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      The antidepressant imipramine inhibits breast cancer growth by targeting estrogen receptor signaling and DNA repair events

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          Abstract

          Aberrant activities of various cell cycle and DNA repair proteins promote cancer growth and progression and render them resistant to therapies. Here, we demonstrate that the anti-depressant imipramine blocks growth of triple-negative (TNBC) and estrogen receptor-positive (ER+) breast cancers by inducing cell cycle arrest and by blocking heightened homologous recombination (HR) and non-homologous end joining-mediated (NHEJ) DNA repair activities. Our results reveal that imipramine inhibits the expression of several cell cycle- and DNA repair-associated proteins including E2F1, CDK1, Cyclin D1, and RAD51. In addition, we show that imipramine inhibits the growth of ER + breast cancers by inhibiting the estrogen receptor- α (ER-α) signaling. Our studies in pre-clinical mouse models and ex vivo explants from breast cancer patients show that imipramine sensitizes TNBC to the PARP inhibitor olaparib and endocrine resistant ER + breast cancer to anti-estrogens. Our studies suggest that repurposing imipramine could enhance routine care for breast cancer patients. Based on these results, we designed an ongoing clinical trial, where we are testing the efficacy of imipramine for treating patients with triple-negative and estrogen receptor-positive breast cancer. Since aberrant DNA repair activity is used by many cancers to survive and become resistant to therapy, imipramine could be used alone and/or with currently used drugs for treating many aggressive cancers.

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          Most cited references45

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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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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              • Record: found
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              • Article: not found

              Differential expression analysis for sequence count data

              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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                Author and article information

                Journal
                7600053
                2780
                Cancer Lett
                Cancer Lett
                Cancer letters
                0304-3835
                1872-7980
                16 June 2023
                01 August 2022
                12 May 2022
                30 June 2023
                : 540
                : 215717
                Affiliations
                [a ]Department of Cell Systems and Anatomy, UT Health, San Antonio, USA
                [b ]Greehey Children’s Cancer Research Institute, USA
                [c ]Department of Molecular Medicine, UT Health, San Antonio, USA
                [d ]Department of Epidemiology and Statistics, UT Health, San Antonio, USA
                [e ]Department of Obstetrics and Gynecology, UT Health, San Antonio, USA
                [f ]Department of Surgery, UT Health, San Antonio, USA
                [g ]Department of Medicine, UT Health, San Antonio, USA
                [h ]Health Careers High School, San Antonio, TX, USA
                [i ]Houston Methodist, Houston, TX, USA
                Author notes
                [* ]Corresponding author. Department of Cell & Structural Biology/Greehey Children’s Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA. raom@ 123456uthscsa.edu , Kaklamani@ 123456uthscsa.edu (M.K. Rao)
                [** ]Corresponding author. Department of Ob-Gyn UT Health, San Antonio, TX, 78229, USA. vadlamudi@ 123456uthscsa.edu (R. Vadlamudi)
                [*** ]Corresponding author. Department of Medicine, Division of Hematology and Oncology, Mays Cancer Center, The University of Texas Health Science Ceter at San Antonio, TX, 78229, USA. Kaklamani@ 123456uthscsa.edu (V. Kaklamani)
                [1]

                Authors contributed equally to the work.

                Author contributions

                S.T., S.R., A.D.R., and M.K.R conceived the study, S.T., S.R., A.D.R., and M.K.R. designed experiments. S.T., and M.K.R. wrote the manuscript. S.T., S.R., P.S., N.A., A.D.R., R.V., S.V., and S.N. performed the experiments. S.T., S.R., P.S., N.A., A.D.R., S.N., Y.C., A.B., R.V., V.K., and M.K.R. analyzed the data. I.J., V.K., and A.B. provided valuable resources. All authors read and approved the final manuscript.

                Article
                NIHMS1821833
                10.1016/j.canlet.2022.215717
                10313451
                35568265
                38be1f0f-a265-4e68-ba6d-bb1d2e5d3476

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

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                Categories
                Article

                Oncology & Radiotherapy
                imipramine,drug repurposing,breast cancer,dna damage,dna repair,estrogen receptor

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