551
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Rugo HS, Jacobs I, Sharma S, Scappaticci F, Paul TA, Jensen-Pergakes K, Malouf GG. The Promise for Histone Methyltransferase Inhibitors for Epigenetic Therapy in Clinical Oncology: A Narrative Review. Adv Ther 2020; 37:3059-3082. [PMID: 32445185 PMCID: PMC7467409 DOI: 10.1007/s12325-020-01379-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Indexed: 12/21/2022]
Abstract
Epigenetic processes are essential for normal development and the maintenance of tissue-specific gene expression in mammals. Changes in gene expression and malignant cellular transformation can result from disruption of epigenetic mechanisms, and global disruption in the epigenetic landscape is a key feature of cancer. The study of epigenetics in cancer has revealed that human cancer cells harbor both genetic alterations and epigenetic abnormalities that interplay at all stages of cancer development. Unlike genetic mutations, epigenetic aberrations are potentially reversible through epigenetic therapy, providing a therapeutically relevant treatment option. Histone methyltransferase inhibitors are emerging as an epigenetic therapy approach with great promise in the field of clinical oncology. The recent accelerated approval of the enhancer of zeste homolog 2 (EZH2; also known as histone-lysine N-methyltransferase EZH2) inhibitor tazemetostat for metastatic or locally advanced epithelioid sarcoma marks the first approval of such a compound for the treatment of cancer. Many other histone methyltransferase inhibitors are currently in development, some of which are being tested in clinical studies. This review focuses on histone methyltransferase inhibitors, highlighting their potential in the treatment of cancer. We also discuss the role for such epigenetic drugs in overcoming epigenetically driven drug resistance mechanisms, and their value in combination with other therapeutic approaches such as immunotherapy.
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552
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Abstract
How ‘difficult’ is it for somatic evolution to produce a cell that is capable of leaving the primary tumor and growing in a distant organ? In this issue, Reiter et al. assess genetic diversity across metastatic lesions and identify a tight selective bottleneck preceding distant metastasis.
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Affiliation(s)
- Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
- Arizona Centre for Cancer Evolution (ACE), Arizona State University, Tempe, AZ, USA.
| | - Darryl Shibata
- Arizona Centre for Cancer Evolution (ACE), Arizona State University, Tempe, AZ, USA.
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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553
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Do C, Dumont ELP, Salas M, Castano A, Mujahed H, Maldonado L, Singh A, DaSilva-Arnold SC, Bhagat G, Lehman S, Christiano AM, Madhavan S, Nagy PL, Green PHR, Feinman R, Trimble C, Illsley NP, Marder K, Honig L, Monk C, Goy A, Chow K, Goldlust S, Kaptain G, Siegel D, Tycko B. Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs. Genome Biol 2020; 21:153. [PMID: 32594908 PMCID: PMC7322865 DOI: 10.1186/s13059-020-02059-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 05/27/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Mapping of allele-specific DNA methylation (ASM) can be a post-GWAS strategy for localizing regulatory sequence polymorphisms (rSNPs). The advantages of this approach, and the mechanisms underlying ASM in normal and neoplastic cells, remain to be clarified. RESULTS We perform whole genome methyl-seq on diverse normal cells and tissues and three cancer types. After excluding imprinting, the data pinpoint 15,112 high-confidence ASM differentially methylated regions, of which 1838 contain SNPs in strong linkage disequilibrium or coinciding with GWAS peaks. ASM frequencies are increased in cancers versus matched normal tissues, due to widespread allele-specific hypomethylation and focal allele-specific hypermethylation in poised chromatin. Cancer cells show increased allele switching at ASM loci, but disruptive SNPs in specific classes of CTCF and transcription factor binding motifs are similarly correlated with ASM in cancer and non-cancer. Rare somatic mutations affecting these same motif classes track with de novo ASM. Allele-specific transcription factor binding from ChIP-seq is enriched among ASM loci, but most ASM differentially methylated regions lack such annotations, and some are found in otherwise uninformative "chromatin deserts." CONCLUSIONS ASM is increased in cancers but occurs by a shared mechanism involving disruptive SNPs in CTCF and transcription factor binding sites in both normal and neoplastic cells. Dense ASM mapping in normal plus cancer samples reveals candidate rSNPs that are difficult to find by other approaches. Together with GWAS data, these rSNPs can nominate specific transcriptional pathways in susceptibility to autoimmune, cardiometabolic, neuropsychiatric, and neoplastic diseases.
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Affiliation(s)
- Catherine Do
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA.
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA.
| | - Emmanuel L P Dumont
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - Martha Salas
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - Angelica Castano
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - Huthayfa Mujahed
- Department of Medicine, Huddinge, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Leonel Maldonado
- Department of Gynecology and Obstetrics, Johns Hopkins Medical Institutions, Baltimore, MD, 21287, USA
| | - Arunjot Singh
- Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Sonia C DaSilva-Arnold
- Department of Obstetrics and Gynecology, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - Govind Bhagat
- Department of Pathology & Cell Biology, Columbia University Medical Center, New York, NY, 10032, USA
- Division of Gastroenterology and Celiac Center, Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA
| | - Soren Lehman
- Department of Medicine, Huddinge, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Angela M Christiano
- Departments of Dermatology and Genetics and Development, Columbia University Medical Center, New York, NY, 10032, USA
| | - Subha Madhavan
- Lombardi Comprehensive Cancer Center of Georgetown University, Washington, DC, 20057, USA
| | | | - Peter H R Green
- Division of Gastroenterology and Celiac Center, Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA
| | - Rena Feinman
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
- Lombardi Comprehensive Cancer Center of Georgetown University, Washington, DC, 20057, USA
| | - Cornelia Trimble
- Department of Gynecology and Obstetrics, Johns Hopkins Medical Institutions, Baltimore, MD, 21287, USA
| | - Nicholas P Illsley
- Department of Obstetrics and Gynecology, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - Karen Marder
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, 10032, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA
| | - Lawrence Honig
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, 10032, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA
| | - Catherine Monk
- Departments of Psychiatry and Behavioral Medicine and Obstetrics and Gynecology, Columbia University Medical Center, New York, NY, 10032, USA
| | - Andre Goy
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
- Lombardi Comprehensive Cancer Center of Georgetown University, Washington, DC, 20057, USA
| | - Kar Chow
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
- Lombardi Comprehensive Cancer Center of Georgetown University, Washington, DC, 20057, USA
| | - Samuel Goldlust
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - George Kaptain
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | - David Siegel
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA
- Lombardi Comprehensive Cancer Center of Georgetown University, Washington, DC, 20057, USA
| | - Benjamin Tycko
- Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, 07110, USA.
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, 07601, USA.
- Lombardi Comprehensive Cancer Center of Georgetown University, Washington, DC, 20057, USA.
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554
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3Scover: Identifying Safeguard TF from Cell Type-TF Specificity Network by an Extended Minimum Set Cover Model. iScience 2020; 23:101227. [PMID: 32554189 PMCID: PMC7303665 DOI: 10.1016/j.isci.2020.101227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 04/27/2020] [Accepted: 05/28/2020] [Indexed: 11/22/2022] Open
Abstract
Transcription factors (TFs) define cellular identity either by activating target cell program or by silencing donor program as demonstrated by intensive cell reprogramming studies. Here, we propose an extended minimum set cover model with stable selection (3Scover) to systematically identify silencing TFs, named safeguard TFs, from omics data. First, a cell type-TF specificity network is constructed to systematically link cell types with their specifically expressed TFs. Then we search the minimum TF set to cover this network with “many but one specificity” characteristic and integrate many subsampling models for a stable solution. 3Scover identified 30 safeguard TFs in human and mouse. These safeguard TFs are significantly enriched in the experimentally discovered reprogramming panel with their protein-protein interactors. In addition, they tend to interact closely with chromatin regulators, negatively regulate transcription, and function earlier in development. Collectively, 3Scover allows us to probe master TFs and combinatorial regulation in controlling cell identity. Cell type-TF specificity networks reveal the relationships among TF and cell identity 3SCover extracts safeguard TFs by “many but one specificity” and parsimony principle Safeguard TFs are enriched in reprogramming panel and interact closely with CR Safeguard TFs are conserved in mouse and human
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555
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Raisner R, Bainer R, Haverty PM, Benedetti KL, Gascoigne KE. Super-enhancer acquisition drives oncogene expression in triple negative breast cancer. PLoS One 2020; 15:e0235343. [PMID: 32584896 PMCID: PMC7316302 DOI: 10.1371/journal.pone.0235343] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/14/2020] [Indexed: 01/12/2023] Open
Abstract
Triple Negative Breast Cancer (TNBC) is a heterogeneous disease lacking known molecular drivers and effective targeted therapies. Cytotoxic chemotherapy remains the mainstay of treatment for TNBCs, which have significantly poorer survival rates compared to other breast cancer subtypes. In addition to changes within the coding genome, aberrant enhancer activity is a well-established contributor to tumorigenesis. Here we use H3K27Ac chromatin immunoprecipitation followed by sequencing (ChIP-Seq) to map the active cis-regulatory landscape in TNBC. We identify distinct disease subtypes associated with specific enhancer activity, and over 2,500 unique superenhancers acquired by tumor cells but absent from normal breast tissue. To identify potential actionable disease drivers, we probed the dependency on genes that associate with tumor-specific enhancers by CRISPR screening. In this way we identify a number of tumor-specific dependencies, including a previously uncharacterized dependency on the TGFβ pseudo-receptor BAMBI.
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Affiliation(s)
- Ryan Raisner
- Department of Discovery Oncology, Genentech, Inc., South San Francisco, California, United States of America
| | - Russell Bainer
- Maze Therapeutics, South San Francisco, California, United States of America
| | - Peter M. Haverty
- Department of Bioinformatics, Genentech, Inc., South San Francisco, California, United States of America
| | - Kelli L. Benedetti
- Department of Cell and Tissue Biology, University of California, San Francisco, California, United States of America
| | - Karen E. Gascoigne
- Department of Discovery Oncology, Genentech, Inc., South San Francisco, California, United States of America
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556
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Dong K, Zhang S. Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data. Brief Bioinform 2020; 22:5860691. [PMID: 32578841 PMCID: PMC8138825 DOI: 10.1093/bib/bbaa120] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/16/2020] [Accepted: 05/18/2020] [Indexed: 12/11/2022] Open
Abstract
The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80 000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory networks show strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue-related TFs, and many of them may reveal novel regulatory mechanisms.
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Affiliation(s)
- Kangning Dong
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences
| | - Shihua Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences
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557
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Michida H, Imoto H, Shinohara H, Yumoto N, Seki M, Umeda M, Hayashi T, Nikaido I, Kasukawa T, Suzuki Y, Okada-Hatakeyama M. The Number of Transcription Factors at an Enhancer Determines Switch-like Gene Expression. Cell Rep 2020; 31:107724. [DOI: 10.1016/j.celrep.2020.107724] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/24/2019] [Accepted: 05/13/2020] [Indexed: 12/15/2022] Open
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558
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Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, Zhu J, Haussler D. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 2020; 38:675-678. [PMID: 32444850 PMCID: PMC7386072 DOI: 10.1038/s41587-020-0546-8] [Citation(s) in RCA: 2000] [Impact Index Per Article: 500.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Mary J Goldman
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.
| | - Brian Craft
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
| | | | | | | | - Akhil Kamath
- Birla Institute of Technology and Science, Goa, India
| | | | - Yunhai Luo
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Angela N Brooks
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jingchun Zhu
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
| | - David Haussler
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
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559
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Smith JP, Sheffield NC. Analytical Approaches for ATAC-seq Data Analysis. CURRENT PROTOCOLS IN HUMAN GENETICS 2020; 106:e101. [PMID: 32543102 PMCID: PMC8191135 DOI: 10.1002/cphg.101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
ATAC-seq, the assay for transposase-accessible chromatin using sequencing, is a quick and efficient approach to investigating the chromatin accessibility landscape. Investigating chromatin accessibility has broad utility for answering many biological questions, such as mapping nucleosomes, identifying transcription factor binding sites, and measuring differential activity of DNA regulatory elements. Because the ATAC-seq protocol is both simple and relatively inexpensive, there has been a rapid increase in the availability of chromatin accessibility data. Furthermore, advances in ATAC-seq protocols are rapidly extending its breadth to additional experimental conditions, cell types, and species. Accompanying the increase in data, there has also been an explosion of new tools and analytical approaches for analyzing it. Here, we explain the fundamentals of ATAC-seq data processing, summarize common analysis approaches, and review computational tools to provide recommendations for different research questions. This primer provides a starting point and a reference for analysis of ATAC-seq data. © 2020 Wiley Periodicals LLC.
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Affiliation(s)
- Jason P. Smith
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia
| | - Nathan C. Sheffield
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
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560
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Mariani L, Weinand K, Gisselbrecht SS, Bulyk ML. MEDEA: analysis of transcription factor binding motifs in accessible chromatin. Genome Res 2020; 30:736-748. [PMID: 32424069 PMCID: PMC7263192 DOI: 10.1101/gr.260877.120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 04/10/2020] [Indexed: 12/15/2022]
Abstract
Deciphering the interplay between chromatin accessibility and transcription factor (TF) binding is fundamental to understanding transcriptional regulation, control of cellular states, and the establishment of new phenotypes. Recent genome-wide chromatin accessibility profiling studies have provided catalogs of putative open regions, where TFs can recognize their motifs and regulate gene expression programs. Here, we present motif enrichment in differential elements of accessibility (MEDEA), a computational tool that analyzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible regions and lineage-specific motifs associated with TF binding therein. To benchmark MEDEA, we used a panel of reference cell lines profiled by ENCODE and curated by the ENCODE Project Consortium for the ENCODE-DREAM Challenge. By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints, we show that MEDEA improves the detection of motifs associated with known lineage specifiers. We then applied MEDEA to 610 ENCODE DNase-seq data sets, where it revealed significant motifs even when absolute enrichment was low and where it identified novel regulators, such as NRF1 in kidney development. Finally, we show that MEDEA performs well on both bulk and single-cell ATAC-seq data. MEDEA is publicly available as part of our Glossary-GENRE suite for motif enrichment analysis.
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Affiliation(s)
- Luca Mariani
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Kathryn Weinand
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.,Bioinformatics and Integrative Genomics PhD Program, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Stephen S Gisselbrecht
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.,Bioinformatics and Integrative Genomics PhD Program, Harvard University, Cambridge, Massachusetts 02138, USA.,Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
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561
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Wang J, Li S, Lin S, Fu S, Qiu L, Ding K, Liang K, Du H. B-cell lymphoma 2 family genes show a molecular pattern of spatiotemporal heterogeneity in gynaecologic and breast cancer. Cell Prolif 2020; 53:e12826. [PMID: 32419250 PMCID: PMC7309952 DOI: 10.1111/cpr.12826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 04/16/2020] [Accepted: 04/18/2020] [Indexed: 12/20/2022] Open
Abstract
Objectives BCL2 family proteins have been widely studied over the past decade due to their essential roles in apoptosis, oncogenesis and anti‐cancer therapy. However, the similarities and differences in the spatial pattern of the BCL2 gene family within the context of chromatin have not been well characterized. We sought to fill this knowledge gap by assessing correlations between gene alteration, gene expression, chromatin accessibility, and clinical outcomes in gynaecologic and breast cancer. Materials and methods In this study, the molecular characteristics of the BCL2 gene family in gynaecologic cancer were systematically analysed by integrating multi‐omics datasets, including transcriptomics, chromatin accessibility, copy number variation, methylomics and clinical outcome. Results We evaluated spatiotemporal associations between long‐range regulation peaks and tumour heterogeneity. Differential expression of the BCL2 family was coupled with widespread chromatin accessibility changes in gynaecologic cancer, accompanied by highly heterogeneous distal non‐coding accessibility surrounding the BCL2L1 gene loci. A relationship was also identified between gene expression, gene amplification, enhancer signatures, DNA methylation and overall patient survival. Prognostic analysis implied clinical correlations with BAD, BIK and BAK1. A shared protein regulatory network was established in which the co‐mutation signature of TP53 and PIK3CA was linked to the BCL2L1 gene. Conclusions Our results provide the first systematic identification of the molecular features of the BCL2 family under the spatial pattern of chromatin in gynaecologic and breast cancer. These findings broaden the therapeutic scope of the BCL2 family to the non‐coding region by including a significantly conserved distal region overlaying an enhancer.
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Affiliation(s)
- Jiajian Wang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Sidi Li
- Department of Obstetrics and Gynecology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Shudai Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Shuying Fu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Li Qiu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Ke Ding
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Keying Liang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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562
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Nordström KJV, Schmidt F, Gasparoni N, Salhab A, Gasparoni G, Kattler K, Müller F, Ebert P, Costa IG, Pfeifer N, Lengauer T, Schulz MH, Walter J. Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data. Nucleic Acids Res 2020; 47:10580-10596. [PMID: 31584093 PMCID: PMC6847574 DOI: 10.1093/nar/gkz799] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 08/31/2019] [Accepted: 09/11/2019] [Indexed: 01/01/2023] Open
Abstract
Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often ‘called’ by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.
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Affiliation(s)
| | - Florian Schmidt
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Excellence Cluster on Multimodal Computing and Interaction, Saarland University, 66123 Saarbrücken, Germany
| | - Nina Gasparoni
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
| | | | - Gilles Gasparoni
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
| | - Kathrin Kattler
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Müller
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Peter Ebert
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074 Aachen, Germany
| | | | - Nico Pfeifer
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Thomas Lengauer
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Marcel H Schulz
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,Excellence Cluster on Multimodal Computing and Interaction, Saarland University, 66123 Saarbrücken, Germany
| | - Jörn Walter
- Department of Genetics, Saarland University, 66123 Saarbrücken, Germany
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563
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Liu Y. Clinical implications of chromatin accessibility in human cancers. Oncotarget 2020; 11:1666-1678. [PMID: 32405341 PMCID: PMC7210018 DOI: 10.18632/oncotarget.27584] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 04/03/2020] [Indexed: 01/19/2023] Open
Abstract
Assay for transposase-accessible chromatin using sequencing (ATAC-seq) has not yet been widely used in cancer research. Clinical implications of chromatin accessibility assessed by ATAC-seq profiling in human cancers especially in a large patient cohort is largely unknown. In this study, we analyzed ATAC-seq data in 404 cancer patients from the Cancer Genome Atlas, representing the largest cancer patient cohort with ATAC-seq data, and correlated chromatin accessibility with patient demographics, tumor histology, molecular subtypes, and survival. Our results showed that chromatin accessibility varies from chromosome to chromosome, and is different in different genomic regions along the same chromosome. Chromatin accessibility especially on the X chromosome is strongly dependent on patient sex, but not much on patient age or tumor stage. Striking difference in chromatin accessibility is observed between lung adenocarcinoma and lung squamous cell carcinoma, the two most common histological subgroups in lung cancer. Furthermore, chromatin accessibility was different between basal and non-basal breast cancer. Finally, we identified prognostic peaks in the promoter regions that were significantly correlated with survival. In particular, we identified six peaks in the ESR1 gene promoter region in the ATAC-seq profiling and found that the peak about 247 bp away from the transcription start site was significantly associated with better survival. In conclusion, our study provides an alternative mechanism underlying tumor prognosis.
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Affiliation(s)
- Yuexin Liu
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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564
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Cistrome Data Browser and Toolkit: analyzing human and mouse genomic data using compendia of ChIP-seq and chromatin accessibility data. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0204-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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565
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Li J, Wang W, Zhang Y, Cieślik M, Guo J, Tan M, Green MD, Wang W, Lin H, Li W, Wei S, Zhou J, Li G, Jing X, Vatan L, Zhao L, Bitler B, Zhang R, Cho KR, Dou Y, Kryczek I, Chan TA, Huntsman D, Chinnaiyan AM, Zou W. Epigenetic driver mutations in ARID1A shape cancer immune phenotype and immunotherapy. J Clin Invest 2020; 130:2712-2726. [PMID: 32027624 PMCID: PMC7190935 DOI: 10.1172/jci134402] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/30/2020] [Indexed: 12/18/2022] Open
Abstract
Whether mutations in cancer driver genes directly affect cancer immune phenotype and T cell immunity remains a standing question. ARID1A is a core member of the polymorphic BRG/BRM-associated factor chromatin remodeling complex. ARID1A mutations occur in human cancers and drive cancer development. Here, we studied the molecular, cellular, and clinical impact of ARID1A aberrations on cancer immunity. We demonstrated that ARID1A aberrations resulted in limited chromatin accessibility to IFN-responsive genes, impaired IFN gene expression, anemic T cell tumor infiltration, poor tumor immunity, and shortened host survival in many human cancer histologies and in murine cancer models. Impaired IFN signaling was associated with poor immunotherapy response. Mechanistically, ARID1A interacted with EZH2 via its carboxyl terminal and antagonized EZH2-mediated IFN responsiveness. Thus, the interaction between ARID1A and EZH2 defines cancer IFN responsiveness and immune evasion. Our work indicates that cancer epigenetic driver mutations can shape cancer immune phenotype and immunotherapy.
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Affiliation(s)
- Jing Li
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Weichao Wang
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | | | - Marcin Cieślik
- Department of Pathology
- Department of Computational Medicine and Bioinformatics
- University of Michigan Rogel Cancer Center, and
| | - Jipeng Guo
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | | | - Michael D. Green
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Weimin Wang
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Heng Lin
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Wei Li
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Shuang Wei
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Jiajia Zhou
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Gaopeng Li
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | | | - Linda Vatan
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Lili Zhao
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Benjamin Bitler
- Gene Expression and Regulation Program, The Wistar Institute, Philadelphia, Pennsylvania, USA
| | - Rugang Zhang
- Gene Expression and Regulation Program, The Wistar Institute, Philadelphia, Pennsylvania, USA
| | - Kathleen R. Cho
- Department of Pathology
- University of Michigan Rogel Cancer Center, and
| | - Yali Dou
- Department of Pathology
- University of Michigan Rogel Cancer Center, and
| | - Ilona Kryczek
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Timothy A. Chan
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - David Huntsman
- Department of Molecular Oncology, British Columbia Cancer, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Arul M. Chinnaiyan
- Department of Pathology
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Department of Urology
- Michigan Center for Translational Pathology
- Howard Hughes Medical Institute, and
| | - Weiping Zou
- Department of Surgery, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Department of Pathology
- University of Michigan Rogel Cancer Center, and
- Graduate Program in Immunology and Graduate Program in Cancer Biology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
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566
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Kumar P, Kiran S, Saha S, Su Z, Paulsen T, Chatrath A, Shibata Y, Shibata E, Dutta A. ATAC-seq identifies thousands of extrachromosomal circular DNA in cancer and cell lines. SCIENCE ADVANCES 2020; 6:eaba2489. [PMID: 32440553 PMCID: PMC7228742 DOI: 10.1126/sciadv.aba2489] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 03/06/2020] [Indexed: 05/17/2023]
Abstract
Extrachromosomal circular DNAs (eccDNAs) are somatically mosaic and contribute to intercellular heterogeneity in normal and tumor cells. Because short eccDNAs are poorly chromatinized, we hypothesized that they are sequenced by tagmentation in ATAC-seq experiments without any enrichment of circular DNA. Indeed, ATAC-seq identified thousands of eccDNAs in cell lines that were validated by inverse PCR and by metaphase FISH. ATAC-seq in gliomas and glioblastomas identify hundreds of eccDNAs, including one containing the well-known EGFR gene amplicon from chr7. More than 18,000 eccDNAs, many carrying known cancer driver genes, are identified in a pan-cancer analysis of ATAC-seq libraries from 23 tumor types. Somatically mosaic eccDNAs are identified by ATAC-seq even before amplification is recognized by genome-wide copy number variation measurements. Thus, ATAC-seq is a sensitive method to detect eccDNA present in a tumor at the pre-amplification stage and can be used to predict resistance to therapy.
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Affiliation(s)
- Pankaj Kumar
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Shashi Kiran
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Shekhar Saha
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Zhangli Su
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Teressa Paulsen
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Ajay Chatrath
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Yoshiyuki Shibata
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Etsuko Shibata
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
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567
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Are Parallel Proliferation Pathways Redundant? Trends Biochem Sci 2020; 45:554-563. [PMID: 32345469 DOI: 10.1016/j.tibs.2020.03.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 03/16/2020] [Accepted: 03/30/2020] [Indexed: 12/14/2022]
Abstract
Are the receptor tyrosine kinase (RTK) and JAK-STAT-driven proliferation pathways 'parallel' or 'redundant'? And what about those of K-Ras4B versus N-Ras? 'Parallel' proliferation pathways accomplish a similar drug resistance outcome. Thus, are they 'redundant'? In this paper, it is argued that there is a fundamental distinction between 'parallel' and 'redundant'. Cellular proliferation pathways are influenced by the genome sequence, 3D organization and chromatin accessibility, and determined by protein availability prior to cancer emergence. In the opinion presented, if they operate the same downstream protein families, they are redundant; if evolutionary-independent, they are parallel. Thus, RTK and JAK-STAT-driven proliferation pathways are parallel; those of Ras isoforms are redundant. Our Precision Medicine Call to map cancer proliferation pathways is vastly important since it can expedite effective therapeutics.
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568
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Pan J, Silva TC, Gull N, Yang Q, Plummer JT, Chen S, Daigo K, Hamakubo T, Gery S, Ding LW, Jiang YY, Hu S, Xu LY, Li EM, Ding Y, Klempner SJ, Gayther SA, Berman BP, Koeffler HP, Lin DC. Lineage-Specific Epigenomic and Genomic Activation of Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas. Cancer Res 2020; 80:2722-2736. [PMID: 32332020 DOI: 10.1158/0008-5472.can-20-0390] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 12/24/2022]
Abstract
Gastrointestinal adenocarcinomas (GIAC) of the tubular gastrointestinal (GI) tract including esophagus, stomach, colon, and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIAC are poorly characterized. Using 907 GIAC samples from The Cancer Genome Atlas, we applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks and identify a list of functionally hyperactive master regulator (MR) TF shared across different GIAC. The top candidate HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. A complex interplay between the HNF4A promoter and three distal enhancer elements was coordinated by GIAC-specific MRTF including ELF3, GATA4, GATA6, and KLF5. HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptional activation of many downstream targets, including HNF1A and factors of interleukin signaling, in a lineage-specific manner. Overall, our study provides new insights into the GIAC-specific gene regulatory networks and identifies potential therapeutic strategies against these common cancers. SIGNIFICANCE: These findings show that GIAC-specific master regulatory transcription factors control HNF4A via three distal enhancers to promote GIAC cell proliferation and survival. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/13/2722/F1.large.jpg.
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Affiliation(s)
- Jian Pan
- Department of Hematology and Oncology, Children's Hospital of Soochow University, Suzhou, China.,Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tiago C Silva
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Nicole Gull
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, California
| | - Qian Yang
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.,Institute of Oncologic Pathology, Medical College of Shantou University, Shantou, China
| | - Jasmine T Plummer
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, California
| | - Stephanie Chen
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, California
| | - Kenji Daigo
- Department of Protein-protein Interaction Research, Institute for Advanced Medical Sciences, Nippon Medical School, Kawasaki, Kanagawa, Japan
| | - Takao Hamakubo
- Department of Protein-protein Interaction Research, Institute for Advanced Medical Sciences, Nippon Medical School, Kawasaki, Kanagawa, Japan
| | - Sigal Gery
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Ling-Wen Ding
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Yan-Yi Jiang
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Shaoyan Hu
- Department of Hematology and Oncology, Children's Hospital of Soochow University, Suzhou, China
| | - Li-Yan Xu
- Institute of Oncologic Pathology, Medical College of Shantou University, Shantou, China
| | - En-Min Li
- Institute of Oncologic Pathology, Medical College of Shantou University, Shantou, China
| | - Yanbing Ding
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou University, Jiangsu, China
| | - Samuel J Klempner
- Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Simon A Gayther
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, California
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, California. .,Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - H Phillip Koeffler
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.,Cancer Science Institute of Singapore, National University of Singapore, Singapore.,National University Cancer Institute, National University Hospital Singapore, Singapore
| | - De-Chen Lin
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
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569
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Reske JJ, Wilson MR, Chandler RL. ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation. Epigenetics Chromatin 2020; 13:22. [PMID: 32321567 PMCID: PMC7178746 DOI: 10.1186/s13072-020-00342-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 04/11/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chromatin dysregulation is associated with developmental disorders and cancer. Numerous methods for measuring genome-wide chromatin accessibility have been developed in the genomic era to interrogate the function of chromatin regulators. A recent technique which has gained widespread use due to speed and low input requirements with native chromatin is the Assay for Transposase-Accessible Chromatin, or ATAC-seq. Biologists have since used this method to compare chromatin accessibility between two cellular conditions. However, approaches for calculating differential accessibility can yield conflicting results, and little emphasis is placed on choice of normalization method during differential ATAC-seq analysis, especially when global chromatin alterations might be expected. RESULTS Using an in vivo ATAC-seq data set generated in our recent report, we observed differences in chromatin accessibility patterns depending on the data normalization method used to calculate differential accessibility. This observation was further verified on published ATAC-seq data from yeast. We propose a generalized workflow for differential accessibility analysis using ATAC-seq data. We further show this workflow identifies sites of differential chromatin accessibility that correlate with gene expression and is sensitive to differential analysis using negative controls. CONCLUSIONS We argue that researchers should systematically compare multiple normalization methods before continuing with differential accessibility analysis. ATAC-seq users should be aware of the interpretations of potential bias within experimental data and the assumptions of the normalization method implemented.
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Affiliation(s)
- Jake J Reske
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Mike R Wilson
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Ronald L Chandler
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA. .,Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, 49503, USA.
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570
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Hook PW, McCallion AS. Leveraging mouse chromatin data for heritability enrichment informs common disease architecture and reveals cortical layer contributions to schizophrenia. Genome Res 2020; 30:528-539. [PMID: 32303558 PMCID: PMC7197474 DOI: 10.1101/gr.256578.119] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 03/30/2020] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies have implicated thousands of noncoding variants across common human phenotypes. However, they cannot directly inform the cellular context in which disease-associated variants act. Here, we use open chromatin profiles from discrete mouse cell populations to address this challenge. We applied stratified linkage disequilibrium score regression and evaluated heritability enrichment in 64 genome-wide association studies, emphasizing schizophrenia. We provide evidence that mouse-derived human open chromatin profiles can serve as powerful proxies for difficult to obtain human cell populations, facilitating the illumination of common disease heritability enrichment across an array of human phenotypes. We demonstrate that signatures from discrete subpopulations of cortical excitatory and inhibitory neurons are significantly enriched for schizophrenia heritability with maximal enrichment in cortical layer V excitatory neurons. We also show that differences between schizophrenia and bipolar disorder are concentrated in excitatory neurons in cortical layers II-III, IV, and V, as well as the dentate gyrus. Finally, we leverage these data to fine-map variants in 177 schizophrenia loci nominating variants in 104/177. We integrate these data with transcription factor binding site, chromatin interaction, and validated enhancer data, placing variants in the cellular context where they may modulate risk.
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Affiliation(s)
- Paul W Hook
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Andrew S McCallion
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Comparative and Molecular Pathobiology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
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571
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Dinh TA, Sritharan R, Smith FD, Francisco AB, Ma RK, Bunaciu RP, Kanke M, Danko CG, Massa AP, Scott JD, Sethupathy P. Hotspots of Aberrant Enhancer Activity in Fibrolamellar Carcinoma Reveal Candidate Oncogenic Pathways and Therapeutic Vulnerabilities. Cell Rep 2020; 31:107509. [PMID: 32294439 PMCID: PMC7474926 DOI: 10.1016/j.celrep.2020.03.073] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/11/2020] [Accepted: 03/23/2020] [Indexed: 02/07/2023] Open
Abstract
Fibrolamellar carcinoma (FLC) is a rare, therapeutically intractable liver cancer that disproportionately affects youth. Although FLC tumors exhibit a distinct gene expression profile, the chromatin regulatory landscape and the genes most critical for tumor cell survival remain unclear. Here, we use chromatin run-on sequencing to discover ∼7,000 enhancers and 141 enhancer hotspots activated in FLC relative to nonmalignant liver. Bioinformatic analyses reveal aberrant ERK/MEK signaling and candidate master transcriptional regulators. We also define the genes most strongly associated with hotspots of FLC enhancer activity, including CA12 and SLC16A14. Treatment of FLC cell models with inhibitors of CA12 or SLC16A14 independently reduce cell viability and/or significantly enhance the effect of the MEK inhibitor cobimetinib. These findings highlight molecular targets for drug development, as well as drug combination approaches.
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Affiliation(s)
- Timothy A Dinh
- Curriculum in Genetics & Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Ramja Sritharan
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - F Donelson Smith
- Department of Pharmacology, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Adam B Francisco
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Rosanna K Ma
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Rodica P Bunaciu
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Matt Kanke
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Charles G Danko
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA; Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Andrew P Massa
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - John D Scott
- Department of Pharmacology, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Praveen Sethupathy
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA.
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572
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Rice SJ, Beier F, Young DA, Loughlin J. Interplay between genetics and epigenetics in osteoarthritis. Nat Rev Rheumatol 2020; 16:268-281. [PMID: 32273577 DOI: 10.1038/s41584-020-0407-3] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2020] [Indexed: 12/15/2022]
Abstract
Research into the molecular genetics of osteoarthritis (OA) has been substantially bolstered in the past few years by the implementation of powerful genome-wide scans that have revealed a large number of novel risk loci associated with the disease. This refreshing wave of discovery has occurred concurrently with epigenetic studies of joint tissues that have examined DNA methylation, histone modifications and regulatory RNAs. These epigenetic analyses have involved investigations of joint development, homeostasis and disease and have used both human samples and animal models. What has become apparent from a comparison of these two complementary approaches is that many OA genetic risk signals interact with, map to or correlate with epigenetic mediators. This discovery implies that epigenetic mechanisms, and their effect on gene expression, are a major conduit through which OA genetic risk polymorphisms exert their functional effects. This observation is particularly exciting as it provides mechanistic insight into OA susceptibility. Furthermore, this knowledge reveals avenues for attenuating the negative effect of risk-conferring alleles by exposing the epigenome as an exploitable target for therapeutic intervention in OA.
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Affiliation(s)
- Sarah J Rice
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Frank Beier
- Department of Physiology and Pharmacology, The University of Western Ontario, London, ON, Canada.,Western Bone and Joint Institute, The University of Western Ontario, London, ON, Canada
| | - David A Young
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - John Loughlin
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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573
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Wu Y, Zhao J, Zhu H, Fan Z, Yuan X, Chen S, Mao R, Fan Y. SPACE: a web server for linking chromatin accessibility with clinical phenotypes and the immune microenvironment in pan-cancer analysis. Cell Mol Immunol 2020; 17:1294-1296. [PMID: 32238917 DOI: 10.1038/s41423-020-0416-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 03/12/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
- Yingcheng Wu
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China.,Laboratory of Medical Science, School of Medicine, Nantong University, Jiangsu, 226001, China
| | - Jingwei Zhao
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China.,Department of Pathophysiology, School of Medicine, Nantong University, Jiangsu, 226001, China
| | - Haoliang Zhu
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China.,Department of Pathophysiology, School of Medicine, Nantong University, Jiangsu, 226001, China
| | - Zhiwei Fan
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China.,Laboratory of Medical Science, School of Medicine, Nantong University, Jiangsu, 226001, China
| | - Xinpei Yuan
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China.,Department of Pathophysiology, School of Medicine, Nantong University, Jiangsu, 226001, China
| | - Shiyin Chen
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China.,Department of Pathophysiology, School of Medicine, Nantong University, Jiangsu, 226001, China
| | - Renfang Mao
- Department of Pathophysiology, School of Medicine, Nantong University, Jiangsu, 226001, China.
| | - Yihui Fan
- Department of Pathogenic Biology, School of Medicine, Nantong University, Jiangsu, 226001, China. .,Laboratory of Medical Science, School of Medicine, Nantong University, Jiangsu, 226001, China.
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574
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Wang Y, Zhang X, Song Q, Hou Y, Liu J, Sun Y, Wang P. Characterization of the chromatin accessibility in an Alzheimer's disease (AD) mouse model. ALZHEIMERS RESEARCH & THERAPY 2020; 12:29. [PMID: 32293531 PMCID: PMC7092509 DOI: 10.1186/s13195-020-00598-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/11/2020] [Indexed: 02/06/2023]
Abstract
Background The pathological hallmarks of Alzheimer’s disease (AD) involve alterations in the expression of numerous genes associated with transcriptional levels, which are determined by chromatin accessibility. Here, the landscape of chromatin accessibility was studied to understand the outline of the transcription and expression of AD-associated metabolism genes in an AD mouse model. Methods The assay for transposase-accessible chromatin by sequencing (ATAC-seq) was used to investigate the AD-associated chromatin reshaping in the APPswe/PS1dE9 (APP/PS1) mouse model. ATAC-seq data in the hippocampus of 8-month-old APP/PS1 mice were generated, and the relationship between chromatin accessibility and gene expression was analyzed in combination with RNA sequencing. Gene ontology (GO) analysis was applied to elucidate biological processes and signaling pathways altered in APP/PS1 mice. Critical transcription factors were identified; alterations in chromatin accessibility were further confirmed using chromatin immunoprecipitation assays. Results We identified 1690 increased AD-associated chromatin-accessible regions in the hippocampal tissues of APP/PS1 mice. These regions were enriched in genes related to diverse signaling pathways, including the PI3K-Akt, Hippo, TGF-β, and Jak-Stat signaling pathways, which play essential roles in regulating cell proliferation, apoptosis, and inflammatory responses. A total of 1003 decreased chromatin-accessible regions were considered to be related with declined AD-associated biological processes including cellular response to hyperoxia and insulin stimulus, synaptic transmission, and positive regulation of autophagy. In the APP/PS1 hippocampus, 1090 genes were found to be upregulated and 1081 downregulated. Interestingly, enhanced ATAC-seq signal was found in approximately 740 genes, with 43 exhibiting upregulated mRNA levels. Several genes involved in AD development were found to have a significantly increased expression in APP/PS1 mice compared to controls, including Sele, Clec7a, Cst7, and Ccr6. The signatures of numerous transcription factors, including Olig2, NeuroD1, TCF4, and NeuroG2, were found enriched in the AD-associated accessible chromatin regions. The transcription-activating marks of H3K4me3 and H3K27ac were also found increased in the promoters of these genes. These results indicate that the mechanism for the upregulation of genes could be attributed to the enrichment of open chromatin regions with transcription factors motifs and the histone marks H3K4me3 and H3K27ac. Conclusion Our study reveals that alterations in chromatin accessibility may be an initial mechanism in AD pathogenesis. Supplementary information Supplementary information accompanies this paper at 10.1186/s13195-020-00598-2.
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Affiliation(s)
- Yaqi Wang
- Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China
| | - Xiaomin Zhang
- Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China
| | - Qiao Song
- Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China
| | - Yuli Hou
- Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China
| | - Jing Liu
- Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China
| | - Yu Sun
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China.
| | - Peichang Wang
- Clinical Laboratory of Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China.
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575
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Guo T, Zambo KDA, Zamuner FT, Ou T, Hopkins C, Kelley DZ, Wulf HA, Winkler E, Erbe R, Danilova L, Considine M, Sidransky D, Favorov A, Florea L, Fertig EJ, Gaykalova DA. Chromatin structure regulates cancer-specific alternative splicing events in primary HPV-related oropharyngeal squamous cell carcinoma. Epigenetics 2020; 15:959-971. [PMID: 32164487 DOI: 10.1080/15592294.2020.1741757] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV+ OPSCC) represents a unique disease entity within head and neck cancer with rising incidence. Previous work has shown that alternative splicing events (ASEs) are prevalent in HPV+ OPSCC, but further validation is needed to understand the regulation of this process and its role in these tumours. In this study, eleven ASEs (GIT2, CTNNB1, MKNK2, MRPL33, SIPA1L3, SNHG6, SYCP2, TPRG1, ZHX2, ZNF331, and ELOVL1) were selected for validation from 109 previously published candidate ASEs to elucidate the post-transcriptional mechanisms of oncogenesis in HPV+ disease. In vitro qRT-PCR confirmed differential expression of 9 of 11 ASE candidates, and in silico analysis within the TCGA cohort confirmed 8 of 11 candidates. Six ASEs (MRPL33, SIPA1L3, SNHG6, TPRG1, ZHX2, and ELOVL1) showed significant differential expression across both methods. Further evaluation of chromatin modification revealed that ASEs strongly correlated with cancer-specific distribution of acetylated lysine 27 of histone 3 (H3K27ac). Subsequent epigenetic treatment of HPV+ HNSCC cell lines (UM-SCC-047 and UPCI-SCC-090) with JQ1 not only induced downregulation of cancer-specific ASE isoforms, but also growth inhibition in both cell lines. The UPCI-SCC-090 cell line, with greater ASE expression, also showed more significant growth inhibition after JQ1 treatment. This study confirms several novel cancer-specific ASEs in HPV+OPSCC and provides evidence for the role of chromatin modifications in regulation of alternative splicing in HPV+OPSCC. This highlights the role of epigenetic changes in the oncogenesis of HPV+OPSCC, which represents a unique, unexplored target for therapeutics that can alter the global post-transcriptional landscape.
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Affiliation(s)
- Theresa Guo
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Kristina Diana A Zambo
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Fernando T Zamuner
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Tingting Ou
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Christopher Hopkins
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Dylan Z Kelley
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Hildegard A Wulf
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Eli Winkler
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Rossin Erbe
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Ludmila Danilova
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA.,Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences , Moscow, Russia
| | - Michael Considine
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - David Sidransky
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA.,Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA
| | - Alexander Favorov
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA.,Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences , Moscow, Russia
| | - Liliana Florea
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University , Baltimore, MD, USA
| | - Elana J Fertig
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA.,Department of Applied Mathematics and Statistics, Johns Hopkins University , Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine , MD, Baltimore, USA
| | - Daria A Gaykalova
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine , Baltimore, MD, USA.,Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, USA
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576
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Reavis HD, Drapkin R. The tubal epigenome - An emerging target for ovarian cancer. Pharmacol Ther 2020; 210:107524. [PMID: 32197795 DOI: 10.1016/j.pharmthera.2020.107524] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 02/18/2020] [Accepted: 02/26/2020] [Indexed: 02/07/2023]
Abstract
Ovarian cancer is the most lethal gynecologic malignancy in the United States. The mortality of this disease is primarily attributed to challenges in early detection and therapeutic resistance. Recent studies indicate that the majority of high-grade serous ovarian carcinomas (HGSCs) originate from aberrant fallopian tube epithelial (FTE) cells. This shift in thinking about ovarian cancer pathogenesis has been met with an effort to identify the early genetic and epigenetic changes that underlie the transformation of normal FTE cells and prompt them to migrate and colonize the ovary, ultimately giving rise to aggressive HGSC. While identification of these early changes is important for biomarker discovery, the emergence of epigenetic alterations in FTE chromatin may also provide new opportunities for early detection, prevention, and therapeutic intervention. Here we provide a comprehensive overview of the current knowledge regarding early epigenetic reprogramming that precedes HGSC tumor development, the way that these alterations affect both intrinsic and extrinsic tumor properties, and how the epigenome may be targeted to thwart HGSC tumorigenesis.
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Affiliation(s)
- Hunter D Reavis
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Graduate Program in Cell and Molecular Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Cancer Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ronny Drapkin
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Graduate Program in Cell and Molecular Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
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577
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Xie L, Dong P, Chen X, Hsieh THS, Banala S, De Marzio M, English BP, Qi Y, Jung SK, Kieffer-Kwon KR, Legant WR, Hansen AS, Schulmann A, Casellas R, Zhang B, Betzig E, Lavis LD, Chang HY, Tjian R, Liu Z. 3D ATAC-PALM: super-resolution imaging of the accessible genome. Nat Methods 2020; 17:430-436. [PMID: 32203384 PMCID: PMC7207063 DOI: 10.1038/s41592-020-0775-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/04/2020] [Accepted: 02/11/2020] [Indexed: 12/22/2022]
Abstract
To image the accessible genome at nanometer scale in situ, we developed 3D ATAC-PALM which integrates Assay for Transposase-Accessible Chromatin with visualization, PALM super-resolution imaging and lattice light-sheet microscopy. Multiplexed with Oligopaint DNA-FISH, RNA-FISH and protein fluorescence, 3D ATAC-PALM connected microscopy and genomic data, revealing spatially-segregated accessible chromatin domains (ACDs) that enclose active chromatin and transcribed genes. Using these methods to analyze genetically perturbed cells, we demonstrated that genome architectural protein CTCF prevents excessive clustering of accessible chromatin and decompacts ACDs. These results highlight 3D ATAC-PALM as a useful tool to probe the structure and organizing mechanism of the genome.
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Affiliation(s)
- Liangqi Xie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA, USA.,Howard Hughes Medical Institute, Berkeley, CA, USA
| | - Peng Dong
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Xingqi Chen
- Center for Personal Dynamic Regulomes and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.,Department of Immunology, Genomics and Pathology, Uppsala University, Uppsala, Sweden
| | - Tsung-Han S Hsieh
- Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA, USA.,Howard Hughes Medical Institute, Berkeley, CA, USA
| | - Sambashiva Banala
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Margherita De Marzio
- Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian P English
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Yifeng Qi
- Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Seol Kyoung Jung
- Lymphocyte Nuclear Biology, NIAMS and Center of Cancer Research, NCI, NIH, Bethesda, MD, USA
| | - Kyong-Rim Kieffer-Kwon
- Lymphocyte Nuclear Biology, NIAMS and Center of Cancer Research, NCI, NIH, Bethesda, MD, USA
| | - Wesley R Legant
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Anders S Hansen
- Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA, USA
| | - Anton Schulmann
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Rafael Casellas
- Lymphocyte Nuclear Biology, NIAMS and Center of Cancer Research, NCI, NIH, Bethesda, MD, USA
| | - Bin Zhang
- Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eric Betzig
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Luke D Lavis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Robert Tjian
- Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA, USA. .,Howard Hughes Medical Institute, Berkeley, CA, USA.
| | - Zhe Liu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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578
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Lorbeer FK, Hockemeyer D. TERT promoter mutations and telomeres during tumorigenesis. Curr Opin Genet Dev 2020; 60:56-62. [PMID: 32163830 DOI: 10.1016/j.gde.2020.02.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/26/2020] [Accepted: 02/02/2020] [Indexed: 01/04/2023]
Abstract
Telomerase regulation and telomere shortening act as a strong tumor suppressor mechanism in human somatic cells. Point mutations in the promoter of telomerase reverse transcriptase (TERT) are the most frequent non-coding mutation in cancer. These TERT promoter mutations (TPMs) create de novo ETS factor binding sites upstream of the start codon of the gene, which can be bound by different ETS factors. TPMs can occur early during tumorigenesis and are thought to be among the first mutations in melanoma, glioblastoma and hepatocellular carcinoma. Despite their association with increased TERT levels, TPMs do not prohibit telomere shortening and TPM-harboring cancers present with short telomeres. Their short telomere length combined with their high prevalence and specificity for cancer makes TPMs an attractive target for future therapeutic exploitation of telomerase inhibition and telomere deprotection-induced cell death.
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Affiliation(s)
- Franziska K Lorbeer
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Dirk Hockemeyer
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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579
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Cai WL, Greer CB, Chen JF, Arnal-Estapé A, Cao J, Yan Q, Nguyen DX. Specific chromatin landscapes and transcription factors couple breast cancer subtype with metastatic relapse to lung or brain. BMC Med Genomics 2020; 13:33. [PMID: 32143622 PMCID: PMC7060551 DOI: 10.1186/s12920-020-0695-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 02/11/2020] [Indexed: 12/17/2022] Open
Abstract
Background Few somatic mutations have been linked to breast cancer metastasis, whereas transcriptomic differences among primary tumors correlate with incidence of metastasis, especially to the lungs and brain. However, the epigenomic alterations and transcription factors (TFs) which underlie these alterations remain unclear. Methods To identify these, we performed RNA-seq, Chromatin Immunoprecipitation and sequencing (ChIP-seq) and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) of the MDA-MB-231 cell line and its brain (BrM2) and lung (LM2) metastatic sub-populations. We incorporated ATAC-seq data from TCGA to assess metastatic open chromatin signatures, and gene expression data from human metastatic datasets to nominate transcription factor biomarkers. Results Our integrated epigenomic analyses found that lung and brain metastatic cells exhibit both shared and distinctive signatures of active chromatin. Notably, metastatic sub-populations exhibit increased activation of both promoters and enhancers. We also integrated these data with chromosome conformation capture coupled with ChIP-seq (HiChIP) derived enhancer-promoter interactions to predict enhancer-controlled pathway alterations. We found that enhancer changes are associated with endothelial cell migration in LM2, and negative regulation of epithelial cell proliferation in BrM2. Promoter changes are associated with vasculature development in LM2 and homophilic cell adhesion in BrM2. Using ATAC-seq, we identified a metastasis open-chromatin signature that is elevated in basal-like and HER2-enriched breast cancer subtypes and associates with worse prognosis in human samples. We further uncovered TFs associated with the open chromatin landscapes of metastatic cells and whose expression correlates with risk for metastasis. While some of these TFs are associated with primary breast tumor subtypes, others more specifically correlate with lung or brain metastasis. Conclusions We identify distinctive epigenomic properties of breast cancer cells that metastasize to the lung and brain. We also demonstrate that signatures of active chromatin sites are partially linked to human breast cancer subtypes with poor prognosis, and that specific TFs can independently distinguish lung and brain relapse.
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Affiliation(s)
- Wesley L Cai
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA
| | - Celeste B Greer
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA.,Present address: Department of Pharmacology, Vanderbilt University School of Medicine, 2209 Garland Ave, Nashville, TN, 37240-0002, USA
| | - Jocelyn F Chen
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA
| | - Anna Arnal-Estapé
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA.,Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA
| | - Jian Cao
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA.,Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA.,Present address: Rutgers Cancer Institute of New Jersey, Rutgers, 195 Little Albany St, New Brunswick, NJ, 08903-2681, USA
| | - Qin Yan
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA. .,Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA. .,Yale Stem Cell Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA. .,Department of Pathology, Yale School of Medicine, P.O. Box 208023, New Haven, CT, 06520-8023, USA.
| | - Don X Nguyen
- Department of Pathology, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA. .,Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA. .,Yale Stem Cell Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA. .,Department of Pathology, Yale School of Medicine, P.O. Box 208023, New Haven, CT, 06520-8023, USA. .,Department of Medicine (Medical Oncology), Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA.
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580
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Przytycki PF, Singh M. Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations. Cell Syst 2020; 10:193-203.e4. [PMID: 32078798 PMCID: PMC7457951 DOI: 10.1016/j.cels.2020.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/04/2019] [Accepted: 01/22/2020] [Indexed: 01/23/2023]
Abstract
Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual's cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.
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Affiliation(s)
- Pawel F Przytycki
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
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581
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Sa JK, Hong JY, Lee IK, Kim JS, Sim MH, Kim HJ, An JY, Sohn TS, Lee JH, Bae JM, Kim S, Kim KM, Kim ST, Park SH, Park JO, Lim HY, Kang WK, Her NG, Lee Y, Cho HJ, Shin YJ, Kim M, Koo H, Kim M, Seo YJ, Kim JY, Choi MG, Nam DH, Lee J. Comprehensive pharmacogenomic characterization of gastric cancer. Genome Med 2020; 12:17. [PMID: 32070411 PMCID: PMC7029441 DOI: 10.1186/s13073-020-0717-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 01/31/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Gastric cancer is among the most lethal human malignancies. Previous studies have identified molecular aberrations that constitute dynamic biological networks and genomic complexities of gastric tumors. However, the clinical translation of molecular-guided targeted therapy is hampered by challenges. Notably, solid tumors often harbor multiple genetic alterations, complicating the development of effective treatments. METHODS To address such challenges, we established a comprehensive dataset of molecularly annotated patient derivatives coupled with pharmacological profiles for 60 targeted agents to explore dynamic pharmacogenomic interactions in gastric cancers. RESULTS We identified lineage-specific drug sensitivities based on histopathological and molecular subclassification, including substantial sensitivities toward VEGFR and EGFR inhibition therapies in diffuse- and signet ring-type gastric tumors, respectively. We identified potential therapeutic opportunities for WNT pathway inhibitors in ALK-mutant tumors, a significant association between PIK3CA-E542K mutation and AZD5363 response, and transcriptome expression of RNF11 as a potential predictor of response to gefitinib. CONCLUSIONS Collectively, our results demonstrate the feasibility of drug screening combined with tumor molecular characterization to facilitate personalized therapeutic regimens for gastric tumors.
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Affiliation(s)
- Jason K Sa
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jung Yong Hong
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - In-Kyoung Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ju-Sun Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Moon-Hee Sim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ha Jung Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji Yeong An
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Tae Sung Sohn
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Joon Ho Lee
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jae Moon Bae
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sung Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyoung-Mee Kim
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Se Hoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Joon Oh Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ho Yeong Lim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Ki Kang
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Nam-Gu Her
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Yeri Lee
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Cho
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Yong Jae Shin
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Misuk Kim
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Harim Koo
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Mirinae Kim
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Yun Jee Seo
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Ja Yeon Kim
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
| | - Min-Gew Choi
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Do-Hyun Nam
- Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Jeeyun Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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582
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Zhou Y, Sun Y, Huang D, Li MJ. epiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis. Front Genet 2020; 11:53. [PMID: 32117461 PMCID: PMC7029718 DOI: 10.3389/fgene.2020.00053] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 01/17/2020] [Indexed: 12/18/2022] Open
Abstract
High-throughput genome-wide epigenomic assays, such as ChIP-seq, DNase-seq and ATAC-seq, have profiled a huge number of functional elements across numerous human tissues/cell types, which provide an unprecedented opportunity to interpret human genome and disease in context-dependent manner. Colocalization analysis determines whether genomic features are functionally related to a given search and will facilitate identifying the underlying biological functions characterizing intricate relationships with queries for genomic regions. Existing colocalization methods leveraged diverse assumptions and background models to assess the significance of enrichment, however, they only provided limited and predefined sets of epigenomic features. Here, we comprehensively collected and integrated over 44,385 bulk or single-cell epigenomic assays across 53 human tissues/cell types, such as transcription factor binding, histone modification, open chromatin and transcriptional event. By classifying these profiles into hierarchy of tissue/cell type, we developed a web portal, epiCOLOC (http://mulinlab.org/epicoloc or http://mulinlab.tmu.edu.cn/epicoloc), for users to perform context-dependent colocalization analysis in a convenient way.
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Affiliation(s)
- Yao Zhou
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yongzheng Sun
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Dandan Huang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.,Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China
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583
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Li Y, Li X, Yang Y, Li M, Qian F, Tang Z, Zhao J, Zhang J, Bai X, Jiang Y, Zhou J, Zhang Y, Zhou L, Xie J, Li E, Wang Q, Li C. TRlnc: a comprehensive database for human transcriptional regulatory information of lncRNAs. Brief Bioinform 2020; 22:1929-1939. [PMID: 32047897 DOI: 10.1093/bib/bbaa011] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/09/2020] [Indexed: 12/20/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
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584
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Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. Genome Biol 2020; 21:22. [PMID: 32014034 PMCID: PMC6996192 DOI: 10.1186/s13059-020-1929-3] [Citation(s) in RCA: 204] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 01/08/2020] [Indexed: 12/16/2022] Open
Abstract
Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.
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Affiliation(s)
- Feng Yan
- Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - David R Powell
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - David J Curtis
- Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia.,Department of Clinical Haematology, Alfred Health, Melbourne, VIC, Australia
| | - Nicholas C Wong
- Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia. .,Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia.
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585
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Li Z, Wang B, Gu S, Jiang P, Sahu A, Chen CH, Han T, Shi S, Wang X, Traugh N, Liu H, Liu Y, Wu Q, Brown M, Xiao T, Boland GM, Shirley Liu X. CRISPR Screens Identify Essential Cell Growth Mediators in BRAF Inhibitor-resistant Melanoma. GENOMICS, PROTEOMICS & BIOINFORMATICS 2020; 18:26-40. [PMID: 32413516 PMCID: PMC7393575 DOI: 10.1016/j.gpb.2020.02.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/20/2020] [Accepted: 02/26/2020] [Indexed: 12/21/2022]
Abstract
BRAF is a serine/threonine kinase that harbors activating mutations in ∼7% of human malignancies and ∼60% of melanomas. Despite initial clinical responses to BRAF inhibitors, patients frequently develop drug resistance. To identify candidate therapeutic targets for BRAF inhibitor resistant melanoma, we conduct CRISPR screens in melanoma cells harboring an activating BRAF mutation that had also acquired resistance to BRAF inhibitors. To investigate the mechanisms and pathways enabling resistance to BRAF inhibitors in melanomas, we integrate expression, ATAC-seq, and CRISPR screen data. We identify the JUN family transcription factors and the ETS family transcription factor ETV5 as key regulators of CDK6, which together enable resistance to BRAF inhibitors in melanoma cells. Our findings reveal genes contributing to resistance to a selective BRAF inhibitor PLX4720, providing new insights into gene regulation in BRAF inhibitor resistant melanoma cells.
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Affiliation(s)
- Ziyi Li
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Binbin Wang
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Shengqing Gu
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Peng Jiang
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Avinash Sahu
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Chen-Hao Chen
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Tong Han
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Sailing Shi
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xiaoqing Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Nicole Traugh
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Hailing Liu
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yin Liu
- Department of Clinical Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Qiu Wu
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Tengfei Xiao
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA.
| | - Genevieve M Boland
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
| | - X Shirley Liu
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
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586
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Xie H, Zhang W, Zhang M, Akhtar T, Li Y, Yi W, Sun X, Zuo Z, Wei M, Fang X, Yao Z, Dong K, Zhong S, Liu Q, Shen Y, Wu Q, Wang X, Zhao H, Bao J, Qu K, Xue T. Chromatin accessibility analysis reveals regulatory dynamics of developing human retina and hiPSC-derived retinal organoids. SCIENCE ADVANCES 2020; 6:eaay5247. [PMID: 32083182 PMCID: PMC7007246 DOI: 10.1126/sciadv.aay5247] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 11/25/2019] [Indexed: 05/06/2023]
Abstract
Retinal organoids (ROs) derived from human induced pluripotent stem cells (hiPSCs) provide potential opportunities for studying human retinal development and disorders; however, to what extent ROs recapitulate the epigenetic features of human retinal development is unknown. In this study, we systematically profiled chromatin accessibility and transcriptional dynamics over long-term human retinal and RO development. Our results showed that ROs recapitulated the human retinogenesis to a great extent, but divergent chromatin features were also discovered. We further reconstructed the transcriptional regulatory network governing human and RO retinogenesis in vivo. Notably, NFIB and THRA were identified as regulators in human retinal development. The chromatin modifications between developing human and mouse retina were also cross-analyzed. Notably, we revealed an enriched bivalent modification of H3K4me3 and H3K27me3 in human but not in murine retinogenesis, suggesting a more dedicated epigenetic regulation on human genome.
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Affiliation(s)
- Haohuan Xie
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Wen Zhang
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- CAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei 230026, China
| | - Mei Zhang
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Tasneem Akhtar
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Young Li
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- CAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei 230026, China
| | - Wenyang Yi
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Xiao Sun
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Zuqi Zuo
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- CAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei 230026, China
| | - Min Wei
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Xin Fang
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Ziqin Yao
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Kai Dong
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Suijuan Zhong
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qiang Liu
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Yong Shen
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Qian Wu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xiaoqun Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Huan Zhao
- Department of Biological and Environmental Engineering, Hefei University, Hefei 230601, China
| | - Jin Bao
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
| | - Kun Qu
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- CAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei 230026, China
- Corresponding author. (T.X.); (K.Q.); (M.Z.)
| | - Tian Xue
- Eye Center, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Neurodegenerative Disorder Research Center, CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, Hefei 230026, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Corresponding author. (T.X.); (K.Q.); (M.Z.)
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587
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Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadley KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark SG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BFF, Wu K, Yang H, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z. Genomic basis for RNA alterations in cancer. Nature 2020; 578:129-136. [PMID: 32025019 PMCID: PMC7054216 DOI: 10.1038/s41586-020-1970-0] [Citation(s) in RCA: 241] [Impact Index Per Article: 60.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 12/11/2019] [Indexed: 01/27/2023]
Abstract
Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.
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Affiliation(s)
| | - Claudia Calabrese
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Natalie R. Davidson
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,000000041936877Xgrid.5386.8Weill Cornell Medical College, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Deniz Demircioğlu
- 0000 0001 2180 6431grid.4280.eNational University of Singapore, Singapore, Singapore ,0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore
| | - Nuno A. Fonseca
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Yao He
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
| | - André Kahles
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Kjong-Van Lehmann
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Fenglin Liu
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
| | - Yuichi Shiraishi
- 0000 0001 2151 536Xgrid.26999.3dThe University of Tokyo, Minato-ku, Japan
| | - Cameron M. Soulette
- 0000 0001 0740 6917grid.205975.cUniversity of California, Santa Cruz, Santa Cruz, CA USA
| | - Lara Urban
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Liliana Greger
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Siliang Li
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Dongbing Liu
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Marc D. Perry
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada ,0000 0001 2297 6811grid.266102.1University of California, San Francisco, San Francisco, CA USA
| | - Qian Xiang
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Fan Zhang
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
| | - Junjun Zhang
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter Bailey
- 0000 0001 2193 314Xgrid.8756.cUniversity of Glasgow, Glasgow, UK
| | - Serap Erkek
- 0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Katherine A. Hoadley
- 0000000122483208grid.10698.36The University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yong Hou
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Matthew R. Huska
- 0000 0001 1014 0849grid.419491.0Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Helena Kilpinen
- 0000000121901201grid.83440.3bUniversity College London, London, UK
| | - Jan O. Korbel
- 0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maximillian G. Marin
- 0000 0001 0740 6917grid.205975.cUniversity of California, Santa Cruz, Santa Cruz, CA USA
| | - Julia Markowski
- 0000 0001 1014 0849grid.419491.0Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Tannistha Nandi
- 0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore
| | - Qiang Pan-Hammarström
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,0000 0004 1937 0626grid.4714.6Karolinska Institutet, Stockholm, Sweden
| | - Chandra Sekhar Pedamallu
- grid.66859.34Broad Institute, Cambridge, MA USA ,0000 0001 2106 9910grid.65499.37Dana-Farber Cancer Institute, Boston, MA USA ,000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA
| | - Reiner Siebert
- grid.410712.1Ulm University and Ulm University Medical Center, Ulm, Germany
| | - Stefan G. Stark
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Hong Su
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Patrick Tan
- 0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore ,0000 0004 0385 0924grid.428397.3Duke-NUS Medical School, Singapore, Singapore
| | - Sebastian M. Waszak
- 0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christina Yung
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shida Zhu
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Philip Awadalla
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada ,0000 0001 2157 2938grid.17063.33University of Toronto, Toronto, Ontario Canada
| | - Chad J. Creighton
- 0000 0001 2160 926Xgrid.39382.33Baylor College of Medicine, Houston, TX USA
| | - Matthew Meyerson
- grid.66859.34Broad Institute, Cambridge, MA USA ,0000 0001 2106 9910grid.65499.37Dana-Farber Cancer Institute, Boston, MA USA ,000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA
| | | | - Kui Wu
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Huanming Yang
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China
| | | | - Alvis Brazma
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Angela N. Brooks
- 0000 0001 0740 6917grid.205975.cUniversity of California, Santa Cruz, Santa Cruz, CA USA ,grid.66859.34Broad Institute, Cambridge, MA USA ,0000 0001 2106 9910grid.65499.37Dana-Farber Cancer Institute, Boston, MA USA
| | - Jonathan Göke
- 0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore ,0000 0004 0620 9745grid.410724.4National Cancer Centre Singapore, Singapore, Singapore
| | - Gunnar Rätsch
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,000000041936877Xgrid.5386.8Weill Cornell Medical College, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Roland F. Schwarz
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,0000 0001 1014 0849grid.419491.0Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany ,0000 0004 0492 0584grid.7497.dGerman Cancer Consortium (DKTK), partner site Berlin, Germany ,0000 0004 0492 0584grid.7497.dGerman Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany ,0000 0004 0492 0584grid.7497.dGerman Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zemin Zhang
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
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588
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The Length and Distribution of Plasma Cell-Free DNA Fragments in Stroke Patients. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9054196. [PMID: 32090114 PMCID: PMC7017581 DOI: 10.1155/2020/9054196] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 10/05/2019] [Indexed: 01/21/2023]
Abstract
A number of studies have shown that plasma cell-free DNA is closely related to the risk of stroke, but the fragmentation status of plasma cell-free DNA and its clinical application value in ischemic stroke are still unclear. In this study, 48 patients with new ischemic stroke and 20 healthy subjects were enrolled. The second-generation high-throughput sequencing technique was used to study the plasma cell-free fragment length and regional distribution of the subjects. As noted in our results, the ratio of plasma cell-free DNA fragments in the disease group was significantly greater than that of the healthy group in the 300–400 bp range; conversely for fragments at the 75–250 bp range, the ratio of plasma cell-free DNA fragments in the patient group was apparently lower than that of the healthy group. In-depth analysis of the proportion of fragments distributed on each component of the genome was carried out. Our results recorded that the plasma cell-free DNA fragments in the disease group were inclined to the EXON, CpG islands, and ALU regions in contrast to that of the healthy group. In particular, fragments within the 300–400 bp range of the disease group were enrichment in the regions of EXON, INTRON, INTERGENIC, LINE, Fragile, ALU, and CpG islands. In summary, our findings suggested that the intracellular DNA degradation profiles could be applied to distinguish the stroke group and the healthy group, which provided a theoretical basis for the clinical diagnosis and prognosis of stroke by profiling the characteristic of plasma cell-free DNA fragments.
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589
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Trevino AE, Sinnott-Armstrong N, Andersen J, Yoon SJ, Huber N, Pritchard JK, Chang HY, Greenleaf WJ, Pașca SP. Chromatin accessibility dynamics in a model of human forebrain development. Science 2020; 367:eaay1645. [PMID: 31974223 PMCID: PMC7313757 DOI: 10.1126/science.aay1645] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 12/16/2019] [Indexed: 12/12/2022]
Abstract
Forebrain development is characterized by highly synchronized cellular processes, which, if perturbed, can cause disease. To chart the regulatory activity underlying these events, we generated a map of accessible chromatin in human three-dimensional forebrain organoids. To capture corticogenesis, we sampled glial and neuronal lineages from dorsal or ventral forebrain organoids over 20 months in vitro. Active chromatin regions identified in human primary brain tissue were observed in organoids at different developmental stages. We used this resource to map genetic risk for disease and to explore evolutionary conservation. Moreover, we integrated chromatin accessibility with transcriptomics to identify putative enhancer-gene linkages and transcription factors that regulate human corticogenesis. Overall, this platform brings insights into gene-regulatory dynamics at previously inaccessible stages of human forebrain development, including signatures of neuropsychiatric disorders.
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Affiliation(s)
- Alexandro E Trevino
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford Human Brain Organogenesis Program, Stanford, CA, USA
| | | | - Jimena Andersen
- Stanford Human Brain Organogenesis Program, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Se-Jin Yoon
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Nina Huber
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Howard Y Chang
- Department of Genetics, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Dermatology, Stanford University, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Sergiu P Pașca
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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590
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Lee JH, Xiong F, Li W. Enhancer RNAs in cancer: regulation, mechanisms and therapeutic potential. RNA Biol 2020; 17:1550-1559. [PMID: 31916476 DOI: 10.1080/15476286.2020.1712895] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Enhancers are distal genomic elements critical for gene regulation and cell identify control during development and diseases. Many human cancers were found to associate with enhancer malfunction, due to genetic and epigenetic alterations, which in some cases directly drive tumour growth. Conventionally, enhancers are known to provide DNA binding motifs to recruit transcription factors (TFs) and to control target genes. However, recent progress found that most, if not all, active enhancers pervasively transcribe noncoding RNAs that are referred to as enhancer RNAs (eRNAs). Increasing evidence points to functional roles of at least a subset of eRNAs in gene regulation in both normal and cancer cells, adding new insights into the action mechanisms of enhancers. eRNA expression was observed to be widespread but also specific to tumour types and individual patients, serving as opportunities to exploit them as potential diagnosis markers or therapeutic targets. In this review, we discuss the brief history of eRNA research, their functional mechanisms and importance in cancer gene regulation, as well as their therapeutic and diagnostic values in cancer. We propose that further studies of eRNAs in cancer will offer a promising 'eRNA targeted therapy' for human cancer intervention.
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Affiliation(s)
- Joo-Hyung Lee
- Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center , Houston, TX, USA
| | - Feng Xiong
- Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center , Houston, TX, USA
| | - Wenbo Li
- Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center , Houston, TX, USA.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center and UTHealth , Houston, TX, USA
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591
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Zhu H, Uusküla-Reimand L, Isaev K, Wadi L, Alizada A, Shuai S, Huang V, Aduluso-Nwaobasi D, Paczkowska M, Abd-Rabbo D, Ocsenas O, Liang M, Thompson JD, Li Y, Ruan L, Krassowski M, Dzneladze I, Simpson JT, Lupien M, Stein LD, Boutros PC, Wilson MD, Reimand J. Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks. Mol Cell 2020; 77:1307-1321.e10. [PMID: 31954095 DOI: 10.1016/j.molcel.2019.12.027] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 06/04/2019] [Accepted: 12/24/2019] [Indexed: 12/17/2022]
Abstract
A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR < 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.
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Affiliation(s)
- Helen Zhu
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, ON M5G 1L7, Canada
| | - Liis Uusküla-Reimand
- Program in Genetics and Genome Biology, SickKids Research Institute, Peter Gilgan Centre for Research and Learning (PGCRL), 686 Bay Street, Toronto, ON M5G 0A4, Canada; Division of Gene Technology, Department of Chemistry and Biotechnology, Tallinn University of Technology, Akadeemia tee 15, Tallinn 12618, Estonia
| | - Keren Isaev
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, ON M5G 1L7, Canada
| | - Lina Wadi
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Azad Alizada
- Program in Genetics and Genome Biology, SickKids Research Institute, Peter Gilgan Centre for Research and Learning (PGCRL), 686 Bay Street, Toronto, ON M5G 0A4, Canada
| | - Shimin Shuai
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada
| | - Vincent Huang
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Dike Aduluso-Nwaobasi
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Marta Paczkowska
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Diala Abd-Rabbo
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Oliver Ocsenas
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, ON M5G 1L7, Canada
| | - Minggao Liang
- Program in Genetics and Genome Biology, SickKids Research Institute, Peter Gilgan Centre for Research and Learning (PGCRL), 686 Bay Street, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada
| | - J Drew Thompson
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Yao Li
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Luyao Ruan
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Michal Krassowski
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Irakli Dzneladze
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Jared T Simpson
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Computer Science, University of Toronto, 214 College Street, Toronto, ON M5T 3A1, Canada
| | - Mathieu Lupien
- Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, ON M5G 1L7, Canada; Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 0A3, Canada
| | - Lincoln D Stein
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, ON M5G 1L7, Canada; Department of Human Genetics, University of California Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA; Department of Urology, University of California Los Angeles, 200 Medical Plaza Driveway #140, Los Angeles, CA 90024, USA; Institute of Precision Health, University of California Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA 90024, USA; Jonsson Comprehensive Cancer Centre, University of California Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA 90024, USA
| | - Michael D Wilson
- Program in Genetics and Genome Biology, SickKids Research Institute, Peter Gilgan Centre for Research and Learning (PGCRL), 686 Bay Street, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada
| | - Jüri Reimand
- Computational Biology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, ON M5G 1L7, Canada.
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592
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Zhou J, Li Y, Cao H, Yang M, Chu L, Li T, Yu Z, Yu R, Qiu B, Wang Q, Li X, Xie J. CATA: a comprehensive chromatin accessibility database for cancer. Database (Oxford) 2020; 2022:6520815. [PMID: 35134148 PMCID: PMC9246274 DOI: 10.1093/database/baab085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/05/2021] [Accepted: 12/29/2021] [Indexed: 11/30/2022]
Abstract
Accessible chromatin refers to the active regions of a chromosome that are bound by many transcription factors (TFs). Changes in chromatin accessibility play a critical role in tumorigenesis. With the emergence of novel methods like Assay for Transposase-accessible Chromatin Sequencing, a sequencing method that maps chromatin-accessible regions (CARs) and enables the computational analysis of TF binding at chromatin-accessible sites, the regulatory landscape in cancer can be dissected. Herein, we developed a comprehensive cancer chromatin accessibility database named CATA, which aims to provide available resources of cancer CARs and to annotate their potential roles in the regulation of genes in a cancer type-specific manner. In this version, CATA stores 2 991 163 CARs from 23 cancer types, binding information of 1398 TFs within the CARs, and provides multiple annotations about these regions, including common single nucleotide polymorphisms (SNPs), risk SNPs, copy number variation, somatic mutations, motif changes, expression quantitative trait loci, methylation and CRISPR/Cas9 target loci. Moreover, CATA supports cancer survival analysis of the CAR-associated genes and provides detailed clinical information of the tumor samples. Database URL: CATA is available at http://www.xiejjlab.bio/cata/.
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Affiliation(s)
- Jianyuan Zhou
- Central Laboratory of Molecular Biology, Medical College of Jiaying University, 146 Huangtang Road, Meizhou 514031, China
- Shandong Provincial Key Laboratory of Radiation Oncology, Cancer Research Center, Shandong Cancer Hospital and Institute, Shandong
- First Medical University, and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan 250000, China
| | | | | | - Min Yang
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, 22 Xinling Road, Shantou 515041, China
| | - Lingyu Chu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, 22 Xinling Road, Shantou 515041, China
| | - Taisong Li
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, 22 Xinling Road, Shantou 515041, China
| | - Zhengmin Yu
- School of Medical Informatics, Harbin Medical University, Daqing Campus, Daqing 163319, China
| | - Rui Yu
- School of Medical Informatics, Harbin Medical University, Daqing Campus, Daqing 163319, China
| | - Bo Qiu
- Central Laboratory of Molecular Biology, Medical College of Jiaying University, 146 Huangtang Road, Meizhou 514031, China
| | - Qiuyu Wang
- School of Medical Informatics, Harbin Medical University, Daqing Campus, Daqing 163319, China
| | - Xuecang Li
- School of Medical Informatics, Harbin Medical University, Daqing Campus, Daqing 163319, China
| | - Jianjun Xie
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, 22 Xinling Road, Shantou 515041, China
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593
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Sabbagh MF, Nathans J. A genome-wide view of the de-differentiation of central nervous system endothelial cells in culture. eLife 2020; 9:e51276. [PMID: 31913116 PMCID: PMC6948952 DOI: 10.7554/elife.51276] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 12/16/2019] [Indexed: 12/11/2022] Open
Abstract
Vascular endothelial cells (ECs) derived from the central nervous system (CNS) variably lose their unique barrier properties during in vitro culture, hindering the development of robust assays for blood-brain barrier (BBB) function, including drug permeability and extrusion assays. In previous work (Sabbagh et al., 2018) we characterized transcriptional and accessible chromatin landscapes of acutely isolated mouse CNS ECs. In this report, we compare transcriptional and accessible chromatin landscapes of acutely isolated mouse CNS ECs versus mouse CNS ECs in short-term in vitro culture. We observe that standard culture conditions are associated with a rapid and selective loss of BBB transcripts and chromatin features, as well as a greatly reduced level of beta-catenin signaling. Interestingly, forced expression of a stabilized derivative of beta-catenin, which in vivo leads to a partial conversion of non-BBB CNS ECs to a BBB-like state, has little or no effect on gene expression or chromatin accessibility in vitro.
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Affiliation(s)
- Mark F Sabbagh
- Department of Molecular Biology and GeneticsJohns Hopkins University School of MedicineBaltimoreUnited States
- Department of NeuroscienceJohns Hopkins University School of MedicineBaltimoreUnited States
| | - Jeremy Nathans
- Department of Molecular Biology and GeneticsJohns Hopkins University School of MedicineBaltimoreUnited States
- Department of NeuroscienceJohns Hopkins University School of MedicineBaltimoreUnited States
- Department of OphthalmologyJohns Hopkins University School of MedicineBaltimoreUnited States
- Howard Hughes Medical Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
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594
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Colaprico A, Olsen C, Bailey MH, Odom GJ, Terkelsen T, Silva TC, Olsen AV, Cantini L, Zinovyev A, Barillot E, Noushmehr H, Bertoli G, Castiglioni I, Cava C, Bontempi G, Chen XS, Papaleo E. Interpreting pathways to discover cancer driver genes with Moonlight. Nat Commun 2020; 11:69. [PMID: 31900418 PMCID: PMC6941958 DOI: 10.1038/s41467-019-13803-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 11/22/2019] [Indexed: 12/28/2022] Open
Abstract
Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.
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Affiliation(s)
- Antonio Colaprico
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.
- Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA.
| | - Catharina Olsen
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium
- Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
- Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), VUB-ULB, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Matthew H Bailey
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University, St. Louis, MO, 63108, USA
| | - Gabriel J Odom
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA
- Department of Biostatistics, Stempel College of Public Health, Florida International University, Miami, FL, 33199, USA
| | - Thilde Terkelsen
- Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Tiago C Silva
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA
- Department of Genetics, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
| | - André V Olsen
- Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Laura Cantini
- Institut Curie, 26 rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris, F-75248, France
- Mines ParisTech, Fontainebleau, F-77300, France
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, Paris Sciences et Lettres Research University, 75005, Paris, France
| | - Andrei Zinovyev
- Institut Curie, 26 rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris, F-75248, France
- Mines ParisTech, Fontainebleau, F-77300, France
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris, F-75248, France
- Mines ParisTech, Fontainebleau, F-77300, France
| | - Houtan Noushmehr
- Department of Genetics, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
- Department of Neurosurgery, Brain Tumor Center, Henry Ford Health System, Detroit, MI, USA
| | - Gloria Bertoli
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
| | - Claudia Cava
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
| | - Gianluca Bontempi
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Xi Steven Chen
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA.
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
| | - Elena Papaleo
- Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.
- Translational Disease System Biology, Faculty of Health and Medical Science, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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595
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Sarkies P. Molecular mechanisms of epigenetic inheritance: Possible evolutionary implications. Semin Cell Dev Biol 2020; 97:106-115. [PMID: 31228598 PMCID: PMC6945114 DOI: 10.1016/j.semcdb.2019.06.005] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 02/04/2019] [Accepted: 06/18/2019] [Indexed: 12/30/2022]
Abstract
Recently interest in multi-generational epigenetic phenomena have been fuelled by highly reproducible intergenerational and transgenerational inheritance paradigms in several model organisms. Such paradigms are essential in order to begin to use genetics to unpick the mechanistic bases of how epigenetic information may be transmitted between generations; indeed great strides have been made towards understanding these mechanisms. Far less well understood is the relationship between epigenetic inheritance, ecology and evolution. In this review I focus on potential connections between laboratory studies of transgenerational epigenetic inheritance phenomena and evolutionary processes that occur in natural populations. In the first section, I consider whether transgenerational epigenetic inheritance might provide an advantage to organisms over the short term in adapting to their environment. Second, I consider whether epigenetic changes can contribute to the evolution of species by contributing to stable phenotypic variation within a population. Finally I discuss whether epigenetic changes could influence evolution by either directly or indirectly promoting DNA sequence changes that could impact phenotypic divergence. Additionally, I will discuss how epigenetic changes could influence the evolution of human cancer and thus be directly relevant for the development of this disease.
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Affiliation(s)
- Peter Sarkies
- MRC London Institute of Medical Sciences, Du Cane Road, London, W120NN, United Kingdom; Institute of Clinical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W120NN, United Kingdom.
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596
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Chung FFL, Herceg Z. The Promises and Challenges of Toxico-Epigenomics: Environmental Chemicals and Their Impacts on the Epigenome. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:15001. [PMID: 31950866 PMCID: PMC7015548 DOI: 10.1289/ehp6104] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/15/2019] [Accepted: 12/16/2019] [Indexed: 05/02/2023]
Abstract
BACKGROUND It has been estimated that a substantial portion of chronic and noncommunicable diseases can be caused or exacerbated by exposure to environmental chemicals. Multiple lines of evidence indicate that early life exposure to environmental chemicals at relatively low concentrations could have lasting effects on individual and population health. Although the potential adverse effects of environmental chemicals are known to the scientific community, regulatory agencies, and the public, little is known about the mechanistic basis by which these chemicals can induce long-term or transgenerational effects. To address this question, epigenetic mechanisms have emerged as the potential link between genetic and environmental factors of health and disease. OBJECTIVES We present an overview of epigenetic regulation and a summary of reported evidence of environmental toxicants as epigenetic disruptors. We also discuss the advantages and challenges of using epigenetic biomarkers as an indicator of toxicant exposure, using measures that can be taken to improve risk assessment, and our perspectives on the future role of epigenetics in toxicology. DISCUSSION Until recently, efforts to apply epigenomic data in toxicology and risk assessment were restricted by an incomplete understanding of epigenomic variability across tissue types and populations. This is poised to change with the development of new tools and concerted efforts by researchers across disciplines that have led to a better understanding of epigenetic mechanisms and comprehensive maps of epigenomic variation. With the foundations now in place, we foresee that unprecedented advancements will take place in the field in the coming years. https://doi.org/10.1289/EHP6104.
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Affiliation(s)
| | - Zdenko Herceg
- Epigenetics Group, International Agency for Research on Cancer (IARC), Lyon, France
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597
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Hamamoto R, Komatsu M, Takasawa K, Asada K, Kaneko S. Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine. Biomolecules 2019; 10:biom10010062. [PMID: 31905969 PMCID: PMC7023005 DOI: 10.3390/biom10010062] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 12/20/2019] [Accepted: 12/27/2019] [Indexed: 12/14/2022] Open
Abstract
To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.
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Affiliation(s)
- Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.K.); (K.T.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Correspondence: ; Tel.: +81-3-3547-5271
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.K.); (K.T.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Takasawa
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.K.); (K.T.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.K.); (K.T.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.K.); (K.T.); (K.A.); (S.K.)
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598
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Suzuki A, Onodera K, Matsui K, Seki M, Esumi H, Soga T, Sugano S, Kohno T, Suzuki Y, Tsuchihara K. Characterization of cancer omics and drug perturbations in panels of lung cancer cells. Sci Rep 2019; 9:19529. [PMID: 31863083 PMCID: PMC6925249 DOI: 10.1038/s41598-019-55692-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 11/21/2019] [Indexed: 01/10/2023] Open
Abstract
To better understand the disruptions of transcriptional regulations and gene expression in lung cancers, we constructed a multi-omics catalogue of the responses of lung cancer cells to a series of chemical compounds. We generated and analyzed 3,240 RNA-seq and 3,393 ATAC-seq libraries obtained from 23 cell lines treated with 95 well-annotated compounds. To demonstrate the power of the created multi-omics resource, we attempted to identify drugs that could induce the designated changes alone or in combination. The basal multi-omics information was first integrated into co-expression modules. Among these modules, we identified a stress response module that may be a promising drug intervention target, as new combinations of compounds that could be used to regulate this module and the consequent phenotypic appearance of cancer cells have been identified. We believe that the multi-omics profiles generated in this study and the strategy used to stratify them will lead to more rational and efficient development of anticancer drugs.
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Affiliation(s)
- Ayako Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.,Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Chiba, Japan
| | - Keiichi Onodera
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.,Bio Science & Engineering Laboratory, Fujifilm Corporation, Kanagawa, Japan
| | - Ken Matsui
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.,Bio Science & Engineering Laboratory, Fujifilm Corporation, Kanagawa, Japan
| | - Masahide Seki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Hiroyasu Esumi
- Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Chiba, Japan.,Research Institute for Biomedical Sciences, Tokyo University of Science, Chiba, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
| | - Sumio Sugano
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
| | - Katsuya Tsuchihara
- Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Chiba, Japan
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599
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Trefflich S, Dalmolin RJS, Ortega JM, Castro MAA. Which came first, the transcriptional regulator or its target genes? An evolutionary perspective into the construction of eukaryotic regulons. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194472. [PMID: 31825805 DOI: 10.1016/j.bbagrm.2019.194472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/06/2019] [Accepted: 11/30/2019] [Indexed: 01/06/2023]
Abstract
Eukaryotic regulons are regulatory units formed by a set of genes under the control of the same transcription factor (TF). Despite the functional plasticity, TFs are highly conserved and recognize the same DNA sequences in different organisms. One of the main factors that confer regulatory specificity is the distribution of the binding sites of the TFs along the genome, allowing the configuration of different transcriptional regulatory networks (TRNs) from the same regulator. A similar scenario occurs between tissues of the same organism, where a TRN can be rewired by epigenetic factors, modulating the accessibility of the TF to its binding sites. In this article we discuss concepts that can help to formulate testable hypotheses about the construction of regulons, exploring the presence and absence of the elements that form a TRN throughout the evolution of an ancestral lineage. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Sheyla Trefflich
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil; Bioinformatics and Systems Biology Laboratory, Federal University of Paraná, Curitiba 81520-260, Brazil
| | - Rodrigo J S Dalmolin
- Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal 59078-400, Brazil
| | - José Miguel Ortega
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Mauro A A Castro
- Bioinformatics and Systems Biology Laboratory, Federal University of Paraná, Curitiba 81520-260, Brazil.
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600
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Al-Ali R, Bauer K, Park JW, Al Abdulla R, Fermi V, von Deimling A, Herold-Mende C, Mallm JP, Herrmann C, Wick W, Turcan Ş. Single-nucleus chromatin accessibility reveals intratumoral epigenetic heterogeneity in IDH1 mutant gliomas. Acta Neuropathol Commun 2019; 7:201. [PMID: 31806013 PMCID: PMC6896263 DOI: 10.1186/s40478-019-0851-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
The presence of genome-wide DNA hypermethylation is a hallmark of lower grade gliomas (LGG) with isocitrate dehydrogenase (IDH) mutations. Further molecular classification of IDH mutant gliomas is defined by the presence (IDHmut-codel) or absence (IDHmut-noncodel) of hemizygous codeletion of chromosome arms 1p and 19q. Despite the DNA hypermethylation seen in bulk tumors, intra-tumoral heterogeneity at the epigenetic level has not been thoroughly analyzed. To address this question, we performed the first epigenetic profiling of single cells in a cohort of 5 gliomas with IDH1 mutation using single nucleus Assay for Transposase-Accessible Chromatin with high-throughput sequencing (snATAC-seq). Using the Fluidigm HT IFC microfluidics platform, we generated chromatin accessibility maps from 336 individual nuclei, and identified variable promoter accessibility of non-coding RNAs in LGGs. Interestingly, local chromatin structures of several non-coding RNAs are significant factors that contribute to heterogeneity, and show increased promoter accessibility in IDHmut-noncodel samples. As an example for clinical significance of this result, we identify CYTOR as a poor prognosis factor in gliomas with IDH mutation. Open chromatin assay points to differential accessibility of non-coding RNAs as an important source of epigenetic heterogeneity within individual tumors and between molecular subgroups. Rare populations of nuclei that resemble either IDH mutant molecular group co-exist within IDHmut-noncodel and IDHmut-codel groups, and along with non-coding RNAs may be an important issue to consider for future studies, as they may help guide predict treatment response and relapse. A web-based explorer for the data is available at shiny.turcanlab.org.
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