Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Converging genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia identified by an information-theoretic analysis

Abstract

In cancer, linking epigenetic alterations to drivers of transformation has been difficult, in part because DNA methylation analyses must capture epigenetic variability, which is central to tumour heterogeneity and tumour plasticity. Here, by conducting a comprehensive analysis, based on information theory, of differences in methylation stochasticity in samples from patients with paediatric acute lymphoblastic leukaemia (ALL), we show that ALL epigenomes are stochastic and marked by increased methylation entropy at specific regulatory regions and genes. By integrating DNA methylation and single-cell gene-expression data, we arrived at a relationship between methylation entropy and gene-expression variability, and found that epigenetic changes in ALL converge on a shared set of genes that overlap with genetic drivers involved in chromosomal translocations across the disease spectrum. Our findings suggest that an epigenetically driven gene-regulation network, with UHRF1 (ubiquitin-like with PHD and RING finger domains 1) as a central node, links genetic drivers and epigenetic mediators in ALL.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Potential energy landscapes explain DNA methylation stochasticity in normal and cancer cells.
Fig. 2: Differential analysis localizes methylation discordance in ALL.
Fig. 3: DNA methylation stochasticity relates to gene expression in ETV6–RUNX1 ALL.
Fig. 4: Methylation discordance and four cytogenetic subtypes of ALL.
Fig. 5: UHRF1 is a target of epigenetic disruption in ALL.
Fig. 6: A plausible regulatory relationship between UHRF1 and in-frame translocation genes identified in ETV6–RUNX1 ALL.

Similar content being viewed by others

Data availability

DNA-methylation and RNA-seq data are available at the Gene Expression Omnibus repository under the accession number GSE116229.

References

  1. Mullighan, C. G. The molecular genetic makeup of acute lymphoblastic leukemia. Hematology Am. Soc. Hematol. Educ. Program 2012, 389–396 (2012).

    Article  PubMed  Google Scholar 

  2. Hunger, S. P. & Mullighan, C. G. Acute lymphoblastic leukemia in children. N. Engl. J. Med. 373, 1541–1552 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Grobner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).

    Article  PubMed  Google Scholar 

  4. Ma, X. et al. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat. Genet. 47, 746–756 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Figueroa, M.E. et al. Integrated genetic and epigenetic analysis of childhood acute lymphoblastic leukemia. J. Clin. Invest. 123, 3099–3111 (2013).

  7. Nordlund, J. et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 14, r105 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mullighan, C. G. et al. CREBBP mutations in relapsed acute lymphoblastic leukaemia. Nature 471, 235–239 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mar, B. G. et al. Mutations in epigenetic regulators including SETD2 are gained during relapse in paediatric acute lymphoblastic leukaemia. Nat. Commun. 5, 3469 (2014).

    Article  PubMed  Google Scholar 

  10. Milani, L. et al. DNA methylation for subtype classification and prediction of treatment outcome in patients with childhood acute lymphoblastic leukemia. Blood 115, 1214–1225 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Hogan, L.E. et al. Integrated genomic analysis of relapsed childhood acute lymphoblastic leukemia reveals therapeutic strategies. Blood 118, 5218–5226 (2011).

  12. Lee, S. T. et al. Epigenetic remodeling in B-cell acute lymphoblastic leukemia occurs in two tracks and employs embryonic stem cell-like signatures. Nucleic Acids Res. 43, 2590–2602 (2015).

  13. Wahlberg, P. et al. DNA methylome analysis of acute lymphoblastic leukemia cells reveals stochastic de novo DNA methylation in CpG islands. Epigenomics 8, 1367–1387 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Jenkinson, G., Pujadas, E., Goutsias, J. & Feinberg, A. P. Potential energy landscapes identify the information-theoretic nature of the epigenome. Nat. Genet. 49, 719–729 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jenkinson, G., Abante, J., Feinberg, A. P. & Goutsias, J. An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data. BMC Bioinformatics 19, 87 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jenkinson, G., Abante, J., Koldobskiy, M. A., Feinberg, A. P. & Goutsias, J. Ranking genomic features using an information-theoretic measure of epigenetic discordance. BMC Bioinformatics 20, 175 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Wang, F. et al. CellMethy: identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues. Sci. Rep. 5, 18037 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lilljebjorn, H. et al. Identification of ETV6-RUNX1-like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia. Nat. Commun. 7, 11790 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang, J. et al. Deregulation of DUX4 and ERG in acute lymphoblastic leukemia. Nat. Genet. 48, 1481–1489 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tsuzuki, S., Taguchi, O. & Seto, M. Promotion and maintenance of leukemia by ERG. Blood 117, 3858–3868 (2011).

  22. Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sharov, A. A. et al. Responsiveness of genes to manipulation of transcription factors in ES cells is associated with histone modifications and tissue specificity. BMC Genomics 12, 102 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Heerema, N. A. et al. Dicentric (9;20)(p11;q11) identified by fluorescence in situ hybridization in four pediatric acute lymphoblastic leukemia patients. Cancer Genet. Cytogenet. 92, 111–115 (1996).

    Article  CAS  PubMed  Google Scholar 

  27. Felice, M. S. et al. Prognostic impact of t(1;19)/TCF3–PBX1 in childhood acute lymphoblastic leukemia in the context of Berlin–Frankfurt–Munster-based protocols. Leuk. Lymphoma 52, 1215–1221 (2011).

    Article  PubMed  Google Scholar 

  28. Pui, C. H., Carroll, W. L., Meshinchi, S. & Arceci, R. J. Biology, risk stratification, and therapy of pediatric acute leukemias: an update. J. Clin. Oncol. 29, 551–565 (2011).

    Article  PubMed  Google Scholar 

  29. Bhojwani, D. et al. ETV6-RUNX1-positive childhood acute lymphoblastic leukemia: improved outcome with contemporary therapy. Leukemia 26, 265–270 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Paulsson, K. et al. The genomic landscape of high hyperdiploid childhood acute lymphoblastic leukemia. Nat. Genet. 47, 672–676 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Greaves, M. A causal mechanism for childhood acute lymphoblastic leukaemia. Nat. Rev. Cancer 18, 471–484 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. De Braekeleer, E. et al. Acute lymphoblastic leukemia associated with RCSD1–ABL1 novel fusion gene has a distinct gene expression profile from BCR–ABL1 fusion. Leukemia 27, 1422–1424 (2013).

    Article  PubMed  Google Scholar 

  33. Goyama, S. et al. UBASH3B/Sts-1–CBL axis regulates myeloid proliferation in human preleukemia induced by AML1–ETO. Leukemia 30, 728–739 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Wernicke, C. M. et al. MondoA is highly overexpressed in acute lymphoblastic leukemia cells and modulates their metabolism, differentiation and survival. Leuk. Res. 36, 1185–1192 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Zhang, R. et al. A possible 5′-NRIP1/UHRF1-3′ fusion gene detected by array CGH analysis in a Ph+ ALL patient. Cancer Genet. 204, 687–691 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Sidhu, H. & Capalash, N. UHRF1: the key regulator of epigenetics and molecular target for cancer therapeutics. Tumour Biol. https://doi.org/10.1177/1010428317692205 (2017).

  37. Ashraf, W. et al. The epigenetic integrator UHRF1: on the road to become a universal biomarker for cancer. Oncotarget 8, 51946–51962 (2017).

  38. Chow, M. et al. Maintenance and pharmacologic targeting of ROR1 protein levels via UHRF1 in t(1;19) pre-B-ALL. Oncogene 37, 5221–5232 (2018).

  39. Gibcus, J. H. & Dekker, J. The hierarchy of the 3D genome. Mol. Cell 49, 773–782 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chow, M. L., Kim, D., Kamath, S., Peng, D. & Luu, M. Use of antiviral medications in drug reaction with eosinophilia and systemic symptoms (DRESS): a case of infantile DRESS. Pediatr. Dermatol. 35, e114–e116 (2018).

    Article  PubMed  Google Scholar 

  41. Yan, F. et al. Inhibition effect of siRNA-downregulated UHRF1 on breast cancer growth. Cancer Biother. Radiopharm. 26, 183–189 (2011).

    CAS  PubMed  Google Scholar 

  42. Yan, F., Wang, X., Shao, L., Ge, M. & Hu, X. Analysis of UHRF1 expression in human ovarian cancer tissues and its regulation in cancer cell growth. Tumour Biol. 36, 8887–8893 (2015).

  43. Ge, T. T., Yang, M., Chen, Z., Lou, G. & Gu, T. UHRF1 gene silencing inhibits cell proliferation and promotes cell apoptosis in human cervical squamous cell carcinoma CaSki cells. J. Ovarian Res. 9, 42 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Iacobucci, I. & Mullighan, C. G. Genetic basis of acute lymphoblastic leukemia. J. Clin. Oncol. 35, 975–983 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lilljebjorn, H. & Fioretos, T. New oncogenic subtypes in pediatric B-cell precursor acute lymphoblastic leukemia. Blood 130, 1395–1401 (2017).

    Article  PubMed  Google Scholar 

  46. Reddy, K. L. & Feinberg, A. P. Higher order chromatin organization in cancer. Semin. Cancer Biol. 23, 109–115 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Shen, H. & Laird, P. W. Interplay between the cancer genome and epigenome. Cell 153, 38–55 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Timp, W. et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 6, 61 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Duran-Ferrer, M. et al. The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome. Nat. Cancer 1, 1066–1081 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Pujadas, E. & Feinberg, A. P. Regulated noise in the epigenetic landscape of development and disease. Cell 148, 1123–1131 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zheng, S. C., Widschwendter, M. & Teschendorff, A. E. Epigenetic drift, epigenetic clocks and cancer risk. Epigenomics 8, 705–719 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. 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 

  54. Li, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat. Med. 22, 792–799 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. Chan, T. E., Stumpf, M. P. H. & Babtie, A. C. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst. 5, 251–267.e3 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Giovinazzo, H. et al. A high-throughput screen of pharmacologically active compounds for inhibitors of UHRF1 reveals epigenetic activity of anthracycline derivative chemotherapeutic drugs. Oncotarget 10, 3040 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Lefebvre, J. L., Kostadinov, D., Chen, W. V., Maniatis, T. & Sanes, J. R. Protocadherins mediate dendritic self-avoidance in the mammalian nervous system. Nature 488, 517–521 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. El Hajj, N., Dittrich, M. & Haaf, T. Epigenetic dysregulation of protocadherins in human disease. Semin. Cell Dev. Biol. 69, 172–182 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Dias, S., Mansson, R., Gurbuxani, S., Sigvardsson, M. & Kee, B. L. E2A proteins promote development of lymphoid-primed multipotent progenitors. Immunity 29, 217–227 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Semerad, C. L., Mercer, E. M., Inlay, M. A., Weissman, I. L. & Murre, C. E2A proteins maintain the hematopoietic stem cell pool and promote the maturation of myelolymphoid and myeloerythroid progenitors. Proc. Natl Acad. Sci. USA 106, 1930–1935 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hunger, S. P. et al. The t(1;19)(q23;p13) results in consistent fusion of E2A and PBX1 coding sequences in acute lymphoblastic leukemias. Blood 77, 687–693 (1991).

    Article  CAS  PubMed  Google Scholar 

  63. Inaba, T. et al. Fusion of the leucine zipper gene HLF to the E2A gene in human acute B-lineage leukemia. Science 257, 531–534 (1992).

    Article  CAS  PubMed  Google Scholar 

  64. Wu, H., Caffo, B., Jaffee, H. A., Irizarry, R. A. & Feinberg, A. P. Redefining CpG islands using hidden Markov models. Biostatistics 11, 499–514 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–2492 (2017).

  66. Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  68. Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes. The Art of Scientific Computing (Cambridge Univ. Press, 2007).

  69. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  70. Stasinopoulos, D. M. & Rigby, R. A. Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23, 1–46 (2007).

    Article  Google Scholar 

  71. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  75. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Guo, G. et al. Serum-based culture conditions provoke gene expression variability in mouse embryonic stem cells as revealed by single-cell analysis. Cell Rep. 14, 956–965 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Jiao, Y., Widschwendter, M. & Teschendorff, A. E. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics 30, 2360–2366 (2014).

  78. Fisher, R. A Statistical Methods, Experimental Design, and Statistical Inference (Oxford Univ. Press, 1990).

Download references

Acknowledgements

This work was supported by US National Institutes of Health grants R01 CA65438 and DP1 DK119129 to A.P.F., R01 HG006282 to H.J., US National Science Foundation grant CCF-1656201 to J.G., St. Baldrick’s Foundation fellowship and funding from Unravel Pediatric Cancer to M.A.K. M.A.K. is a Damon Runyon–Sohn Pediatric Cancer Fellow supported by the Damon Runyon Cancer Research Foundation (DRSG-15P-16). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

A.P.F. and M.A.K. designed the study and led the biological experiments. A.P.F. and J.G. supervised all aspects of the research. C.L.B., K.R.R. and P.A.B. provided the primary patient material and disease-specific expertise. A.I., C.C. and R.T. performed library preparation and sequencing. V.A.R.D. performed the single-cell RNA-seq experiments. E.P. performed WGBS quality control, preprocessing and bisulfite alignment. H.J. and W.Z. performed statistical analysis of bulk and single-cell RNA-seq data. G.J., J.A. and J.G. developed the data analysis methods. G.J. and J.A. implemented the data analysis methods. A.P.F., G.J., J.G. and M.A.K. analysed the data and wrote the manuscript.

Corresponding authors

Correspondence to John Goutsias or Andrew P. Feinberg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Biomedical Engineering thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary figures and table captions.

Reporting Summary

Supplementary Tables

WGBS samples, statistics and clinical features, ranked lists of genes, bulk RNA-seq results, single-cell RNA-seq data, gene regulatory-network modules, median JSD of comparisons, and more.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koldobskiy, M.A., Jenkinson, G., Abante, J. et al. Converging genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia identified by an information-theoretic analysis. Nat Biomed Eng 5, 360–376 (2021). https://doi.org/10.1038/s41551-021-00703-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-021-00703-2

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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