Abstract

In acute myeloid leukemia (AML), therapy resistance frequently occurs, leading to high mortality among patients. However, the mechanisms that render leukemic cells drug resistant remain largely undefined. Here, we identified loss of the histone methyltransferase EZH2 and subsequent reduction of histone H3K27 trimethylation as a novel pathway of acquired resistance to tyrosine kinase inhibitors (TKIs) and cytotoxic drugs in AML. Low EZH2 protein levels correlated with poor prognosis in AML patients. Suppression of EZH2 protein expression induced chemoresistance of AML cell lines and primary cells in vitro and in vivo. Low EZH2 levels resulted in derepression of HOX genes, and knockdown of HOXB7 and HOXA9 in the resistant cells was sufficient to improve sensitivity to TKIs and cytotoxic drugs. The endogenous loss of EZH2 expression in resistant cells and primary blasts from a subset of relapsed AML patients resulted from enhanced CDK1-dependent phosphorylation of EZH2 at Thr487. This interaction was stabilized by heat shock protein 90 (HSP90) and followed by proteasomal degradation of EZH2 in drug-resistant cells. Accordingly, inhibitors of HSP90, CDK1 and the proteasome prevented EZH2 degradation, decreased HOX gene expression and restored drug sensitivity. Finally, patients with reduced EZH2 levels at progression to standard therapy responded to the combination of bortezomib and cytarabine, concomitant with the re-establishment of EZH2 expression and blast clearance. These data suggest restoration of EZH2 protein as a viable approach to overcome treatment resistance in this AML patient population.

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Acknowledgements

We are grateful to J.H. Song and T.S. Kim (Korea University, Seoul, Republic of Korea) and to S. Chouaib (INSERM U1186, Gustave Roussy Cancer Campus, Villejuif, France) for providing cell lines. The authors thank D. Wendt-Cousin and A. Küttner for excellent technical assistance, and K. Agelopoulos and I. Schulze for assistance with exome library preparation and processing. We are grateful to the University of Münster core facilities IFG/IZKF for performing the Affymetrix gene expression arrays and the LIFA for Illumina sequencing of Exome libraries. We thank atelier42 for designing the graphical model of chemoresistance. S.G. and C.M.-T. hold the copyright for the graphic art. The Münster laboratory of W.E.B. was supported by the Deutsche Forschungsgemeinschaft, DFG EXC 1003 Cells in Motion–Cluster of Excellence. This work was further supported by grants from the Deutsche Forschungsgemeinschaft (SPP1463, MU1328/9-2 to C.M.-T.), the German Cancer Aid Foundation (111286 to C.M.-T.), the German José-Carreras Leukemia Foundation (DJCLS R 13/04 to C.M.-T.) and the state of Sachsen-Anhalt (FZK 28/43 to C.M.-T.).

Author information

Author notes

    • Tino Schenk
    • , Hans-Ulrich Klein
    • , Sigal Tavor
    •  & Gabriele Köhler

    Present addresses: Department of Medicine II, Hematology and Oncology, University Hospital of Jena, Jena, Germany (T.S.); Brigham and Women's Hospital, Harvard Medical School, Department of Neurology, Boston, Massachusetts, USA (H.-U.K.); Department of Hemato-Oncology, Assuta Medical Center, Tel-Aviv, Israel (S.T.); Institute of Pathology, Klinikum Fulda gAG, Fulda, Germany (G.K.).

    • Thomas Oellerich
    •  & Shuchi Agrawal-Singh

    These authors contributed equally to this work.

Affiliations

  1. Department of Medicine IV, Hematology and Oncology, University Hospital of Halle (Saale), Halle (Saale), Germany.

    • Stefanie Göllner
    • , Christian Rohde
    • , Caroline Pabst
    • , Lutz P Müller
    •  & Carsten Müller-Tidow
  2. Department of Medicine II, Hematology/Oncology, Goethe University, Frankfurt, Germany.

    • Thomas Oellerich
    •  & Hubert Serve
  3. German Cancer Consortium (DKTK), Heidelberg, Germany, and German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Thomas Oellerich
    • , Hubert Serve
    • , Karsten Spiekermann
    • , Binje Vick
    •  & Irmela Jeremias
  4. Biotech Research and Innovation Centre and Centre for Epigenetics, University of Copenhagen, Copenhagen, Denmark.

    • Shuchi Agrawal-Singh
    • , Mads Lerdrup
    •  & Klaus Hansen
  5. Institute of Cancer Research (ICR), Molecular Pathology, London, UK.

    • Tino Schenk
  6. Institute of Medical Informatics, University Hospital of Münster, Münster, Germany.

    • Hans-Ulrich Klein
    •  & Martin Dugas
  7. Department of Medicine A, Hematology and Oncology, University Hospital of Münster, Münster, Germany.

    • Tim Sauer
    •  & Wolfgang E Berdel
  8. Goldyne Savad Institute of Gene Therapy, Jerusalem, Israel.

    • Sigal Tavor
  9. Department of Medicine, Molecular Hematology, University Hospital Carl Gustav Carus, Dresden, Germany.

    • Friedrich Stölzel
    • , Sylvia Herold
    • , Gerhard Ehninger
    •  & Christian Thiede
  10. Gerhard Domagk Institute of Pathology, University of Münster, Münster, Germany.

    • Gabriele Köhler
  11. Bioanalytical Mass Spectrometry Group, Max Plank Institute for Biophysical Chemistry, Goettingen, Germany.

    • Kuan-Ting Pan
    •  & Henning Urlaub
  12. Bioanalytics, Institute for Clinical Chemistry, University Medical Center Göttingen, Germany.

    • Henning Urlaub
  13. Department of Medicine III, University Hospital of Munich, Munich, Germany.

    • Karsten Spiekermann
  14. Research Unit Gene Vectors, Helmholtz Center Munich, Munich, Germany.

    • Binje Vick
    •  & Irmela Jeremias
  15. Sylvester Comprehensive Cancer Center (UMHC), University of Miami Hospital and Clinics, Miami, Florida, USA.

    • Arthur Zelent
  16. Department of Pathology, University Hospital of Halle (Saale), Halle (Saale), Germany.

    • Claudia Wickenhauser

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Contributions

S.G., S.A.-S. and T. Schenk performed cell culture experiments, ChIP-seq, quantitative PCR, RNAi, western blotting, immunoprecipitations and flow cytometry; T. Sauer, S.T., G.K. and C.W. performed tissue microarray production, staining and analysis. F.S., G.E. and C.T. performed EZH2 mRNA expression analysis of AML patients and established the MV4-11R cell line. C.T., W.E.B., A.Z., L.P.M. and K.S. provided patient samples. H.-U.K., M.D., S.A.-S., K.H., C.R. and M.L. performed bioinformatic analysis of mRNA expression microarrays, ChIP-Seq and Exome-Seq data. T.O., H.S., K.-T.P. and H.U. performed and analyzed label free and SILAC-labeled mass spectrometry of EZH2. C.T. and S.H. performed diagnostic sequencing of primary AML samples. S.G., C.P., B.V. and I.J. performed and analyzed mouse experiments. All authors discussed the results and commented on the manuscript. S.G. and C.M.-T. designed the study, analyzed the data and wrote the paper. T.O. and S.A.-S. share second authorship of this paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Stefanie Göllner or Carsten Müller-Tidow.

Supplementary information

PDF files

  1. 1.

    Supplementary Figures and Tables

    Supplementary Figures 1–8, Supplementary Tables 1, 2, 5, 7, and 16

Excel files

  1. 1.

    Supplementary Table 3

    Mutations in matched diagnosis and relapse samples

  2. 2.

    Supplementary Table 4

    Clinical and cytogenetics data of patient samples used for in vitro DZNep treatments.

  3. 3.

    Supplementary Table 6

    Target genes determined by integrated analysis of gene expression and ChIP-seq

  4. 4.

    Supplementary Table 8

    Data sets and statistics of SILAC (pT487-EZH2) immunoprecipitation quantitative proteomics in MV4-11R EV cells

  5. 5.

    Supplementary Table 9

    Data sets and statistics of SILAC (EZH2) immunoprecipitation quantitative proteomics in MV4-11R EV cells

  6. 6.

    Supplementary Table 10

    Data sets and statistics of SILAC (pT487-EZH2) immunoprecipitation quantitative proteomics in MV4-11R T487A cells

  7. 7.

    Supplementary Table 11

    Data sets and statistics of SILAC (EZH2) immunoprecipitation quantitative proteomics in MV4-11R T487A cells

  8. 8.

    Supplementary Table 12

    Data sets and statistics of SILAC (EZH2) immunoprecipitation quantitative proteomics in MV4-11 cells

  9. 9.

    Supplementary Table 13

    Data sets and statistics of SILAC (EZH2) immunoprecipitation quantitative proteomics in MV4-11R cells

  10. 10.

    Supplementary Table 14

    Data sets and statistics of SILAC (EZH2) immunoprecipitation quantitative proteomics in MV4-11R cells + carfilzomib

  11. 11.

    Supplementary Table 15

    Clinical and cytogenetics data of patient samples used for in vitro bortezomib treatment studies

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https://doi.org/10.1038/nm.4247

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