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Supplementary Note 1. List of members Full list of members of the ICGC MMML-Seq project Coordination (C1): Gesine Richter1, Reiner Siebert1, Susanne Wagner1, Andrea Haake1, Julia Richter1 Data Center (C2): Roland Eils2,3, Chris Lawerenz2, Sylwester Radomski2, Ingrid Scholz2
Clinical Centers (WP1): Christoph Borst4, Birgit Burkhardt5,6, Alexander Claviez7, Martin Dreyling8, Sonja Eberth9, Hermann Einsele10, Norbert Frickhofen11, Siegfried Haas4, Martin-Leo Hansmann12, Dennis Karsch13, Michael Kneba13, Jasmin Lisfeld6
, Luisa Mantovani-Löffler14, Marius Rohde5, Christina Stadler9, Peter Staib15, Stephan Stilgenbauer16, German Ott17, Lorenz Trümper9 , Thorsen Zenz35 Normal Cells (WPN): Martin-Leo Hansmann12, Dieter Kube9, Ralf Küppers18, Marc Weniger18
Pathology and Analyte Preparation (WP2-3): Siegfried Haas4, Michael Hummel19, Wolfram Klapper20, Ulrike Kostezka21, Dido Lenze19, Peter Möller22, Andreas Rosenwald23, Monika Szczepanowski20 Sequencing and genomics (WP4-7): Ole Ammerpohl1, Sietse Aukema1, Vera Binder24, Arndt Borkhardt24, Andrea Haake1, Kebria Hezaveh24, Jessica Hoell24; Ellen Leich23, Peter Lichter2, Christina Lopez1, Inga Nagel1, Jordan Pischimariov23, Bernhard Radlwimmer2, Julia Richter1, Philip Rosenstiel25, Andreas Rosenwald23, Markus Schilhabel25, Stefan Schreiber26, Inga Vater1, Rabea Wagner1, Reiner Siebert1
Bioinformatics (WP8-9): Stephan H. Bernhart27-29, Hans Binder28, Benedikt Brors2, Gero Doose27-29, Jürgen Eils2, Roland Eils2,3, Steve Hoffmann27-29, Lydia Hopp28, Helene Kretzmer27-29, Markus Kreuz30, Jan Korbel31, David Langenberger27-29, Markus Loeffler30, Sylwester Radomski2, Maciej Rosolowski30, Matthias Schlesner2 , Peter F. Stadler27-29,32-34,
Stefanie Sungalee31 1Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel/ Christian-Albrechts University
Kiel, Kiel, Germany; 2German Cancer Research Center (DKFZ), Division Theoretical Bioinformatics, Heidelberg, Germany;
3Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology
and Bioquant, University of Heidelberg, Heidelberg, Germany; 4Friedrich-Ebert Hospital Neumünster, Clinics for Hematology, Oncology and Nephrology, Neumünster, Germany;
5Department of Pediatric Hematology and Oncology, University Hospital Münster, Münster, Germany;
6Department of Pediatric Hematology and Oncology University Hospital Giessen, Giessen, Germany;
7Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Germany;
8Department of Medicine III - Campus Grosshadern, University Hospital Munich, Munich, Germany;
9Department of Hematology and Oncology, Georg-August-University of Göttingen, Göttingen, Germany;
10University Hospital Würzburg, Department of Medicine and Poliklinik II, University of Würzburg, Würzburg,
Germany; 11
Department of Medicine III, Hematology and Oncology, Dr. Horst-Schmidt-Kliniken of Wiesbaden, Wiesbaden, Germany; 12
Senckenberg Institute of Pathology, University of Frankfurt Medical School, Frankfurt am Main, Germany; 13
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, Campus Kiel, Kiel, Germany;
14Hospital of Internal Medicine II, Hematology and Oncology, St-Georg Hospital Leipzig, Leipzig, Germany;
15Univesity Hospital Aachen, St.-Antonius Hospital, Department of Oncology, Hematology and stem cell
transplantation, University of Aachen, Aachen, Germany; 16
Department of Internal Medicine III, University of Ulm, Ulm, Germany; 17
Robert-Bosch Hospital Stuttgart, Department of Pathology, Stuttgart, Germany; 18
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany; 19
Institute of Pathology, Charité – University Medicine Berlin, Berlin, Germany; 20
Hematopathology Section, University Hospital Schleswig-Holstein Campus Kiel/ Christian-Albrechts University Kiel, Kiel, Germany; 21
Comprehensive Cancer Center Ulm (CCCU), University Hospital Ulm, Ulm, Germany;
22Institute of Pathology, Medical Faculty of the Ulm University, Ulm, Germany;
23Institute of Pathology, University of Würzburg, Würzburg, Germany;
24Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, Düsseldorf,
Germany;
25Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein Campus Kiel/ Christian-Albrechts
University Kiel,, Kiel, Germany;
Nature Genetics: doi:10.1038/ng.3413
26Department of General Internal Medicine, University Hospital Schleswig-Holstein Campus Kiel/ Christian-
Albrechts University Kiel, Kiel, Germany; 27
Transcriptome Bioinformatics Group, LIFE Research Center for Civilization Diseases, Leipzig, Germany; 28
Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany; 29
Bioinformatics Group, Department of Computer, University of Leipzig, Leipzig, Germany 30
Institute for Medical Informatics Statistics and Epidemiology, Leipzig, Germany;31
EMBL Heidelberg, Genome Biology, Heidelberg, Germany; 32
RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany 33
Santa Fe Institute, Santa Fe, New Mexico, United States of America 34
Max-Planck-Institute for Mathematics in Sciences, Leipzig, Germany 35
Department of Medicine V, University of Heidelberg, Heidelberg, Germany
Nature Genetics doi:10.1038/ng.3413
Full list of members of the MMML project Pathology group and analytes preparation: Thomas F.E. Barth1, Heinz-Wolfram Bernd2, Sergio B. Cogliatti3, Alfred C. Feller2, Martin L. Hansmann4, Michael Hummel5, Wolfram Klapper6, Dido Lenze5, Peter Möller1, Hans-Konrad Müller-Hermelink7, German Ott7, Andreas Rosenwald7, Harald Stein5, Monika Szczepanowski6, Hans-Heinrich Wacker6. Genetics group: Thomas F.E. Barth1, Petra Behrmann8, Peter Daniel10, Judith Dierlammm8, Eugenia Haralambieva7, Lana Harder11, Paul-Martin Holterhus12, Ralf Küppers13, Dieter Kube13, Peter Lichter14, Jose I. Martín-Subero11, Peter Möller1, Eva M. Murga-Peñas9, German Ott7, Christiane Pott16, Armin Pscherer15, Andreas Rosenwald7, Carsten Schwaenen17, Reiner Siebert11, Heiko Trautmann16, Martina Vockerodt18, Swen Wessendorf16. Bioinformatics group: Stefan Bentink19, Hilmar Berger20, Dirk Hasenclever20, Markus Kreuz20, Markus Loeffler20, Maciej Rosolowski20, Rainer Spang19. Project coordination: Benjamin Stürzenhofecker14, Lorenz Trümper14, Maren Wehner14. Steering committee: Markus Loeffler19, Reiner Siebert11, Harald Stein5, Lorenz Trümper14.
1Institute of Pathology, University Hospital of Ulm, Ulm, Germany;
2Institute of Pathology, University Hospital Schleswig-Holstein Campus Lübeck, Lübeck, Germany;
3Institute of Pathology, Kantonsspital St. Gallen, St.Gallen, Switzerland;
4Institute of Pathology, University Hospital of Frankfurt, Frankfurt, Germany;
5Institute of Pathology, Campus Benjamin Franklin, Charité–Universitätsmedizin Berlin, Berlin, Germany;
6Institute of Hematopathology, University Hospital Schleswig-Holstein Campus Kiel/ Christian-Albrechts University
Kiel, Kiel, Germany; 7Institute of Pathology, University of Würzburg, Würzburg, Germany;
8Cytogenetic and Molecular Diagnostics, Internal Medicine III, University Hospital of Ulm, Ulm, Germany;
9University Medical Center Hamburg-Eppendorf, Hamburg, Germany;
10Department of Hematology, Oncology and Tumor Immunology, University Medical Center Charité, Berlin,
Germany; 11
Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Kiel, Germany; 12
Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University Hospital Schleswig-Holstein Campus Kiel / Christian-Albrechts University Kiel, Kiel, Germany; 13
Institute for Cell Biology (Tumor Research), University of Duisburg-Essen, Essen, Germany; 14
Department of Hematology and Oncology, Georg-August University of Göttingen, Göttingen, Germany; 15
German Cancer Research Center (DKFZ), Heidelberg, Germany; 16
Second Medical Department, University Hospital Schleswig-Holstein Campus Kiel/ Christian-Albrechts University Kiel, Kiel, Germany; 17
Cytogenetic and Molecular Diagnostics, Internal Medicine III, University Hospital of Ulm, Ulm, Germany; 18
Department of Pediatrics I, Georg-August University of Göttingen, Göttingen, Germany; 19
Institute of Functional Genomics, University of Regensburg, Regensburg, Germany; 20
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
Nature Genetics: doi:10.1038/ng.3413
Full list of principal investigators and involved members of the BLUEPRINT project Project coordinator: Hendrik Stunnenberg1 Standardization and Quality control (WP1-2): Xavier Estivill2, Ivo Gut3, Hélène Pendeville4, Joost H.A. Martens1, Hendrik Stunnenberg1 Epigenome of haematopoietic cells (WP3): Frank Grosveld5, Willem Ouwehand6, Joost Martens1, Hendrik Stunnenberg1 Epigenome of normal and neoplastic B- and T-cells (WP4): Elias Campo Guerri7, Jude Fitzgibbon8, Ralf Küppers9, Markus Loeffler10, Elizabeth Macintyre11, Jose Ignacio Martin Subero7, Marta Kulis7, Reiner Siebert12, Salvatore Spicuglia13, Hendrik Stunnenberg1 Epigenome of acute myeloid leukaemia (WP5): Tariq Enver14, Joost Martens1, Hendrik Stunnenberg1, Edo Vellenga15 Data coordination and analysis (WP6-7): Paul Flicek16, Roderic Guigo17, Ivo Gut3, Markus Loeffler10, Joost H.A. Martens1, Martin Seifert18, Amos Tanay19, David Torrents20, Alfonso Valencia21, Martin Vingron22 DNA methylation variation in T1DM (WP8): Stephan Beck23, Bernhard Boehm24, Åke Lernmark25, David Leslie26, Vardham Rakyan26 Biomarker development (WP9): Christoph Bock27, Manel Esteller Badosa28, Thomas Lengauer29, Edo Vellenga15 The effect of common sequence variation on the epigenome landscape (WP10): Stylianos E. Antonarakis30, Manolis Dermitzakis30, Hans Lehrach31, Willem Ouwehand6 Nicole Soranzo32, Hendrik Stunnenberg1 Mouse models to quantify variation in reference epigenomes (WP11): David Adams32, Anne Ferguson-Smith33, Salvatore Spicuglia13 Technology development for genome wide and selected profiling of cytosine (hydroxy)methylation (WP12): Adrian Bird34, Wolf Reik35, Dirk Schübeler36, Michael Stratton32 Technology optimization for microscale application (WP13): Hélène Pendeville4, Hendrik Stunnenberg1, Eileen Furlong37, Christoph Merten37 Identification/ validation of Epi-targets and Compound development and screening (WP14-15): Lucia Altucci38, Gerard Drewes39, Laura Maccari40, Thomas Graf41, Kristian Helin42, Antonello Mai43, Saverio Minucci44, Pier Giuseppe Pelicci45 Training and dissemination (WP16, 17 and 19): Claudia Schacht46, Martin Seifert18, Hendrik Stunnenberg1, Jörn Walter47 ChIP-Seq of cell lines: Anke K. Bergmann12,47, Hindrik. H.D. Kerstens1, Laura Clarke16, David
Richardson16, Enrique Carrillo-de Santa Pau21, Daniel Rico21
1Radboud University, Department of Molecular Biology, Nijmegen, Netherlands;
2Centre for Genomic Regulation, Genes and Diseases Program, Barcelona, Spain;
3Centro Nacional de Analisis Genomico, Barcelona, Spain;
4Diagenode SA, Epigenetic Laboratory, Liege, Belgium;
5Erasmus University Medical Centre Rotterdam, Department of Cell Biology, Rotterdam, Netherlands;
6University of Cambridge, Department of Haematology, Cambridge, United Kingdom;
7Institut D’investigacions Biomediques August Pi I Sunyer, Center for Biomedical Diagnosis, Barcelona, Spain;
8Queen Mary University of London, Institute of Cancer, London, United Kingdom;
9Universitätsklinikum Essen, Institute of Cell Biology (Cancer research), Essen, Germany;
10University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiologie (IMISE), Leipzig, Germany;
11Centre National de la Recherche Scientifique, UMR-814 and Paris Descartes Université, Laboratoire
d'hématologie, CNRS 8147, Paris, France; 12
Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Kiel, Germany; 13
Institut National de la Sante et de la Recherche Medicale, TAGC Laboratory, Inserm U1090, Marseille, France; 14
University College London, Department of Cancer Biology, London, United Kingdom; 15
University Medical Centre Groningen, Department of Hematology, Groningen, Netherlands; 16
European Bioinformatics Institute, Hinxton, United Kingdom; 17
Centre for Genomic Regulation, Bioinformatics and Genomics Program, Barcelona, Spain 18
Genomatix Software GmbH, Munich, Germany; 19
Weizmann Institute of Science, Department of Science and Applied Mathematics, Rehovot, Israel; 20
Barcelona Supercomputing Center, Department of Life Sciences - Computational Genomics, Barcelona, Spain; 21
Structural Biology and Bio Computing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain; 22
Max Planck Institute for Molecular Genetics, Department of Computational Molecular Biology, Berlin, Germany;
Nature Genetics: doi:10.1038/ng.3413
23University College London, Cancer Institute, London, United Kingdom;
24University of Ulm, Department of Internal Medicine, Ulm, Germany;
25Lund University, Department of Clinical Sciences Malmö, Malmö, Sweden;
26Queen Mary University of London, Blizard Institute of Cell and Molecular Sciences, London, United Kingdom;
27CeMM Research Center for Molecular Medicine, Vienna, Austria;
28Institut d’Investigacio Biomedica de Bellvitge, Department of Cancer Epigenetics and Biology, Barcelona, Spain;
29Max Planck Institute for Informatics, Department of Cancer Epigenetics and Biology, Saarbrücken, Germany;
30University of Geneva, Department of Genetic Medicine and Development, Geneva, Switzerland;
31Max Planck Institute for Molecular Genetics, Department of Vertebrate Genomics, Berlin, Germany;
32Wellcome Trust Sanger Institute, Hinxton, United Kingdom;
33University of Cambridge, Department of Genetics, Cambridge, United Kingdom;
34University of Edinburgh, Wellcome Trust Centre for Cell Biology, Edinburgh, United Kingdom;
35The Babraham Institute, Cambridge, United Kingdom;
36Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
37European Molecular Biology Laboratory, Heidelberg, Germany
38Second University of Naples, Department of Pathology, Napoli, Italy;
39Cellzome AG, Heidelberg, Germany;
40Siena Biotech SPA, Siena, Italy;
41Centre for Genomic Regulation, Differentiation and Cancer Program, Barcelona, Spain;
42University of Copenhagen, Biotech Research and Innovation Centre, Copenhagen, Denmark;
43Sapienza University of Rome, Department of Drug Chemistry and Technologies, Rome, Italy;
44European Institute of Oncology, Department of Experimental Oncology, Milan, Italy;
45European Research and Project Office GmbH, Saarbrücken, Germany;
46University of Saarland, Genetics Institute, Saarbrücken, Germany;
47Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany;
Nature Genetics: doi:10.1038/ng.3413
2. Supplementary Tables
Supplementary Table1:Clinical and molecular characteristics
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 2: Statistics of whole-genome bisulfite sequencing.
PID type total read count % mapped % uniquely
mapped R2 array correlation
4118819 GC B 991,190,927 96.48 90.99 na4122131 GC B 1,099,283,967 96.0 89.23 0.887 4160735 GC B 831,812,581 85.09 79.78 0.91 4174884 GC B 806,131,461 83.98 78.41 0.921 4112512 BL 745,957,354 89.41 78.68 0.929 4119027 BL 865,369,134 94.41 87.95 0.939 4125240 BL 728,907,037 90.39 83.13 0.922 4133511 BL 1,092,415,562 94.60 88.55 0.931 4142267 BL 1,027,372,107 96.99 91.09 0.907 4177434 BL 1,051,677,637 89.95 82.92 0.943 4177856 BL 1,088,090,219 95.58 88.88 0.942 4182393 BL 822,877,534 94.01 87.72 0.932 4189998 BL 1,103,050,124 96.04 89.56 0.935 4190495 BL 922,078,617 85.41 77.85 0.915 4193278 BL 910,056,125 95.48 88.75 0.930 4194218 BL 979,263,566 96.25 89.70 0.934 4194891 BL 765,168,759 86.49 78.99 0.935 4105105 FL 1,049,794,226 94.46 86.67 0.932 4121361 FL 975,443,989 96.21 89.79 0.915 4134005 FL 1,064,047,805 93.16 85.43 0.920 4158726 FL 1,050,825,693 95.46 88.97 0.919 4159170 FL 1,140,282,923 96.33 89.56 0.934 4175837 FL 1,004,341,522 96.19 90.56 0.908 4177376 FL 872,114,064 84.59 76.92 0.913 4188900 FL 861,302,178 85.52 77.13 0.910 4189200 FL 1,048,046,414 94.18 88.07 0.931
total read count: number of reads obtained from sequencer; % mapped: percentage of reads which could be mapped to reference genome; % uniquely mapped: percentage of reads which could be mapped uniquely to reference genome; % array correlation: R2 of linear model of whole-genome bisulfite sequencing methylation rates and Illumina HumanMethylation450K BeadChip beta rates.
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 3: Statistics of HumanMethylation450k BeadChips.
PID type Detected CpG
(0.01) Detected CpG (0.01), [%]
Detected CpG (0.05)
Detected CpG (0.05), [%]
4122131 GC B 485518 99.988 485541 99.993 4160735 GC B 485507 99.986 485536 99.992 4174884 GC B 485525 99.989 485536 99.992 4112512 BL 485235 99.93 485282 99.939 4119027 BL 485541 99.993 485549 99.994 4125240 BL 485516 99.987 485533 99.991 4133511 BL 485472 99.978 485506 99.985 4142267 BL 485504 99.985 485528 99.990 4177434 BL 485067 99.895 485150 99.912 4177856 BL 485490 99.982 485514 99.987 4182393 BL 485544 99.993 485554 99.995 4189998 BL 485500 99.984 485524 99.989 4190495 BL 485504 99.985 485524 99.989 4193278 BL 485519 99.988 485540 99.992 4194218 BL 485477 99.979 485499 99.984 4194891 BL 485478 99.98 485506 99.985 4105105 FL 485164 99.915 485208 99.924 4121361 FL 485467 99.977 485494 99.946 4134005 FL 485493 99.983 485522 99.989 4158726 FL 485550 99.994 485557 99.996 4159170 FL 485539 99.992 485554 99.995 4175837 FL 485214 99.925 485275 99.938 4177376 FL 485202 99.923 485274 99.938 4188900 FL 485500 99.984 485521 99.988 4189200 FL 485187 99.92 485259 99.935
The number and the percentage (based on a total number of 485577 loci) of detected CpGs with a detection p-value of 0.01 and 0.05 are listed.
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 4: Statistics of whole-transcriptome sequencing.
PID type total read count
% mapped
% properly paired
% uniquely mapped
% single-tons
4118819 GC B 152,163,762 95.39 88.72 88.74 1.19 4122131 GC B 143,139,572 94.07 88.60 88.32 1.84 4174884 GC B 143,681,684 95.05 88.49 88.72 1.38 4112512 BL 100,040,596 95.92 91.96 88.75 1.21 4119027 BL 122,070,424 96.30 92.56 89.78 1.10 4125240 BL 118,552,896 92.88 89.35 85.79 2.92 4133511 BL 180,464,110 94.97 90.26 88.96 1.66 4142267 BL 147,502,168 93.93 88.95 84.78 2.10 4177434 BL 130,335,292 95.84 91.55 89.49 1.31 4177856 BL 147,673,376 94.95 90.33 88.18 1.67 4182393 BL 134,646,148 96.62 92.83 90.18 0.99 4189998 BL 168,560,268 94.81 90.26 87.22 1.78 4190495 BL 126,625,502 96.93 92.29 91.17 0.80 4193278 BL 173,013,412 93.42 88.47 85.58 2.38 4194218 BL 148,876,052 93.68 88.93 85.11 2.18 4194891 BL 170,697,768 93.82 88.77 86.79 2.19 4105105 FL 173,994,462 96.56 92.52 90.45 1.04 4121361 FL 140,160,636 96.74 92.68 89.10 0.92 4134005 FL 105,581,544 94.63 90.23 88.84 1.78 4158726 FL 190,901,256 94.14 89.07 88.85 1.96 4159170 FL 149,299,090 94.60 89.63 88.90 1.75 4175837 FL 183,318,574 93.42 87.86 87.49 2.24 4177376 FL 160,775,046 92.45 86.47 85.43 2.66 4188900 FL 168,897,502 93.23 87.70 87.61 2.31 4189200 FL 171,840,556 94.44 89.49 87.38 1.92
total read count: number of reads obtained from sequencer; %mapped: percentage of reads which could be mapped to reference genome; %properly paired: percentage of reads which form a proper pair on reference genome; %uniquely mapped: percentage of reads which could be mapped uniquely to reference genome; %singletons: percentage of reads for which no mate was aligned by the mapper. Supplementary Table 5 (available online):Statistics of whole-genome sequencing (SupplementaryTable5.xlsx)
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 6: Mapping of chromatin states
ENCODE Blueprint H3K27me3 H3K36me3 H3K4me1 H3K4me3 H3K27ac
1 Active Promoter Apro + +++ +++
2 Weak Promoter Apro ++ +++ ++
3 Poised Promoter Rpro +++ ++ ++
4 Strong Enhancer Apro + +++ +++ +++
5 Strong Enhancer RegE ~ +++ +++
6 Weak Enhancer RegE ++
7 Weak Enhancer RegE ++
8 Insulator -
9 Txn transition TranR ++ ++
10 Txn elongation TranR ++
11 Weak Txn TranR ~
12 Repressed RHet +
13 Hetero-chromatin RHet
14 Repetitive - + + +
15 Repetitive - +++ +++ +++ +++ +++
Blueprint Assignment
ENCODE H3K27me3 H3K36me3 H3K4me1 H3K4me3 H3K27ac
RHet 13
RHet 12 +
Rpro 3 + + +
Apro 2 + +
Apro 4 +
Apro 1 + +
RegE 5 + +
RegE 6,7 +
TranR 9,10 +
TranR 11 ~ Assignment of chromatin states based on histone modifications (found by CHiP-Seq) in [1] and [2], together with the mapping used in this paper. Shade of blue corresponds to the abundance of the respective modification in the segment. Top, assignment in [1], "~" percentage<=10, "+" percentage <=30; "++" percentage<=60. "+++" percentage >60. Bottom: assignment in [2]. Marks that are mentioned in (12) are assigned a "+". [1] Ernst, Jason, et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473(7345): 43-49. (2011) [2] Ernst, J., and Manolis K. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nature biotechnology 28(8):817-825. (2010)
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 7 (available online): Annotation DMRs (SupplementaryTable7.xlsx). List of locations of differentially methylated regions (DMRs) with average methylation rates for GC-B, BL and FL with overlapping ENSEMBL gene annotations including the type, accession, name and strand of the annotation. Supplementary Table 8 (available online): Correlating DMRs (SupplementaryTable8.xlsx). List of correlating DMRs (cDMRs) and adjacent genes along with test results for the correlation of expression and methylation (spearman) for the cDMR-gene pairs. Supplementary Table 9: Number of significant enriched pathways by Ingenuity Pathway Analysis. Difference of IPA pathway analyses, that were generated through the use of QIAGEN's Ingenuity Pathway Analysis (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity). In the analysis based on differential expression we identified 60 activated pathways. Using negative correlating DMRs (cDMRs) 58 of these pathways were recovered. As expected, the analysis based on positively correlated cDMRs only revealed fewer (17) of these pathways.
all diff. expr. neg. cDMR pos. cDMR pathways 60 58 17
-log(p-value) 1.60 3.56 0.60 z-score 2.75 2.07 -
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 10: Ingenuity Pathway Analysis scores. Strongly inactivated (z-score<-3) and activated (z-score>2) pathways based on gene expression in Burkitt lymphoma versus follicular lymphoma. Data were analyzed through the use of QIAGEN's Ingenuity®Pathway Analysis (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity).
Ingenuity Canonical Pathways -log(p-value) z-score Inactivated in BL vs. FL
NF-κB Signaling 2.93 -4.84 iCOS-iCOSL Signaling in T Helper Cells 6.14 -4.49 Tec Kinase Signaling 2.91 -4.27 HMGB1 Signaling 1.22 -3.80 Phospholipase C Signaling 2.16 -3.78 PKCθ Signaling in T Lymphocytes 4.70 -3.67 cAMP-mediated signaling 0.91 -3.57 CD28 Signaling in T Helper Cells 4.30 -3.55 TREM1 Signaling 2.86 -3.41 IL-9 Signaling 3.04 -3.32 Production of Nitric Oxide and Reactive Oxygen Species in Macrophages 0.84 -3.31 Role of NFAT in Regulation of the Immune Response 3.93 -3.29 PI3K Signaling in B Lymphocytes 0.52 -3.27 Activation of IRF by Cytosolic Pattern Recognition Receptors 0.42 -3.16 Gαi Signaling 1.91 -3.13 Eicosanoid Signaling 3.57 -3.00 NF-κB Activation by Viruses 1.35 -3.00 p38 MAPK Signaling 0.62 -2.98 Colorectal Cancer Metastasis Signaling 1.20 -2.97
Activated in BL vs. FL Role of BRCA1 in DNA Damage Response 4.43 2.00 Cyclins and Cell Cycle Regulation 3.40 2.24 Estrogen-mediated S-phase Entry 4.17 2.53 Antioxidant Action of Vitamin C 0.84 2.98
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 11: Ranking of transcription factors. Correlation of transcription factor (TF) expression with the expression of cDMR – genes in the corresponding quadrant with a transcription factor binding site in the cDMR. Analysis was done for all enriched TFs and their potentially regulated genes. The list is sorted by correlation coefficient (rho). TF rho pval STAT5A 0.876923077 2.24E-‐06 TCF3 0.818461538 2.30E-‐06 MEF2 0.796153846 4.03E-‐06 BCL3 0.793846154 4.38E-‐06 PML 0.783076923 6.69E-‐06 BATF 0.776923077 8.64E-‐06 FOXM1 -‐0.766153846 1.35E-‐05 RXRA 0.700769231 1.44E-‐04 SMARCA4 -‐0.688461538 2.08E-‐04 YY1 -‐0.683076923 2.43E-‐04 RAD21 -‐0.646153846 6.48E-‐04 ZEB1 -‐0.610769231 1.48E-‐03 ETS1 0.553076923 4.74E-‐03 NFIC 0.543076923 5.69E-‐03 EBF1 0.52 8.51E-‐03 RUNX3 0.519230769 8.62E-‐03 PAX5 -‐0.48 1.62E-‐02 ZBTB33 0.464615385 2.03E-‐02 TAF1 0.457692308 2.25E-‐02 POLE4 -‐0.449230769 2.54E-‐02 SIX5 0.425384615 3.51E-‐02 NFATC1 -‐0.390769231 5.44E-‐02 ATF2 0.388461538 5.59E-‐02 MTA3 -‐0.385384615 5.80E-‐02 PU1 -‐0.303846154 1.40E-‐01 SRF 0.290769231 1.58E-‐01 ELF1 -‐0.276923077 1.80E-‐01 BCLAF1 -‐0.231538462 2.64E-‐01 CREB1 0.223076923 2.82E-‐01 IRF4 0.221538462 2.86E-‐01 PBX3 0.203076923 3.29E-‐01 CEBPB 0.201538462 3.32E-‐01 POU2 -‐0.183846154 3.77E-‐01 POL2 0.106923077 6.10E-‐01 SP1 0.099230769 6.36E-‐01 GABPA -‐0.097692308 6.41E-‐01 EGR1 0.073076923 7.28E-‐01 TCF12 0.04 8.50E-‐01 ATF3 0.031538462 8.82E-‐01 BCL11 -‐0.007692308 9.72E-‐01
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 12 (available online): Statistics of transcriptome arrays (SupplementaryTable12.xlsx). Overview of extended cohort used for array-based expression analysis with pathological and molecular diagnosis. Supplementary Table 13: SMARCA4 immunohistochemistry in TMAs
Molecular diagnosis*
mBL (%) intermediate (%) non mBL (%) p-‐value
Age < 60 30/33 (91) 5/22 (23) 27/70 (39)
p<0.001 ≥ 60 3/33 (9) 17/22 (77) 43/70 (61)
Gender male 26/33 (79) 11/22 (50) 36/71 (51)
p=0.018 female 7/33 (21) 11/22 (50) 35/71 (49)
Molecular subtypes**
GCB 30/33 (91) 15/22 (68) 30/72 (42) p<0.001 ABC 0/33 (0) 3/22 (14) 25/72 (35)
unclassifed 3/33 (9) 4/22 (18) 17/72 (24)
PAP groups***
PAP-‐1 0/33 (0) 3/22 (14) 30/72 (42)
p<0.001
PAP-‐2 0/33 (0) 3/22 (14) 9/72 (12) PAP-‐3 0/33 (0) 0/22 (0) 9/72 (12) PAP-‐4 0/33 (0) 3/22 (14) 9/72 (12) BL-‐PAP 31/33 (94) 2/22 (9) 0/72 (0) mind-‐L 2/33 (6) 11/22 (50) 15/72 (21)
SMARCA4 immunohistochemistry score#
0 (0%) 1/28 (4) 2/20 (10) 2/54 (4)
p=0.1919 1 (1-‐25%) 2/28 (7) 3/20 (15) 2/54 (4) 2 (26-‐50%) 1/28 (4) 1/20 (5) 4/54 (7) 3 (51-‐75%) 1/28 (4) 1/20 (5) 11/54 (20) 4 (76-‐100%) 23/28 (82) 13/20 (65) 35/54 (65)
MYC status
IG-‐MYC 30/31 (97) 9/21 (43) 3/67 (4,5) p<0.001 neg 1/31 (3) 9/21 (43) 63/67 (94)
non-‐IG-‐MYC 0/31 (0) 3/21 (14) 1/67 (1,5) The staining criteria for SMARCA4 were as follows: 0=no staining, 1=1–25%, 2=26–50%, 3=51–75%, 4= >75%. For the score “no staining” an internal staining control must be present. * according to Hummel M. et al. A biologic definition of Burkitt's lymphoma from transcriptional and genomic profiling.N Engl J Med 354, 2419-‐2430 (2006); **according to Wright G. et al., A gene expression-‐based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci USA. 100(17):9991–6. (2003); ***according to Bentink S. et al., Pathway activation patterns in diffuse large B-‐cell lymphomas. Leukemia. 22(9):1746–54 (2008); # SMARCA4 immunohistochemistry failed to be scored in 25 cases due to: no internal control, too much background, other artefacts, non representative staining, non representative core or missing core.
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 14:Detection and verification of mutations in SMARCA4 gene
Love C. et al., The genetic landscape of mutations in Burkitt Lymphoma. Nat Gent. 44 (2012);Schmitz et al., Burkitt Lymphoma pathogenesis and therapeutic targets from structural and functional genomics. Nature. 490 (2012)Morin et al., Frequent mutation of histone-modifying gene in non-Hodgkin lymphoma. Nature. 476 (2011)Forbes et al., COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. (2014)
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 15 (available online): SWI/SNF SNVs (SupplementaryTable15.xlsx). Summary of 19 somatic SNVs identified by genome and exome sequencing in SWI/SNF associated proteins in the investigated BL cases. The table gives information on the location and type of the mutations. Supplementary Table 16: Run identifiers of cell line ChIP-seq data (European Nucleotide Archive;ERP002586). Cell line Input H3K27ac H3K27me3 H3K36me3 H3K4me1 H3K4me3 H3K9me3 BL-2 ERR324294 ERR324306 ERR324300 ERR324287 ERR324266 ERR324263 ERR324280 DG-75 ERR324296 ERR324307 ERR324302 ERR324289 ERR324304 ERR324262 ERR324282 KARPAS-422 ERR324297 ERR324271 ERR324276 ERR324290 ERR324268 ERR324270 ERR324283
Nature Genetics: doi:10.1038/ng.3413
Supplementary Table 17: Primer sequences used for SMARC4 mutation analysis
primer name forward primer (5´- 3´sequence) reverse primer (5´- 3´sequence) PCR product lenght (bp)
SMARCA4_Ex9 gccttgcggggagatgtgtccaccatgctg ggggagtgacccctggagcccgcagtacc 315 SMARCA4_Ex15 gtcaggagccagcacattgtcacagatag cgcaccacctgggaacacctgcaccgagg 287 SMARCA4_Ex16 aggaccctctggtgtccgacccggccttc ttgtggtattctactgcggcaaacttagg 311 SMARCA4_Ex17 ttgcacagtgagccattgatgagagaccg tcactgtccagaggtatgtgtggacgtc 215 SMARCA4_Ex18 gtgcctgtgcccctcttgccacctggcc aacttgtaggggctttggaggagacgggc 273 SMARCA4_Ex19 ctccccatgtgccgggccacctgctgccc ccagctgtagctggtgctcaacacgttcc 392 SMARCA4_Ex20 ccttctagtgagacctctgtcgccctcc tggggagaggccctgagcacgcccagccc 271 SMARCA4_Ex21 gggttcggatggggggagtcaggcctcaag ctgcctgccacgctgccggccttggacac 243 SMARCA4_Ex22 agcccaccccaccccaggagggcaagacc gagctgtcgaggagaagccagctctgcc 227 SMARCA4_Ex23 ggaccgcagcggggcccggtggcctgctc gcaataaagccaacaaaacgacagaaaac 189 SMARCA4_Ex24 cctgccttacctgcctgcagggttccagg gtgaggagcttctgtggcagccacaacaac 307 SMARCA4_Ex25 tccttggtgtccccactctacccctgagg ggccgtctcctcgaggttttgcaggcacc 299 SMARCA4_Ex26 cagaggccaccttcccttttatgacctcc gaaagccgctcacgcgtccaccattcacgc 357 SMARCA4_Ex27 aactgctggtgaaagacgccggattgacag ggcccttgctggccgtctcagccgagaag 233 SMARCA4_Ex28 gctcggccgccgcccaccccggcccctcc ctagggataccaccatgggcactaggacg 234 SMARCA4_Ex30 cggcctctgcttgtcgacctgggtgctgg gagtgcagatgccaggcctgctcccacgg 383 SMARCA4_Ex33 ggccgggcaggcagccctccagtcgggcc aaagctggggccttgggggctctcgggcc 262
Supplementary Table 18: Biotypes of GENCODE V14 genes associated with BL:FL DMRs and cDMRs.
DMR BL hypo
DMR BL hyper
cDMRs Q1
cDMRs Q2
cDMRs Q3
cDMRs Q4
3prime over- lapping ncrna
2 5
antisense 345 474 26 110 51 66 lincRNA 372 540 27 116 49 66 miRNA 60 48 misc RNA 33 18 non coding 2 1 polymorphic pseudogene
3 3
processed transcript
137 188 11 35 25 25
protein coding 3864 4729 438 1380 837 889 pseudogene 223 269 27 48 25 57 rRNA 4 4 sense intronic 32 22 2 2 5 sense overlapping 10 7 1 4 1 snoRNA 13 16 snRNA 18 10 IG/TR (pseudo) gene
25 25 19 2 3
Nature Genetics: doi:10.1038/ng.3413
0.00 0.25 0.50 0.75 1.00
0.00
0.25
0.50
0.75
1.00
methylation rate
beta
val
ue
Supplementary Figure 1: Correlation of WGBS methylation rates with Illumina HumanMethylation450K BeadChip beta rates. Calculated for n=26 sample pairs. Linear model resulted in mean adjusted R2 of 0.921.
3. Supplementary Figures
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 2: Examples of gene expression (rpm, reads per million) and methylation (rate) at genes with poised promoter chromatin segments. In contrast to the GC-B cells increased methylation is observed in the BL and FL at poised promoters. In addition, the expression of associated genes is activated in the lymphomas.
86,046,500 86,047,500 86,048,500 86,049,500Poised PromoterRepressed
chr1
Gm12878
1
0BL rate
1
0FL rate
1
0GC B rate
CYR61GENCODE
4.8
0GC B rpm
4.8
0FL rpm
BL rpm4.8
0
A
chr2
Gm12878
GENCODE IGFBP5
FL rate1
0
GC B rate0
1
BL rate0
1
BL rpm0
4
FL rpm0
4
GC B rpm0
4
217,545,000 217,555,000B Poised PromoterRepressedInsulatorHeterochromatin
C chr20
Gm12878
GENCODE MMP9
BL rate0
1
FL rate0
1
GC B rate0
1
BL rpm0
22
FL rpm0
22
GC B rpm0
22
44,639,000 44,643,000 Poised PromoterRepressedWeak EnhancerHeterochromatinStrong Enhancer
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 3: DNA methylation in gene regions varies dependent on gene expression levels. Highlyexpressed genes show the strongest loss of methylation at the TSS as compared to genes expressed at lowerlevels in all entities. Strong hypermethylation is seen in the gene bodies of highly expressed genes.
A
0.00
0.25
0.50
0.75
1.00
−15 0 10 20 30 40 50 60 70 80 90 100 115percent
met
hyla
tion
BLB
C D
0.00
0.25
0.50
0.75
1.00
−15 0 10 20 30 40 50 60 70 80 90 100 115percent
met
hyla
tion
FL
0.00
0.25
0.50
0.75
1.00
−15 0 10 20 30 40 50 60 70 80 90 100 115percent
met
hyla
tion
GCB
expr
low
medium
high
not
met
hyla
tion
0.00
0.25
0.50
0.75
1.00
−15 0 10 20 30 40 50 60 70 80 90 100 115percent
low
medium
high
not
BLFLGCB
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 4: Length and position distribution of DMRs/cDMRS. A) Density distribution of cDMRs in gene regions. Positively correlated DMRs (blue) have a peak density upstream of a TSS, while negatively correlating DMRs (red) are most frequently found just downstream of the TSS within the gene body. B) The length distribution of DMRs (blue line) and cDMRs (red line) are virtually identical. C) The length distribution of DMRs in the vicinity or inside genes (red) shows no apparent differences to inter-genic DMRs.
B
length [nt]
cDMRDMR
0 2500 5000 7500 10000
0.0000
0.0005
0.0010
0.0015
densi
ty
C
0.0000
0.0005
0.0010
0.0015
densi
ty
0 2500 5000 7500 10000length of DMR [nt]
gene associatedintergenic
A
neg. cor. cDMRpos. cor. cDMR
-5000 0 5000 10000 15000 20000distance to TSS [nt]
densi
ty
0
3*10-5
6*10-5
9*10-5
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 5: Examples of cDMRs downstream of the TSS. 85% of cDMRs are located downstream of the TSS. cDMRs downstream the TSS are for example located in the following genes: INSR (A), LEF1 (B), SMARCA4 (C) and TERT (D). Methylation heatmap on the left site (blue - low methylation, red - high methylation) and correlation plot for the cDMR on the right, showing methylation of the cDMR and expression of the associated gene.
105kb
chr19 7.140 7.180 7.220 7.260 [kb]
GC-B
BL
FL
A
cDMR
expression
methylation
INSR
0.00 0.25 0.50 0.75 1.00
0.0
0.1
0.2
0.3
0.4
125kb
chr4 108.980 109.000 190.020 190.040 [kb]
GC-B
BL
FL
190.060 190.080
B
cDMR
methylation0.00 0.25 0.50 0.75 1.00
expression
0.0
0.5
1.0
1.5
LEF1
105kb
chr19 11.080 11.100 11.120 11.140 [kb]
GC-B
BL
FL
11.160
C
cDMR
methylation0.00 0.25 0.50 0.75 1.00
expression
2
4
6
SMARCA4
45kb
chr5 1.260 1.270 1.280 1.290 [kb]
GC-B
BL
FL
cDMR
D
methylation0.00 0.25 0.50 0.75 1.00
expression
2
4
0
TERT
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 6: Examples of cDMR correlation at dark zone (A) and light zone (B) genes. Expression of dark zone genes is facilitated by cDMR hypomethylation in BL and suppressed by cDMR hypermethylation in FL, while the pattern is reversed for light zone specific genes.
A
Bexpression
methylation0.00 0.25 0.50 0.75 1.00
BLFLGC-B
5
10
15
20
NFKB2
expression
methylation0.00 0.25 0.50 0.75 1.00
BLFLGC-B
0
4
8
12
16
TNFAIP3
expression
methylation0.00 0.25 0.50 0.75 1.00
BLFLGC-B
2
4
6
SMARCA4
expression
methylation0.00 0.25 0.50 0.75 1.00
BLFLGC-B
10
20
TCF3
Nature Genetics: doi:10.1038/ng.3413
DDX58
ZBP1
TANK
IFIH1
IL10
TNF
LTA
CD40
IRF7
NFKB2
IRF9
Activation of IRF by Cytosolic
Pattern Recognition Receptors
PLCB1
PLCB4
PLA2G4A
PLA2R1
PLD1
PLA2G16
PLA2G4C
NFKB2
PLD6
GPLD1
PLCD1
RARRES3
STAT5B
PLA2G4B
PLD2
TNF
CSF2RB
PLCB2
STAT5A
Antioxidant Action
of Vitamin C
GRM2
VIPR1
TULP2
PDE9A
GRM3
ENPP6
CHRM3
RAPGEF4
OPRM1
NPY1R
PDE2A
PTH1R
LPAR1
P2RY14
PTGDR
PDE3A
PDE4A
GPER1
SSTR3
P2RY12
CNGB3
PTGER2
XCR1
ADRA2C
ADCY6
PTGIR
ADCY4
AKAP5
PTGER4
ADRA2A
CCR4
PRKAR2B
AKAP1
CNR1
STAT3
GABBR1
P2RY13
S1PR1
DUSP6
APEX1
cAMP mediated signaling
IL2
AKT3
PRKCQ
TRGV9
GRAP2
CD28
ITK
CD247
ITPR1
CD80
FYN
CD86
MALT1
NFATC4
PIK3CG
ATM
PIK3CD
LCP2
ZAP70
CD3G
CTLA4
CD4
CD3E
CD3D
LAT
PTPN6
PTPRC
NFKB2
CD28 Signaling in T Helper Cells
WNT16
NOS2
GNG4
CDH1
WNT1
WNT10B
FZD10
VEGFC
RND3
AKT3
MMP17
WNT3
SMO
GNG7
GNG13
PTGER2
ADCY6
IFNG
FZD8
GNG2
TLR6
ADCY4
CCND1
TGFBR2
PTGER4
FNBP1
TLR1
PIK3CG
LEF1
PRKAR2B
MSH6
GNB3
IFNGR1
TNF
JAK3
RHOF
JAK1
STAT3
ATM
PIK3CD
HRAS
BIRC5
NFKB2
TCF3
MYC
Colorectal Cancer Metastasis Signaling
MYT1
HDAC11
CDK6
CCND2
CCND1
PPP2R5B
ATM
CDKN2A
CCNE1
CDC25A
SKP2
SUV39H1
E2F1
CCNB2
E2F2
TFDP1
CDKN2C
CDK4
PA2G4
CDK1
CDK2
CCNA2
CCNB1
Cyclins and Cell Cycle Regulation
−2 0 2
Row Z−Score
−2 0 2
Row Z−Score
−4 −2 0 2 4
Row Z−Score
−4 −2 0 2 4
Row Z−Score
−2 0 2
Row Z−Score
−3 −1 1 3
Row Z−Score
Nature Genetics: doi:10.1038/ng.3413
PTGFR
DPEP1
PLA2G4A
PLA2R1
DPEP3
CYSLTR1
PTGDR
PTGES
GGT1
PTGER2
PLA2G16
PLA2G4C
RARRES3
CYSLTR2
LTC4S
ALOX12
PTGIR
DPEP2
PLA2G4B
PTGER4
PTGDS
−2 0 2
Row Z−Score
CCND1
E2F2
CCNE1
TFDP1
SKP2
CDC25A
E2F1
CDK1
CDK2
CCNA2
CDK4
MYC
−2 0 1 2
Row Z−Score
Eicosanoid Signaling Estrogen mediated S phase Entry
PPP2R2B
GRM3
GRM2
OPRM1
GNG4
LPAR1
P2RY14
NPY1R
SSTR3
P2RY12
GNG7
GNG13
PRKAR2B
OPN1SW
ADCY6
GNG2
RALB
ADCY4
ADRA2C
XCR1
ADRA2A
CCR4
GNB3
HRAS
S1PR1
CNR1
STAT3
GABBR1
P2RY13
−4 −2 0 2 4
Row Z−Score
IL25
IL12B
IL4
IL2
TNFRSF11B
SELE
IL12A
AKT3
RND3
HMGB1
HRAS
NFKB2
IFNG
CNTF
KAT2B
TNFRSF1B
FNBP1
PIK3CG
ATM
PIK3CD
TNF
LTA
VCAM1
RHOF
IFNGR1
−4 −2 0 2 4
Row Z−Score
Gai Signaling HMGB1 Signaling
IL2
AKT3
PRKCQ
TRGV9
GRAP2
CD40LG
TRAT1
ITK
IL2RA
ITPR1
CD80
ICOS
CD28
CD247
IL2RB
PIK3CG
ATM
PIK3CD
CD3G
LCP2
ZAP70
CD4
CD3E
CD3D
IL2RG
NFATC4
LAT
NFKB2
PTPRC
CD40
−4 −2 0 2 4
Row Z−Score
SOCS2
CISH
TNF
STAT5A
JAK3
PIK3CG
JAK1
STAT5B
STAT3
ATM
PIK3CD
NFKB2
IL2RG
−3 −1 1 2 3
Row Z−Score
iCOS iCOSL Signaling in T Helper Cells IL9 Signaling
Nature Genetics: doi:10.1038/ng.3413
AKT3
PRKCQ
PRKCA
PRKCH
ITGA6
CR2
CCR5
MAP3K14
PIK3CG
ATM
PIK3CD
CD4
ITGAL
ITGB2
HRAS
TNFRSF14
NFKB2
−2 0 1 2
Row Z−Score
NTRK2
TNFRSF11A
FGFR2
BMP2
TNFRSF11B
TRGV9
BMP4
FLT4
TNFSF11
IL1R2
TGFA
FGFR3
AKT3
PRKCQ
CD40LG
INSR
FGFR4
TNFSF13B
KDR
IL33
TNFRSF1B
ZAP70
TNF
LTA
MAP3K8
PIK3CG
ATM
PIK3CD
TLR6
CASP8
TGFBR2
TLR1
TANK
MAP3K14
MALT1
SIGIRR
TRADD
TNFAIP3
CD40
NFKB2
HRAS
TNFRSF17
−4 −2 0 2 4
Row Z−Score
PLA2G4A
IL1R2
IRAK2
HIST2H3C
HSPB2
MAP3K5
PLA2G4C
HIST1H3C
FASLG
IL33
TRADD
TNF
MEF2C
TNFRSF1B
FAS
PLA2G4B
MKNK1
TGFBR2
CDC25B
MYC
−2 0 2
Row Z−Score
PLCB1
MYL3
PLCB4
PLA2G4A
GNG4
PRKCA
PRKCQ
ARHGEF10
PLD1
RND3
TRGV9
HDAC11
PLA2G4C
ARHGEF3
GRAP2
PRKCH
LCP2
CD3G
ZAP70
PLCB2
RHOF
ITK
CD247
ADCY6
ITPR1
GNG2
GPLD1
GNG7
GNG13
PLD6
FCGR2A
ADCY4
FYN
RALB
PLA2G4B
PLD2
MEF2C
FNBP1
CD3E
CD3D
LAT
NFKB2
IGHG4
HRAS
GNB3
NFATC4
IGHG3
IGHG1
IGHG2
−4 −2 0 2 4
Row Z−Score
PLCB1
PLCB4
IL4
AKT3
VAV3
VAV2
ITPR1
PLCD1
FYN
CR2
SH2B2
MALT1
PIK3CG
PIK3CD
PLCB2
NFATC4
HRAS
IL4R
NFKB2
CD180
PTPRC
CD40
−4 −2 0 2 4
Row Z−Score
IL2
PRKCQ
TRGV9
VAV3
MAP3K5
GRAP2
VAV2
CD80
CD247
CD28
FYN
CD86
MAP3K14
MALT1
CD3G
LCP2
ZAP70
MAP3K8
PIK3CG
ATM
PIK3CD
RAC3
NFATC4
HRAS
CD4
CD3E
CD3D
LAT
NFKB2
−4 −2 0 2 4
Row Z−Score
NFKB Activation by Viruses NFKB Signaling
p38 MAPK Signaling Phospholipase C Signaling
PI3K Signaling in B Lymphocytes PKC Signaling in T Lymphocytes
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 7: Gene exrpession heatmaps
of genes enriched in IPA pathways (Supplementary
Table 10).
ALB
PPP2R2B
NOS2
IL4
PRKCA
IFNG
RND3
AKT3
PRKCQ
HOXA10
TNFRSF11B
PRKCH
MAP3K5
PPARA
CLU
TNFRSF1B
MAP3K14
PPP2R5B
PIK3CG
FNBP1
MAP3K8
TNF
JAK3
RHOF
JAK1
PPP1R3D
ATM
PIK3CD
IFNGR1
CYBB
PPP1R14B
NFKB2
PTPN6
−4 −2 0 2 4
Row Z−Score
−2 0 1 2
Row Z−Score
PLCB1
PLCB4
RCAN2
GNG4
AKT3
PRKCQ
TRGV9
CSNK1G1
GNG7
GNG13
ITK
CD247
GNA12
FYN
CD80
ITPR1
GNG2
CD28
CD3G
FCGR2A
MEF2C
PIK3CG
AKAP5
CD86
ORAI1
LCP2
ZAP70
PLCB2
ATM
PIK3CD
CD4
CD3E
CD3D
HRAS
GNB3
NFATC4
LAT
NFKB2
−2 0 2
Row Z−Score
PAK3
GNG4
TRGV9
RND3
PRKCQ
STAT4
PRKCA
VAV3
TXK
VAV2
PRKCH
ITK
IGHE
FGR
GNB3
NFKB2
PAK6
GNG7
GNG13
FASLG
HCK
GNG2
GNA12
FYN
FNBP1
PIK3CG
TNFRSF25
TNF
STAT5A
JAK3
TNFSF10
FAS
TNFSF12
RHOF
JAK1
STAT5B
STAT3
ATM
PIK3CD
−2 0 2
Row Z−Score
CCL7
NLRP14
NLRP6
AKT3
NOD2
CASP1
IL10
TLR6
CD86
TLR1
SIGIRR
NLRC5
ITGAX
STAT5A
STAT5B
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TNF
CIITA
LAT2
NFKB2
CD83
CD40
−4 −2 0 2 4
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Role of NFAT in Regulation
of the Immune Response
Role of BRCA1 in
DNA Damage Response
Production of Nitric Oxide and Reactive
Oxygen Species in Macrophages
TREM1 Signaling
BRIP1
RBBP8
FANCB
BRCA2
RFC3
BLM
CHEK2
SMARCA2
ATM
E2F2
RAD51
CHEK1
MSH6
RFC5
RFC2
FANCA
RFC4
FANCG
E2F1
SMARCA4
PLK1
Tec Kinase Signaling
IFNG
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 8: Gene expression heatmap of a subset genes enriched in IPA pathways (Supplementary Table 10). A) Genes specifically downregulated in FL are enriched in GO Terms related to cell cycle and DNA repair. Most frequent genes in GO Terms are dislayed. B) Genes specifically downregulated in BL are enriched in GO Terms related to the immune system and inflammatory and wounding response. Most frequent genes in GO Terms are dislayed.
A B
STAT5ASTAT5BPTPRCCD80CD86CD83TNFTLR6TLR3ITGALCYBBCR2CIITACD180
Suppressed in BL and DZ
−2 0 2Raw Z −Score
CDK2CDC25ABIRC5CCNB1CDK1CCNA2CHEK1BRIP1BLMMSH6BRCA2
Suppressed in FL and LZInflam
mati
on
Imm
un
ity
Cell
cycl
eD
NA
repair
BL FL GC-B
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 9: Integrative genome browser view of the IGF2BP1 locus. Top: chromatin segmentations
of GM12878, BL2, DG-75 and KARPAS-422 cell lines; middle: Average CpG methylation of BL, FL and GC-B
samples; bottom: Average RNA expression of BL, FL and GC-B samples.
chr17
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GENCODE
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RepressedPoised Promoter
Heterochromatin
Insulator
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IGF2BP1
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Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 10: NF-kappa-B (NFKB) signaling pathway (Ingenuity Pathway Analysis Canonical
Pathway). Shadings signify significantly different RNA expression in pBL vs. pFL (red: pBL>pFL; green:
pBL<pFL). Blue edges mark cDMR-associated genes pairs that show significantly negative correlation.
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 11: Cyclins and Cell Cycle Regulation (Ingenuity Pathway Analysis Canonical Pathway).
Shadings signify significantly different RNA expression in pBL vs. pFL (red: pBL>pFL; green: pBL<pFL). Blue edges
mark cDMR-associated genes pairs that show significantly negative correlation.
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 12: TCF3 binding site in TCF3 intron. TCF3 ChIP-Seq experiments [1] for the BL cell lines BL-41 (A) and NAMALWA (B). As compared to three control conditions (anti-Flag, anti-TCF3 and Biotag Control) coverage profiles peak inside cDMR (white background) for wild type TCF3 as well as TCF3 mutants (D561E, N551K, V557E) in BL-41 (A). In NAMALWA, only a weak TCF3 signal can be observed. C) TCF3/E2A binding motif annotated by Motifmap (motifmap.ics.uci.edu) using a 46way-Multiz alignment. (D) TCF3 TFBS motif overlaps with ChIP-Seq peak in BL-41. The region is annotated as active promoter in BL (DG-75, BL2), DLBCL (KARPAS-422) and lymphoblastoid (GM12878) cell lines. (E) Array experiments confirm a higher expression of TCF3 in BL cell lines (BL-41, DG-75, NAMALWA) as compared to non-BL cell lines KARPAS-422 and KARPAS-106 (MMML cohort). (F) The scatterplot confirms the negative relation between the expression of TCF3 (measured with RNASeq in RPM) and the cDMR methylation (measured with HumanMethylation450 BeadChip) from samples of the ICGC-MMML-Seq cohort. Note that the 450k array covers the cDMR with 2 CpG (chr19:1648682,chr19:1649123). [1] Schmitz et al., Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics. Nature. 490 (2012)
A
CACCTGCC100 bases
TCF3 binding site motifs
TCF3 peaks in BL41 and Namalwa
UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)
BL2 chromatin states
DG-75 chromatin states
KARPAS-422 chromatin states
AProAProAProTSS
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Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 13: Suppression of inhibitory G-protein-alpha-i signaling and activation of protein kinase A (PKA) signaling in BL. (A) Correlation of methylation and expression for PRKAR2B. (B) RNA expression of PRKAR2B in BL, FL and GC-B determined by microarray analysis in an extended lymphoma cohort. (C) Treatment of the Burkitt lymphoma cell line Ca46 with the protein kinase A inhibitor PKI-6-22 (IC50=2nM) [1] did not affect cell viability. Burkitt lymphoma cell lines BL-2 and BL-41 were not affected, either (data not shown).[1] Glass, D.B., Lundquist, L.J., Katz, B.M. & Walsh, D.A. Protein kinase inhibitor-(6-22)-amide peptide analogs with standard and nonstandard amino acid substitutions for phenylalanine 10. Inhibition of cAMP-dependent protein kinase. JBiol Chem. 264(1989)
methylation
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Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 14: Examples of methylation profiles at enriched TFBS in cDMRs. The regions 5k up-
and downstream of the annotated TFBS were used to analyze the methylation profile. We found the lowest
methylation rates located precisely in the middle of the TFBS. Up- and downstream of the TFBS the methylation
rates increase steeply and return to background levels.
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Nature Genetics: doi:10.1038/ng.3413
A B
C D
E F
Supplementary Figure 15: Analysis of KI-67 as a confounding factor. A) Correlation of the TF expression of TCF3, YY1, FOXM1, ZEB1, SMARCA4, STAT5A, PML, BATF and BCL3 with the proliferation status measured by Ki-67 staining. All available probesets for the TF genes were pooled and correlated with the proliferation marker (grey line). Apparently, no relevant correlation between TF expression and proliferation was observed (Spearman cor=0.02). Ki67 values were jittered to avoid overlaps as KI67 was measured in 5% steps. B) Correlation of target gene expression for the targets of TCF3, YY1, FOXM1, ZEB2, SMARCA4, STAT5A, PML, BATF and BCL3 with proliferation status measured by Ki-67 staining. All available probesets for these target genes were pooled and correlated with the proliferation marker (grey line). No apparent correlation was detected (Spearman cor=-0.043). KI67 values were jittered to avoid overlaps as KI67 was measured in 5% steps. C) Unsupervised clustering of TF expression with proliferation marker. Samples with a Ki-67 level of >= 80% are labeled with red blocks. No clusters of such proliferated samples are apparent when clustering for TF gene expression. D) Proliferation sorted heatmap of TF gene expression data. No apparent clusters that link proliferation and TF expression can be observed. E) Unsupervised clustering of TF target gene expression with proliferation marker. Samples with a Ki-67 level of >= 80% are labeled with red blocks. No clusters of such proliferated samples are apparent when clustering for TF target gene expression. F) Proliferation sorted heatmap of TF target gene expression data. No apparent clusters that link proliferation and TF expression can be observed.
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 16: SMARCA4 expression of an extended lymphoma cohort (Affymetrix hgu133a). mBL show a clear upregulation of SMARCA4 in comparison to DLBCL (ABC, GCB, unclassified), FL and healthy cells (tonsil, naive, GC, post GC).
12
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mBL(n=84)
ABC(n=106)
uncl(n=80)
GCB(n=176)
FL(n=144)
tonsil(n=10)
naive(n=8)
GC(n=10)
post GC(n=9)
gene
exp
ress
ion
[vsn
sca
le]
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 17: SMARCA4 and SMARCB1 immunohistochemistry.Left:1st upper panel: General view on two reactive follicles stained for SMARCA4 (= BRG1) (A) and at higher magnification on the germinal center and mantle zone (B); the conventional immunohistochemistry shows a strong nuclear staining in germinal center B-cells, whereas SMARCA4 expression is much less pronounced in the mantle zone and in the interfollicular space (reactive tonsil, original magnification 100x and 400x, respectively).2nd and 3rd panel: double immunofluorescence staining for SMARCA4 (AlexaFluor 555, red) (C), DNA (DAPI, blue) (D), and c-MYC (Alexa Fluor 488, green) (E); the merged picture (F) shows SMARCA4 and c-MYC double positive cells (yellow/orange) (reactive tonsil, original magnification 1000x).4th panel: General view on two reactive follicles stained for SMARCB1 (=INI1, BD clone 25/BAF47 Cat. No. #612111); the conventional immunohistochemistry shows nuclear staining with an ubiquitous distribution pattern throughout the reactive tissue (reactive tonsil, original magnification 100x (G) and 400x (H), respectively).Right:1st upper panel: Immunofluorescence staining of Burkitt lymphoma. Strong nuclear SMARCA4 staining of a BL case with SMARCA4 R973W; Infiltrated lymph node, original magnification (I) 100x and (J) 400x.2nd and 3rd panel: Immunohistochemistry stainings of (K) SMARCA4 (Alexa Fluor 555, red), (L) MYC (Alexa Fluor 488, green), (M) SMARCA4/MYC double staining showing double positive lymphoma cells and (N) DNA (DAPI, blue); abdominal tumor, original magnification 1000x.
A
C
D
G
B
E
F
H
I
K
M
J
L
N
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 18: DNA methylation patterns of lymphomas and in normal B-cell differentiation.A) Correlation of DNA methylation levels in GC-B-cells from Kulis et al. 2015 [1] and the current study at CpG resolution (blue=low density, orange=high density). B) Unsupervised principle component analysis (PCA) of microarray methylation data for all lymphoma and GC-B samples used in the current study and all normal B-cell samples of Kulis et al. 2015. PCA was performed as described in [1]. SHPCs, hematopoietic progenitor cells; preB1Cs, pre-BI cells; preB2Cs, pre-BII cells; iBCs, immature B cells; naiBCs, naive B cells from peripheral blood; t-naiBCs, naive B cells from tonsil; gcBCs, germinal center B cells; t-PCs, plasma cells from tonsil; memBCs, memory B cells from peripheral blood; bm-PCs, plasma cells from bone marrow. C) WGBS methylation differences of all CpGs of the modules defined by Kulis et al. 2015. Top: BL against GCB, center: BL against FL, bottom: FL against GCB. D) 450k methylation differences of all CpGs of the modules defined by Kulis et al. 2015. Top: BL against GCB, center: BL against FL, bottom: FL against GCB. E) Enrichment or depletion of cDMRs in the modules defined in Kulis et al. 2015 for the four quadrants defined in the cDMR analysis. All DMRs were used as background. [1] Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat Genet 47, 746-56 (2015).
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0.50
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1 2 3 4 5 6 7 8 9 1011121314151617181920
Met
hyla
tion
diffe
renc
e
BL-FL
FL-GC
B
Module
fold
enr
ichm
ent
A B
C D
E
HPCs
preB1Cs
-50 500 100
20
0
-20
-40
Principal component 1 (54.38%)
Prin
cipa
l com
pone
nt 2
(6.9
1%)
memBCs
bm-PCs
FLBL
HPCs
preB1Cs
preB2Cs
iBCs
naiBCs
t-naiBCs
t-PCs
gcBCs
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 19: Model of SMARCA4, TCF3 and ID3 interaction. A) In normal GC-B-cells TCF3 induces its own inhibitor ID3, creating an autoregulatory loop that results in modulate expression of target genes, e.g. SMARCA4. SMARCA4 is a component of the SWI/SNF complex, regulating the expression of target genes by its helicase function. B) In Burkitt lymphoma mutations (red bolt) affecting ID3, negative regulator of TCF3, foster TCF3 dependent gene expression. The same effect might also be caused by mutations of TCF3 (not recurrently mutated in this data set, grey bolt). Furthermore, the TCF3 gene harbours a hypomethylated cDMR for amplifying TCF3 transcription. High TCF3 expression results in higher expression of target genes. High SMARC4 expression and mutations in SMARCA4 ablate helicase function directly, through interference with ATP-binding or indirectly by obstructing the interaction of the helicase domains resulting in low expression of target genes.red and black arrows in different size indicating the protein level of expression, white lollipops in headlong direction indicating an unmethylated status.
TCF3
TCF3 ID3
ID3
target genes
target genes
TCF3cDMR 1
SWI/SNF
SMARCA4 ATP
SMARCA4
target genes
SMARCA4
SMARCA4
SWI/SNF
ATP
target genes
TCF3
A
B
cDMR 113/13 BL hypo-
methylated
13/13 BL hypo-methylated
1/13 BL 5/13 BL
6/13 BL
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 20: Landscape of the genes mutated in at least four of the lymphomas included in combined WGBS, WGS and transcriptome analyses. Upper heatmap shows the cytogenetic status of the samples. The barplot indicates the number of SNVs and indels per sample. The complete set of raw data are available from EGA and the complete mutational calls from the ICGC data portal (www.icgc.org).
BCL2 break
BCL6 break
0255075
100
DDX3X
RHOA
ID3
BCL2
SMARCA4
FBXO11
CREBBP
CCND3
TP53
MLL2
MYC
4142
267
4194
218
4177
856
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891
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998
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511
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434
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105
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726
4121
361
4177
376
positive negative
BL_leukemia BL FL FL/DLBCL
IG-MYC translocation
#S
NV
s, IN
DELs
Nature Genetics: doi:10.1038/ng.3413
A
Supplementary Figure 21: DNA methylation of 37 BL as determined by HumanMethylation450 BeadChip analysis. Only loci located in (A) DMR or (B) cDMR are included. Green boxes on top of the heatmap: juvenile donors (<18 years), black boxes on top of the heatmap: adult donors (>17 years), heatmap: blue: low methylation; yellow: high DNA methylation.
B
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 22: Analysis of tumor content and KI-67 as a confounding factor. A) Analysis of tumor content and its effects on average per-sample methylation and transcription. No apparent relationship between the average genome wide methylation rate and tumor content (left) nor the differential expression between tumor and healthy control (right) is observed. B) Analysis of the correlation of proliferation rate (as measured by Ki67 expression) and average methylation rate. No significant p-values were observed using spearman correlation (BL p-val = 0.33, FL p-val = 0.77, both p-val = 0.11).
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0.58 0.60 0.62 0.64 0.66 0.68 0.70
6065
7075
8085
9095
average methylation
tum
or c
onte
nt (k
ryo)
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0.75 0.80 0.85
● BL ● FLPearson's correlation coefficient
tumor expression ~ GC-B expression
A
B
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0.60
0.65
0.70
0 25 50 75 100
● BL● FL
proliferation rate
genom
e w
ide m
eth
yla
tion
Nature Genetics: doi:10.1038/ng.3413
Supplementary Figure 23: Analysis of DMR and cDMR homogeneity. Unsupervised hierarchical cluster analysis of HumanMethylation450 BeadChip data obtained from 37 BL and 52 FL samples of CpG loci located in (left) DMRs and (right) cDMR. Blue boxes on top of the heatmap indicate BL samples, grey boxes FL; blue: low, yellow: high DNA methylation.
Nature Genetics: doi:10.1038/ng.3413