Abstract

Genetic heterogeneity contributes to clinical outcome and progression of most tumors, but little is known about allelic diversity for epigenetic compartments, and almost no data exist for acute myeloid leukemia (AML). We examined epigenetic heterogeneity as assessed by cytosine methylation within defined genomic loci with four CpGs (epialleles), somatic mutations, and transcriptomes of AML patient samples at serial time points. We observed that epigenetic allele burden is linked to inferior outcome and varies considerably during disease progression. Epigenetic and genetic allelic burden and patterning followed different patterns and kinetics during disease progression. We observed a subset of AMLs with high epiallele and low somatic mutation burden at diagnosis, a subset with high somatic mutation and lower epiallele burdens at diagnosis, and a subset with a mixed profile, suggesting distinct modes of tumor heterogeneity. Genes linked to promoter-associated epiallele shifts during tumor progression showed increased single-cell transcriptional variance and differential expression, suggesting functional impact on gene regulation. Thus, genetic and epigenetic heterogeneity can occur with distinct kinetics likely to affect the biological and clinical features of tumors.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Current treatment of acute myeloid leukemia. Curr. Opin. Oncol. 24, 711–719 (2012).

  2. 2.

    et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5,876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood 116, 354–365 (2010).

  3. 3.

    et al. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood 115, 453–474 (2010).

  4. 4.

    et al. Chemotherapy-resistant human AML stem cells home to and engraft within the bone marrow endosteal region. Nat. Biotechnol. 25, 1315–1321 (2007).

  5. 5.

    et al. Clonal evolution in relapsed acute myeloid leukemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

  6. 6.

    et al. Leukemia-associated somatic mutations drive distinct patterns of age-related clonal hemopoiesis. Cell Rep. 10, 1239–1245 (2015).

  7. 7.

    et al. Tet2 loss leads to increased hematopoietic stem cell self-renewal and myeloid transformation. Cancer Cell 20, 11–24 (2011).

  8. 8.

    et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).

  9. 9.

    , , & Clonal evolution in hematological malignancies and therapeutic implications. Leukemia 28, 34–43 (2014).

  10. 10.

    et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 25, 379–392 (2014).

  11. 11.

    Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  12. 12.

    , , , & Evolution of karyotypes in acute nonlymphocytic leukemia. Cancer Res. 39, 3619–3627 (1979).

  13. 13.

    Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

  14. 14.

    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature 490, 61–70 (2012).

  15. 15.

    Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

  16. 16.

    et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

  17. 17.

    et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl. Acad. Sci. USA 110, 4009–4014 (2013).

  18. 18.

    et al. Genetic heterogeneity of diffuse large B cell lymphoma. Proc. Natl. Acad. Sci. USA 110, 1398–1403 (2013).

  19. 19.

    et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

  20. 20.

    , , , & Intratumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med. 12, e1001786 (2015).

  21. 21.

    et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

  22. 22.

    et al. Discovery and saturation analysis of cancer genes across 21 tumor types. Nature 505, 495–501 (2014).

  23. 23.

    et al. DNA methylation signatures identify biologically distinct subtypes in acute myeloid leukemia. Cancer Cell 17, 13–27 (2010).

  24. 24.

    et al. Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell 18, 553–567 (2010).

  25. 25.

    et al. DNA hydroxymethylation profiling reveals that WT1 mutations result in loss of TET2 function in acute myeloid leukemia. Cell Rep. 9, 1841–1855 (2014).

  26. 26.

    et al. Mutational cooperativity linked to combinatorial epigenetic gain of function in acute myeloid leukemia. Cancer Cell 27, 502–515 (2015).

  27. 27.

    et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012).

  28. 28.

    et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014).

  29. 29.

    et al. Epigenomic evolution in diffuse large B cell lymphomas. Nat. Commun. 6, 6921 (2015).

  30. 30.

    et al. Variability in DNA methylation defines novel epigenetic subgroups of DLBCL associated with different clinical outcomes. Blood 123, 1699–1708 (2014).

  31. 31.

    et al. Aberration in DNA methylation in B cell lymphomas has a complex origin and increases with disease severity. PLoS Genet. 9, e1003137 (2013).

  32. 32.

    et al. DNA methyltransferase 1 and DNA methylation patterning contribute to germinal center B cell differentiation. Blood 118, 3559–3569 (2011).

  33. 33.

    et al. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28, 307–317 (2015).

  34. 34.

    & Clonal evolution in cancer. Nature 481, 306–313 (2012).

  35. 35.

    , & Epigenetic modulators, modifiers and mediators in cancer etiology and progression. Nat. Rev. Genet. 17, 284–299 (2016).

  36. 36.

    et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).

  37. 37.

    et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).

  38. 38.

    et al. Base-pair resolution DNA methylation sequencing reveals profoundly divergent epigenetic landscapes in acute myeloid leukemia. PLoS Genet. 8, e1002781 (2012).

  39. 39.

    et al. Enhanced reduced representation bisulfite sequencing for assessment of DNA methylation at base-pair resolution. J. Vis. Exp. 96, e52246 (2015).

  40. 40.

    et al. Dynamic evolution of clonal epi-alleles revealed by methclone. Genome Biol. 15, 472 (2014).

  41. 41.

    et al. Clonal evolution and devolution after chemotherapy in adult acute myelogenous leukemia. Blood 121, 369–377 (2013).

  42. 42.

    et al. Clonal evolution in relapsed NPM1-mutated acute myeloid leukemia. Blood 122, 100–108 (2013).

  43. 43.

    et al. Disease evolution and outcomes in familial AML with germline CEBPA mutations. Blood 126, 1214–1223 (2015).

  44. 44.

    et al. The prognostic impact and stability of isocitrate dehydrogenase 2 mutation in adult patients with acute myeloid leukemia. Leukemia 25, 246–253 (2011).

  45. 45.

    et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N. Engl. J. Med. 366, 1079–1089 (2012).

  46. 46.

    et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput. Biol. 10, e1003665 (2014).

  47. 47.

    & CTCF: an architectural protein bridging genome topology and function. Nat. Rev. Genet. 15, 234–246 (2014).

  48. 48.

    et al. CTCF haploinsufficiency destabilizes DNA methylation and predisposes to cancer. Cell Rep. 7, 1020–1029 (2014).

  49. 49.

    , & Use of CD45 fluorescence and side-scatter characteristics for gating lymphocytes when using the whole-blood lysis procedure and flow cytometry. Cytometry 26, 16–21 (1996).

  50. 50.

    A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2012).

  51. 51.

    , , & Flexbar—flexible barcode and adapter processing for next-generation sequencing platforms. Biology 1, 895–905 (2012).

  52. 52.

    et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

  53. 53.

    et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493–D496 (2004).

  54. 54.

    et al. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  55. 55.

    et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  56. 56.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  57. 57.

    , , & WEB-based gene set analysis toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41, W77–W83 (2013).

  58. 58.

    & Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

  59. 59.

    & Fast and accurate short-read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  60. 60.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  61. 61.

    et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  62. 62.

    et al. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 11, 10.1 (2013).

  63. 63.

    et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  64. 64.

    et al. VarScan 2: somatic mutation and copy-number-alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  65. 65.

    et al. SomaticSniper: identification of somatic point mutations in whole-genome sequencing data. Bioinformatics 28, 311–317 (2012).

  66. 66.

    et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118;iso-2;iso-3. Fly (Austin) 6, 80–92 (2012).

  67. 67.

    et al. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet. 91, 597–607 (2012).

  68. 68.

    & A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007).

  69. 69.

    et al. Mutational spectrum of myeloid malignancies with inv(3)/t(3;3) reveals a predominant involvement of RAS/RTK signaling pathways. Blood 125, 133–139 (2015).

  70. 70.

    A statistical framework for SNP calling, mutation discovery, association mapping and population genetical-parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

  71. 71.

    , , , & Pindel: a pattern growth approach to detect break points of large deletions and medium-sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

  72. 72.

    et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

  73. 73.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  74. 74.

    et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

  75. 75.

    et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

  76. 76.

    A nonparametric test of independence. Ann. Math. Statist. 19, 546–557 (1948).

Download references

Acknowledgements

We thank J. Phillips, J. Ishii, L. Wang, J. Busuttil, T. Lee, P. Zumbo, J. Gandara, and A. Zeilemaker for technical support; C. Sheridan for technical support, assistance with organization, and maintenance of sample database and banking; M. Perugini, D. Iarossi, and I.S. Tiong for assistance with clinical database management; and Y. Neelamraju, Z. Li, J. Glass, and M.R. De Massy for data annotation and management. Next-generation sequencing protocols and sequencing were performed by the Weill Cornell Medicine Epigenomics Core and the New York Genome Center. We thank A. Viale from the Integrated Genomics Operation and N. Socci from the bioinformatics core at Memorial Sloan Kettering Cancer Center for sequencing services. We thank the South Australian Cancer Research Biobank for access to clinical samples. We thank F. Michor for recommendations regarding data analyses. This work was supported by Starr Cancer Consortium grant I4-A442 (A.M.M., R.L., and C.E.M.), STARR Cancer Consortium grant I7-A765 and I9-A9-071 (C.E.M.), the Irma T. Hirschl and Monique Weill-Caulier Charitable Trusts, Bert L. and N. Kuggie Vallee Foundation and the WorldQuant Foundation, Pershing Square Sohn Cancer Research Alliance, and NASA (NNX14AH50G) (C.E.M.); LLS SCOR 7006-13 (A.M.M.); NCI K08CA169055 (F.E.G.-B.); an American Society of Hematology (ASHAMFDP-20121) award under the ASH-AMFDP partnership with the Robert Wood Johnson Foundation and ASH/EHA TRTH (F.E.G.-B.); a Doris Duke Medical Foundation, Leukemia and Lymphoma Society Translational Research Program, and Geoffrey Beene Cancer Center (C.Y.P.); a Leukaemia and Lymphoma Research award (D.G. and R. Dillon); German Research Foundation (DFG) grant SFB 1074 (project B3; K.D. and L.B.); DFG Heisenberg-Stipendium BU 1339/3-1 (L.B.); an Australian National Health and Medical Research Council and the Royal Adelaide Hospital Contributing Haematologists Fund financial support (R.J.D., A.L.B., and I.D.L.); US National Institutes of Health R01CA102031 (G.J.R. and M.L.G.) and R01NS076465 (C.E.M. and A.M.M.); and Leukemia Fighters funding (G.J.R., M.L.G., and D.C.H.).

Author information

Author notes

    • Sheng Li

    Present address: Department of Neurosurgery, Weill Cornell Medicine, New York, New York, USA.

    • Sheng Li
    •  & Francine E Garrett-Bakelman

    These authors contributed equally to this work.

Affiliations

  1. Department of Physiology and Biophysics and the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA.

    • Sheng Li
    •  & Christopher E Mason
  2. Division of Hematology–Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York, USA.

    • Francine E Garrett-Bakelman
    • , Monica L Guzman
    • , Duane C Hassane
    • , Gail J Roboz
    •  & Ari M Melnick
  3. Leukemia Service, Department of Medicine, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Stephen S Chung
    • , Todd Hricik
    • , Franck Rapaport
    • , Jay Patel
    •  & Ross Levine
  4. Erasmus University Medical Center, Department of Hematology, Rotterdam, the Netherlands.

    • Mathijs A Sanders
    • , Ruud Delwel
    • , Bob Löwenberg
    •  & Peter J M Valk
  5. Department of Medical and Molecular Genetics, King's College London, Faculty of Life Sciences and Medicine, London, UK.

    • Richard Dillon
    •  & David Grimwade
  6. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, New York, USA.

    • Priyanka Vijay
  7. Center for Cancer Biology, SA Pathology and University of South Australia, Adelaide, South Australia, Australia.

    • Anna L Brown
    • , Ian D Lewis
    •  & Richard J D'Andrea
  8. School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia.

    • Anna L Brown
    •  & Richard J D'Andrea
  9. Department of Hematology, SA Pathology and Royal Adelaide Hospital, Adelaide, South Australia, Australia.

    • Anna L Brown
    • , Ian D Lewis
    • , Luen Bik To
    •  & Richard J D'Andrea
  10. Division of Hematology and Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Alexander E Perl
    • , Joy Cannon
    • , Selina Luger
    •  & Martin Carroll
  11. Department of Internal Medicine III, University Hospital of Ulm, Ulm, Germany.

    • Lars Bullinger
    • , Hartmut Döhner
    •  & Konstanze Döhner
  12. University of Rochester Medical Center, Rochester, New York, USA.

    • Michael Becker
  13. School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.

    • Ian D Lewis
    •  & Luen Bik To
  14. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Christopher Y Park
  15. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Christopher Y Park
  16. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Donna Neuberg
  17. The Feil Family Brain and Mind Research Institute, New York, New York, USA.

    • Christopher E Mason

Authors

  1. Search for Sheng Li in:

  2. Search for Francine E Garrett-Bakelman in:

  3. Search for Stephen S Chung in:

  4. Search for Mathijs A Sanders in:

  5. Search for Todd Hricik in:

  6. Search for Franck Rapaport in:

  7. Search for Jay Patel in:

  8. Search for Richard Dillon in:

  9. Search for Priyanka Vijay in:

  10. Search for Anna L Brown in:

  11. Search for Alexander E Perl in:

  12. Search for Joy Cannon in:

  13. Search for Lars Bullinger in:

  14. Search for Selina Luger in:

  15. Search for Michael Becker in:

  16. Search for Ian D Lewis in:

  17. Search for Luen Bik To in:

  18. Search for Ruud Delwel in:

  19. Search for Bob Löwenberg in:

  20. Search for Hartmut Döhner in:

  21. Search for Konstanze Döhner in:

  22. Search for Monica L Guzman in:

  23. Search for Duane C Hassane in:

  24. Search for Gail J Roboz in:

  25. Search for David Grimwade in:

  26. Search for Peter J M Valk in:

  27. Search for Richard J D'Andrea in:

  28. Search for Martin Carroll in:

  29. Search for Christopher Y Park in:

  30. Search for Donna Neuberg in:

  31. Search for Ross Levine in:

  32. Search for Ari M Melnick in:

  33. Search for Christopher E Mason in:

Contributions

A.M.M. and C.E.M. conceived of the studies, designed analytical approaches, analyzed results, wrote the manuscript, and designed experiments with F.E.G.-B. S. Li conceived of computational analyses, wrote code and performed computational analysis, wrote the manuscript, and generated figures. F.E.G.-B. performed, coordinated and/or supervised all patient sample experimental procedures, performed computational and bench experimental data management and analyses, and wrote manuscript. S.S.C. and R. Dillon performed experiments (flow cytometry and sorting of subject samples) and associated data analysis. T.H., F.R., and J.P. performed computational analyses. M.A.S. and P.J.M.V. performed experiments (exome capture) and associated computational analysis. A.L.B., A.E.P., J.C., L.B., S. Luger, M.B., I.D.L., L.B.T., B.L., H.D., K.D., P.J.M.V., R.J.D., and M.C. coordinated patient sample collection and analyzed clinical data. D.N. assisted with statistical analyses. P.V. performed single-cell RNA-seq library preparation. R. Delwel, M.L.G., D.C.H., G.J.R., D.G., C.Y.P., and R.L. helped with sample collection, writing, analysis, and patient annotation. All authors read, edited, and approved the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Ari M Melnick or Christopher E Mason.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Methods and Supplementary Figures 1–10

Excel files

  1. 1.

    Supplementary Table 1

    Summary of patient characteristics

  2. 2.

    Supplementary Table 2

    Detailed description of patient characteristics and genomics assays performed

  3. 3.

    Supplementary Table 3

    Sequencing statistics from ERRBS

  4. 4.

    Supplementary Table 4

    Sequencing statistics from genomic sequencing

  5. 5.

    Supplementary Table 5

    Patterns of EPM and genetic changes in epigenetic clusters 1 and 3

  6. 6.

    Supplementary Table 6

    Somatic mutations in recurrently affected AML genes

  7. 7.

    Supplementary Table 7

    Sequencing statistics from RNA-seq

  8. 8.

    Supplementary Table 8

    Differentially expressed genes between epigenetic clusters 3 and 1

  9. 9.

    Supplementary Table 9

    GO term enrichment analysis of differentially expressed genes between epigenetic clusters 1 and 3

  10. 10.

    Supplementary Table 10

    Somatic mutations gained in AML_130 at first relapse time point (T2)

  11. 11.

    Supplementary Table 11

    Somatic mutations detected in AML_130 at time points T1–T5

  12. 12.

    Supplementary Table 12

    Genes associated with eloci linked to clinical outcomes

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nm.4125

Further reading

Newsletter Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing