells and plasma cells in the tumor-draining lymph nodes (tDLNs) as early as 7 days after tumor challenge in both murine models, whereas changes in the spleen are less pronounced. Surprisingly, within the tumor microenvironment (TME) of both models, we detect distinct B cell subpopulations, but tumor development does not appear to cause major alterations in their frequency over time. Furthermore, our investigation into B cell regulatory phenotypes highlights that the B10 Breg phenotype remains unaffected in the evaluated tissues. Most importantly, we identified an increase in CD19 + LAG-3 + cells in tDLNs of both murine models. Interestingly, although CD19 + LAG-3 + cells represent a minor subset of total B cells (< 3%) in all evaluated tissues, most of these cells exhibit elevated expression of IgD, suggesting that LAG-3 may serve as an activation marker for B cells. Corroborating with these findings, we detected distinct cell cycle and proliferation genes alongside LAG-3 analyzing scRNA-Seq data from a cohort of TNBC patients. More importantly, our study suggests that the presence of LAG-3 B cells in breast tumors could be associated with a good prognosis, as patients with higher levels of LAG-3 B cell transcripts had a longer progression-free interval (PFI). This novel insight could pave the way for targeted therapies that harness the unique properties of LAG-3 + B cells, potentially offering new avenues for improving patient outcomes in TNBC. Further research is warranted to unravel the mechanistic pathways of these cells and to validate their prognostic value in larger, diverse patient cohorts.

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          Integrated analysis of multimodal single-cell data

          Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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            Hallmarks of Cancer: New Dimensions

            The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.
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              TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

              The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
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                Author and article information

                Contributors
                liza@icb.ufmg.br
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 May 2024
                21 May 2024
                2024
                : 14
                : 11576
                Affiliations
                [1 ]Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, ( https://ror.org/0176yjw32) Av. Pres. Antônio Carlos, 6627 – Pampulha, Belo Horizonte, MG 31270-901 Brazil
                [2 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, NYU Grossman School of Medicine, NYU Langone Health, New York University, ; 550 1st Ave, New York, NY 10016 USA
                [3 ]Department of Pharmacology, Universidade Federal de Minas Gerais, ( https://ror.org/0176yjw32) Av. Pres. Antônio Carlos, 6627 – Pampulha, Belo Horizonte, MG 31270-901 Brazil
                [4 ]GRID grid.419166.d, Instituto Nacional de Câncer, Ministério da Saúde, Coordenação de Pesquisa, Laboratório de Bioinformática e Biologia Computacional - Rua André Cavalcanti, ; 37, 1 Andar, Centro, Rio de Janeiro, RJ 20231050 Brasil
                Article
                60243
                10.1038/s41598-024-60243-y
                11109097
                38773133
                a5be54a8-354a-412e-8cc3-3ef16aceefcf
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 September 2023
                : 19 April 2024
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                © Springer Nature Limited 2024

                Uncategorized
                triple-negative breast cancer,b lag-3,b-til,humoral immune response,tumor-infiltrating lymphocytes,4t1,e0771,immunology,adaptive immunity,cellular immunity,molecular medicine,computational biology and bioinformatics,biochemical reaction networks,cellular signalling networks

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