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Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia

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.

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Figure 1: EPM levels at diagnosis compared to normal bone marrow controls segregate patients into two groups with distinct clinical outcomes.
Figure 2: AML is characterized by high epiallele shift and variance.
Figure 3: Disease-stage-specific epiallele patterns define unique subsets of AML patients.
Figure 4: Assessment of epiallele shifts and genetic changes in serial samples from patient AML_130.
Figure 5: Transcriptional variance is associated with high epiallele shift at promoters.

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Grimwade, D. 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).

    Article  CAS  PubMed  Google Scholar 

  3. Döhner, H. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Landau, D.A., Carter, S.L., Getz, G. & Wu, C.J. Clonal evolution in hematological malignancies and therapeutic implications. Leukemia 28, 34–43 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. Testa, J.R., Mintz, U., Rowley, J.D., Vardiman, J.W. & Golomb, H.M. Evolution of karyotypes in acute nonlymphocytic leukemia. Cancer Res. 39, 3619–3627 (1979).

    CAS  PubMed  Google Scholar 

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

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

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

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mroz, E.A., Tward, A.D., Hammon, R.J., Ren, Y. & Rocco, J.W. Intratumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med. 12, e1001786 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Figueroa, M.E. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rampal, R. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mazor, T. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Greaves, M. & Maley, C.C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Feinberg, A.P., Koldobskiy, M.A. & Göndör, A. Epigenetic modulators, modifiers and mediators in cancer etiology and progression. Nat. Rev. Genet. 17, 284–299 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ong, C.T. & Corces, V.G. CTCF: an architectural protein bridging genome topology and function. Nat. Rev. Genet. 15, 234–246 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Nicholson, J.K., Hubbard, M. & Jones, B.M. 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).

    Article  CAS  PubMed  Google Scholar 

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

  51. Matthias, D., Roehr, J.T., Ahmed, R. & Dieterich, C. Flexbar—flexible barcode and adapter processing for next-generation sequencing platforms. Biology 1, 895–905 (2012).

    Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, J., Duncan, D., Shi, Z. & Zhang, B. WEB-based gene set analysis toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41, W77–W83 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Benjamini, Y. & Hochberg, Y. 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).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  66. Cingolani, P. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  69. Gröschel, S. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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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.).

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Authors

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.

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Correspondence to Ari M Melnick or Christopher E Mason.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Methods and Supplementary Figures 1–10 (PDF 5242 kb)

Supplementary Table 1

Summary of patient characteristics (XLS 36 kb)

Supplementary Table 2

Detailed description of patient characteristics and genomics assays performed (XLSX 74 kb)

Supplementary Table 3

Sequencing statistics from ERRBS (XLSX 81 kb)

Supplementary Table 4

Sequencing statistics from genomic sequencing (XLS 61 kb)

Supplementary Table 5

Patterns of EPM and genetic changes in epigenetic clusters 1 and 3 (XLSX 9 kb)

Supplementary Table 6

Somatic mutations in recurrently affected AML genes (XLSX 30 kb)

Supplementary Table 7

Sequencing statistics from RNA-seq (XLSX 61 kb)

Supplementary Table 8

Differentially expressed genes between epigenetic clusters 3 and 1 (XLSX 67 kb)

Supplementary Table 9

GO term enrichment analysis of differentially expressed genes between epigenetic clusters 1 and 3 (XLSX 38 kb)

Supplementary Table 10

Somatic mutations gained in AML_130 at first relapse time point (T2) (XLSX 42 kb)

Supplementary Table 11

Somatic mutations detected in AML_130 at time points T1–T5 (XLSX 57 kb)

Supplementary Table 12

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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