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The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia

Nature Medicinevolume 24pages868880 (2018) | Download Citation


Chronic lymphocytic leukemia (CLL) is a frequent hematological neoplasm in which underlying epigenetic alterations are only partially understood. Here, we analyze the reference epigenome of seven primary CLLs and the regulatory chromatin landscape of 107 primary cases in the context of normal B cell differentiation. We identify that the CLL chromatin landscape is largely influenced by distinct dynamics during normal B cell maturation. Beyond this, we define extensive catalogues of regulatory elements de novo reprogrammed in CLL as a whole and in its major clinico-biological subtypes classified by IGHV somatic hypermutation levels. We uncover that IGHV-unmutated CLLs harbor more active and open chromatin than IGHV-mutated cases. Furthermore, we show that de novo active regions in CLL are enriched for NFAT, FOX and TCF/LEF transcription factor family binding sites. Although most genetic alterations are not associated with consistent epigenetic profiles, CLLs with MYD88 mutations and trisomy 12 show distinct chromatin configurations. Furthermore, we observe that non-coding mutations in IGHV-mutated CLLs are enriched in H3K27ac-associated regulatory elements outside accessible chromatin. Overall, this study provides an integrative portrait of the CLL epigenome, identifies extensive networks of altered regulatory elements and sheds light on the relationship between the genetic and epigenetic architecture of the disease.

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This work was funded by the European Union’s Seventh Framework Programme through the Blueprint Consortium (grant agreement 282510), the International Cancer Genome Consortium (Chronic Lymphocytic Leukemia Genome consortium to E.C. and C.L.-O.), the European Hematology Association (Non-Clinical Advanced Research Fellowship to J.I.M.-S.), the World Wide Cancer Research Foundation Grant No. 16-1285 (to J.I.M.-S.), the Spanish Ministerio de Economía y Competitividad (MINECO) Grant No. SAF2015-64885-R (to E.C.) and Grant No. PMP15/00007, part of Plan Nacional de I+D+I and co-financed by the ISCIII-Sub-Directorate General for Evaluation and the European Regional Development Fund (FEDER–“Una manera de hacer Europa”) (to E.C.), the Generalitat de Catalunya Suport Grups de Recerca AGAUR 2014-SGR-795 (to E.C.), the CERCA Programme/Generalitat de Catalunya and CIBERONC. R.B. was supported by fellowships from the EU (Marie Skłodowska-Curie Inter European Fellowship) and the Lady TATA Memorial Trust (International Award), N.R. by the Acció instrumental d’incorporació de científics i tecnòlegs PERIS 2016 from the Generalitat de Catalunya and M.Ku. by an AOI grant of the Spanish Association Against Cancer. E.C. is an Academia Researcher of the “Institució Catalana de Recerca i Estudis Avançats” (ICREA) of the Generalitat de Catalunya. This work was partially developed at the Centro Esther Koplowitz (CEK, Barcelona, Spain). We are indebted to the HCB-IDIBAPS Biobank-Tumor Bank and Hematopathology Collection for sample procurement.

Author information


  1. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

    • Renée Beekman
    • , Núria Russiñol
    • , Guillem Clot
    • , Ana C. Queirós
    • , Anna Enjuanes
    • , David Martín-García
    • , Sílvia Beà
    • , Magda Pinyol
    • , Elias Campo
    •  & Jose I. Martin-Subero
  2. Centro de Investigación Biomédica en Red de Cáncer, Universitat de Barcelona, Barcelona, Spain

    • Renée Beekman
    • , Guillem Clot
    • , Anna Enjuanes
    • , David Martín-García
    • , Sílvia Beà
    • , Magda Pinyol
    • , Marta Aymerich
    • , Xabier Agirre
    • , Felipe Prosper
    • , Tycho Baumann
    • , Julio Delgado
    • , Armando López-Guillermo
    • , Xose S. Puente
    • , Carlos López-Otín
    • , Elias Campo
    •  & Jose I. Martin-Subero
  3. Departament de Fonaments Clinics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain

    • Vicente Chapaprieta
    • , Roser Vilarrasa-Blasi
    • , Núria Verdaguer-Dot
    • , Martí Duran-Ferrer
    • , Elias Campo
    •  & Jose I. Martin-Subero
  4. Molecular Biology, NCMLS, FNWI, Radboud University, Nijmegen, The Netherlands

    • Joost H. A. Martens
    •  & Hendrik G. Stunnenberg
  5. Fundació Clínic per a la Recerca Biomèdica, Barcelona, Spain

    • Marta Kulis
    •  & Elias Campo
  6. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    • François Serra
    • , Julie Blanc
    • , Marta Gut
    • , Angelika Merkel
    • , Simon Heath
    • , Marc A. Martí-Renom
    •  & Ivo Gut
  7. Structural Genomics Group, CNAG-CRG, The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

    • François Serra
    •  & Marc A. Martí-Renom
  8. Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

    • François Serra
    •  & Marc A. Martí-Renom
  9. Nuclear Dynamics Program, Babraham Institute, Babraham Research Campus, Cambridge, UK

    • Biola M. Javierre
    • , Steven W. Wingett
    •  & Peter Fraser
  10. Core Biología Molecular, CDB, Hospital Clínic, Barcelona, Spain

    • Giancarlo Castellano
  11. CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

    • Julie Blanc
    • , Marta Gut
    • , Angelika Merkel
    • , Simon Heath
    •  & Ivo Gut
  12. Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology and UPF, Barcelona, Spain

    • Anna Vlasova
    • , Sebastian Ullrich
    • , Emilio Palumbo
    •  & Roderic Guigó
  13. Unitat de Hematología, Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain

    • Marta Aymerich
  14. Programa Conjunto de Biología Computacional, Barcelona Supercomputing Center (BSC), Institut de Recerca Biomèdica (IRB), Spanish National Bioinformatics Institute, Universitat de Barcelona, Barcelona, Spain

    • Romina Royo
    • , Montserrat Puiggros
    •  & David Torrents
  15. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    • David Torrents
    •  & Marc A. Martí-Renom
  16. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK

    • Avik Datta
    • , Ernesto Lowy
    • , Myrto Kostadima
    • , Maša Roller
    • , Laura Clarke
    •  & Paul Flicek
  17. Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, Pamplona, Spain

    • Xabier Agirre
    •  & Felipe Prosper
  18. Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain

    • Felipe Prosper
  19. Servicio de Hematología, Hospital Clínic, IDIBAPS, Barcelona, Spain

    • Tycho Baumann
    • , Julio Delgado
    •  & Armando López-Guillermo
  20. Department of Biological Science, Florida State University, Tallahassee, FL, USA

    • Peter Fraser
  21. Max Planck Institut for Molecular Genetics, Berlin, Germany

    • Marie-Laure Yaspo
  22. Institute of Human Genetics, University of Ulm and University Hospital of Ulm, Ulm, Germany

    • Reiner Siebert
  23. Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, Oviedo, Spain

    • Xose S. Puente
    •  & Carlos López-Otín
  24. Hematopathology Section, Hospital Clinic of Barcelona, Barcelona, Spain

    • Elias Campo


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The Chronic Lymphocytic Leukemia Genome consortium and the BLUEPRINT consortium contributed to this study respectively as part of the International Cancer Genome Consortium and the International Human Epigenome Consortium. Investigator contributions were as follows: T.B., J.D., A.L.-G., D.M.-G., S.B., M.P., M.A., M.Ku., N.V.-D., X.A. and F.P. contributed to sample collection (CLL and normal B cells) as well as to their biological and clinical annotation. M.P., N.V.-D., M.G. and I.G. contributed to WGS data generation. N.R., N.V.-D., J.H.A.M., H.G.S., J.I.M.-S., M.G., I.G. and M.-L.Y. contributed to histone mark, ATAC-seq, methylome and transcriptome data generation. R.V.-B., J.B., M.G., I.G., J.I.M.-S., B.M.J., P.Fr., N.V.-D., A.E., A.C.Q. and R.B. contributed to in situ HiC, promoter capture HiC and 4C-seq data generation. X.S.P. and C.L.-O. contributed to WGS data analysis. R.B., V.C., J.H.A.M., M.D.-F., M.Ku., G.Cl., G.Ca., A.M., S.H., A.V., S.U., E.P., R.G., R.R., M.P., D.T., A.D., E.L., M.Ko., M.R., L.C., P.Fl. and J.I.M.-S. contributed to histone mark, ATAC-seq, methylome and transcriptome data analysis. R.V.-B., F.S., M.A.M.-R., S.W.W., B.M.J., P.Fr. and R.B. contributed to in situ HiC, promoter capture HiC and 4C-seq data analysis. C.L.-O., E.C., H.G.S. and R.S. participated in the study design and data interpretation together with R.B. and J.I.M.-S. R.B. and J.I.M.-S. directed the research and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Jose I. Martin-Subero.

Supplementary information

  1. Supplementary Figures

    Supplementary Figures 1-19

  2. Reporting Summary

  3. Supplementary Table 1

    Patient characteristics

  4. Supplementary Table 2

    K-means clusters

  5. Supplementary Table 3

    Differential DNA methylation in CLL

  6. Supplementary Table 4

    Genes related to K-means clusters histone marks

  7. Supplementary Table 5

    Gene ontology terms

  8. Supplementary Table 6

    Chromatin states related to K-means clusters histone marks

  9. Supplementary Table 7

    De novo active and inactive regulatory elements in CLL

  10. Supplementary Table 8

    Enriched transcription factor motifs

  11. Supplementary Table 9

    Differentially active/accessible regions U-CLL and M-CLL

  12. Supplementary Table 10

    Overlap cell of origin signature DNA methylation and ATACseq

  13. Supplementary Table 11

    Patient groups somatic mutations and structural variants

  14. Supplementary Table 12

    Differential regions and target genes related to somatic genetic

  15. Supplementary Table 13

    Regions considered IG or SHM target loci in this study

  16. Supplementary Table 14

    Data quality measures

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