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      Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells

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          Abstract

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          Triple negative breast cancer is a disease with limited treatment options and the poorest outcome across all breast cancer subtypes, thus the need for new effective therapies is high. We recently found that navitoclax displays synergistic anti-proliferative and apoptotic activities with other drugs in treatment of triple negative breast cancer cells, but the resistance to treatment is still a limiting factor. Therefore, we investigated the effects of navitoclax treatment on the transcriptome, genome and epigenome in vitro to better understand the process of developing resistance. We discovered and validated a list of multiple, previously unknown markers of drug resistance that can help in patient selection in future clinical trials involving navitoclax.

          Abstract

          Cancer cells employ various defense mechanisms against drug-induced cell death. Investigating multi-omics landscapes of cancer cells before and after treatment can reveal resistance mechanisms and inform new therapeutic strategies. We assessed the effects of navitoclax, a BCL2 family inhibitor, on the transcriptome, methylome, chromatin structure, and copy number variations of MDA-MB-231 triple-negative breast cancer (TNBC) cells. Cells were sampled before treatment, at 72 h of exposure, and after 10-day drug-free recovery from treatment. We observed transient alterations in the expression of stress response genes that were accompanied by corresponding changes in chromatin accessibility. Most of these changes returned to baseline after the recovery period. We also detected lasting alterations in methylation states and genome structure that suggest permanent changes in cell population composition. Using single-cell analyses, we identified 2350 genes significantly upregulated in navitoclax-resistant cells and derived an 18-gene navitoclax resistance signature. We assessed the navitoclax-response-predictive function of this signature in four additional TNBC cell lines in vitro and in silico in 619 cell lines treated with 251 different drugs. We observed a drug-specific predictive value in both experiments, suggesting that this signature could help guiding clinical biomarker studies involving navitoclax.

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          Most cited references74

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              The Sequence Alignment/Map format and SAMtools

              Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                08 September 2020
                September 2020
                : 12
                : 9
                : 2551
                Affiliations
                [1 ]Yale Cancer Center, Yale School of Medicine, New Haven, CT 06511, USA; michal.marczyk@ 123456yale.edu (M.M.); gpatward@ 123456its.jnj.com (G.A.P.); xiaotong.li@ 123456yale.edu (X.L.); vwali1@ 123456its.jnj.com (V.B.W.); abhishek.gupta@ 123456yale.edu (A.K.G.); manoj.pillai@ 123456yale.edu (M.M.P.); christos.hatzis@ 123456yale.edu (C.H.); vignesh.gunasekharan@ 123456yale.edu (V.G.)
                [2 ]Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
                [3 ]Computational Biology & Bioinformatics Program, Yale University, New Haven, CT 06511, USA; jun.zhao@ 123456yale.edu (J.Z.); rihao.qu@ 123456yale.edu (R.Q.); yuval.kluger@ 123456yale.edu (Y.K.)
                [4 ]Department of Pathology, Yale School of Medicine, New Haven, CT 06511, USA; qin.yan@ 123456yale.edu
                Author notes
                Author information
                https://orcid.org/0000-0003-2508-5736
                https://orcid.org/0000-0003-1644-6835
                https://orcid.org/0000-0003-4077-453X
                https://orcid.org/0000-0002-2471-8485
                Article
                cancers-12-02551
                10.3390/cancers12092551
                7563413
                32911681
                b97a6027-56b4-4cd8-937b-33cd6ba35784
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 August 2020
                : 04 September 2020
                Categories
                Article

                navitoclax,drug resistance,cancer therapy,signature,multi-omics,triple-negative breast cancer

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