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Passemiers A, Tuveri S, Sudhakaran D, Jatsenko T, Laga T, Punie K, Hatse S, Tejpar S, Coosemans A, Van Nieuwenhuysen E, Timmerman D, Floris G, Van Rompuy AS, Sagaert X, Testa A, Ficherova D, Raimondi D, Amant F, Lenaerts L, Moreau Y, Vermeesch JR. MetDecode: methylation-based deconvolution of cell-free DNA for noninvasive multi-cancer typing. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae522. [PMID: 39177091 PMCID: PMC11379469 DOI: 10.1093/bioinformatics/btae522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 07/24/2024] [Accepted: 08/20/2024] [Indexed: 08/24/2024]
Abstract
MOTIVATION Circulating-cell free DNA (cfDNA) is widely explored as a noninvasive biomarker for cancer screening and diagnosis. The ability to decode the cells of origin in cfDNA would provide biological insights into pathophysiological mechanisms, aiding in cancer characterization and directing clinical management and follow-up. RESULTS We developed a DNA methylation signature-based deconvolution algorithm, MetDecode, for cancer tissue origin identification. We built a reference atlas exploiting de novo and published whole-genome methylation sequencing data for colorectal, breast, ovarian, and cervical cancer, and blood-cell-derived entities. MetDecode models the contributors absent in the atlas with methylation patterns learnt on-the-fly from the input cfDNA methylation profiles. In addition, our model accounts for the coverage of each marker region to alleviate potential sources of noise. In-silico experiments showed a limit of detection down to 2.88% of tumor tissue contribution in cfDNA. MetDecode produced Pearson correlation coefficients above 0.95 and outperformed other methods in simulations (P < 0.001; T-test; one-sided). In plasma cfDNA profiles from cancer patients, MetDecode assigned the correct tissue-of-origin in 84.2% of cases. In conclusion, MetDecode can unravel alterations in the cfDNA pool components by accurately estimating the contribution of multiple tissues, while supplied with an imperfect reference atlas. AVAILABILITY AND IMPLEMENTATION MetDecode is available at https://github.com/JorisVermeeschLab/MetDecode.
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Affiliation(s)
- Antoine Passemiers
- Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Department of Electrical Engineering, KU Leuven, Leuven, 3001, Belgium
| | - Stefania Tuveri
- Laboratory for Cytogenetics and Genome Research, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Dhanya Sudhakaran
- Laboratory for Cytogenetics and Genome Research, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Tatjana Jatsenko
- Laboratory for Cytogenetics and Genome Research, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
| | - Tina Laga
- Gynaecological Oncology, Department of Oncology, KU Leuven, Leuven, 3000, Belgium
- Gynaecology and Obstetrics, University Hospitals KU Leuven, Leuven, 3000, Belgium
| | - Kevin Punie
- Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven, 3000, Belgium
- Laboratory of Experimental Oncology, Department of General Medical Oncology, University Hospitals Leuven, KU Leuven, Leuven, 3000, Belgium
- Department of Oncology, GZA Ziekenhuis, Antwerp, 2610, Belgium
| | - Sigrid Hatse
- Laboratory of Experimental Oncology, Department of General Medical Oncology, University Hospitals Leuven, KU Leuven, Leuven, 3000, Belgium
| | - Sabine Tejpar
- Digestive Oncology Unit, University Hospital Gasthuisberg, Leuven, 3000, Belgium
| | - An Coosemans
- Laboratory of Tumour Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, 3000, Belgium
| | - Els Van Nieuwenhuysen
- Gynaecological Oncology, Department of Oncology, KU Leuven, Leuven, 3000, Belgium
- Gynaecology and Obstetrics, University Hospitals KU Leuven, Leuven, 3000, Belgium
| | - Dirk Timmerman
- Gynaecology and Obstetrics, University Hospitals KU Leuven, Leuven, 3000, Belgium
| | - Giuseppe Floris
- Translational Cell & Tissue Research, Department of Pathology, KU Leuven, Leuven, 3000, Belgium
| | - Anne-Sophie Van Rompuy
- Translational Cell & Tissue Research, Department of Pathology, KU Leuven, Leuven, 3000, Belgium
| | - Xavier Sagaert
- Translational Cell & Tissue Research, Department of Pathology, KU Leuven, Leuven, 3000, Belgium
| | - Antonia Testa
- Department of Woman, Child and Public Health, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, 00168, Italy
| | - Daniela Ficherova
- Obstetrics and Gynaecology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Daniele Raimondi
- Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Department of Electrical Engineering, KU Leuven, Leuven, 3001, Belgium
| | - Frederic Amant
- Gynaecological Oncology, Department of Oncology, KU Leuven, Leuven, 3000, Belgium
- Gynaecology and Obstetrics, University Hospitals KU Leuven, Leuven, 3000, Belgium
- Department of Gynaecologic Oncology, Netherlands Cancer Institute, Amsterdam, 1066 CX, The Netherlands
| | - Liesbeth Lenaerts
- Gynaecological Oncology, Department of Oncology, KU Leuven, Leuven, 3000, Belgium
| | - Yves Moreau
- Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Department of Electrical Engineering, KU Leuven, Leuven, 3001, Belgium
| | - Joris R Vermeesch
- Laboratory for Cytogenetics and Genome Research, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
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Ferro dos Santos MR, Giuili E, De Koker A, Everaert C, De Preter K. Computational deconvolution of DNA methylation data from mixed DNA samples. Brief Bioinform 2024; 25:bbae234. [PMID: 38762790 PMCID: PMC11102637 DOI: 10.1093/bib/bbae234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.
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Affiliation(s)
- Maísa R Ferro dos Santos
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Edoardo Giuili
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Andries De Koker
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Celine Everaert
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Katleen De Preter
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
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Shimada M, Omae Y, Kakita A, Gabdulkhaev R, Hitomi Y, Miyagawa T, Honda M, Fujimoto A, Tokunaga K. Identification of region-specific gene isoforms in the human brain using long-read transcriptome sequencing. SCIENCE ADVANCES 2024; 10:eadj5279. [PMID: 38266094 PMCID: PMC10807796 DOI: 10.1126/sciadv.adj5279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 12/21/2023] [Indexed: 01/26/2024]
Abstract
In neurological and neuropsychiatric diseases, different brain regions are affected, and differences in gene expression patterns could potentially explain this mechanism. However, limited studies have precisely explored gene expression in different regions of the human brain. In this study, we performed long-read RNA sequencing on three different brain regions of the same individuals: the cerebellum, hypothalamus, and temporal cortex. Despite stringent filtering criteria excluding isoforms predicted to be artifacts, over half of the isoforms expressed in multiple samples across multiple regions were found to be unregistered in the GENCODE reference. We then especially focused on genes with different major isoforms in each brain region, even with similar overall expression levels, and identified that many of such genes including GAS7 might have distinct roles in dendritic spine and neuronal formation in each region. We also found that DNA methylation might, in part, drive different isoform expressions in different regions. These findings highlight the significance of analyzing isoforms expressed in disease-relevant sites.
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Affiliation(s)
- Mihoko Shimada
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
- Center for Clinical Sciences, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Yosuke Omae
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Ramil Gabdulkhaev
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Yuki Hitomi
- Department of Human Genetics, Research Institute, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Taku Miyagawa
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Makoto Honda
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Japan Somnology Center and Seiwa Hospital, Institute of Neuropsychiatry, Tokyo, Japan
| | - Akihiro Fujimoto
- Department of Human Genetics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
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4
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Muench A, Teichmann D, Spille D, Kuzman P, Perez E, May SA, Mueller WC, Kombos T, Nazari-Dehkordi S, Onken J, Vajkoczy P, Ntoulias G, Bettencourt C, von Deimling A, Paulus W, Heppner FL, Koch A, Capper D, Kaul D, Thomas C, Schweizer L. A Novel Type of IDH-wildtype Glioma Characterized by Gliomatosis Cerebri-like Growth Pattern, TERT Promoter Mutation, and Distinct Epigenetic Profile. Am J Surg Pathol 2023; 47:1364-1375. [PMID: 37737691 DOI: 10.1097/pas.0000000000002118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Diffuse gliomas in adults encompass a heterogenous group of central nervous system neoplasms. In recent years, extensive (epi-)genomic profiling has identified several glioma subgroups characterized by distinct molecular characteristics, most importantly IDH1/2 and histone H3 mutations. A group of 16 diffuse gliomas classified as "adult-type diffuse high-grade glioma, IDH-wildtype, subtype F (HGG-F)" was identified by the DKFZ v12.5 Brain Tumor Classifier . Histopathologic characterization, exome sequencing, and review of clinical data was performed in all cases. Based on unsupervised t -distributed stochastic neighbor embedding and clustering analysis of genome-wide DNA methylation data, HGG-F shows distinct epigenetic profiles separate from established central nervous system tumors. Exome sequencing demonstrated frequent TERT promoter (12/15 cases), PIK3R1 (11/16), and TP53 mutations (5/16). Radiologic characteristics were reminiscent of gliomatosis cerebri in 9/14 cases (64%). Histopathologically, most cases were classified as diffuse gliomas (7/16, 44%) or were suspicious for the infiltration zone of a diffuse glioma (5/16, 31%). None of the cases demonstrated microvascular proliferation or necrosis. Outcome of 14 patients with follow-up data was better compared to IDH-wildtype glioblastomas with a median progression-free survival of 58 months and overall survival of 74 months (both P <0.0001). Our series represents a novel type of adult-type diffuse glioma with distinct molecular and clinical features. Importantly, we provide evidence that TERT promoter mutations in diffuse gliomas without further morphologic or molecular signs of high-grade glioma should be interpreted in the context of the clinicoradiologic presentation as well as epigenetic profile and may not be suitable as a standalone marker for glioblastoma, IDH-wildtype.
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Affiliation(s)
- Amos Muench
- Edinger Institute, Institute of Neurology, University of Frankfurt am Main
| | | | | | - Peter Kuzman
- Institute of Neuropathology, University Hospital Leipzig, Leipzig
| | | | - Sven-Axel May
- Department of Neurosurgery, Klinikum Chemnitz, Chemnitz
| | - Wolf C Mueller
- Institute of Neuropathology, University Hospital Leipzig, Leipzig
| | | | | | | | | | - Georgios Ntoulias
- Department of Neurosurgery, Schlosspark-Klinik Charlottenburg, Berlin
| | - Conceição Bettencourt
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Werner Paulus
- Institute of Neuropathology, University Hospital Münster, Münster
| | - Frank L Heppner
- Departments of Neuropathology
- Cluster of Excellence, NeuroCure
- German Center for Neurodegenerative Diseases (DZNE)
- German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ)
| | - Arend Koch
- Departments of Neuropathology
- German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ)
| | - David Capper
- Departments of Neuropathology
- German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ)
| | - David Kaul
- Radiation Oncology and Radiotherapy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin
| | - Christian Thomas
- Institute of Neuropathology, University Hospital Münster, Münster
| | - Leonille Schweizer
- Edinger Institute, Institute of Neurology, University of Frankfurt am Main
- Frankfurt Cancer Institute (FCI), Frankfurt am Main
- Departments of Neuropathology
- German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ)
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Zheng Y, Jun J, Brennan K, Gevaert O. EpiMix is an integrative tool for epigenomic subtyping using DNA methylation. CELL REPORTS METHODS 2023; 3:100515. [PMID: 37533639 PMCID: PMC10391348 DOI: 10.1016/j.crmeth.2023.100515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/12/2023] [Accepted: 06/01/2023] [Indexed: 08/04/2023]
Abstract
DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer and immunological and cardiovascular diseases. Recent technological advances have enabled genome-wide profiling of DNAme in large human cohorts. There is a need for analytical methods that can more sensitively detect differential methylation profiles present in subsets of individuals from these heterogeneous, population-level datasets. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared with existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and long non-coding RNAs (lncRNAs). Using cell-type-specific data from two separate studies, we discover epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven ncRNAs in non-small cell lung cancer.
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Affiliation(s)
- Yuanning Zheng
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - John Jun
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Kevin Brennan
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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Zheng Y, Jun J, Brennan K, Gevaert O. EpiMix: an integrative tool for epigenomic subtyping using DNA methylation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.03.522660. [PMID: 36711917 PMCID: PMC9881910 DOI: 10.1101/2023.01.03.522660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer, immunological, and cardiovascular diseases. Recent technological advances enable genome-wide quantification of DNAme in large human cohorts. So far, existing methods have not been evaluated to identify differential DNAme present in large and heterogeneous patient cohorts. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared to existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. Using cell-type specific data from two separate studies, we discovered novel epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven non-coding RNAs in non-small cell lung cancer.
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Affiliation(s)
- Yuanning Zheng
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - John Jun
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Kevin Brennan
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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7
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Divé I, Weber KJ, Hartung TI, Steidl E, Wagner M, Hattingen E, Franz K, Fokas E, Ronellenfitsch MW, Herrlinger U, Harter PN, Steinbach JP. Gliomatosis cerebri (GC) growth pattern: A single-center analysis of clinical, histological, and molecular characteristics of GC and non-GC glioblastoma. Neurooncol Adv 2023; 5:vdad131. [PMID: 38024242 PMCID: PMC10676054 DOI: 10.1093/noajnl/vdad131] [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] [Indexed: 12/01/2023] Open
Abstract
Background The biological understanding of glioblastoma (GB) with gliomatosis cerebri (GC) pattern is poor due to the absence of GC-specific studies. Here, we aimed to identify molecular or clinical parameters that drive GC growth. Methods From our methylome database of IDH (isocitrate dehydrogenase)-wildtype GB, we identified 158 non-GC and 65 GC cases. GC cases were subdivided into diffuse-infiltrative (subtype 1), multifocal (subtype 2), or tumors with 1 solid mass (subtype 3). We compared clinical, histological, and molecular parameters and conducted a reference-free tumor deconvolution of DNA methylation data based on latent methylation components (LMC). Results GC subtype 1 less frequently showed contrast-enhancing tumors, and more frequently lacked morphological GB criteria despite displaying GB DNA methylation profile. However, the tumor deconvolution did not deliver a specific LMC cluster for either of the GC subtypes. Employing the reference-based analysis MethylCIBERSORT, we did not identify significant differences in tumor cell composition. The majority of both GC and non-GC patients received radiochemotherapy as first-line treatment, but there was a major imbalance for resection. The entire GC cohort had significantly shorter overall survival (OS) and time to treatment failure (TTF) than the non-GC cohort. However, when filtering for cases in which only stereotactic biopsy was performed, the comparison of OS and TTF lost statistical significance. Conclusions Our study offers clinically relevant information by demonstrating a similar outcome for GB with GC growth pattern in the surgically matched analysis. The limited number of cases in the GC subgroups encourages the validation of our DNA methylation analysis in larger cohorts.
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Affiliation(s)
- Iris Divé
- Dr. Senckenberg Institute of Neurooncology, Goethe University, Frankfurt am Main, Germany
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
| | - Katharina J Weber
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
- Institute of Neurology (Edinger-Institute), Goethe University, Frankfurt am Main, Germany
| | - Tabea I Hartung
- Institute of Neurology (Edinger-Institute), Goethe University, Frankfurt am Main, Germany
| | - Eike Steidl
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
- Institute of Neuroradiology, Goethe University, Frankfurt am Main, Germany
| | - Marlies Wagner
- Institute of Neuroradiology, Goethe University, Frankfurt am Main, Germany
| | - Elke Hattingen
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
- Institute of Neuroradiology, Goethe University, Frankfurt am Main, Germany
| | - Kea Franz
- Dr. Senckenberg Institute of Neurooncology, Goethe University, Frankfurt am Main, Germany
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
- Department of Neurosurgery, Goethe University, Frankfurt am Main, Germany
| | - Emmanouil Fokas
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
- Department of Radiotherapy and Oncology, Goethe University, Frankfurt am Main, Germany
| | - Michael W Ronellenfitsch
- Dr. Senckenberg Institute of Neurooncology, Goethe University, Frankfurt am Main, Germany
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
| | | | - Patrick N Harter
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
- Institute of Neurology (Edinger-Institute), Goethe University, Frankfurt am Main, Germany
- Center for Neuropathology and Prion Research, Ludwig-Maximilians University, Munich, Germany (P.N.H.)
| | - Joachim P Steinbach
- Dr. Senckenberg Institute of Neurooncology, Goethe University, Frankfurt am Main, Germany
- University Cancer Center Frankfurt (UCT), Goethe University, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany
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Kern F, Kuhn T, Ludwig N, Simon M, Gröger L, Fabis N, Aparicio-Puerta E, Salhab A, Fehlmann T, Hahn O, Engel A, Wagner V, Koch M, Winek K, Soreq H, Nazarenko I, Fuhrmann G, Wyss-Coray T, Meese E, Keller V, Laschke MW, Keller A. Ageing-associated small RNA cargo of extracellular vesicles. RNA Biol 2023; 20:482-494. [PMID: 37498213 PMCID: PMC10376918 DOI: 10.1080/15476286.2023.2234713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 07/28/2023] Open
Abstract
Previous work on murine models and humans demonstrated global as well as tissue-specific molecular ageing trajectories of RNAs. Extracellular vesicles (EVs) are membrane vesicles mediating the horizontal transfer of genetic information between different tissues. We sequenced small regulatory RNAs (sncRNAs) in two mouse plasma fractions at five time points across the lifespan from 2-18 months: (1) sncRNAs that are free-circulating (fc-RNA) and (2) sncRNAs bound outside or inside EVs (EV-RNA). Different sncRNA classes exhibit unique ageing patterns that vary between the fcRNA and EV-RNA fractions. While tRNAs showed the highest correlation with ageing in both fractions, rRNAs exhibited inverse correlation trajectories between the EV- and fc-fractions. For miRNAs, the EV-RNA fraction was exceptionally strongly associated with ageing, especially the miR-29 family in adipose tissues. Sequencing of sncRNAs and coding genes in fat tissue of an independent cohort of aged mice up to 27 months highlighted the pivotal role of miR-29a-3p and miR-29b-3p in ageing-related gene regulation that we validated in a third cohort by RT-qPCR.
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Affiliation(s)
- Fabian Kern
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz-Centre for Infection Research (HZI), Department for Clinical Bioinformatics, Saarbrücken, Germany
| | - Thomas Kuhn
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz-Centre for Infection Research (HZI), Biogenic Nanotherapeutics Group (BION), Saarbrücken, Germany
- Department of Pharmacy, Saarland University, Saarbrücken, Germany
| | - Nicole Ludwig
- Department of Human Genetics, Saarland University, Homburg, Germany
- Center for Human and Molecular Biology, Saarland University, Homburg, Germany
| | - Martin Simon
- Molecular Cell Biology and Microbiology, Wuppertal University, Wuppertal, Germany
| | - Laura Gröger
- Department of Human Genetics, Saarland University, Homburg, Germany
| | - Natalie Fabis
- Molecular Cell Biology and Microbiology, Wuppertal University, Wuppertal, Germany
| | - Ernesto Aparicio-Puerta
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Abdulrahman Salhab
- Department of Genetics and Epigenetics, Saarland University, Saarbrücken, Germany
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Oliver Hahn
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, USA
| | - Annika Engel
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Viktoria Wagner
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Marcus Koch
- INM – Leibniz Institute for New Materials, Saarbrücken, Germany
| | - Katarzyna Winek
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hermona Soreq
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Irina Nazarenko
- Faculty of Medicine, Institute for Infection Prevention and Control; Medical Center - University of Freiburg, Freiburg, Germany
| | - Gregor Fuhrmann
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz-Centre for Infection Research (HZI), Biogenic Nanotherapeutics Group (BION), Saarbrücken, Germany
- Department of Pharmacy, Saarland University, Saarbrücken, Germany
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, USA
| | - Eckart Meese
- Department of Human Genetics, Saarland University, Homburg, Germany
| | - Verena Keller
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Matthias W. Laschke
- Institute for Clinical and Experimental Surgery, Saarland University, Homburg, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz-Centre for Infection Research (HZI), Department for Clinical Bioinformatics, Saarbrücken, Germany
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
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9
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Li W, Shao C, Zhou H, Du H, Chen H, Wan H, He Y. Multi-omics research strategies in ischemic stroke: A multidimensional perspective. Ageing Res Rev 2022; 81:101730. [PMID: 36087702 DOI: 10.1016/j.arr.2022.101730] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/23/2022] [Accepted: 09/03/2022] [Indexed: 01/31/2023]
Abstract
Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high rate of death and long-term impairment. Despite years of studies, there are still no stroke biomarkers for clinical practice, and the molecular mechanisms of stroke remain largely unclear. The high-throughput omics approach provides new avenues for discovering biomarkers of IS and explaining its pathological mechanisms. However, single-omics approaches only provide a limited understanding of the biological pathways of diseases. The integration of multiple omics data means the simultaneous analysis of thousands of genes, RNAs, proteins and metabolites, revealing networks of interactions between multiple molecular levels. Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their use in discovering the biomarkers and pathological mechanisms of IS. We then delineate strategies for achieving integration at the multi-omics level and discuss how integrative omics and systems biology can contribute to our understanding and management of IS.
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Affiliation(s)
- Wentao Li
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Chongyu Shao
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Huifen Zhou
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Haixia Du
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Haiyang Chen
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Haitong Wan
- School of Life Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
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10
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Zaremba A, Jansen P, Murali R, Mayakonda A, Riedel A, Philip M, Rose C, Schaller J, Müller H, Kutzner H, Möller I, Stadtler N, Kretz J, Sucker A, Bankfalvi A, Livingstone E, Zimmer L, Horn S, Paschen A, Plass C, Schadendorf D, Hadaschik E, Lutsik P, Griewank K. Genetic and methylation profiles distinguish benign, malignant and spitzoid melanocytic tumors. Int J Cancer 2022; 151:1542-1554. [PMID: 35737508 PMCID: PMC9474633 DOI: 10.1002/ijc.34187] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/26/2022] [Accepted: 05/04/2022] [Indexed: 11/07/2022]
Abstract
Accurate classification of melanocytic tumors is important for prognostic evaluation, treatment and follow-up protocols of patients. The majority of melanocytic proliferations can be classified solely based on clinical and pathological criteria, however in select cases a definitive diagnostic assessment remains challenging and additional diagnostic biomarkers would be advantageous. We analyzed melanomas, nevi, Spitz nevi and atypical spitzoid tumors using parallel sequencing (exons of 611 genes and 507 gene translocation analysis) and methylation arrays (850k Illumina EPIC). By combining detailed genetic and epigenetic analysis with reference-based and reference-free DNA methylome deconvolution we compared Spitz nevi to nevi and melanoma and assessed the potential for these methods in classifying challenging spitzoid tumors. Results were correlated with clinical and histologic features. Spitz nevi were found to cluster independently of nevi and melanoma and demonstrated a different mutation profile. Multiple copy number alterations and TERT promoter mutations were identified only in melanomas. Genome-wide methylation in Spitz nevi was comparable to benign nevi while the Leukocytes UnMethylation for Purity (LUMP) algorithm in Spitz nevi was comparable to melanoma. Histologically difficult to classify Spitz tumor cases were assessed which, based on methylation arrays, clustered between Spitz nevi and melanoma and in terms of genetic profile or copy number variations demonstrated worrisome features suggesting a malignant neoplasm. Comprehensive sequencing and methylation analysis verify Spitz nevi as an independent melanocytic entity distinct from both nevi and melanoma. Combined genetic and methylation assays can offer additional insights in diagnosing difficult to classify Spitzoid tumors.
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Affiliation(s)
- Anne Zaremba
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Philipp Jansen
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Rajmohan Murali
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Anand Mayakonda
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz International Graduate School for Cancer Research, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Anna Riedel
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz International Graduate School for Cancer Research, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Manuel Philip
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | | | | | | | - Heinz Kutzner
- Dermatopathologie Friedrichshafen, Medical faculty of the University Leipzig, Leipzig, Germany
| | - Inga Möller
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Nadine Stadtler
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Julia Kretz
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Antje Sucker
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Agnes Bankfalvi
- Department of Pathology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Elisabeth Livingstone
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Lisa Zimmer
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Susanne Horn
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
- Rudolf-Schönheimer-Institute of Biochemistry, Medical faculty of the University Leipzig, Leipzig, Germany
| | - Annette Paschen
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Eva Hadaschik
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus Griewank
- Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Germany, and German Cancer Consortium (DKTK), Heidelberg, Germany
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11
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He D, Chen M, Wang W, Song C, Qin Y. Deconvolution of tumor composition using partially available DNA methylation data. BMC Bioinformatics 2022; 23:355. [PMID: 36002797 PMCID: PMC9400327 DOI: 10.1186/s12859-022-04893-7] [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: 06/07/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Deciphering proportions of constitutional cell types in tumor tissues is a crucial step for the analysis of tumor heterogeneity and the prediction of response to immunotherapy. In the process of measuring cell population proportions, traditional experimental methods have been greatly hampered by the cost and extensive dropout events. At present, the public availability of large amounts of DNA methylation data makes it possible to use computational methods to predict proportions. Results In this paper, we proposed PRMeth, a method to deconvolve tumor mixtures using partially available DNA methylation data. By adopting an iteratively optimized non-negative matrix factorization framework, PRMeth took DNA methylation profiles of a portion of the cell types in the tissue mixtures (including blood and solid tumors) as input to estimate the proportions of all cell types as well as the methylation profiles of unknown cell types simultaneously. We compared PRMeth with five different methods through three benchmark datasets and the results show that PRMeth could infer the proportions of all cell types and recover the methylation profiles of unknown cell types effectively. Then, applying PRMeth to four types of tumors from The Cancer Genome Atlas (TCGA) database, we found that the immune cell proportions estimated by PRMeth were largely consistent with previous studies and met biological significance. Conclusions Our method can circumvent the difficulty of obtaining complete DNA methylation reference data and obtain satisfactory deconvolution accuracy, which will be conducive to exploring the new directions of cancer immunotherapy. PRMeth is implemented in R and is freely available from GitHub (https://github.com/hedingqin/PRMeth). Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04893-7.
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Affiliation(s)
- Dingqin He
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Ming Chen
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Wenjuan Wang
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Chunhui Song
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Yufang Qin
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China. .,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China.
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12
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Genetic and Methylation Analysis of CTNNB1 in Benign and Malignant Melanocytic Lesions. Cancers (Basel) 2022; 14:cancers14174066. [PMID: 36077603 PMCID: PMC9454999 DOI: 10.3390/cancers14174066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/19/2022] [Accepted: 07/26/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology and in 1–2% of advanced melanoma. We performed a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations and found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanoma) demonstrated a different genetic profile, grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (55%) and NRAS Q61 (34%). Abstract Melanocytic neoplasms have been genetically characterized in detail during the last decade. Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology. In addition, they have been identified in 1–2% of advanced melanoma. Performing a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations, we found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanomas) demonstrated a different genetic profile, instead grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (n = 21, 55%) and NRAS Q61 (n = 13, 34%). In our cohort, four of seven (58%) and one of nine (11%) patients treated with targeted therapy (BRAF and MEK Inhibitors) or immune-checkpoint therapy, respectively, showed disease control (partial response or stable disease). In summary, CTNNB1 mutations are associated with a unique melanocytic tumor type in benign tumors (nevi), which can be applied in a diagnostic setting. In advanced disease, no clear characteristics distinguishing CTNNB1-mutant from other melanomas were observed; however, studies of larger, optimally prospective, cohorts are warranted.
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13
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Chen Y, Toth R, Chocarro S, Weichenhan D, Hey J, Lutsik P, Sawall S, Stathopoulos GT, Plass C, Sotillo R. Club cells employ regeneration mechanisms during lung tumorigenesis. Nat Commun 2022; 13:4557. [PMID: 35931677 PMCID: PMC9356049 DOI: 10.1038/s41467-022-32052-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 07/12/2022] [Indexed: 11/09/2022] Open
Abstract
The high plasticity of lung epithelial cells, has for many years, confounded the correct identification of the cell-of-origin of lung adenocarcinoma (LUAD), one of the deadliest malignancies worldwide. Here, we employ lineage-tracing mouse models to investigate the cell of origin of Eml4-Alk LUAD, and show that Club and Alveolar type 2 (AT2) cells give rise to tumours. We focus on Club cell originated tumours and find that Club cells experience an epigenetic switch by which they lose their lineage fidelity and gain an AT2-like phenotype after oncogenic transformation. Single-cell transcriptomic analyses identified two trajectories of Club cell evolution which are similar to the ones used during lung regeneration, suggesting that lung epithelial cells leverage on their plasticity and intrinsic regeneration mechanisms to give rise to a tumour. Together, this study highlights the role of Club cells in LUAD initiation, identifies the mechanism of Club cell lineage infidelity, confirms the presence of these features in human tumours, and unveils key mechanisms conferring LUAD heterogeneity.
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Affiliation(s)
- Yuanyuan Chen
- Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Reka Toth
- Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Sara Chocarro
- Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Ruprecht Karl University of Heidelberg, Heidelberg, Germany
| | - Dieter Weichenhan
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Joschka Hey
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Ruprecht Karl University of Heidelberg, Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Stefan Sawall
- X-Ray Imaging and CT, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Georgios T Stathopoulos
- Comprehensive Pneumology Center (CPC) and Institute for Lung Biology and Disease (iLBD), Helmholtz Center Munich-German Research Center for Environmental Health (HMGU), Max-Lebsche-Platz 31, 81377, Munich, Bavaria, Germany.,German Center for Lung Research (DZL), Heidelberg, Germany
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,German Center for Lung Research (DZL), Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TRLC), Heidelberg, Germany.,German Consortium for Translational Cancer Research (DKTK), 69120, Heidelberg, Germany
| | - Rocio Sotillo
- Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. .,German Center for Lung Research (DZL), Heidelberg, Germany. .,Translational Lung Research Center Heidelberg (TRLC), Heidelberg, Germany. .,German Consortium for Translational Cancer Research (DKTK), 69120, Heidelberg, Germany.
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14
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Jeong Y, de Andrade e Sousa LB, Thalmeier D, Toth R, Ganslmeier M, Breuer K, Plass C, Lutsik P. Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes. Brief Bioinform 2022; 23:bbac248. [PMID: 35794707 PMCID: PMC9294431 DOI: 10.1093/bib/bbac248] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022] Open
Abstract
DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods-both reference-based and reference-free-generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.
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Affiliation(s)
- Yunhee Jeong
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Faculty of Mathematics and Informatics, Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, Germany
| | | | - Dominik Thalmeier
- Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraβ e 1, 85764, Neuherberg, Germany
| | - Reka Toth
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Marlene Ganslmeier
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Kersten Breuer
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
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15
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Woltering N, Albers A, Müther M, Stummer W, Paulus W, Hasselblatt M, Holling M, Thomas C. DNA
methylation profiling of central nervous system hemangioblastomas identifies two distinct subgroups. Brain Pathol 2022; 32:e13083. [PMID: 35637626 PMCID: PMC9616087 DOI: 10.1111/bpa.13083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/10/2022] [Indexed: 12/01/2022] Open
Abstract
Hemangioblastomas (HBs) of the central nervous system are highly vascular neoplasms that occur sporadically or as a manifestation of von Hippel–Lindau (VHL) disease. Despite their benign nature, HBs are clinically heterogeneous and can be associated with significant morbidity due to mass effects of peritumoral cysts or tumor progression. Underlying molecular factors involved in HB tumor biology remain elusive. We investigated genome‐wide DNA methylation profiles and clinical and histopathological features in a series of 47 HBs from 42 patients, including 28 individuals with VHL disease. Thirty tumors occurred in the cerebellum, 8 in the brainstem and 8 HBs were of spinal location, while 1 HB was located in the cerebrum. Histologically, 12 HBs (26%) belonged to the cellular subtype and exclusively occurred in the cerebellum, whereas 35 HBs were reticular (74%). Unsupervised clustering and dimensionality reduction of DNA methylation profiles revealed two distinct subgroups. Methylation cluster 1 comprised 30 HBs of mainly cerebellar location (29/30, 97%), whereas methylation cluster 2 contained 17 HBs predominantly located in non‐cerebellar compartments (16/17, 94%). The sum of chromosomal regions being affected by copy‐number alterations was significantly higher in methylation cluster 1 compared to cluster 2 (mean 262 vs. 109 Mb, p = 0.001). Of note, loss of chromosome 6 occurred in 9/30 tumors (30%) of methylation cluster 1 and was not observed in cluster 2 tumors (p = 0.01). No relevant methylation differences between sporadic and VHL‐related HBs or cystic and non‐cystic HBs could be detected. Deconvolution of the bulk DNA methylation profiles revealed four methylation components that were associated with the two methylation clusters suggesting cluster‐specific cell‐type compositions. In conclusion, methylation profiling of HBs reveals 2 distinct subgroups that mainly associate with anatomical location, cytogenetic profiles and differences in cell type composition, potentially reflecting different cells of origin.
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Affiliation(s)
- Niklas Woltering
- Institute of Neuropathology University Hospital Münster Münster Germany
| | - Anne Albers
- Institute of Neuropathology University Hospital Münster Münster Germany
| | - Michael Müther
- Department of Neurosurgery University Hospital Münster Münster Germany
| | - Walter Stummer
- Department of Neurosurgery University Hospital Münster Münster Germany
| | - Werner Paulus
- Institute of Neuropathology University Hospital Münster Münster Germany
| | | | - Markus Holling
- Department of Neurosurgery University Hospital Münster Münster Germany
| | - Christian Thomas
- Institute of Neuropathology University Hospital Münster Münster Germany
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16
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Zhu T, Liu J, Beck S, Pan S, Capper D, Lechner M, Thirlwell C, Breeze CE, Teschendorff AE. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat Methods 2022; 19:296-306. [PMID: 35277705 PMCID: PMC8916958 DOI: 10.1038/s41592-022-01412-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 01/28/2022] [Indexed: 02/07/2023]
Abstract
Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. This resource presents an in silico generated DNA methylation atlas that can be used for cell-type deconvolution of human tissues.
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17
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Maity AK, Stone TC, Ward V, Webster AP, Yang Z, Hogan A, McBain H, Duku M, Ho KMA, Wolfson P, Graham DG, Beck S, Teschendorff AE, Lovat LB. Novel epigenetic network biomarkers for early detection of esophageal cancer. Clin Epigenetics 2022; 14:23. [PMID: 35164838 PMCID: PMC8845366 DOI: 10.1186/s13148-022-01243-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/04/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Early detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity. RESULTS Here we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett's Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status. CONCLUSIONS Given our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation.
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Affiliation(s)
- Alok K Maity
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Timothy C Stone
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Vanessa Ward
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Amy P Webster
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Zhen Yang
- Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Aine Hogan
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Hazel McBain
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Margaraet Duku
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Kai Man Alexander Ho
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Paul Wolfson
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - David G Graham
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK.,Division of GI Services, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | | | - Stephan Beck
- UCL Cancer Institute, University College London, Gower Street, London, WC1E 6BT, UK
| | - Andrew E Teschendorff
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
| | - Laurence B Lovat
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK. .,Division of GI Services, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK.
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18
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Goeppert B, Stichel D, Toth R, Fritzsche S, Loeffler MA, Schlitter AM, Neumann O, Assenov Y, Vogel MN, Mehrabi A, Hoffmann K, Köhler B, Springfeld C, Weichenhan D, Plass C, Esposito I, Schirmacher P, von Deimling A, Roessler S. Integrative analysis reveals early and distinct genetic and epigenetic changes in intraductal papillary and tubulopapillary cholangiocarcinogenesis. Gut 2022; 71:391-401. [PMID: 33468537 PMCID: PMC8762040 DOI: 10.1136/gutjnl-2020-322983] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/21/2020] [Accepted: 01/02/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE A detailed understanding of the molecular alterations in different forms of cholangiocarcinogenesis is crucial for a better understanding of cholangiocarcinoma (CCA) and may pave the way to early diagnosis and better treatment options. DESIGN We analysed a clinicopathologically well-characterised patient cohort (n=54) with high-grade intraductal papillary (IPNB) or tubulopapillary (ITPN) neoplastic precursor lesions of the biliary tract and correlated the results with an independent non-IPNB/ITPN associated CCA cohort (n=294). The triplet sample set of non-neoplastic biliary epithelium, precursor and invasive CCA was analysed by next generation sequencing, DNA copy number and genome-wide methylation profiling. RESULTS Patients with invasive CCA arising from IPNB/ITPN had better prognosis than patients with CCA not associated with IPNB/ITPN. ITPN was localised mostly intrahepatic, whereas IPNB was mostly of extrahepatic origin. IPNB/ITPN were equally associated with small-duct and large-duct type intrahepatic CCA. IPNB exhibited mutational profiles of extrahepatic CCA, while ITPN had significantly fewer mutations. Most mutations were shared between precursor lesions and corresponding invasive CCA but ROBO2 mutations occurred exclusively in invasive CCA and CTNNB1 mutations were mainly present in precursor lesions. In addition, IPNB and ITPN differed in their DNA methylation profiles and analyses of latent methylation components suggested that IPNB and ITPN may have different cells-of-origin. CONCLUSION Integrative analysis revealed that IPNB and ITPN harbour distinct early genetic alterations, IPNB are enriched in mutations typical for extrahepatic CCA, whereas ITPN exhibited few genetic alterations and showed distinct epigenetic profiles. In conclusion, IPNB/ITPN may represent a distinctive, intermediate form of intrahepatic and extrahepatic cholangiocarcinogenesis.
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Affiliation(s)
- Benjamin Goeppert
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany .,Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany
| | - Damian Stichel
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany,German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany,Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Reka Toth
- Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sarah Fritzsche
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | | | | | - Olaf Neumann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Yassen Assenov
- Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Monika Nadja Vogel
- Diagnostic and Interventional Radiology, Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany
| | - Arianeb Mehrabi
- Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany,Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, Germany
| | - Katrin Hoffmann
- Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany,Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, Germany
| | - Bruno Köhler
- Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany,Department of Medical Oncology, National Center of Tumor Diseases, Heidelberg, Germany
| | - Christoph Springfeld
- Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany,Department of Medical Oncology, National Center of Tumor Diseases, Heidelberg, Germany
| | - Dieter Weichenhan
- Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christoph Plass
- German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany,Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Irene Esposito
- Institute of Pathology, Heinrich-Heine-Universitat Dusseldorf, Dusseldorf, Germany
| | - Peter Schirmacher
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany,Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany
| | - Andreas von Deimling
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany,German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany,Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Stephanie Roessler
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany .,Liver Cancer Center Heidelberg (LCCH), Heidelberg, Germany
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19
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Ko H, Ahn HJ, Kim YI. Methylation and mutation of the inhibin‑α gene in human melanoma cells and regulation of PTEN expression and AKT/PI3K signaling by a demethylating agent. Oncol Rep 2021; 47:37. [PMID: 34958114 DOI: 10.3892/or.2021.8248] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/23/2021] [Indexed: 11/06/2022] Open
Abstract
Inhibin suppresses the pituitary secretion of follicle‑stimulating hormone and has been reported to act as a tumor suppressor gene in the gonad in mice. Epigenetic modifications, mutations, changes in the loss of heterozygosity (LOH) of the inhibin‑α gene and regulation of gene expression in response to a demethylating agent [5‑aza‑2'‑deoxycytidine (5‑Aza‑dC)] in human melanoma cells were assessed. In addition, the association between a mutation in the 5'‑untranslated region (5'‑UTR) of the inhibin‑α subunit and the expression of phosphatidylinositol 3,4,5‑trisphosphate‑dependent Rac exchanger 2 (PREX2) and phosphatase and tensin homolog (PTEN) as well as AKT/PI3K signaling was determined. The methylation status of the CpG sites of the inhibin‑α promoter was analyzed by methylation‑specific PCR in bisulfite‑treated DNA. Cell viability was counted using the trypan blue assay, mRNA expression was examined via reverse transcription‑quantitative PCR, and protein expression was examined via western blot analysis. The inhibin‑α promoter was hypermethylated in G361, SK‑MEL‑3, SK‑MEL‑24 and SK‑MEL‑28 cells and moderately methylated in SK‑MEL‑5 cells. Inhibin‑α gene mutations were observed in the 5'‑UTR exon 1 of G361, SK‑MEL‑5, SK‑MEL‑24 and SK‑MEL‑28 cells as well as in exon 2 of SK‑MEL‑3 cells. Allelic imbalance, including LOH, in the inhibin‑α gene was detected in human melanoma cells. Treatment with 5‑Aza‑dC increased inhibin‑α mRNA and protein levels, inhibited cell proliferation, and delayed the doubling times of surviving melanoma cells. In 5‑Aza‑dC‑treated cells, PREX2 protein expression was slightly increased in G361 and SK‑MEL‑24 cells and decreased in SK‑MEL3, SK‑MEL‑5 and SK‑MEL‑28 cells. However, the protein expression of PTEN was decreased in melanoma cells. In addition, AKT and PI3K protein phosphorylation levels increased in all melanoma cells, except of G361 cells, demonstrating decreased PI3K protein phosphorylation. These data provided evidence that methylation, mutation and LOH are observed in the inhibin α‑subunit gene and gene locus in human melanoma cells. Furthermore, the demethylating agent reactivated inhibin‑α gene expression and regulated PREX2 expression. AKT/PI3K signaling increased as PTEN expression decreased. In addition, mutations in the tumor suppressor inhibin‑α, PTEN and p53 genes were not associated with transcriptional silencing, gene expression and cell growth as analyzed through experiments and literature reviews. These data demonstrated that methylation and mutations were associated with the inhibin‑α gene in human melanoma cells and indicated the regulation of PTEN expression and AKT/PI3K signaling by a demethylating agent.
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Affiliation(s)
- Hyunmin Ko
- Department of Surgery, College of Medicine, Kyung Hee University, Dongdaemun, Seoul 02447, Republic of Korea
| | - Hyung Joon Ahn
- Department of Surgery, College of Medicine, Kyung Hee University, Dongdaemun, Seoul 02447, Republic of Korea
| | - Young Il Kim
- Medical Science Research Institute, Kyung Hee University Medical Center, Dongdaemun, Seoul 02447, Republic of Korea
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20
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Salas LA, Peres LC, Thayer ZM, Smith RWA, Guo Y, Chung W, Si J, Liang L. A transdisciplinary approach to understand the epigenetic basis of race/ethnicity health disparities. Epigenomics 2021; 13:1761-1770. [PMID: 33719520 PMCID: PMC8579937 DOI: 10.2217/epi-2020-0080] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 04/07/2020] [Indexed: 11/21/2022] Open
Abstract
Health disparities correspond to differences in disease burden and mortality among socially defined population groups. Such disparities may emerge according to race/ethnicity, socioeconomic status and a variety of other social contexts, and are documented for a wide range of diseases. Here, we provide a transdisciplinary perspective on the contribution of epigenetics to the understanding of health disparities, with a special emphasis on disparities across socially defined racial/ethnic groups. Scientists in the fields of biological anthropology, bioinformatics and molecular epidemiology provide a summary of theoretical, statistical and practical considerations for conducting epigenetic health disparities research, and provide examples of successful applications from cancer research using this approach.
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Affiliation(s)
- Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA
| | - Lauren C Peres
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Zaneta M Thayer
- Department of Anthropology, Dartmouth College, Hanover, NH 03755, USA
| | - Rick WA Smith
- Department of Anthropology, Dartmouth College, Hanover, NH 03755, USA
- The William H. Neukom Institute for Computational Science, Dartmouth College, Hanover, NH 03755, USA
| | | | - Wonil Chung
- Department of Statistics & Actuarial Science, Soongsil University, Seoul, 06478, Korea
- Program in Genetic Epidemiology & Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jiahui Si
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics & Epidemiology, Peking University School of Public Health, Beijing, 100191, China
| | - Liming Liang
- Program in Genetic Epidemiology & Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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21
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Decamps C, Arnaud A, Petitprez F, Ayadi M, Baurès A, Armenoult L, Escalera S, Guyon I, Nicolle R, Tomasini R, de Reyniès A, Cros J, Blum Y, Richard M. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification. BMC Bioinformatics 2021; 22:473. [PMID: 34600479 PMCID: PMC8487526 DOI: 10.1186/s12859-021-04381-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 09/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. RESULTS We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. CONCLUSION DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 .
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Affiliation(s)
- Clémentine Decamps
- Laboratory TIMC-IMAG, UMR 5525, CNRS, Univ. Grenoble Alpes, Grenoble, France
| | - Alexis Arnaud
- Data Institute, Univ. Grenoble Alpes, Grenoble, France
| | - Florent Petitprez
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | - Mira Ayadi
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | - Aurélia Baurès
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | - Lucile Armenoult
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | | | - Sergio Escalera
- Universitat de Barcelona and Computer Vision Center, Barcelona, Spain
| | - Isabelle Guyon
- LISN (INRIA/CNRS), Université Paris-Saclay, Gif-sur-Yvette, France
| | - Rémy Nicolle
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | | | - Aurélien de Reyniès
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | - Jérôme Cros
- Dpt of Pathology, Beaujon Hospital, Univ. Paris-INSERM U1149, Clichy, France
| | - Yuna Blum
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France. .,IGDR UMR 6290, CNRS, Université de Rennes 1, Rennes, France.
| | - Magali Richard
- Laboratory TIMC-IMAG, UMR 5525, CNRS, Univ. Grenoble Alpes, Grenoble, France.
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22
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Wu Z, Abdullaev Z, Pratt D, Chung HJ, Skarshaug S, Zgonc V, Perry C, Pack S, Saidkhodjaeva L, Nagaraj S, Tyagi M, Gangalapudi V, Valdez K, Turakulov R, Xi L, Raffeld M, Papanicolau-Sengos A, O'Donnell K, Newford M, Gilbert MR, Sahm F, Suwala AK, von Deimling A, Mamatjan Y, Karimi S, Nassiri F, Zadeh G, Ruppin E, Quezado M, Aldape K. Impact of the methylation classifier and ancillary methods on CNS tumor diagnostics. Neuro Oncol 2021; 24:571-581. [PMID: 34555175 DOI: 10.1093/neuonc/noab227] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases. METHODS An integrated diagnostic approach was employed for a consecutive series of 1,258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed. RESULTS Among the received cases in consultation, a high confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.5% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (p = 1.15e-11). Deconvolution demonstrated that suspected GBMs matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity. CONCLUSIONS This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs. change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.
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Affiliation(s)
- Zhichao Wu
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Zied Abdullaev
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Drew Pratt
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Hye-Jung Chung
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Shannon Skarshaug
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Valerie Zgonc
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Candice Perry
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Svetlana Pack
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lola Saidkhodjaeva
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sushma Nagaraj
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Manoj Tyagi
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Vineela Gangalapudi
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kristin Valdez
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Rust Turakulov
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Liqiang Xi
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark Raffeld
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Antonios Papanicolau-Sengos
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kayla O'Donnell
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Michael Newford
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Felix Sahm
- Department of Neuropathology, Institute of Pathology, University Hospital of Heidelberg, Im Neuenheimer Feld, Heidelberg, Germany
| | - Abigail K Suwala
- Department of Neuropathology, Institute of Pathology, University Hospital of Heidelberg, Im Neuenheimer Feld, Heidelberg, Germany
| | - Andreas von Deimling
- Department of Neuropathology, Institute of Pathology, University Hospital of Heidelberg, Im Neuenheimer Feld, Heidelberg, Germany
| | - Yasin Mamatjan
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Shirin Karimi
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Farshad Nassiri
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Gelareh Zadeh
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Martha Quezado
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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23
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Filipski K, Scherer M, Zeiner KN, Bucher A, Kleemann J, Jurmeister P, Hartung TI, Meissner M, Plate KH, Fenton TR, Walter J, Tierling S, Schilling B, Zeiner PS, Harter PN. DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma. J Immunother Cancer 2021; 9:jitc-2020-002226. [PMID: 34281986 PMCID: PMC8291310 DOI: 10.1136/jitc-2020-002226] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2021] [Indexed: 12/18/2022] Open
Abstract
Background Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. Methods A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). Results We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. Conclusions These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.
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Affiliation(s)
- Katharina Filipski
- Neurological Institute (Edinger Institute), University Hospital, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Michael Scherer
- Department of Genetics, University of Saarland, Saarbrücken, Germany.,Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.,Graduate School of Computer Science, Saarland Informatics Campus, Saabrücken, Germany
| | - Kim N Zeiner
- Department of Dermatology, University Hospital, Frankfurt am Main, Germany
| | - Andreas Bucher
- Department of Radiology, University Hospital, Frankfurt am Main, Germany
| | - Johannes Kleemann
- Department of Dermatology, University Hospital, Frankfurt am Main, Germany
| | - Philipp Jurmeister
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany
| | - Tabea I Hartung
- Neurological Institute (Edinger Institute), University Hospital, Frankfurt am Main, Germany
| | - Markus Meissner
- Department of Dermatology, University Hospital, Frankfurt am Main, Germany
| | - Karl H Plate
- Neurological Institute (Edinger Institute), University Hospital, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Tim R Fenton
- School of Biosciences, University of Kent, Kent, UK
| | - Jörn Walter
- Department of Genetics, University of Saarland, Saarbrücken, Germany
| | - Sascha Tierling
- Department of Genetics, University of Saarland, Saarbrücken, Germany
| | - Bastian Schilling
- Department of Dermatology, University Hospital Würzburg, Würzburg, Germany
| | - Pia S Zeiner
- German Cancer Consortium (DKTK) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany.,Dr. Senckenberg Institute of Neurooncology, University Hospital, Frankfurt am Main, Germany
| | - Patrick N Harter
- Neurological Institute (Edinger Institute), University Hospital, Frankfurt am Main, Germany .,German Cancer Consortium (DKTK) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
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24
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Lu T, Cardenas A, Perron P, Hivert MF, Bouchard L, Greenwood CMT. Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth. Clin Epigenetics 2021; 13:131. [PMID: 34174944 PMCID: PMC8236204 DOI: 10.1186/s13148-021-01114-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 06/14/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner. RESULTS We analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates. CONCLUSION Compared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Patrice Perron
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalier, Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Marie-France Hivert
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Luigi Bouchard
- Centre de Recherche du Centre Hospitalier, Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
- Department of Medical Biology, Centre Intégré Universitaire de Santé et de Services Sociaux Saguenay-Lac-Saint-Jean - Hôpital Universitaire de Chicoutimi, Saguenay, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada.
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25
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Soares-Lima SC, Mehanna H, Camuzi D, de Souza-Santos PT, Simão TDA, Nicolau-Neto P, Almeida Lopes MDS, Cuenin C, Talukdar FR, Batis N, Costa I, Dias F, Degli Esposti D, Boroni M, Herceg Z, Ribeiro Pinto LF. Upper Aerodigestive Tract Squamous Cell Carcinomas Show Distinct Overall DNA Methylation Profiles and Different Molecular Mechanisms behind WNT Signaling Disruption. Cancers (Basel) 2021; 13:3014. [PMID: 34208581 PMCID: PMC8234055 DOI: 10.3390/cancers13123014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/27/2021] [Accepted: 06/08/2021] [Indexed: 12/28/2022] Open
Abstract
Upper aerodigestive tract (UADT) tumors present different biological behavior and prognosis, suggesting specific molecular mechanisms underlying their development. However, they are rarely considered as single entities (particularly head and neck subsites) and share the most common genetic alterations. Therefore, there is a need for a better understanding of the global DNA methylation differences among UADT tumors. We performed a genome-wide DNA methylation analysis of esophageal (ESCC), laryngeal (LSCC), oral (OSCC) and oropharyngeal (OPSCC) squamous cell carcinomas, and their non-tumor counterparts. The unsupervised analysis showed that non-tumor tissues present markedly distinct DNA methylation profiles, while tumors are highly heterogeneous. Hypomethylation was more frequent in LSCC and OPSCC, while ESCC and OSCC presented mostly hypermethylation, with the latter showing a CpG island overrepresentation. Differentially methylated regions affected genes in 127 signaling pathways, with only 3.1% of these being common among different tumor subsites, but with different genes affected. The WNT signaling pathway, known to be dysregulated in different epithelial tumors, is a frequent hit for DNA methylation and gene expression alterations in ESCC and OPSCC, but mostly for genetic alterations in LSCC and OSCC. UADT tumor subsites present differences in genome-wide methylation regarding their profile, intensity, genomic regions and signaling pathways affected.
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Affiliation(s)
- Sheila Coelho Soares-Lima
- Molecular Carcinogenesis Program, Brazilian National Cancer Institute, Rua André Cavalcanti, 37–6° Andar, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil; (S.C.S.-L.); (D.C.); (P.N.-N.); (M.d.S.A.L.)
| | - Hisham Mehanna
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (H.M.); (N.B.)
| | - Diego Camuzi
- Molecular Carcinogenesis Program, Brazilian National Cancer Institute, Rua André Cavalcanti, 37–6° Andar, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil; (S.C.S.-L.); (D.C.); (P.N.-N.); (M.d.S.A.L.)
| | | | - Tatiana de Almeida Simão
- Departamento de Bioquímica, Instituto de Biologia Roberto Alcantara Gomes, Universidade do Estado do Rio de Janeiro, Av. 28 de Setembro 87 fundos, Vila Isabel, Rio de Janeiro 20551-013, Brazil;
| | - Pedro Nicolau-Neto
- Molecular Carcinogenesis Program, Brazilian National Cancer Institute, Rua André Cavalcanti, 37–6° Andar, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil; (S.C.S.-L.); (D.C.); (P.N.-N.); (M.d.S.A.L.)
| | - Monique de Souza Almeida Lopes
- Molecular Carcinogenesis Program, Brazilian National Cancer Institute, Rua André Cavalcanti, 37–6° Andar, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil; (S.C.S.-L.); (D.C.); (P.N.-N.); (M.d.S.A.L.)
| | - Cyrille Cuenin
- Epigenetics Group, International Agency for Research on Cancer, 150 Cours Albert Thomas, CEDEX 08, 69372 Lyon, France; (C.C.); (F.R.T.); (D.D.E.); (Z.H.)
| | - Fazlur Rahman Talukdar
- Epigenetics Group, International Agency for Research on Cancer, 150 Cours Albert Thomas, CEDEX 08, 69372 Lyon, France; (C.C.); (F.R.T.); (D.D.E.); (Z.H.)
| | - Nikolaos Batis
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (H.M.); (N.B.)
| | - Izabella Costa
- Seção de Cirurgia de Cabeça e Pescoço, Instituto Nacional de Câncer—INCA, Praça da Cruz Vermelha, Rio de Janeiro 20230-130, Brazil; (I.C.); (F.D.)
| | - Fernando Dias
- Seção de Cirurgia de Cabeça e Pescoço, Instituto Nacional de Câncer—INCA, Praça da Cruz Vermelha, Rio de Janeiro 20230-130, Brazil; (I.C.); (F.D.)
| | - Davide Degli Esposti
- Epigenetics Group, International Agency for Research on Cancer, 150 Cours Albert Thomas, CEDEX 08, 69372 Lyon, France; (C.C.); (F.R.T.); (D.D.E.); (Z.H.)
| | - Mariana Boroni
- Bioinformatics and Computational Biology Lab, Brazilian National Cancer Institute, Rua André Cavalcanti, 37–1° Andar, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil;
| | - Zdenko Herceg
- Epigenetics Group, International Agency for Research on Cancer, 150 Cours Albert Thomas, CEDEX 08, 69372 Lyon, France; (C.C.); (F.R.T.); (D.D.E.); (Z.H.)
| | - Luis Felipe Ribeiro Pinto
- Molecular Carcinogenesis Program, Brazilian National Cancer Institute, Rua André Cavalcanti, 37–6° Andar, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil; (S.C.S.-L.); (D.C.); (P.N.-N.); (M.d.S.A.L.)
- Departamento de Bioquímica, Instituto de Biologia Roberto Alcantara Gomes, Universidade do Estado do Rio de Janeiro, Av. 28 de Setembro 87 fundos, Vila Isabel, Rio de Janeiro 20551-013, Brazil;
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26
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Simon M, Mughal SS, Horak P, Uhrig S, Buchloh J, Aybey B, Stenzinger A, Glimm H, Fröhling S, Brors B, Imbusch CD. Deconvolution of sarcoma methylomes reveals varying degrees of immune cell infiltrates with association to genomic aberrations. J Transl Med 2021; 19:204. [PMID: 33980253 PMCID: PMC8117561 DOI: 10.1186/s12967-021-02858-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 04/26/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Soft-tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors for which response to immunotherapies is not well established. Therefore, it is important to risk-stratify and identify STS patients who will most likely benefit from these treatments. RESULTS To reveal shared and distinct methylation signatures present in STS, we performed unsupervised deconvolution of DNA methylation data from the TCGA sarcoma and an independent validation cohort. We showed that leiomyosarcoma can be subclassified into three distinct methylation groups. More importantly, we identified a component associated with tumor-infiltrating leukocytes, which suggests varying degrees of immune cell infiltration in STS subtypes and an association with prognosis. We further investigated the genomic alterations that may influence tumor infiltration by leukocytes including RB1 loss in undifferentiated pleomorphic sarcomas and ELK3 amplification in dedifferentiated liposarcomas. CONCLUSIONS In summary, we have leveraged unsupervised methylation-based deconvolution to characterize the immune compartment and molecularly stratify subtypes in STS, which may benefit precision medicine in the future.
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Affiliation(s)
- Malte Simon
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Sadaf S Mughal
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Horak
- Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Uhrig
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jonas Buchloh
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bogac Aybey
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Hanno Glimm
- Department of Translational Medical Oncology, NCT Dresden, Dresden, Germany.,University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Stefan Fröhling
- Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Charles D Imbusch
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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27
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Scherer M, Schmidt F, Lazareva O, Walter J, Baumbach J, Schulz MH, List M. Machine learning for deciphering cell heterogeneity and gene regulation. NATURE COMPUTATIONAL SCIENCE 2021; 1:183-191. [PMID: 38183187 DOI: 10.1038/s43588-021-00038-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/08/2021] [Indexed: 12/14/2022]
Abstract
Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.
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Affiliation(s)
- Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
- Computational Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany
| | | | - Olga Lazareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jörn Walter
- Computational Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Computational BioMedicine Lab, Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Marcel H Schulz
- Institute of Cardiovascular Regeneration, University Hospital and Goethe University Frankfurt, Frankfurt, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
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28
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Mancarella D, Plass C. Epigenetic signatures in cancer: proper controls, current challenges and the potential for clinical translation. Genome Med 2021; 13:23. [PMID: 33568205 PMCID: PMC7874645 DOI: 10.1186/s13073-021-00837-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 01/21/2021] [Indexed: 12/26/2022] Open
Abstract
Epigenetic alterations are associated with normal biological processes such as aging or differentiation. Changes in global epigenetic signatures, together with genetic alterations, are driving events in several diseases including cancer. Comparative studies of cancer and healthy tissues found alterations in patterns of DNA methylation, histone posttranslational modifications, and changes in chromatin accessibility. Driven by sophisticated, next-generation sequencing-based technologies, recent studies discovered cancer epigenomes to be dominated by epigenetic patterns already present in the cell-of-origin, which transformed into a neoplastic cell. Tumor-specific epigenetic changes therefore need to be redefined and factors influencing epigenetic patterns need to be studied to unmask truly disease-specific alterations. The underlying mechanisms inducing cancer-associated epigenetic alterations are poorly understood. Studies of mutated epigenetic modifiers, enzymes that write, read, or edit epigenetic patterns, or mutated chromatin components, for example oncohistones, help to provide functional insights on how cancer epigenomes arise. In this review, we highlight the importance and define challenges of proper control tissues and cell populations to exploit cancer epigenomes. We summarize recent advances describing mechanisms leading to epigenetic changes in tumorigenesis and briefly discuss advances in investigating their translational potential.
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Affiliation(s)
- Daniela Mancarella
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. .,Faculty of Biosciences, Ruprecht-Karls-University of Heidelberg, 69120, Heidelberg, Germany.
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,German Consortium for Translational Cancer Research (DKTK), 69120, Heidelberg, Germany
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29
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Abstract
Ewing sarcoma (EwS) is a highly aggressive pediatric bone cancer that is defined by a somatic fusion between the EWSR1 gene and an ETS family member, most frequently the FLI1 gene, leading to expression of a chimeric transcription factor EWSR1-FLI1. Otherwise, EwS is one of the most genetically stable cancers. The situation when the major cancer driver is well known looks like a unique opportunity for applying the systems biology approach in order to understand the EwS mechanisms as well as to uncover some general mechanistic principles of carcinogenesis. A number of studies have been performed revealing the direct and indirect effects of EWSR1-FLI1 on multiple aspects of cellular life. Nevertheless, the emerging picture of the oncogene action appears to be highly complex and systemic, with multiple reciprocal influences between the immediate consequences of the driver mutation and intracellular and intercellular molecular mechanisms, including regulation of transcription, epigenome, and tumoral microenvironment. In this chapter, we present an overview of existing molecular profiling resources available for EwS tumors and cell lines and provide an online comprehensive catalogue of publicly available omics and other datasets. We further highlight the systems biology studies of EwS, involving mathematical modeling of networks and integration of molecular data. We conclude that despite the seeming simplicity, a lot has yet to be understood on the systems-wide mechanisms connecting the driver mutation and the major cellular phenotypes of this pediatric cancer. Overall, this chapter can serve as a guide for a systems biology researcher to start working on EwS.
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30
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Qin Y, Zhang W, Sun X, Nan S, Wei N, Wu HJ, Zheng X. Deconvolution of heterogeneous tumor samples using partial reference signals. PLoS Comput Biol 2020; 16:e1008452. [PMID: 33253170 PMCID: PMC7728196 DOI: 10.1371/journal.pcbi.1008452] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/10/2020] [Accepted: 10/19/2020] [Indexed: 12/16/2022] Open
Abstract
Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE). Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods.
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Affiliation(s)
- Yufang Qin
- College of Information Technology, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Weiwei Zhang
- School of Science, East China University of Technology, Nanchang, Jiangxi, China
| | - Xiaoqiang Sun
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Siwei Nan
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Nana Wei
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Hua-Jun Wu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai, China
- * E-mail:
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31
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Scherer M, Nazarov PV, Toth R, Sahay S, Kaoma T, Maurer V, Vedeneev N, Plass C, Lengauer T, Walter J, Lutsik P. Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz. Nat Protoc 2020; 15:3240-3263. [PMID: 32978601 DOI: 10.1038/s41596-020-0369-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 05/29/2020] [Indexed: 12/13/2022]
Abstract
DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.
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Affiliation(s)
- Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.,Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Petr V Nazarov
- Quantitative Biology Unit, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Reka Toth
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Division of Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Shashwat Sahay
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.,Center for Digital Health, Berlin Institute of Health and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Tony Kaoma
- Quantitative Biology Unit, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Valentin Maurer
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Lengauer
- Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Jörn Walter
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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32
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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33
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Allum F, Grundberg E. Capturing functional epigenomes for insight into metabolic diseases. Mol Metab 2020; 38:100936. [PMID: 32199819 PMCID: PMC7300388 DOI: 10.1016/j.molmet.2019.12.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/23/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Metabolic diseases such as obesity are known to be driven by both environmental and genetic factors. Although genome-wide association studies of common variants and their impact on complex traits have provided some biological insight into disease etiology, identified genetic variants have been found to contribute only a small proportion to disease heritability, and to map mainly to non-coding regions of the genome. To link variants to function, association studies of cellular traits, such as epigenetic marks, in disease-relevant tissues are commonly applied. SCOPE OF THE REVIEW We review large-scale efforts to generate genome-wide maps of coordinated epigenetic marks and their utility in complex disease dissection with a focus on DNA methylation. We contrast DNA methylation profiling methods and discuss the advantages of using targeted methods for single-base resolution assessments of methylation levels across tissue-specific regulatory regions to deepen our understanding of contributing factors leading to complex diseases. MAJOR CONCLUSIONS Large-scale assessments of DNA methylation patterns in metabolic disease-linked study cohorts have provided insight into the impact of variable epigenetic variants in disease etiology. In-depth profiling of epigenetic marks at regulatory regions, particularly at tissue-specific elements, will be key to dissect the genetic and environmental components contributing to metabolic disease onset and progression.
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Affiliation(s)
- Fiona Allum
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0C7, Canada; McGill University and Genome Quebec Innovation Centre, Montréal, Québec, H3A 0G1, Canada
| | - Elin Grundberg
- Children's Mercy Kansas City, Kansas City, MO, 64108, United States.
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Scherer M, Nebel A, Franke A, Walter J, Lengauer T, Bock C, Müller F, List M. Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic Acids Res 2020; 48:e46. [PMID: 32103242 PMCID: PMC7192612 DOI: 10.1093/nar/gkaa120] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/14/2020] [Indexed: 12/13/2022] Open
Abstract
DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.
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Affiliation(s)
- Michael Scherer
- Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Department of Genetics/Epigenetics, Saarland University, 66123 Saarbrücken, Germany
| | - Almut Nebel
- Institute of Clinical Molecular Biology, Kiel University, 24105 Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, 24105 Kiel, Germany
| | - Jörn Walter
- Department of Genetics/Epigenetics, Saarland University, 66123 Saarbrücken, Germany
| | - Thomas Lengauer
- Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
- Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Fabian Müller
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
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35
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Abstract
The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy.
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36
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Feng H, Jin P, Wu H. Disease prediction by cell-free DNA methylation. Brief Bioinform 2020; 20:585-597. [PMID: 29672679 DOI: 10.1093/bib/bby029] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 03/06/2018] [Indexed: 12/24/2022] Open
Abstract
Disease diagnosis using cell-free DNA (cfDNA) has been an active research field recently. Most existing approaches perform diagnosis based on the detection of sequence variants on cfDNA; thus, their applications are limited to diseases associated with high mutation rate such as cancer. Recent developments start to exploit the epigenetic information on cfDNA, which could have substantially wider applications. In this work, we provide thorough reviews and discussions on the statistical method developments and data analysis strategies for using cfDNA epigenetic profiles, in particular DNA methylation, to construct disease diagnostic models. We focus on two important aspects: marker selection and prediction model construction, under different scenarios. We perform simulations and real data analysis to compare different approaches, and provide recommendations for data analysis.
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Affiliation(s)
- Hao Feng
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
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Decamps C, Privé F, Bacher R, Jost D, Waguet A, Houseman EA, Lurie E, Lutsik P, Milosavljevic A, Scherer M, Blum MGB, Richard M. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinformatics 2020; 21:16. [PMID: 31931698 PMCID: PMC6958785 DOI: 10.1186/s12859-019-3307-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 12/03/2019] [Indexed: 12/24/2022] Open
Abstract
Background Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. Results Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell’s rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms’ performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Conclusion Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.
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Affiliation(s)
- Clémentine Decamps
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France
| | - Florian Privé
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France
| | - Raphael Bacher
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France
| | - Daniel Jost
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France
| | - Arthur Waguet
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France
| | | | | | - Eugene Lurie
- Bioinformatics Research Laboratory, Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX, USA
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Aleksandar Milosavljevic
- Bioinformatics Research Laboratory, Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX, USA
| | - Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, 66123, Saarbruecken, Germany
| | - Michael G B Blum
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France
| | - Magali Richard
- Laboratory TIMC-IMAG, UMR 5525, Univ. Grenoble Alpes, CNRS, F-38700, Grenoble, France.
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Yin L, Luo Y, Xu X, Wen S, Wu X, Lu X, Xie H. Virtual methylome dissection facilitated by single-cell analyses. Epigenetics Chromatin 2019; 12:66. [PMID: 31711526 PMCID: PMC6844058 DOI: 10.1186/s13072-019-0310-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 10/21/2019] [Indexed: 12/31/2022] Open
Abstract
Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.
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Affiliation(s)
- Liduo Yin
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, 100101, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
| | - Yanting Luo
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiguang Xu
- Epigenomics and Computational Biology Lab, Fralin Life Sciences Institute at Virginia Tech, Virginia Tech, Blacksburg, VA, 24061, USA.,Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Shiyu Wen
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiaowei Wu
- Department of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Xuemei Lu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China. .,School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100101, China.
| | - Hehuang Xie
- Epigenomics and Computational Biology Lab, Fralin Life Sciences Institute at Virginia Tech, Virginia Tech, Blacksburg, VA, 24061, USA. .,Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA. .,Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, 24061, USA.
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39
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Shimada M, Miyagawa T, Takeshima A, Kakita A, Toyoda H, Niizato K, Oshima K, Tokunaga K, Honda M. Epigenome-wide association study of narcolepsy-affected lateral hypothalamic brains, and overlapping DNA methylation profiles between narcolepsy and multiple sclerosis. Sleep 2019; 43:5574506. [DOI: 10.1093/sleep/zsz198] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/07/2019] [Indexed: 01/05/2023] Open
Abstract
Abstract
Narcolepsy with cataplexy is a sleep disorder caused by a deficiency in hypocretin neurons in the lateral hypothalamus (LH). Here we performed an epigenome-wide association study (EWAS) of DNA methylation for narcolepsy and replication analyses using DNA samples extracted from two brain regions: LH (Cases: N = 4; Controls: N = 4) and temporal cortex (Cases: N = 7; Controls: N = 7). Seventy-seven differentially methylated regions (DMRs) were identified in the LH analysis, with the top association of a DMR in the myelin basic protein (MBP) region. Only five DMRs were detected in the temporal cortex analysis. Genes annotated to LH DMRs were significantly associated with pathways related to fatty acid response or metabolism. Two additional analyses applying the EWAS data were performed: (1) investigation of methylation profiles shared between narcolepsy and other disorders and (2) an integrative analysis of DNA methylation data and a genome-wide association study for narcolepsy. The results of the two approaches, which included significant overlap of methylated positions associated with narcolepsy and multiple sclerosis, indicated that the two diseases may partly share their pathogenesis. In conclusion, DNA methylation in LH where loss of orexin-producing neurons occurs may play a role in the pathophysiology of the disease.
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Affiliation(s)
- Mihoko Shimada
- Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Department of Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Taku Miyagawa
- Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Department of Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Akari Takeshima
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hiromi Toyoda
- Department of Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Kazuhiro Niizato
- Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Tokyo, Japan
| | - Kenichi Oshima
- Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Tokyo, Japan
| | - Katsushi Tokunaga
- Department of Human Genetics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Makoto Honda
- Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Seiwa Hospital, Institute of Neuropsychiatry, Tokyo, Japan
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40
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Li Z, Wu H. TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis. Genome Biol 2019; 20:190. [PMID: 31484546 PMCID: PMC6727351 DOI: 10.1186/s13059-019-1778-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/30/2019] [Indexed: 02/07/2023] Open
Abstract
In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, 30322, GA, USA
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, 30322, GA, USA.
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41
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Avila Cobos F, Vandesompele J, Mestdagh P, De Preter K. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 2019; 34:1969-1979. [PMID: 29351586 DOI: 10.1093/bioinformatics/bty019] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/10/2018] [Indexed: 12/22/2022] Open
Abstract
Summary Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results. Contact katleen.depreter@ugent.be. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francisco Avila Cobos
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Jo Vandesompele
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Pieter Mestdagh
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Katleen De Preter
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
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42
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Thompson M, Chen ZJ, Rahmani E, Halperin E. CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets. Genome Biol 2019; 20:138. [PMID: 31300005 PMCID: PMC6624895 DOI: 10.1186/s13059-019-1743-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/21/2019] [Indexed: 12/11/2022] Open
Abstract
Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED.
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Affiliation(s)
- Mike Thompson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Johnson Chen
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Elior Rahmani
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Biomathematics, University of California Los Angeles, Los Angeles, CA, USA.
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43
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Kumsta R. The role of epigenetics for understanding mental health difficulties and its implications for psychotherapy research. Psychol Psychother 2019; 92:190-207. [PMID: 30924323 DOI: 10.1111/papt.12227] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Indexed: 12/14/2022]
Abstract
Many mental health difficulties have developmental origins. Understanding the mechanisms for how psychosocial experiences are biologically embedded and influence lifelong development is a key challenge for the mental health disciplines. In recent years, epigenetic processes have emerged as a potential mechanism mediating the long-lasting vulnerability following the experience of adversity. Animal models provide evidence that early-life adversity can produce enduring epigenetic modifications in the brain, which mediate disorder-like behaviours, and there is emerging evidence to support that environmental factors influence epigenetic processes in humans. The investigation of DNA methylation, a chemical modification of the DNA with a role in gene regulatory processes, is becoming increasingly popular in psychological studies. A particular interest for the psychotherapy field lies in the potential for psychological interventions to influence epigenetic processes. Hence, the focus of this review will be on studies that have investigated intervention-associated changes in DNA methylation. Results of the first few studies will be critically reviewed, and a model of how therapy-associated changes of DNA methylation in peripheral, non-brain tissue might be useful as epigenetic biomarkers of treatment outcome will be presented. PRACTITIONER POINTS: Many mental health difficulties have substantial developmental origin. Epigenetic processes have emerged as a potential mechanism mediating the long-term effects of early adversity Epigenetic refers to cellular mechanisms that control gene expression states, independent of changes to the underlying DNA sequence. The epigenome can be highly dynamic and potentially influenced by external factors A particular interest for the psychotherapy field lies in the potential for psychological interventions to influence epigenetic processes.
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Affiliation(s)
- Robert Kumsta
- Department of Genetic Psychology, Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany
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44
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Goeppert B, Toth R, Singer S, Albrecht T, Lipka DB, Lutsik P, Brocks D, Baehr M, Muecke O, Assenov Y, Gu L, Endris V, Stenzinger A, Mehrabi A, Schirmacher P, Plass C, Weichenhan D, Roessler S. Integrative Analysis Defines Distinct Prognostic Subgroups of Intrahepatic Cholangiocarcinoma. Hepatology 2019; 69:2091-2106. [PMID: 30615206 PMCID: PMC6594081 DOI: 10.1002/hep.30493] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 01/03/2019] [Indexed: 12/11/2022]
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer. It is defined by cholangiocytic differentiation and has poor prognosis. Recently, epigenetic processes have been shown to play an important role in cholangiocarcinogenesis. We performed an integrative analysis on 52 iCCAs using both genetic and epigenetic data with a specific focus on DNA methylation components. We found recurrent isocitrate dehydrogenase 1 (IDH1) and IDH2 (28%) gene mutations, recurrent arm-length copy number alterations (CNAs), and focal alterations such as deletion of 3p21 or amplification of 12q15, which affect BRCA1 Associated Protein 1, polybromo 1, and mouse double minute 2 homolog. DNA methylome analysis revealed excessive hypermethylation of iCCA, affecting primarily the bivalent genomic regions marked with both active and repressive histone modifications. Integrative clustering of genetic and epigenetic data identified four iCCA subgroups with prognostic relevance further designated as IDH, high (H), medium (M), and low (L) alteration groups. The IDH group consisted of all samples with IDH1 or IDH2 mutations and showed, together with the H group, a highly disrupted genome, characterized by frequent deletions of chromosome arms 3p and 6q. Both groups showed excessive hypermethylation with distinct patterns. The M group showed intermediate characteristics regarding both genetic and epigenetic marks, whereas the L group exhibited few methylation changes and mutations and a lack of CNAs. Methylation-based latent component analysis of cell-type composition identified differences among these four groups. Prognosis of the H and M groups was significantly worse than that of the L group. Conclusion: Using an integrative genomic and epigenomic analysis approach, we identified four major iCCA subgroups with widespread genomic and epigenomic differences and prognostic implications. Furthermore, our data suggest differences in the cell-of-origin of the iCCA subtypes.
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Affiliation(s)
- Benjamin Goeppert
- Institute of PathologyUniversity Clinic of HeidelbergHeidelbergGermany,Liver Cancer Center HeidelbergHeidelbergGermany
| | - Reka Toth
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Stephan Singer
- Institute of PathologyUniversity Clinic of HeidelbergHeidelbergGermany,Institute of PathologyErnst‐Moritz‐Arndt UniversityGreifswaldGermany
| | - Thomas Albrecht
- Institute of PathologyUniversity Clinic of HeidelbergHeidelbergGermany
| | - Daniel B. Lipka
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Pavlo Lutsik
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - David Brocks
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Marion Baehr
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Oliver Muecke
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Yassen Assenov
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Lei Gu
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany,Boston Children's HospitalBostonMA
| | - Volker Endris
- Institute of PathologyUniversity Clinic of HeidelbergHeidelbergGermany
| | | | - Arianeb Mehrabi
- Liver Cancer Center HeidelbergHeidelbergGermany,Department of General Visceral and Transplantation SurgeryUniversity Hospital HeidelbergHeidelbergGermany
| | - Peter Schirmacher
- Institute of PathologyUniversity Clinic of HeidelbergHeidelbergGermany,Liver Cancer Center HeidelbergHeidelbergGermany,German Consortium for Translational Cancer ResearchHeidelbergGermany
| | - Christoph Plass
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany,German Consortium for Translational Cancer ResearchHeidelbergGermany
| | - Dieter Weichenhan
- Division of Cancer EpigenomicsGerman Cancer Research CenterHeidelbergGermany
| | - Stephanie Roessler
- Institute of PathologyUniversity Clinic of HeidelbergHeidelbergGermany,Liver Cancer Center HeidelbergHeidelbergGermany
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45
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Identification of differentially methylated cell types in epigenome-wide association studies. Nat Methods 2018; 15:1059-1066. [PMID: 30504870 PMCID: PMC6277016 DOI: 10.1038/s41592-018-0213-x] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 10/09/2018] [Indexed: 12/22/2022]
Abstract
An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present a statistical algorithm called CellDMC ( https://github.com/sjczheng/EpiDISH ), which can identify differentially methylated positions and the specific cell type(s) driving the differential methylation. We validated CellDMC on in silico mixtures of DNA methylation data generated with different technologies, as well as on real mixtures from epigenome-wide association and cancer epigenome studies. CellDMC achieved over 90% sensitivity and specificity in scenarios where current state-of-the-art methods did not identify differential methylation. By applying CellDMC to an EWAS performed in buccal swabs, we identified smoking-associated differentially methylated positions occurring in the epithelial compartment, which we validated in smoking-related lung cancer. CellDMC may be useful in the identification of causal DNA-methylation alterations in disease.
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46
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BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference. Genome Biol 2018; 19:141. [PMID: 30241486 PMCID: PMC6151042 DOI: 10.1186/s13059-018-1513-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 08/20/2018] [Indexed: 11/10/2022] Open
Abstract
We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.
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Davis Armstrong NM, Chen WM, Brewer MS, Williams SR, Sale MM, Worrall BB, Keene KL. Epigenome-Wide Analyses Identify Two Novel Associations With Recurrent Stroke in the Vitamin Intervention for Stroke Prevention Clinical Trial. Front Genet 2018; 9:358. [PMID: 30237808 PMCID: PMC6135883 DOI: 10.3389/fgene.2018.00358] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 08/20/2018] [Indexed: 12/17/2022] Open
Abstract
DNA methylation, a well-characterized epigenetic modification that is influenced by both environment and genetic variation, has previously been implicated in a number of complex diseases, including cardiovascular disease and stroke. The goal of this study was to evaluate epigenome-wide associations with recurrent stroke and the folate one-carbon metabolism-related trait, plasma homocysteine (hcy). Differential methylation analyses were performed on 473,864 autosomal CpG loci, using Illumina HumanMethylation 450K array data in 180 ischemic stroke cases from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial. Linear regression was used to assess associations between number of strokes prior to VISP enrollment and measures of hcy with degree of methylation (β-values), while logistic regression was used to evaluate recurrent stroke status and incident recurrent stroke associations. All regression analyses were stratified by race. Two differentially methylated CpG sites exceeded epigenome-wide significance (p ≤ 1.055 × 10−7) for prior number of strokes (PNS) in European Americans. The top locus, cg22812874, was located in the ankyrin repeat and SOCS box containing 10 gene (ASB10; p = 3.4 × 10−9; β = −0.0308; 95% CI = −0.040, −0.002). Methylation locus cg00340919, located in an intron of the tetratricopeptide repeat domain 37 gene, was also statistically significant (TTC37; p = 8.74 × 10−8; β = −0.0517; 95% CI = −0.069, −0.034). An additional 138 CpG sites met our threshold for suggestive significance (p ≤ 5 × 10−5). We evaluated DNA methylation associated with recurrent stroke and hcy phenotypes across the epigenome. Hypermethylation at two CpG sites located in ASB10 and TTC37 was associated with fewer strokes prior to VISP enrollment. Our findings present a foundation for additional epigenome-wide studies, as well as mechanistic studies into epigenetic marks that influence recurrent stroke risk.
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Affiliation(s)
| | - Wei-Min Chen
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States.,Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Michael S Brewer
- Department of Biology, East Carolina University, Greenville, NC, United States
| | - Stephen R Williams
- Department of Neurology, University of Virginia, Charlottesville, VA, United States
| | - Michèle M Sale
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States.,Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Bradford B Worrall
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States.,Department of Neurology, University of Virginia, Charlottesville, VA, United States
| | - Keith L Keene
- Department of Biology, East Carolina University, Greenville, NC, United States.,Center for Health Disparities, East Carolina University, Greenville, NC, United States
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DNA methylation analysis on purified neurons and glia dissects age and Alzheimer's disease-specific changes in the human cortex. Epigenetics Chromatin 2018; 11:41. [PMID: 30045751 PMCID: PMC6058387 DOI: 10.1186/s13072-018-0211-3] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/17/2018] [Indexed: 12/30/2022] Open
Abstract
Background Epigenome-wide association studies (EWAS) based on human brain samples allow a deep and direct understanding of epigenetic dysregulation in Alzheimer’s disease (AD). However, strong variation of cell-type proportions across brain tissue samples represents a significant source of data noise. Here, we report the first EWAS based on sorted neuronal and non-neuronal (mostly glia) nuclei from postmortem human brain tissues. Results We show that cell sorting strongly enhances the robust detection of disease-related DNA methylation changes even in a relatively small cohort. We identify numerous genes with cell-type-specific methylation signatures and document differential methylation dynamics associated with aging specifically in neurons such as CLU, SYNJ2 and NCOR2 or in glia RAI1,CXXC5 and INPP5A. Further, we found neuron or glia-specific associations with AD Braak stage progression at genes such as MCF2L, ANK1, MAP2, LRRC8B, STK32C and S100B. A comparison of our study with previous tissue-based EWAS validates multiple AD-associated DNA methylation signals and additionally specifies their origin to neuron, e.g., HOXA3 or glia (ANK1). In a meta-analysis, we reveal two novel previously unrecognized methylation changes at the key AD risk genes APP and ADAM17. Conclusions Our data highlight the complex interplay between disease, age and cell-type-specific methylation changes in AD risk genes thus offering new perspectives for the validation and interpretation of large EWAS results. Electronic supplementary material The online version of this article (10.1186/s13072-018-0211-3) contains supplementary material, which is available to authorized users.
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Zheng SC, Webster AP, Dong D, Feber A, Graham DG, Sullivan R, Jevons S, Lovat LB, Beck S, Widschwendter M, Teschendorff AE. A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics 2018; 10:925-940. [DOI: 10.2217/epi-2018-0037] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Aim: An outstanding challenge in epigenome studies is the estimation of cell-type proportions in complex epithelial tissues. Materials & methods: Here, we construct and validate a DNA methylation reference and algorithm for complex tissues that contain epithelial, immune and nonimmune stromal cells. Results: Using this reference, we show that easily accessible tissues such as saliva, buccal and cervix exhibit substantial variation in immune cell (IC) contamination. We further validate our reference in the context of oral cancer, where it correctly predicts an increased IC infiltration in cancer but suppressed in patients with highest smoking exposure. Finally, our method can improve the specificity of differentially methylated CpG calls in epithelial cancer. Conclusion: The degree and variation of IC contamination in complex epithelial tissues is substantial. We provide a valuable resource and tool for assessing the epithelial purity and IC contamination of samples and for identifying differential methylation in such complex tissues.
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Affiliation(s)
- Shijie C Zheng
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, PR China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, PR China
| | - Amy P Webster
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Danyue Dong
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, PR China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, PR China
| | - Andy Feber
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - David G Graham
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Roisin Sullivan
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Sarah Jevons
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Laurence B Lovat
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Stephan Beck
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Martin Widschwendter
- Department of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, PR China
- Department of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK
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50
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Deng G, Yang J, Zhang Q, Xiao ZX, Cai H. MethCNA: a database for integrating genomic and epigenomic data in human cancer. BMC Genomics 2018; 19:138. [PMID: 29433427 PMCID: PMC5810021 DOI: 10.1186/s12864-018-4525-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 02/05/2018] [Indexed: 12/23/2022] Open
Abstract
Background The integration of DNA methylation and copy number alteration data promises to provide valuable insight into the underlying molecular mechanisms responsible for cancer initiation and progression. However, the generation and processing of these datasets are costly and time-consuming if carried out separately. The Illumina Infinium HumanMethylation450 BeadChip, initially designed for the evaluation of DNA methylation levels, allows copy number variant calling using bioinformatics tools. Results A substantial amount of Infinium HumanMethylation450 data across various cancer types has been accumulated in recent years and is a valuable resource for large-scale data analysis. Here we present MethCNA, a comprehensive database for genomic and epigenomic data integration in human cancer. In the current release, MethCNA contains about 10,000 tumor samples representing 37 cancer types. All raw array data were collected from The Cancer Genome Atlas and NCBI Gene Expression Omnibus database and analyzed using a pipeline that integrated multiple computational resources and tools. The normalized copy number aberration data and DNA methylation alterations were obtained. We provide a user-friendly web-interface for data mining and visualization. Conclusions The Illumina Infinium HumanMethylation450 BeadChip enables the interrogation and integration of both genomic and epigenomic data from exactly the same DNA specimen, and thus can aid in distinguishing driver from passenger mutations in cancer. We expect MethCNA will enable researchers to explore DNA methylation and copy number alteration patterns, identify key oncogenic drivers in cancer, and assist in the development of targeted therapies. MethCNA is publicly available online at http://cgma.scu.edu.cn/MethCNA. Electronic supplementary material The online version of this article (10.1186/s12864-018-4525-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gaofeng Deng
- Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Jian Yang
- Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Qing Zhang
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou, Jiangsu, 221002, China.,Cancer Institute, Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China
| | - Zhi-Xiong Xiao
- Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Haoyang Cai
- Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan, 610064, China.
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