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      Immunological Hallmarks for Clinical Response to BCG in Bladder Cancer

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          Abstract

          Intravesical Bacillus Calmette-Guerin (BCG) is an effective immunotherapy for non-muscle invasive bladder cancer (NMIBC). However, recurrence and progression remain frequent warranting deeper insights into its mechanism. We herein comprehensively profiled blood and tissues obtained from NMIBC patients before, during and after BCG treatment using cytometry by time-of-flight (CyTOF) and RNA sequencing to identify the key immune subsets crucial for anti-tumor activity. We observed the temporal changes of peripheral immune subsets including NKT cells, central memory CD4 + T cells, CD8 + T cells and regulatory T cells (Treg) during the course of BCG. Gene expression analysis revealed enriched immune pathways involving in T cell activation and chemotaxis, as well as a more diversified T cell receptor repertoire in post-BCG tissues. Moreover, tissue multiplexed-immunofluorescence (mIF) showed baseline densities of non-Treg and CD8 +PD-1 + T cells were predictive of response and better recurrence-free survival after BCG. Remarkably, post-BCG tissues from responders were found to be infiltrated with more active CD8 +PD-1 - T cells and non-Treg CD4 +FOXP3 - T cells; but increased exhausted CD8 +PD-1 + T cells were found in non-responders. Taken together, we identified predictive biomarkers for response and uncovered the post-treatment expansion of exhausted PD-1 +CD8 + T cells as key to BCG resistance, which could potentially be restored by combining with anti-PD-1 immunotherapy.

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          Most cited references41

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          HTSeq—a Python framework to work with high-throughput sequencing data

          Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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            FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

            The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.
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              EAU Guidelines on Non-Muscle-invasive Urothelial Carcinoma of the Bladder: Update 2016.

              The European Association of Urology (EAU) panel on Non-muscle-invasive Bladder Cancer (NMIBC) released an updated version of the guidelines on Non-muscle-invasive Bladder Cancer.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                29 January 2021
                2020
                : 11
                : 615091
                Affiliations
                [1] 1 Translational Immunology Institute (TII), SingHealth-DukeNUS Academic Medical Centre , Singapore, Singapore
                [2] 2 Duke-NUS Medical School , Singapore, Singapore
                [3] 3 Division of Pathology, Singapore General Hospital , Singapore, Singapore
                [4] 4 Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (ASTAR) , Singapore, Singapore
                [5] 5 Department of Urology, Singapore General Hospital , Singapore, Singapore
                Author notes

                Edited by: Ilaria Marigo, Veneto Institute of Oncology (IRCCS), Italy

                Reviewed by: Antonella Sistigu, Catholic University of the Sacred Heart, Italy; María Marcela Barrio, Fundación Cáncer, Argentina; Sergei Kusmartsev, University of Florida, United States

                *Correspondence: Valerie Chew, valerie.chew.s.p@ 123456singhealth.com.sg

                This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2020.615091
                7879685
                33584702
                465350f2-908a-47f5-97c7-abd89d15006d
                Copyright © 2021 Lim, Nguyen, Wasser, Kumar, Lee, Nasir, Chua, Lai, Hazirah, Loh, Khor, Yeong, Lim, Low, Albani, Chong and Chew

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 October 2020
                : 10 December 2020
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 41, Pages: 130, Words: 6705
                Funding
                Funded by: National Medical Research Council 10.13039/501100001349
                Categories
                Immunology
                Original Research

                Immunology
                bacillus calmette–guerin (bcg),bladder cancer,immunotherapy,biomarkers,pd-1
                Immunology
                bacillus calmette–guerin (bcg), bladder cancer, immunotherapy, biomarkers, pd-1

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