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.
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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.).
The authors declare no competing financial interests.
Supplementary Methods and Supplementary Figures 1–10 (PDF 5242 kb)
Summary of patient characteristics (XLS 36 kb)
Detailed description of patient characteristics and genomics assays performed (XLSX 74 kb)
Sequencing statistics from ERRBS (XLSX 81 kb)
Sequencing statistics from genomic sequencing (XLS 61 kb)
Patterns of EPM and genetic changes in epigenetic clusters 1 and 3 (XLSX 9 kb)
Somatic mutations in recurrently affected AML genes (XLSX 30 kb)
Sequencing statistics from RNA-seq (XLSX 61 kb)
Differentially expressed genes between epigenetic clusters 3 and 1 (XLSX 67 kb)
GO term enrichment analysis of differentially expressed genes between epigenetic clusters 1 and 3 (XLSX 38 kb)
Somatic mutations gained in AML_130 at first relapse time point (T2) (XLSX 42 kb)
Somatic mutations detected in AML_130 at time points T1–T5 (XLSX 57 kb)
Genes associated with eloci linked to clinical outcomes (XLSX 52 kb)
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Li, S., Garrett-Bakelman, F., Chung, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat Med 22, 792–799 (2016). https://doi.org/10.1038/nm.4125
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