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1.
Front Immunol. 2018 Jul 17;9:1620. doi: 10.3389/fimmu.2018.01620. eCollection 2018.

Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany.
2
Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia.

Abstract

We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host's response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes.

KEYWORDS:

blood disturbances; community-acquired pneumonia severity; epigenetics; immune suppression; infections; molecular subtypes; prognostic impact

2.
Oncogene. 2018 Jul 11. doi: 10.1038/s41388-018-0385-y. [Epub ahead of print]

RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.

Author information

1
Department of Dermatology, Venereology and Allergology, University of Leipzig, Philipp-Rosenthal-Str. 23-25, 04103, Leipzig, Germany. Manfred.kunz@medizin.uni-leipzig.de.
2
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
3
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103, Leipzig, Germany.
4
Bioinformatics Group, Faculty for Mathematics and Computer Science, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
5
Department of Dermatology, Venereology and Allergology, University of Leipzig, Philipp-Rosenthal-Str. 23-25, 04103, Leipzig, Germany.
6
Department of Dermatology and Allergy, University of Bonn, Sigmund-Freud-Strasse 25, 53127, Bonn, Germany.
7
Department of Dermatology, University of Magdeburg, Leipziger Strasse 44, 39120, Magdeburg, Germany.
8
Department of Dermatology, Venereology and Allergology, University of Köln, 50937 Köln, Germany.
9
Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, and Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, 68167 Mannheim, Germany.
10
Department of Dermatology, Fachklinik Hornheide, Dorbaumstrasse 300, 48157, Münster, Germany.
11
Unit for RNA Biology, Department of Clinical Chemistry and Clinical Pharmacology, University of Bonn, 53127 Bonn, Germany.
12
Department of Dermatology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen and the German Cancer Consortium (DKTK), University of Duisburg-Essen, 45122 Essen, Germany.
13
Department of Physiological Chemistry, University of Würzburg, Biozentrum, Am Hubland, 97074, Würzburg, Germany.
14
Comprehensive Cancer Center Mainfranken, University Clinic Würzburg, 97080, Würzburg, Germany.
15
Institute of Biochemistry, Emil-Fischer Zentrum, University of Erlangen, Fahrstraße 17, 91054, Erlangen, Germany.
16
Hagler Institute for Advanced Study and Department of Biology, Texas A&M University, College Station, TX, 77843-3572, USA.

Abstract

Recent studies revealed trajectories of mutational events in early melanomagenesis, but the accompanying changes in gene expression are far less understood. Therefore, we performed a comprehensive RNA-seq analysis of laser-microdissected melanocytic nevi (n = 23) and primary melanoma samples (n = 57) and characterized the molecular mechanisms of early melanoma development. Using self-organizing maps, unsupervised clustering, and analysis of pseudotime (PT) dynamics to identify evolutionary trajectories, we describe here two transcriptomic types of melanocytic nevi (N1 and N2) and primary melanomas (M1 and M2). N1/M1 lesions are characterized by pigmentation-type and MITF gene signatures, and a high prevalence of NRAS mutations in M1 melanomas. N2/M2 lesions are characterized by inflammatory-type and AXL gene signatures with an equal distribution of wild-type and mutated BRAF and low prevalence of NRAS mutations in M2 melanomas. Interestingly, N1 nevi and M1 melanomas and N2 nevi and M2 melanomas, respectively, cluster together, but there is no clustering in a stage-dependent manner. Transcriptional signatures of M1 melanomas harbor signatures of BRAF/MEK inhibitor resistance and M2 melanomas harbor signatures of anti-PD-1 antibody treatment resistance. Pseudotime dynamics of nevus and melanoma samples are suggestive for a switch-like immune-escape mechanism in melanoma development with downregulation of immune genes paralleled by an increasing expression of a cell cycle signature in late-stage melanomas. Taken together, the transcriptome analysis identifies gene signatures and mechanisms underlying development of melanoma in early and late stages with relevance for diagnostics and therapy.

3.
Epigenomics. 2018 Jun;10(6):745-764. doi: 10.2217/epi-2017-0140. Epub 2018 Jun 11.

Combined SOM-portrayal of gene expression and DNA methylation landscapes disentangles modes of epigenetic regulation in glioblastoma.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.

Abstract

AIM:

We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer.

MATERIALS & METHODS:

The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'.

RESULTS:

We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states.

CONCLUSION:

The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

KEYWORDS:

cancer heterogeneity; gene regulation; integrative bioinformatics; machine learning; molecular subtypes; transcriptome and methylome

4.
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.

5.
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
6.
Sci Rep. 2017 Sep 28;7(1):12369. doi: 10.1038/s41598-017-12393-5.

IRS1 DNA promoter methylation and expression in human adipose tissue are related to fat distribution and metabolic traits.

Author information

1
IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany.
2
Department of Clinical and Molecular Biology and Akershus University Hospital, University of Oslo, Lørenskog, Norway.
3
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
4
Department of Computer Science, University of Leipzig, Leipzig, Germany.
5
Department of Clinical Sciences, Skåne University Hospital, Malmö, Sweden.
6
Department of Medicine, University of Leipzig, Leipzig, Germany.
7
Department of Surgery, University of Leipzig, Leipzig, Germany.
8
Clinic of Visceral Surgery, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany.
9
Municipal Clinic Dresden-Neustadt, Dresden, Germany.
10
IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany. yvonne.bottcher@medisin.uio.no.
11
University of Oslo, Institute of Clinical Medicine, Department of Clinical and Molecular Biology; Akershus University Hospital, 1478, Lørenskog, Norway. yvonne.bottcher@medisin.uio.no.

Abstract

The SNP variant rs2943650 near IRS1 gene locus was previously associated with decreased body fat and IRS1 gene expression as well as an adverse metabolic profile in humans. Here, we hypothesize that these effects may be mediated by an interplay with epigenetic alterations. We measured IRS1 promoter DNA methylation and mRNA expression in paired human subcutaneous and omental visceral adipose tissue samples (SAT and OVAT) from 146 and 41 individuals, respectively. Genotyping of rs2943650 was performed in all individuals (N = 146). We observed a significantly higher IRS1 promoter DNA methylation in OVAT compared to SAT (N = 146, P = 8.0 × 10-6), while expression levels show the opposite effect direction (N = 41, P = 0.011). OVAT and SAT methylation correlated negatively with IRS1 gene expression in obese subjects (N = 16, P = 0.007 and P = 0.010). The major T-allele is related to increased DNA methylation in OVAT (N = 146, P = 0.019). Finally, DNA methylation and gene expression in OVAT correlated with anthropometric traits (waist- circumference waist-to-hip ratio) and parameters of glucose metabolism in obese individuals. Our data suggest that the association between rs2943650 near the IRS1 gene locus with clinically relevant variables may at least be modulated by changes in DNA methylation that translates into altered IRS1 gene expression.

7.
Epigenetics. 2017;12(10):886-896. doi: 10.1080/15592294.2017.1361090. Epub 2017 Oct 6.

Targeting DNA hypermethylation: Computational modeling of DNA demethylation treatment of acute myeloid leukemia.

Author information

1
a Interdisciplinary Center for Bioinformatics, University of Leipzig , Leipzig , Germany.
2
d Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig , Leipzig , Germany.
3
b Division of Hematology, Oncology and Stem Cell Transplantation, Faculty of Medicine, University of Freiburg , Freiburg , Germany.
4
c German Cancer Consortium (DKTK) , Freiburg , Germany.

Abstract

In acute myeloid leukemia (AML) DNA hypermethylation of gene promoters is frequently observed and often correlates with a block of differentiation. Treatment of AML patients with DNA methyltransferase inhibitors results in global hypomethylation of genes and, thereby, can lead to a reactivation of the differentiation capability. Unfortunately, after termination of treatment both hypermethylation and differentiation block return in most cases. Here, we apply, for the first time, a computational model of epigenetic regulation of transcription to: i) provide a mechanistic understanding of the DNA (de-) methylation process in AML and; ii) improve DNA demethylation treatment strategies. By in silico simulation, we analyze promoter hypermethylation scenarios referring to DNMT dysfunction, decreased H3K4me3 and increased H3K27me3 modification activity, and accelerated cell proliferation. We quantify differences between these scenarios with respect to gene repression and activation. Moreover, we compare the scenarios regarding their response to DNMT inhibitor treatment alone and in combination with inhibitors of H3K27me3 histone methyltransferases and of H3K4me3 histone demethylases. We find that the different hypermethylation scenarios respond specifically to therapy, suggesting that failure of remission originates in patient-specific deregulation. We observe that inappropriate demethylation therapy can result even in enforced deregulation. As an example, our results suggest that application of high DNMT inhibitor concentration can induce unwanted global gene activation if hypermethylation originates in increased H3K27me3 modification. Our results underline the importance of a personalized therapy requiring knowledge about the patient-specific mechanism of epigenetic deregulation.

KEYWORDS:

5-aza-2'-deoxycytidine; 5-azacytidine; DNA methylation; DNMT inhibitors; Decitabine; acute myeloid leukemia; computational modeling; demethylation therapy; histone modification; mathematical modeling

PMID:
28758855
PMCID:
PMC5788435
DOI:
10.1080/15592294.2017.1361090
[Indexed for MEDLINE]
Free PMC Article
Icon for Taylor & Francis Icon for PubMed Central
8.
J Pathol. 2017 Oct;243(2):242-254. doi: 10.1002/path.4948. Epub 2017 Sep 5.

Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome.

Author information

1
Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany.
2
Institute of Pathology, Centre for Integrated Oncology, University Hospital Cologne, Cologne, Germany.
3
Translational Epigenomics, University Hospital Cologne, Cologne, Germany.
4
Max Planck Institute for Molecular Genetics, Berlin, Germany.
5
Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, France.
6
Université de Strasbourg, Strasbourg, France.
7
Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia.
8
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
9
Department of Hereditary Tumour Syndromes, Surgical Centre, HELIOS Clinic, University Witten/Herdecke, Wuppertal, Germany.
10
Institute of Human Genetics, University Hospital Bonn, Centre for Hereditary Tumour Syndromes, University of Bonn, Bonn, Germany.
11
Department of Internal Medicine I, University Hospital Bonn, Centre for Hereditary Tumour Syndromes, University of Bonn, Bonn, Germany.
12
Department of Medicine, Haematology and Oncology, Ruhr-University of Bochum, Knappschaftskrankenhaus, Bochum, Germany.
13
Institute of Human Genetics and Anthropology, Heinrich-Heine University, Düsseldorf, Germany.
14
Department of Applied Tumour Biology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
15
Clinical Cooperation Unit of Applied Tumour Biology, DKFZ (German Cancer Research Centre) Heidelberg, Germany.
16
Molecular Medicine Partnership Unit, University Hospital Heidelberg and EMBL Heidelberg, Heidelberg, Germany.

Abstract

Colorectal cancer (CRC) arising in Lynch syndrome (LS) comprises tumours with constitutional mutations in DNA mismatch repair genes. There is still a lack of whole-genome and transcriptome studies of LS-CRC to address questions about similarities and differences in mutation and gene expression characteristics between LS-CRC and sporadic CRC, about the molecular heterogeneity of LS-CRC, and about specific mechanisms of LS-CRC genesis linked to dysfunctional mismatch repair in LS colonic mucosa and the possible role of immune editing. Here, we provide a first molecular characterization of LS tumours and of matched tumour-distant reference colonic mucosa based on whole-genome DNA-sequencing and RNA-sequencing analyses. Our data support two subgroups of LS-CRCs, G1 and G2, whereby G1 tumours show a higher number of somatic mutations, a higher amount of microsatellite slippage, and a different mutation spectrum. The gene expression phenotypes support this difference. Reference mucosa of G1 shows a strong immune response associated with the expression of HLA and immune checkpoint genes and the invasion of CD4+ T cells. Such an immune response is not observed in LS tumours, G2 reference and normal (non-Lynch) mucosa, and sporadic CRC. We hypothesize that G1 tumours are edited for escape from a highly immunogenic microenvironment via loss of HLA presentation and T-cell exhaustion. In contrast, G2 tumours seem to develop in a less immunogenic microenvironment where tumour-promoting inflammation parallels tumourigenesis. Larger studies on non-neoplastic mucosa tissue of mutation carriers are required to better understand the early phases of emerging tumours. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

KEYWORDS:

hereditary cancer; immune editing; mismatch repair; tumour heterogeneity

PMID:
28727142
DOI:
10.1002/path.4948
[Indexed for MEDLINE]
Icon for Wiley
9.
Mol Metab. 2016 Nov 16;6(1):86-100. doi: 10.1016/j.molmet.2016.11.003. eCollection 2017 Jan.

Genome-wide DNA promoter methylation and transcriptome analysis in human adipose tissue unravels novel candidate genes for obesity.

Author information

1
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany.
2
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, 04103, Germany.
3
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Bioinformatics Group, Department of Computer Science, University of Leipzig, 04107, Leipzig, Germany.
4
Molecular Biology Laboratory, Istituto Auxologico Italiano IRCCS, Milan, 20149, Italy.
5
Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 205 02, Malmoe, Sweden.
6
Department of Medicine, University of Leipzig, Leipzig, 04103, Germany.
7
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Department of Surgery, University of Leipzig, Leipzig, 04103, Germany.
8
Städtisches Klinikum Karlsruhe, Clinic of Visceral Surgery, Karlsruhe, 76133, Germany.
9
Municipal Clinic Dresden-Neustadt, Dresden, 01129, Germany.
10
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Department of Medicine, University of Leipzig, Leipzig, 04103, Germany.
11
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Department of Medicine, University of Leipzig, Leipzig, 04103, Germany. Electronic address: matthias.blueher@medizin.uni-leipzig.de.
12
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany. Electronic address: yvonne.boettcher@medizin.uni-leipzig.de.

Abstract

OBJECTIVE/METHODS:

DNA methylation plays an important role in obesity and related metabolic complications. We examined genome-wide DNA promoter methylation along with mRNA profiles in paired samples of human subcutaneous adipose tissue (SAT) and omental visceral adipose tissue (OVAT) from non-obese vs. obese individuals.

RESULTS:

We identified negatively correlated methylation and expression of several obesity-associated genes in our discovery dataset and in silico replicated ETV6 in two independent cohorts. Further, we identified six adipose tissue depot-specific genes (HAND2, HOXC6, PPARG, SORBS2, CD36, and CLDN1). The effects were further supported in additional independent cohorts. Our top hits might play a role in adipogenesis and differentiation, obesity, lipid metabolism, and adipose tissue expandability. Finally, we show that in vitro methylation of SORBS2 directly represses gene expression.

CONCLUSIONS:

Taken together, our data show distinct tissue specific epigenetic alterations which associate with obesity.

KEYWORDS:

DNA methylation; Epigenetic mechanisms; Human adipose tissue depots; Obesity-related co-morbidities; mRNA expression

PMID:
28123940
PMCID:
PMC5220399
DOI:
10.1016/j.molmet.2016.11.003
[Indexed for MEDLINE]
Free PMC Article
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10.
Oncotarget. 2017 Jan 3;8(1):846-862. doi: 10.18632/oncotarget.13666.

Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq.

Author information

1
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology Leipzig, 04103 Leipzig, Germany.
2
Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany.
3
Department of Dermatology, Venereology and Allergology, University Hospital Essen, 45147 Essen, Germany.
4
Department of Physiological Chemistry, University of Würzburg, Biozentrum, Am Hubland, 97074 Würzburg, Germany.
5
Comprehensive Cancer Center Mainfranken, University Clinic Würzburg, 97080 Würzburg, Germany.
6
Institute for Advanced Study, 3572 Texas A&M University, College Station, Texas 77843-3572, USA.
7
Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany.

Abstract

Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules involved in proliferation, oxidative phosphorylation, pigmentation and cellular stroma. Gene expression modules had prognostic relevance when compared with gene expression data from published melanoma samples and patient survival data. We surveyed kinome expression patterns across sub-populations of the BRAF/NRAS wild type sample and found that CDK4 and CDK2 were consistently highly expressed in the majority of cells, suggesting that these kinases might be involved in melanoma progression. Treatment of cells with the CDK4 inhibitor palbociclib restricted cell proliferation to a similar, and in some cases greater, extent than MAPK inhibitors. Finally, we identified a low abundant sub-population in this sample that highly expressed a module containing ABC transporter ABCB5, surface markers CD271 and CD133, and multiple aldehyde dehydrogenases (ALDHs). Patient-derived cultures of the BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant metastases showed more homogeneous single-cell gene expression patterns with gene expression modules for proliferation and ABC transporters. Taken together, our results describe an intertumor and intratumor heterogeneity in melanoma short-term cultures which might be relevant for patient survival, and suggest promising targets for new treatment approaches in melanoma therapy.

KEYWORDS:

melanoma; single cell transcriptome sequencing; stem cells

PMID:
27903987
PMCID:
PMC5352202
DOI:
10.18632/oncotarget.13666
[Indexed for MEDLINE]
Free PMC Article
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11.
Stem Cells. 2017 Mar;35(3):694-704. doi: 10.1002/stem.2514. Epub 2016 Nov 8.

Bistable Epigenetic States Explain Age-Dependent Decline in Mesenchymal Stem Cell Heterogeneity.

Author information

1
INSERM U972, University Paris 11, Hôpital Paul Brousse, Villejuif, France.
2
Faculty of Biology, Mouloud Mammeri University, Tizi-ouzou, Algeria.
3
Interdisciplinary Center for Bioinformatics, University Leipzig, Germany.
4
LIFE: Leipzig Research Center for Civilization Diseases, University Leipzig, Germany.
5
Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
6
IBPS Laboratory of Developmental Biology, University Pierre & Marie Curie, Paris, France.

Abstract

The molecular mechanisms by which heterogeneity, a major characteristic of stem cells, is achieved are yet unclear. We here study the expression of the membrane stem cell antigen-1 (Sca-1) in mouse bone marrow mesenchymal stem cell (MSC) clones. We show that subpopulations with varying Sca-1 expression profiles regenerate the Sca-1 profile of the mother population within a few days. However, after extensive replication in vitro, the expression profiles shift to lower values and the regeneration time increases. Study of the promoter of Ly6a unravels that the expression level of Sca-1 is related to the promoter occupancy by the activating histone mark H3K4me3. We demonstrate that these findings can be consistently explained by a computational model that considers positive feedback between promoter H3K4me3 modification and gene transcription. This feedback implicates bistable epigenetic states which the cells occupy with an age-dependent frequency due to persistent histone (de-)modification. Our results provide evidence that MSC heterogeneity, and presumably that of other stem cells, is associated with bistable epigenetic states and suggest that MSCs are subject to permanent state fluctuations. Stem Cells 2017;35:694-704.

KEYWORDS:

Aging; Epigenetics; FACS; Mesenchymal stem cells; Methylation

PMID:
27734598
PMCID:
PMC5347872
DOI:
10.1002/stem.2514
[Indexed for MEDLINE]
Free PMC Article
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12.
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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13.
Genes (Basel). 2015 Oct 21;6(4):1076-112. doi: 10.3390/genes6041076.

Epigenetic Heterogeneity of B-Cell Lymphoma: Chromatin Modifiers.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. hopp@izbi.uni-leipzig.de.
2
Group of Bioinformatics, Institute of Molecular Biology NAS RA, 7 Hasratyan St, Yerevan 0014, Armenia. l_nersisyan@mb.sci.am.
3
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. wirth@izbi.uni-leipzig.de.
4
Group of Bioinformatics, Institute of Molecular Biology NAS RA, 7 Hasratyan St, Yerevan 0014, Armenia. aarakelyan@sci.am.
5
College of Science and Engineering, American University of Armenia, 40 Baghramyan Ave, Yerevan 0019, Armenia. aarakelyan@sci.am.
6
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. binder@izbi.uni-leipzig.de.

Abstract

We systematically studied the expression of more than fifty histone and DNA (de)methylating enzymes in lymphoma and healthy controls. As a main result, we found that the expression levels of nearly all enzymes become markedly disturbed in lymphoma, suggesting deregulation of large parts of the epigenetic machinery. We discuss the effect of DNA promoter methylation and of transcriptional activity in the context of mutated epigenetic modifiers such as EZH2 and MLL2. As another mechanism, we studied the coupling between the energy metabolism and epigenetics via metabolites that act as cofactors of JmjC-type demethylases. Our study results suggest that Burkitt's lymphoma and diffuse large B-cell Lymphoma differ by an imbalance of repressive and poised promoters, which is governed predominantly by the activity of methyltransferases and the underrepresentation of demethylases in this regulation. The data further suggest that coupling of epigenetics with the energy metabolism can also be an important factor in lymphomagenesis in the absence of direct mutations of genes in metabolic pathways. Understanding of epigenetic deregulation in lymphoma and possibly in cancers in general must go beyond simple schemes using only a few modes of regulation.

KEYWORDS:

B cell maturation; coupling between energy metabolism and epigenetics; methylation of histone-lysine side chains; plasticity of cell function; regulation of gene expression; writers and erasers of epigenetic marks

14.
Genes (Basel). 2015 Sep 7;6(3):812-40. doi: 10.3390/genes6030812.

Epigenetic Heterogeneity of B-Cell Lymphoma: DNA Methylation, Gene Expression and Chromatin States.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany. hopp@izbi.uni-leipzig.de.
2
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany. wirth@izbi.uni-leipzig.de.
3
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany. binder@izbi.uni-leipzig.de.

Abstract

Mature B-cell lymphoma is a clinically and biologically highly diverse disease. Its diagnosis and prognosis is a challenge due to its molecular heterogeneity and diverse regimes of biological dysfunctions, which are partly driven by epigenetic mechanisms. We here present an integrative analysis of DNA methylation and gene expression data of several lymphoma subtypes. Our study confirms previous results about the role of stemness genes during development and maturation of B-cells and their dysfunction in lymphoma locking in more proliferative or immune-reactive states referring to B-cell functionalities in the dark and light zone of the germinal center and also in plasma cells. These dysfunctions are governed by widespread epigenetic effects altering the promoter methylation of the involved genes, their activity status as moderated by histone modifications and also by chromatin remodeling. We identified four groups of genes showing characteristic expression and methylation signatures among Burkitt's lymphoma, diffuse large B cell lymphoma, follicular lymphoma and multiple myeloma. These signatures are associated with epigenetic effects such as remodeling from transcriptionally inactive into active chromatin states, differential promoter methylation and the enrichment of targets of transcription factors such as EZH2 and SUZ12.

KEYWORDS:

epigenetic reprogramming; gene expression; germinal center; high dimensional data portraying; machine learning; promoter methylation; stemness

15.
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
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16.
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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17.
Methods Mol Biol. 2014;1107:279-302. doi: 10.1007/978-1-62703-748-8_17.

MicroRNA expression landscapes in stem cells, tissues, and cancer.

Author information

1
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.

Abstract

MicroRNAs play critical roles in the regulation of gene expression with two major functions: marking mRNA for degradation in a sequence-specific manner or repressing translation. Publicly available data sets on miRNA and mRNA expression in embryonal and induced stem cells, human tissues, and solid tumors are analyzed in this case study using self-organizing maps (SOMs) to characterize miRNA expression landscapes in the context of cell fate commitment, tissue-specific differentiation, and its dysfunction in cancer. The SOM portraits of the individual samples clearly reveal groups of miRNA specifically overexpressed without the need of additional pairwise comparisons between the different systems. Sets of miRNA differentially over- and underexpressed in different systems have been detected in this study. The individual portraits of the expression landscapes enable a very intuitive, image-based perception which clearly promotes the discovery of qualitative relationships between the systems studied. We see perspectives for broad applications of this method in standard analysis to many kinds of high-throughput data of single miRNA and especially combined miRNA/mRNA data sets.

PMID:
24272444
DOI:
10.1007/978-1-62703-748-8_17
[Indexed for MEDLINE]
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18.
Methods Mol Biol. 2014;1107:257-78. doi: 10.1007/978-1-62703-748-8_16.

Analysis of microRNA expression using machine learning.

Author information

1
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.

Abstract

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

PMID:
24272443
DOI:
10.1007/978-1-62703-748-8_16
[Indexed for MEDLINE]
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19.
Bioinformatics. 2013 Nov 15;29(22):2892-9. doi: 10.1093/bioinformatics/btt492. Epub 2013 Aug 20.

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.

Author information

1
Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.

Abstract

MOTIVATION:

After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein.

RESULTS:

Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams.

AVAILABILITY:

The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.

PMID:
23966112
PMCID:
PMC3810846
DOI:
10.1093/bioinformatics/btt492
[Indexed for MEDLINE]
Free PMC Article
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20.
Biology (Basel). 2013 Dec 2;2(4):1411-37. doi: 10.3390/biology2041411.

Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. hopp@izbi.uni-leipzig.de.
2
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. lembcke@izbi.uni-leipzig.de.
3
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. binder@izbi.uni-leipzig.de.
4
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. wirth@izbi.uni-leipzig.de.

Abstract

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics.

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