5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      H3K9 methyltransferases and demethylases control lung tumor-propagating cells and lung cancer progression

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Epigenetic regulators are attractive anticancer targets, but the promise of therapeutic strategies inhibiting some of these factors has not been proven in vivo or taken into account tumor cell heterogeneity. Here we show that the histone methyltransferase G9a, reported to be a therapeutic target in many cancers, is a suppressor of aggressive lung tumor-propagating cells (TPCs). Inhibition of G9a drives lung adenocarcinoma cells towards the TPC phenotype by de-repressing genes which regulate the extracellular matrix. Depletion of G9a during tumorigenesis enriches tumors in TPCs and accelerates disease progression metastasis. Depleting histone demethylases represses G9a-regulated genes and TPC phenotypes. Demethylase inhibition impairs lung adenocarcinoma progression in vivo. Therefore, inhibition of G9a is dangerous in certain cancer contexts, and targeting the histone demethylases is a more suitable approach for lung cancer treatment. Understanding cellular context and specific tumor populations is critical when targeting epigenetic regulators in cancer for future therapeutic development.

          Abstract

          The effects of epigenetic regulators on different tumor cell populations can affect their potential as anticancer targets. In this study, the authors demonstrate that the histone methyltransferase G9a is a suppressor of lung tumor-propagating cells and tumor progression, acting through chromatin modification with MMP10 as one of its targets for metastasis regulation.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: found
          • Article: not found

          Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

          Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            G9a interacts with Snail and is critical for Snail-mediated E-cadherin repression in human breast cancer.

            Breast cancers are highly heterogeneous but can be grouped into subtypes based on several criteria, including level of expression of certain markers. Claudin-low breast cancer (CLBC) is associated with early metastasis and resistance to chemotherapy, while gene profiling indicates it is characterized by the expression of markers of epithelial-mesenchymal transition (EMT) - a phenotypic conversion linked with metastasis. Although the epigenetic program controlling the phenotypic and cellular plasticity of EMT remains unclear, one contributor may be methylation of the E-cadherin promoter, resulting in decreased E-cadherin expression, a hallmark of EMT. Indeed, reduced E-cadherin often occurs in CLBC and may contribute to the early metastasis and poor patient survival associated with this disease. Here, we have determined that methylation of histone H3 on lysine 9 (H3K9me2) is critical for promoter DNA methylation of E-cadherin in three TGF-β-induced EMT model cell lines, as well as in CLBC cell lines. Further, Snail interacted with G9a, a major euchromatin methyltransferase responsible for H3K9me2, and recruited G9a and DNA methyltransferases to the E-cadherin promoter for DNA methylation. Knockdown of G9a restored E-cadherin expression by suppressing H3K9me2 and blocking DNA methylation. This resulted in inhibition of cell migration and invasion in vitro and suppression of tumor growth and lung colonization in in vivo models of CLBC metastasis. Our study not only reveals a critical mechanism underlying the epigenetic regulation of EMT but also paves a way for the development of new treatment strategies for CLBC.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection

              Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
                Bookmark

                Author and article information

                Contributors
                carla.kim@childrens.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 November 2018
                19 November 2018
                2018
                : 9
                : 4559
                Affiliations
                [1 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Stem Cell Program, Division of Hematology/Oncology and Pulmonary and Respiratory Diseases, , Children’s Hospital Boston, ; Boston, MA 02115 USA
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Genetics, , Harvard Medical School, ; Boston, MA 02115 USA
                [3 ]ISNI 0000 0001 2109 4251, GRID grid.240324.3, Laura and Isaac Perlmutter Cancer Center, , New York University Langone Medical Center, ; New York, NY 10016 USA
                [4 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, Department of Toxicology and Cancer Biology, , University of Kentucky, ; Lexington, KY 40536 USA
                [5 ]ISNI 0000 0001 0670 2351, GRID grid.59734.3c, Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Tisch Cancer Institute, , Icahn School of Medicine at Mount Sinai, ; New York, NY 10029 USA
                [6 ]ISNI 0000 0004 0454 0768, GRID grid.412701.1, Department of Cancer Biology, Perelman School of Medicine at the University of Pennsylvania, , Abramson Cancer Center, ; Philadelphia, PA 19104 USA
                [7 ]ISNI 0000 0001 2179 9593, GRID grid.24827.3b, Division of Biomedical Informatics, Cincinnati Children’s Research Foundation, , University of Cincinnati College of Medicine, ; Cincinnati, OH 45229 USA
                [8 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Stem Cell Institute, ; Cambridge, MA 02138 USA
                Author information
                http://orcid.org/0000-0003-2000-5290
                Article
                7077
                10.1038/s41467-018-07077-1
                6242814
                30455465
                254748d7-93b1-4c01-8a4b-04557008a5ad
                © The Author(s) 2018

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 August 2018
                : 10 October 2018
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                Uncategorized
                Uncategorized

                Comments

                Comment on this article