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1.
Nature. 2018 Jul;559(7714):E10. doi: 10.1038/s41586-018-0167-2.

Author Correction: The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Wuerzburg University, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr. von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Muenster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
46
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany. s.pfister@dkfz.de.
47
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
48
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
49
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany. s.pfister@dkfz.de.

Abstract

In this Article, author Benedikt Brors was erroneously associated with affiliation number '8' (Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA); the author's two other affiliations (affiliations '3' and '7', both at the German Cancer Research Center (DKFZ)) were correct. This has been corrected online.

2.
Nature. 2018 Mar 15;555(7696):321-327. doi: 10.1038/nature25480. Epub 2018 Feb 28.

The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Münster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Abstract

Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

Comment in

PMID:
29489754
DOI:
10.1038/nature25480
[Indexed for MEDLINE]
Icon for Nature Publishing Group
3.
F1000Res. 2017 Aug 16;6:1490. doi: 10.12688/f1000research.12302.1. eCollection 2017.

BAT: Bisulfite Analysis Toolkit: BAT is a toolkit to analyze DNA methylation sequencing data accurately and reproducibly. It covers standard processing and analysis steps from raw read mapping up to annotation data integration and calculation of correlating DMRs.

Author information

1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, 04109, Germany.
2
Transcriptome Bioinformatics, Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, 04109, Germany.
3
ecSeq GmbH, Leipzig, 04275, Germany.

Abstract

Here, we present BAT, a modular bisulfite analysis toolkit, that facilitates the analysis of bisulfite sequencing data. It covers the essential analysis steps of read alignment, quality control, extraction of methylation information, and calling of differentially methylated regions, as well as biologically relevant downstream analyses, such as data integration with gene expression, histone modification data, or transcription factor binding site annotation.

KEYWORDS:

DMRs; DNA methylation; RRBS; WGBS; bisulfite sequencing; epigenetics; integrative analysis; software

Conflict of interest statement

Competing interests: No competing interests were disclosed.

4.
Sci Rep. 2016 Nov 23;6:37393. doi: 10.1038/srep37393.

Changes of bivalent chromatin coincide with increased expression of developmental genes in cancer.

Author information

1
Leipzig University, Chair of Bioinformatics, Leipzig, 04107, Germany.
2
Leipzig University, Transcriptome Bioinformatics Group - Interdisciplinary Center for Bioinformatics, Leipzig, 04107, Germany.
3
Ludwig-Maximilians-University, Institute of Laboratory Medicine, Munich, 81377, Germany.
4
Inserm, U1110 - Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, 67000, France.
5
Université de Strasbourg, Strasbourg, 67000, France.
6
Christian Albrechts University &University Hospital Schleswig-Holstein - Campus Kiel, Institute of Human Genetics, Kiel, 24105, Germany.
7
Christian Albrechts University Kiel &University Hospital Schleswig-Holstein - Campus Kiel, Department of Pediatrics, Kiel, 24105, Germany.
8
Ulm University &Ulm University Medical Center, Institute for Human Genetics, Ulm, 89081, Germany.
9
Leipzig University, LIFE - Leipzig Research Center for Civilization Diseases, Leipzig, 04107, Germany.
10
University of Vienna, Department of Theoretical Chemistry, Vienna, 1090, Austria.
11
Max-Planck-Institute for Mathematics in Sciences, Leipzig, 04103, Germany.
12
Santa Fe Institute, Santa Fe, NM 87501, USA.

Abstract

Bivalent (poised or paused) chromatin comprises activating and repressing histone modifications at the same location. This combination of epigenetic marks at promoter or enhancer regions keeps genes expressed at low levels but poised for rapid activation. Typically, DNA at bivalent promoters is only lowly methylated in normal cells, but frequently shows elevated methylation levels in cancer samples. Here, we developed a universal classifier built from chromatin data that can identify cancer samples solely from hypermethylation of bivalent chromatin. Tested on over 7,000 DNA methylation data sets from several cancer types, it reaches an AUC of 0.92. Although higher levels of DNA methylation are often associated with transcriptional silencing, counter-intuitive positive statistical dependencies between DNA methylation and expression levels have been recently reported for two cancer types. Here, we re-analyze combined expression and DNA methylation data sets, comprising over 5,000 samples, and demonstrate that the conjunction of hypermethylation of bivalent chromatin and up-regulation of the corresponding genes is a general phenomenon in cancer. This up-regulation affects many developmental genes and transcription factors, including dozens of homeobox genes and other genes implicated in cancer. Thus, we reason that the disturbance of bivalent chromatin may be intimately linked to tumorigenesis.

PMID:
27876760
PMCID:
PMC5120258
DOI:
10.1038/srep37393
[Indexed for MEDLINE]
Free PMC Article
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5.
Haematologica. 2016 Nov;101(11):1380-1389. Epub 2016 Jul 6.

Alterations of microRNA and microRNA-regulated messenger RNA expression in germinal center B-cell lymphomas determined by integrative sequencing analysis.

Author information

1
Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, Medical Faculty, Düsseldorf, Germany.
2
Department of Algorithmic Bioinformatics, Heinrich-Heine University, Duesseldorf, Germany.
3
Transcriptome Bioinformatics Group, LIFE Research Center for Civilization Diseases, University of Leipzig, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany.
5
Interdisciplinary Center for Bioinformatics, University of Leipzig, Germany.
6
Institute of Pathology, Charité - University Medicine Berlin, Germany.
7
Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
8
Division Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
9
National Center for Tumor Diseases (NCT), Heidelberg, Germany.
10
German Cancer Consortium (DKTK), Heidelberg, Germany.
11
Department of Pediatric Hematology and Oncology, University Hospital Münster, Germany.
12
Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Germany.
13
Department of Human and Animal Cell Cultures, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
14
Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Heidelberg, Germany.
15
Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology and Bioquant, Heidelberg University, Germany.
16
Friedrich-Ebert Hospital Neumünster, Clinics for Hematology, Oncology and Nephrology, Neumünster, Germany.
17
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, Campus Kiel, Germany.
18
Hematopathology Section, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
19
EMBL Heidelberg, Genome Biology, Heidelberg, Germany.
20
Institute for Medical Informatics Statistics and Epidemiology, Leipzig, Germany.
21
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany.
22
Institute of Pathology, University of Würzburg, and Comprehensive Cancer Center Mainfranken, Würzburg, Germany.
23
Hospital of Internal Medicine II, Hematology and Oncology, St-Georg Hospital Leipzig, Germany.
24
Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany.
25
Institute of Pathology, Medical Faculty of the Ulm University, Germany.
26
Department of Pediatric Hematology and Oncology University Hospital Giessen, Germany.
27
Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
28
RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.
29
Max-Planck-Institute for Mathematics in Sciences, Leipzig, Germany.
30
Santa Fe Institute, NM, USA.
31
Department of Internal Medicine III, University of Ulm, Germany.
32
Department of Hematology and Oncology, Georg-August-University of Göttingen, Germany.

Abstract

MicroRNA are well-established players in post-transcriptional gene regulation. However, information on the effects of microRNA deregulation mainly relies on bioinformatic prediction of potential targets, whereas proof of the direct physical microRNA/target messenger RNA interaction is mostly lacking. Within the International Cancer Genome Consortium Project "Determining Molecular Mechanisms in Malignant Lymphoma by Sequencing", we performed miRnome sequencing from 16 Burkitt lymphomas, 19 diffuse large B-cell lymphomas, and 21 follicular lymphomas. Twenty-two miRNA separated Burkitt lymphomas from diffuse large B-cell lymphomas/follicular lymphomas, of which 13 have shown regulation by MYC. Moreover, we found expression of three hitherto unreported microRNA. Additionally, we detected recurrent mutations of hsa-miR-142 in diffuse large B-cell lymphomas and follicular lymphomas, and editing of the hsa-miR-376 cluster, providing evidence for microRNA editing in lymphomagenesis. To interrogate the direct physical interactions of microRNA with messenger RNA, we performed Argonaute-2 photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation experiments. MicroRNA directly targeted 208 messsenger RNA in the Burkitt lymphomas and 328 messenger RNA in the non-Burkitt lymphoma models. This integrative analysis discovered several regulatory pathways of relevance in lymphomagenesis including Ras, PI3K-Akt and MAPK signaling pathways, also recurrently deregulated in lymphomas by mutations. Our dataset reveals that messenger RNA deregulation through microRNA is a highly relevant mechanism in lymphomagenesis.

PMID:
27390358
PMCID:
PMC5394868
DOI:
10.3324/haematol.2016.143891
[Indexed for MEDLINE]
Free PMC Article
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6.
Nat Commun. 2016 Jun 24;7:11807. doi: 10.1038/ncomms11807.

MYC/MIZ1-dependent gene repression inversely coordinates the circadian clock with cell cycle and proliferation.

Author information

1
Heidelberg University, Biochemistry Center, Im Neuenheimer Feld 328, D-69120 Heidelberg, Germany.
2
Division Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.
3
Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany.

Abstract

The circadian clock and the cell cycle are major cellular systems that organize global physiology in temporal fashion. It seems conceivable that the potentially conflicting programs are coordinated. We show here that overexpression of MYC in U2OS cells attenuates the clock and conversely promotes cell proliferation while downregulation of MYC strengthens the clock and reduces proliferation. Inhibition of the circadian clock is crucially dependent on the formation of repressive complexes of MYC with MIZ1 and subsequent downregulation of the core clock genes BMAL1 (ARNTL), CLOCK and NPAS2. We show furthermore that BMAL1 expression levels correlate inversely with MYC levels in 102 human lymphomas. Our data suggest that MYC acts as a master coordinator that inversely modulates the impact of cell cycle and circadian clock on gene expression.

PMID:
27339797
PMCID:
PMC4931031
DOI:
10.1038/ncomms11807
[Indexed for MEDLINE]
Free PMC Article
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7.
Genome Res. 2016 Feb;26(2):256-62. doi: 10.1101/gr.196394.115. Epub 2015 Dec 2.

metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data.

Author information

1
Transcriptome Bioinformatics Group, LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, 04107 Leipzig, Germany; Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Faculty of Computer Science, University of Leipzig, 04107 Leipzig, Germany;
2
Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Faculty of Computer Science, University of Leipzig, 04107 Leipzig, Germany; RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology - IZI, 04103 Leipzig, Germany; Santa Fe Institute, Santa Fe, New Mexico 87501, USA; Department of Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria; Max Planck Institute for Mathematics in Sciences, 04103 Leipzig, Germany.

Abstract

The detection of differentially methylated regions (DMRs) is a necessary prerequisite for characterizing different epigenetic states. We present a novel program, metilene, to identify DMRs within whole-genome and targeted data with unrivaled specificity and sensitivity. A binary segmentation algorithm combined with a two-dimensional statistical test allows the detection of DMRs in large methylation experiments with multiple groups of samples in minutes rather than days using off-the-shelf hardware. metilene outperforms other state-of-the-art tools for low coverage data and can estimate missing data. Hence, metilene is a versatile tool to study the effect of epigenetic modifications in differentiation/development, tumorigenesis, and systems biology on a global, genome-wide level. Whether in the framework of international consortia with dozens of samples per group, or even without biological replicates, it produces highly significant and reliable results.

PMID:
26631489
PMCID:
PMC4728377
DOI:
10.1101/gr.196394.115
[Indexed for MEDLINE]
Free PMC Article
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8.
Nat Genet. 2015 Nov;47(11):1316-1325. doi: 10.1038/ng.3413. Epub 2015 Oct 5.

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control.

Author information

1
Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
2
Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
3
Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
4
German ICGC MMML-Seq-project.
5
German Cancer Research Center (DKFZ), Division Molecular Genetics, Heidelberg, Germany.
6
Institute of Human Genetics, Christian-Albrechts-University, Kiel, Germany.
7
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany.
8
Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
9
BLUEPRINT project.
10
Cell Networks, Bioquant, University of Heidelberg, Heidelberg, Germany.
11
Structural Biology and BioComputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.
12
Deutsches Krebsforschungszentrum Heidelberg (DKFZ), Division Theoretical Bioinformatics, Heidelberg, Germany.
13
Department of Otorhinolaryngology, University of Duisburg-Essen, Essen, Germany.
14
University Hospital Muenster - Pediatric Hematology and Oncology, Münster Germany.
15
Leibniz-Institut DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
16
Department of Hematology and Oncology, Georg-Augusts-University of Göttingen, Göttingen, Germany.
17
Institute of Pharmacy and Molecular Biotechnology, Bioquant, University of Heidelberg, Heidelberg, Germany.
18
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
19
Friedrich-Ebert Hospital Neumuenster, Clinics for Haematology, Oncology and Nephrology, Neumünster, Germany.
20
Institute of Pathology, Charité - University Medicine Berlin, Berlin, Germany.
21
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, Campus Kiel, Kiel, Germany.
22
Radboud University, Department of Molecular Biology, Faculty of Science, Nijmegen, Netherlands.
23
Hematopathology Section, Christian-Albrechts-University, Kiel, Germany.
24
Institute for Medical Informatics Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
25
Departamento de Anatomía Patológica, Farmacología y Microbiología, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
26
Institute of Pathology, Medical Faculty of the Ulm University, Ulm, Germany.
27
University Hospital Giessen, Pediatric Hematology and Oncology, Giessen, Germany.
28
Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany.
29
RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.
30
Santa Fe Institute, Santa Fe, New Mexico, United States of America.
31
Max-Planck-Institute for Mathematics in Sciences, Leipzig, Germany.
#
Contributed equally

Abstract

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas.

PMID:
26437030
PMCID:
PMC5444523
DOI:
10.1038/ng.3413
[Indexed for MEDLINE]
Free PMC Article
Icon for Nature Publishing Group Icon for PubMed Central
9.
Proc Natl Acad Sci U S A. 2015 Sep 22;112(38):E5261-70. doi: 10.1073/pnas.1505753112. Epub 2015 Sep 8.

MINCR is a MYC-induced lncRNA able to modulate MYC's transcriptional network in Burkitt lymphoma cells.

Author information

1
Transcriptome Bioinformatics, Leipzig Research Center for Civilization Diseases, University of Leipzig, D-04107 Leipzig, Germany;
2
Institute of Human Genetics, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany;
3
Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children's Hospital, Heinrich Heine University, Medical Faculty, D-40225 Düsseldorf, Germany;
4
Institute of Human Genetics, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany; Department of Pediatrics, University Hospital Schleswig-Holstein, D-24105 Kiel, Germany;
5
Department of Pediatric Hematology and Oncology, University Hospital Münster, D-48149 Munster, Germany; Department of Pediatric Hematology and Oncology, University Hospital Giessen, D-35392 Giessen, Germany;
6
Department of Pediatrics, University Hospital Schleswig-Holstein, D-24105 Kiel, Germany;
7
Institute of Pathology, Charité University Medicine Berlin, D-12200 Berlin, Germany;
8
Friedrich-Ebert Hospital Neumünster, Clinics for Hematology, Oncology and Nephrology, D-24534 Neumünster, Germany;
9
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, D-24105 Kiel, Germany;
10
Hematopathology Section, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany;
11
Institute for Medical Informatics Statistics and Epidemiology, University of Leipzig, D-04107 Leipzig, Germany;
12
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, D-45122 Essen, Germany;
13
Division Theoretical Bioinformatics, German Cancer Research Center, D-69120 Heidelberg, Germany;
14
Hospital of Internal Medicine II, Hematology and Oncology, St. Georg Hospital Leipzig, D-04129 Leipzig, Germany;
15
Institute of Pathology, Ulm University, D-89070 Ulm, Germany;
16
Department of Clinical Pathology, Robert Bosch Hospital and Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, D-70376 Stuttgart, Germany;
17
Department of Pediatric Hematology and Oncology, University Hospital Giessen, D-35392 Giessen, Germany;
18
Institute of Clinical Molecular Biology, Christian Albrechts University, D-24105 Kiel, Germany;
19
Institute of Pathology, University of Würzburg and Comprehensive Cancer Center Mainfranken, D-97080 Würzburg, Germany;
20
Department of Internal Medicine III, University of Ulm, D-89081 Ulm, Germany;
21
Department of Molecular Biology, Radboud Institute for Molecular Life Sciences, Radboud University, 6525 Nijmegen, The Netherlands;
22
Department of Hematology and Oncology, Georg August University of Göttingen, D-37075 Göttingen, Germany;
23
Institute of Human Genetics, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany; Institute of Genetics and Biophysics "A.Buzzati-Traverso," Consiglio Nazionale delle Ricerche, I-80131 Naples, Italy iiaccarino@medgen.uni-kiel.de.

Abstract

Despite the established role of the transcription factor MYC in cancer, little is known about the impact of a new class of transcriptional regulators, the long noncoding RNAs (lncRNAs), on MYC ability to influence the cellular transcriptome. Here, we have intersected RNA-sequencing data from two MYC-inducible cell lines and a cohort of 91 B-cell lymphomas with or without genetic variants resulting in MYC overexpression. We identified 13 lncRNAs differentially expressed in IG-MYC-positive Burkitt lymphoma and regulated in the same direction by MYC in the model cell lines. Among them, we focused on a lncRNA that we named MYC-induced long noncoding RNA (MINCR), showing a strong correlation with MYC expression in MYC-positive lymphomas. To understand its cellular role, we performed RNAi and found that MINCR knockdown is associated with an impairment in cell cycle progression. Differential gene expression analysis after RNAi showed a significant enrichment of cell cycle genes among the genes down-regulated after MINCR knockdown. Interestingly, these genes are enriched in MYC binding sites in their promoters, suggesting that MINCR acts as a modulator of the MYC transcriptional program. Accordingly, MINCR knockdown was associated with a reduction in MYC binding to the promoters of selected cell cycle genes. Finally, we show that down-regulation of Aurora kinases A and B and chromatin licensing and DNA replication factor 1 may explain the reduction in cellular proliferation observed on MINCR knockdown. We, therefore, suggest that MINCR is a newly identified player in the MYC transcriptional network able to control the expression of cell cycle genes.

KEYWORDS:

B-cell lymphoma; MYC; cell cycle; lncRNA

Comment in

PMID:
26351698
PMCID:
PMC4586867
DOI:
10.1073/pnas.1505753112
[Indexed for MEDLINE]
Free PMC Article
Icon for HighWire Icon for PubMed Central
10.
Genes Chromosomes Cancer. 2015 Sep;54(9):555-64. doi: 10.1002/gcc.22268. Epub 2015 Jul 14.

The PCBP1 gene encoding poly(rC) binding protein I is recurrently mutated in Burkitt lymphoma.

Collaborators (131)

Richter G, Siebert R, Wagner S, Haake A, Richter J, Eils R, Lawerenz C, Radomski S, Scholz I, Borst C, Burkhardt B, Claviez A, Dreyling M, Eberth S, Einsele H, Frickhofen N, Haas S, Hansmann ML, Karsch D, Kneba M, Lisfeld J, Mantovani-Löffler L, Rohde M, Stadler C, Staib P, Stilgenbauer S, Ott G, Trümper L, Zenz T, Hansmann ML, Kube D, Küppers R, Weniger M, Haas S, Hummel M, Klapper W, Kostezka U, Lenze D, Möller P, Rosenwald A, Szczepanowski M, Ammerpohl O, Aukema SM, Binder V, Borkhardt A, Haake A, Hezaveh K, Hoell J, Leich E, Lichter P, Lopez C, Nagel I, Pischimariov J, Radlwimmer B, Richter J, Rosenstiel P, Rosenwald A, Schilhabel M, Schreiber S, Vater I, Wagner R, Siebert R, Bernhart SH, Binder H, Brors B, Doose G, Eils J, Eils R, Hoffmann S, Hopp L, Kretzmer H, Kreuz M, Korbel J, Langenberger D, Loeffler M, Radomski S, Rosolowski M, Schlesner M, Stadler PF, Sungalee S, Barth TF, Bernd HW, Cogliatti SB, Feller AC, Hansmann ML, Hummel M, Klapper W, Lenze D, Möller P, Müller-Hermelink HK, Ott G, Rosenwald A, Stein H, Szczepanowski M, Wacker HH, Barth TF, Behrmann P, Daniel P, Dierlammm J, Haralambieva E, Harder L, Holterhus PM, Küppers R, Kube D, Lichter P, Martín-Subero JI, Möller P, Murga-Peñas EM, Ott G, Pott C, Pscherer A, Rosenwald A, Schwaenen C, Siebert R, Trautmann H, Vockerodt M, Wessendorf S, Bentink S, Berger H, Hasenclever D, Kreuz M, Loeffler M, Rosolowski M, Spang R, Stürzenhofecker B, Trümper L, Wehner M, Loeffler M, Siebert R, Stein H, Trümper L.

Author information

1
Institute of Human Genetics, Christian-Albrechts-University Kiel and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
2
Deutsches Krebsforschungszentrum Heidelberg (DKFZ), Division Theoretical Bioinformatics, Heidelberg, Germany.
3
Non-Hodgkin Lymphoma Berlin-Frankfurt-Münster Group Study Center, Department of Pediatric Hematology and Oncology, University Children's Hospital, Münster, Germany.
4
Department of Pediatrics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University, Kiel, Germany.
5
Leibniz-Institute DSMZ- German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany.
6
Institute of Pathology, Campus Benjamin Franklin, Charité-Universitätsmedizin, Berlin, Germany.
7
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germany.
8
Institute of Pathology, Universitätsklinikum Ulm, Ulm, Germany.
9
Department of Pediatric Hematology and Oncology, Justus Liebig University, Giessen, Germany.
10
Cell Networks, Bioquant, University of Heidelberg, Heidelberg, Germany.
11
Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
12
Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Kiel, Germany.
13
Institute of Hematopathology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
14
Department of Hematology and Oncology, Georg-August University of Göttingen, Germany.

Abstract

The genetic hallmark of Burkitt lymphoma is the translocation t(8;14)(q24;q32), or one of its light chain variants, resulting in IG-MYC juxtaposition. However, these translocations alone are insufficient to drive lymphomagenesis, which requires additional genetic changes for malignant transformation. Recent studies of Burkitt lymphoma using next generation sequencing approaches have identified various recurrently mutated genes including ID3, TCF3, CCND3, and TP53. Here, by using similar approaches, we show that PCBP1 is a recurrently mutated gene in Burkitt lymphoma. By whole-genome sequencing, we identified somatic mutations in PCBP1 in 3/17 (18%) Burkitt lymphomas. We confirmed the recurrence of PCBP1 mutations by Sanger sequencing in an independent validation cohort, finding mutations in 3/28 (11%) Burkitt lymphomas and in 6/16 (38%) Burkitt lymphoma cell lines. PCBP1 is an intron-less gene encoding the 356 amino acid poly(rC) binding protein 1, which contains three K-Homology (KH) domains and two nuclear localization signals. The mutations predominantly (10/12, 83%) affect the KH III domain, either by complete domain loss or amino acid changes. Thus, these changes are predicted to alter the various functions of PCBP1, including nuclear trafficking and pre-mRNA splicing. Remarkably, all six primary Burkitt lymphomas with a PCBP1 mutation expressed MUM1/IRF4, which is otherwise detected in around 20-40% of Burkitt lymphomas. We conclude that PCBP1 mutations are recurrent in Burkitt lymphomas and might contribute, in cooperation with other mutations, to its pathogenesis.

PMID:
26173642
DOI:
10.1002/gcc.22268
[Indexed for MEDLINE]
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