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      Targeting mutant p53 with arsenic trioxide: A preclinical study focusing on triple negative breast cancer

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          Highlights

          • Arsenic trioxide (ATO) was recently shown to reactivate mutant p53 and restore wild-type functionality.

          • ATO was a more potent inhibitor of the proliferation of mutant p53 cell lines than wild-type p53 cell lines.

          • Triple-negative breast cancer (TNBC) cell lines were more sensitive to ATO than non-TNBC cell lines.

          • ATO induced wild-type p53 canonical target genes such as CDKN1A, SLC7A11, HMOX1, BBC3, PMAIP1, SESN2, SRXN1 and TXNRD1.

          • Our findings support the activation of mutant p53 by ATO and, furthermore, the possible repurposing of ATO to treat TP53-mutated TNBC.

          Abstract

          New treatments are urgently required for triple-negative breast cancer (TNBC). As TP53 is mutated in approximately 80% of TNBC, it is theoretically an attractive target for new drugs for this disease. Arsenic trioxide (ATO), which is used to treat promyelocytic leukaemia, was recently shown to reactivate mutant p53 and restore wild-type functionality. The aim of this study was to evaluate ATO as a potential new treatment for TNBC. Using a panel of 20 cell lines, we found that TNBC cell lines were more sensitive to ATO than non-TNBC cell lines ( P = 0.045). Consistent with its ability to reactivate mutant p53, ATO was a more potent inhibitor of proliferation in cell lines with mutant TP53 than the wildtype TP53 ( P = 0.027). Direct evidence of mutant p53 reactivation was the induction of multiple wild-type p53 canonical target genes such as CDKN1A, SLC7A11, BBC3, PMAIP1, SESN2, SRXN1 and TXNRD1. Our findings support the activation of mutant p53 by ATO and, furthermore, the possible repurposing of ATO to treat TP53-mutated TNBC.

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          Most cited references42

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

              Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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                Author and article information

                Contributors
                Journal
                Transl Oncol
                Transl Oncol
                Translational Oncology
                Neoplasia Press
                1936-5233
                12 June 2024
                August 2024
                12 June 2024
                : 46
                : 102025
                Affiliations
                [a ]UCD School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
                [b ]Department of Medical Oncology, St. Vincent's University Hospital, Dublin D04 T6F4, Ireland
                [c ]Data Science Centre, School of Population Health, RCSI University of Medicine and Health Sciences, Dublin D02 YN77, Ireland
                [d ]UCD Clinical Research Centre, St. Vincent's University Hospital, Dublin D04 T6F4, Ireland
                Author notes
                [* ]Corresponding author at: UCD Clinical Research Centre, St. Vincent's University Hospital, Dublin D04 T6F4, Ireland. Michael.J.Duffy@ 123456ucd.ie
                [1]

                Joint senior authors.

                Article
                S1936-5233(24)00152-9 102025
                10.1016/j.tranon.2024.102025
                11225897
                38870678
                345a39c4-ee20-4622-b057-23e209b3f166
                © 2024 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 3 January 2024
                : 29 May 2024
                : 3 June 2024
                Categories
                Original Research

                mutant p53,ato,triple-negative breast cancer,therapy,rna-seq,apr-246

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