DNA methylation epitypes highlight underlying developmental and disease pathways in acute myeloid leukemia

  1. Christopher C. Oakes1,2,3
  1. 1Division of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio 43210, USA;
  2. 2The Ohio State University Comprehensive Cancer Center, Columbus, Ohio 43210, USA;
  3. 3Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA;
  4. 4Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon 97239, USA;
  5. 5Hematology and Oncology, Medical Faculty, University of Augsburg, 86159 Augsburg, Germany;
  6. 6Department of Medicine III, University Hospital, LMU Munich, 80539 Munich, Germany;
  7. 7Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Munich, Germany;
  8. 8German Cancer Consortium (DKTK), Partner Site Munich, 69120 Heidelberg, Germany;
  9. 9German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany;
  10. 10Department of Medicine II, Stem Cell Transplantation Unit, Klinikum Augsburg, Ludwig-Maximilians University Munich, 86156 Munich, Germany;
  11. 11Department of Hematology, Oncology and Tumorimmunology, Charité–Universitätsmedizin, 13353 Berlin, Germany
  • Corresponding author: Christopher.Oakes{at}osumc.edu
  • Abstract

    Acute myeloid leukemia (AML) is a molecularly complex disease characterized by heterogeneous tumor genetic profiles and involving numerous pathogenic mechanisms and pathways. Integration of molecular data types across multiple patient cohorts may advance current genetic approaches for improved subclassification and understanding of the biology of the disease. Here, we analyzed genome-wide DNA methylation in 649 AML patients using Illumina arrays and identified a configuration of 13 subtypes (termed “epitypes”) using unbiased clustering. Integration of genetic data revealed that most epitypes were associated with a certain recurrent mutation (or combination) in a majority of patients, yet other epitypes were largely independent. Epitypes showed developmental blockage at discrete stages of myeloid differentiation, revealing epitypes that retain arrested hematopoietic stem-cell-like phenotypes. Detailed analyses of DNA methylation patterns identified unique patterns of aberrant hyper- and hypomethylation among epitypes, with variable involvement of transcription factors influencing promoter, enhancer, and repressed regions. Patients in epitypes with stem-cell-like methylation features showed inferior overall survival along with up-regulated stem cell gene expression signatures. We further identified a DNA methylation signature involving STAT motifs associated with FLT3-ITD mutations. Finally, DNA methylation signatures were stable at relapse for the large majority of patients, and rare epitype switching accompanied loss of the dominant epitype mutations and reversion to stem-cell-like methylation patterns. These results show that DNA methylation-based classification integrates important molecular features of AML to reveal the diverse pathogenic and biological aspects of the disease.

    Footnotes

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.269233.120.

    • Freely available online through the Genome Research Open Access option.

    • Received July 23, 2020.
    • Accepted March 9, 2021.

    This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

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