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EZH2 oncogenic mutations drive epigenetic, transcriptional, and structural changes within chromatin domains

Nature Genetics volume 51, pages 517–528 (2019)Cite this article

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Abstract

Chromatin is organized into topologically associating domains (TADs) enriched in distinct histone marks. In cancer, gain-of-function mutations in the gene encoding the enhancer of zeste homolog 2 protein (EZH2) lead to a genome-wide increase in histone-3 Lys27 trimethylation (H3K27me3) associated with transcriptional repression. However, the effects of these epigenetic changes on the structure and function of chromatin domains have not been explored. Here, we found a functional interplay between TADs and epigenetic and transcriptional changes mediated by mutated EZH2. Altered EZH2 (p.Tyr646* (EZH2Y646X)) led to silencing of entire domains, synergistically inactivating multiple tumor suppressors. Intra-TAD gene silencing was coupled with changes of interactions between gene promoter regions. Notably, gene expression and chromatin interactions were restored by pharmacological inhibition of EZH2Y646X. Our results indicate that EZH2Y646X alters the topology and function of chromatin domains to promote synergistic oncogenic programs.

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Fig. 1: Comparison of the genome 3D organization in EZH2WT and EZH2Y646X cells.
Fig. 2: Epigenetic and transcriptional changes in EZH2Y646X cells occur within TADs.
Fig. 3: Inactivation of the tumor-suppressive TAD comprising FOXO3 and SESN1.
Fig. 4: Concurrent downregulation of genes within tumor-suppressive TADs accelerates B-cell proliferation and lymphoma progression.
Fig. 5: Inhibition of EZH2 abrogates H3K27me3 and reactivates inactive TADs.
Fig. 6: Intra-TAD structural changes in EZH2WT and EZH2Y646X cells.

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Data availability

In this study, we used the following mRNA expression datasets: GSE23501 for wild-type and EZH2-mutated GCB-DLBCL primary human samples, PRJNA278311 (NCBI-BioProject) for wild-type and EZH2-mutated FL primary human samples, GSE40792 for wild-type and EZH2-mutated cell lines before and after treatment with GSK126, GSE49284 for EZH2-mutated cell lines before and after treatment with EPZ6438, and GSE12195 for centrocytes, centroblasts, and memory B cells. ChIP-seq data for H3K27me3 in OCI-Ly7, DOHH-2, and Karpas-422 were downloaded from ENCODE; H3K27me3 in WSU-DLCL2 was downloaded from GSE40970; H3K4me3 in OCI-Ly7 and Karpas-422 were downloaded from ENCODE. ChIP-seq data for H3K27me3 and RNA-seq data for OCI-Ly19 and OCI-Ly19-EZH2Y646F were generated as described in the manuscript and have been deposited at GSE114270. HiC matrices and UMI-4C data have been deposited at Zenodo: https://doi.org/10.5281/zenodo.1244182. Custom scripts are available through a public GitHub repository at: https://github.com/CSOgroup/Donaldson-et-al-scripts/.

References

  1. Bonev, B. & Cavalli, G. Organization and function of the 3D genome. Nat. Rev. Genet. 17, 661–678 (2016).

    Article  CAS  Google Scholar 

  2. Dixon, J. R., Gorkin, D. U. & Ren, B. Chromatin domains: the unit of chromosome organization. Mol. Cell 62, 668–680 (2016).

    Article  CAS  Google Scholar 

  3. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  Google Scholar 

  4. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  Google Scholar 

  5. Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

    Article  CAS  Google Scholar 

  6. Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    Article  CAS  Google Scholar 

  7. Le Dily, F. et al. Distinct structural transitions of chromatin topological domains correlate with coordinated hormone-induced gene regulation. Genes Dev. 28, 2151–2162 (2014).

    Article  Google Scholar 

  8. de Laat, W. & Duboule, D. Topology of mammalian developmental enhancers and their regulatory landscapes. Nature 502, 499–506 (2013).

    Article  Google Scholar 

  9. Fraser, J. et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol. Syst. Biol. 11, 852 (2015).

    Article  Google Scholar 

  10. Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods.Science 351, 1454–1458 (2016).

    Article  CAS  Google Scholar 

  11. Weischenfeldt, J. et al. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat. Genet. 49, 65–74 (2017).

    Article  CAS  Google Scholar 

  12. Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016).

    Article  CAS  Google Scholar 

  13. Taberlay, P. C. et al. Three-dimensional disorganisation of the cancer genome occurs coincident with long range genetic and epigenetic alterations. Genome Res. 26, 719–731 (2016).

    Article  CAS  Google Scholar 

  14. Plass, C. et al. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat. Rev. Genet. 14, 765–780 (2013).

    Article  CAS  Google Scholar 

  15. Morin, R. D. et al. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nat. Genet. 42, 181–185 (2010).

    Article  CAS  Google Scholar 

  16. Souroullas, G. P. et al. An oncogenic Ezh2 mutation induces tumors through global redistribution of histone 3 lysine 27 trimethylation. Nat. Med. 22, 632–640 (2016).

    Article  CAS  Google Scholar 

  17. Tirode, F. et al. Genomic landscape of Ewing sarcoma defines an aggressive subtype with co-association of STAG2 and TP53 mutations. Cancer Discov. 4, 1342–1353 (2014).

    Article  CAS  Google Scholar 

  18. Comet, I., Riising, E. M., Leblanc, B. & Helin, K. Maintaining cell identity: PRC2-mediated regulation of transcription and cancer. Nat. Rev. Cancer 16, 803–810 (2016).

    Article  CAS  Google Scholar 

  19. Sneeringer, C. J. et al. Coordinated activities of wild-type plus mutant EZH2 drive tumor-associated hypertrimethylation of lysine 27 on histone H3 (H3K27) in human B-cell lymphomas. Proc. Natl Acad. Sci. USA 107, 20980–20985 (2010).

    Article  CAS  Google Scholar 

  20. Yap, D. B. et al. Somatic mutations at EZH2 Y641 act dominantly through a mechanism of selectively altered PRC2 catalytic activity, to increase H3K27 trimethylation. Blood 117, 2451–2459 (2011).

    Article  CAS  Google Scholar 

  21. Béguelin, W. et al. EZH2 is required for germinal center formation and somatic EZH2 mutations promote lymphoid transformation. Cancer Cell 23, 677–692 (2013).

    Article  Google Scholar 

  22. Oricchio, E. et al. Genetic and epigenetic inactivation of SESTRIN1 controls mTORC1 and response toEZH2 inhibition in follicular lymphoma. Sci. Transl. Med. 9, eaak9969 (2017).

    Article  Google Scholar 

  23. Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  Google Scholar 

  24. Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).

    Article  CAS  Google Scholar 

  25. Pfitzner, D., Leibbrandt, R. & Powers, D. Characterization and evaluation of similarity measures for pairs of clusterings. Knowl. Inf. Syst. 19, 361 (2009).

    Article  Google Scholar 

  26. Shin, H. et al. TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 44, e70 (2016).

    Article  Google Scholar 

  27. Zufferey, M., Tavernari, D., Oricchio, E. & Ciriello, G. Comparison of computational methods for the identification of topologically associating domains. Genome. Biol. 19, 217 (2018).

    Article  CAS  Google Scholar 

  28. Carty, M. et al. An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data. Nat. Commun. 8, 15454 (2017).

    Article  CAS  Google Scholar 

  29. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    Article  CAS  Google Scholar 

  30. McCabe, M. T. et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature 492, 108–112 (2012).

    Article  CAS  Google Scholar 

  31. Ortega-Molina, A. et al. The histone lysine methyltransferase KMT2D sustains a gene expression program that represses B cell lymphoma development. Nat. Med. 21, 1199–1208 (2015).

    Article  CAS  Google Scholar 

  32. Nuytten, M. et al. The transcriptional repressor NIPP1 is an essential player in EZH2-mediated gene silencing. Oncogene 27, 1449–1460 (2008).

    Article  CAS  Google Scholar 

  33. Klein, U. et al. Transcriptional analysis of the B cell germinal center reaction. Proc. Natl Acad. Sci. 100, 2639–2644 (2003).

    Article  CAS  Google Scholar 

  34. Yang, C.-S. et al. Ubiquitin modification by the E3 ligase/ADP-ribosyltransferase Dtx3L/Parp9. Mol. Cell 66, 503–516.e5 (2017).

    Article  CAS  Google Scholar 

  35. Matloubian, M. et al. Lymphocyte egress from thymus and peripheral lymphoid organs is dependent on S1P receptor 1. Nature 427, 355–360 (2004).

    Article  CAS  Google Scholar 

  36. Dansen, T. B. & Burgering, B. M. T. Unravelling the tumor-suppressive functions of FOXO proteins. Trends. Cell Biol. 18, 421–429 (2008).

    Article  CAS  Google Scholar 

  37. Okosun, J. et al. Integrated genomic analysis identifies recurrent mutations and evolution patterns driving the initiation and progression of follicular lymphoma. Nat. Genet. 46, 176–181 (2014).

    Article  CAS  Google Scholar 

  38. Oricchio, E. et al. The Eph-receptor A7 is a soluble tumor suppressor for follicular lymphoma. Cell 147, 554–564 (2011).

    Article  CAS  Google Scholar 

  39. Chen, B. B., Glasser, J. R., Coon, T. A. & Mallampalli, R. K. F-box protein FBXL2 exerts human lung tumor suppressor-like activity by ubiquitin-mediated degradation of cyclin D3 resulting in cell cycle arrest. Oncogene 31, 2566–2579 (2012).

    Article  CAS  Google Scholar 

  40. Li, L., Pan, D., Chen, H., Zhang, L. & Xie, W. F-box protein FBXL2 inhibits gastric cancer proliferation by ubiquitin-mediated degradation of forkhead box M1. FEBS Lett. 590, 445–452 (2016).

    Article  CAS  Google Scholar 

  41. Hatzimichael, E. et al. The collagen prolyl hydroxylases are novel transcriptionally silenced genes in lymphoma. Br. J. Cancer 107, 1423–1432 (2012).

    Article  CAS  Google Scholar 

  42. 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  Google Scholar 

  43. Oricchio, E. et al. Frequent disruption of the RB pathway in indolent follicular lymphoma suggests a new combination therapy. J. Exp. Med. 211, 1379–1391 (2014).

    Article  CAS  Google Scholar 

  44. Mavrakis, K. J. et al. Genome-wide RNA-mediated interference screen identifies miR-19 targets in Notch-induced T-cell acute lymphoblastic leukaemia. Nat. Cell Biol. 12, 372–379 (2010).

    Article  CAS  Google Scholar 

  45. Scuoppo, C. et al. A tumour suppressor network relying on the polyamine-hypusine axis. Nature 487, 244–248 (2012).

    Article  CAS  Google Scholar 

  46. Schatz, J. H. et al. Targeting cap-dependent translation blocks converging survival signals by AKT and PIM kinases in lymphoma. J. Exp. Med. 208, 1799–1807 (2011).

    Article  CAS  Google Scholar 

  47. Lupiáñez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).

    Article  Google Scholar 

  48. Dao, L. T. M. et al. Genome-wide characterization of mammalian promoters with distal enhancer functions. Nat. Genet. 49, 1073–1081 (2017).

    Article  CAS  Google Scholar 

  49. Boettiger, A. N. et al. Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature 529, 418–422 (2016).

    Article  CAS  Google Scholar 

  50. Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–796 (2006).

    Article  CAS  Google Scholar 

  51. Budanov, A. V. & Karin, M. The p53-regulated Sestrin gene products inhibit mTOR signaling. Cell 134, 451–460 (2008).

    Article  CAS  Google Scholar 

  52. Renault, V. M. et al. The pro-longevity gene FoxO3 is a direct target of the p53 tumor suppressor. Oncogene 30, 3207–3221 (2011).

    Article  CAS  Google Scholar 

  53. Schwartzman, O. et al. UMI-4C for quantitative and targeted chromosomal contact profiling. Nat. Methods 13, 685–691 (2016).

    Article  CAS  Google Scholar 

  54. Kleinberg, J. & Tardos, É. Algorithm Design (Pearson, Boston, 2005).

Download references

Acknowledgements

We thank B. Ren and A. D. Schmitt for help with Hi-C library preparation; C. Bolt for help with the UMI-4C protocol; J. Lingner, D. Trono, and F. Radtke for critical reading of the manuscript; and D. Duboule and J. Huelsken for useful discussions. We thank the EPFL research animal, flow cytometry, histology, and sequencing facilities. This work is supported by the ISREC Foundation (E.O.), the Swiss National Science Foundation (E.O. and M.C.D.-C. SNF-31003A_159637) and Swiss Cancer League (E.O. KFS-3982-08-2016). G.C. is supported by the Giorgi-Cavaglieri Foundation. D.T. is supported by the Swiss National Science Foundation (SNSF, SNF-310030_169519), M.Z. is supported by the Swiss Cancer League (KFS-3983-08-2016), and S.S. is supported by European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 665667.

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Author notes

  1. These authors contributed equally: Maria C. Donaldson-Collier, Stephanie Sungalee, Marie Zufferey, Daniele Tavernari.

Authors and Affiliations

  1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Science, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Maria C. Donaldson-Collier, Stephanie Sungalee, Natalya Katanayeva, Elena Battistello & Elisa Oricchio

  2. Department of Computational Biology, University of Lausanne (UNIL), Lausanne, Switzerland

    Marie Zufferey, Daniele Tavernari, Elena Battistello, Marco Mina, Franck Raynaud & Giovanni Ciriello

  3. Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

    Marie Zufferey, Daniele Tavernari, Elena Battistello, Marco Mina, Franck Raynaud & Giovanni Ciriello

  4. Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Kyle M. Douglass, Timo Rey & Suliana Manley

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Contributions

M.C.D-C. prepared Hi-C, UMI-4C, ChIP-seq and RNA-seq libraries and performed in vitro validation experiments and DNA FISH experiments. S.S. analyzed Hi-C, UMI-4C, and ChIP-seq data. M.Z. performed the comparison of Hi-C contact maps, TAD calling, and all analyses based on mRNA expression data. D.T. performed interactome analyses and all analyses based on ChIP-seq data. K.M.D. and T.R. acquired and analyzed STORM images. N.K. and E.B. performed in vitro and in vivo experiments. M.M. assisted in the analysis of Hi-C data. F.R. assisted in the analysis of STORM data. S.M. supervised in STORM acquisition and image analyses. G.C and E.O. designed the study and wrote the manuscript with comments from all authors.

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Correspondence to Giovanni Ciriello or Elisa Oricchio.

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Integrated supplementary information

Supplementary Figure 1 Detailed comparison of the 3D genome organization in EZH2WT and EZH2Y646X cells.

a, Immunoblot of H3K27me3, H3K27me2, and Histone-3 in EZH2 wild-type (WT) and EZH2 mutated (Y646X) cells (n = 2 experiments). b-e, Comparisons of intra-chromosomal maps (n = 22 maps in each comparison) of EZH2Y646X cell lines (Karpas422 and WSU-DLCL2) and of the indicated cell lines based on (b) stratum-adjusted correlation coefficient (SCC), (c) fraction of 1Mb bins assigned to different compartments, (d) TAD similarity by Measure of Concordance, and (e) fraction of shared significant interactions. The thick central line of each box plot represents the median, the bounding box corresponds to the 25th–75th percentiles, and the whiskers extend up to 1.5 times the interquartile range, f, Fraction of RNA-seq reads mapping to EZH2WT and EZH2Y646F in OCI-Ly19 cells in three independent replicates (Rep1, Rep2, Rep3). g, Immunoblot of H3K27me3, Histone-3, EZH2, and tubulin in OCI-Ly19 EZH2 wild-type (WT), OCI-Ly19 EZH2 wild-type transduced with the vector, and OCI-Ly19 EZH2Y646F cells. h, Comparisons of intra-chromosomal Hi-C matrices (n = 22 maps per comparison) between OCI-Ly19-EZH2Y646F cells and the indicated cell lines by: SCC, fraction of 1Mb bins assigned to different compartments, TAD similarity, and fraction of shared significant interactions. The thick central line of each box plot represents the median, the bounding box corresponds to the 25th–75th percentiles, and the whiskers extend up to 1.5 times the interquartile range.

Supplementary Figure 2 H3K27me3 across subcompartments and TADs in EZH2WT and EZH2Y646F cells.

a-b, Distribution of (a) mean H3K27me3 and (b) mean H3K27me3 fold-changes between OCI-Ly19-EZH2Y646F and OCI-Ly19-EZH2WT in regions within sub-compartments A1 (n = 490), A2 (n = 1249), B1 (n = 896), B2 (n = 504), B3 (n = 685), and B4 (n = 25) in OCI-Ly19-EZH2WT and OCI-Ly19-EZH2Y646F. The thick central line of each box plot represents the median value, the bounding box corresponds to the 25th–75th percentiles, and the whiskers extend up to 1.5 times the interquartile range. Dots are values extending beyond such range. c, Pearson’s correlation between TAD mean H3K27me3 in OCI-Ly19-EZH2Y646F and the mean of the corresponding values in Karpas-422 and WSU-DLCL2 (n = 2,038 TADs). d-e, Correlation across n = 2,038 TADs of H3K27me3 in (d) OCI-Ly19-EZH2WT and (e) OCI-Ly19-EZH2Y646F in loci within the same TAD and loci separated by one TAD boundary. f, Distribution of TAD mean H3K27me3 fold-changes (log2FC) within = 2,038 TADs in OCI-Ly19 EZH2Y646F versus OCI-Ly19 EZH2WT compared to fold-change distributions obtained after permuting H3K27me3 bins (50kb or 100kb) preserving the compartment composition. g, Ratio between the expected number of TADs with >2-fold H3K27me3 increase in OCI-Ly19-EZH2Y646F compared to OCI-Ly19-EZH2WT (log2(FC) > 1, Y-axis) based on permutation of H3K27me3 bins of sizes ranging from 50 kb to 2 Mb (interval size is on the X-axis) and the observed number of TADs with log2(FC) > 1.

Supplementary Figure 3 Transcriptionally inactive TADs in EZH2WT and EZH2Y646X cells.

a, Formula of mRNA Fold-Change Concordance (FCC) score and graphical representation of the cumulative sum curves used to quantify the concordance of gene expression fold-changes within TADs. b, Cumulative sum curve of mRNA FCC scores derived from the comparison of OCI-Ly19-EZH2Y646F versus OCI-Ly19-EZH2WT cells in n = 900 TADs. c-e, Pearson’s correlation of mRNA expression profiles (Y-axis) between gene-pairs located in the same TAD and in different TADs as a function of their genomic distance (X-axis) in (c) DLBCL cell lines (n = 16), (d) GC-DLBCL patient samples (n = 37), and (e) FL patient samples (n = 23). Curves were fitted with the loess R function. f, Comparison of TAD mean H3K27me3 fold-changes (X-axis) versus the TAD mean mRNA expression fold-changes (Y-axis) in OCI-Ly19-EZH2Y646F versus OCI-Ly19-EZH2WT cells (n = 900 TADs). Points are colored to reflect point density: cold colors indicate low density; warm colors indicate high density. Highlighted areas correspond to regions with a >2-fold difference in both H3K27me3 and mRNA expression. g-h, Comparison of TAD mean H3K27me3 fold-changes (X-axis) versus the TAD mean mRNA expression fold-changes (Y-axis) in OCI-Ly19-EZH2Y646F versus OCI-Ly19-EZH2WT cells considering only TADs including genes exhibiting a mRNA expression fold change |log2 FC| >1 (n = 187 TADs), or an adjusted p-value < 0.1 (n = 166 TADs). Only TADs with at least 3 genes are considered. i, (Left) Overlap between 37 out of 72 inactive TADs containing at least 3 genes with detectable expression in OCI-Ly19-EZHY646F and OCI-Ly19-EZHWT and 89 inactive TADs defined based on H3K27me3 and mRNA fold-changes in OCI-Ly19. (Right): Overlap of 72 inactive TADs with 57 inactive TADs defined based on H3K27me3 fold-changes obtained by comparing EZH2 mutated cell lines (Karpas-422 and WSU-DLCL2) and OCI-Ly19 EZH2WT. P-value determined by two-tailed Fisher’s exact test. OR: odd ratios. j, Mean mRNA expression difference in inactive TADs in normal centroblast and centrocytes (n = 10 samples of germinal center, GC, cells) versus memory B-cells (n = 5 samples, X-axis) and corresponding p-value determined by two-tailed T-test. k, mRNA expression heatmap of genes in the top 3 differentially expressed inactive TADs in centroblast/centrocytes versus memory B-cells.

Supplementary Figure 4 Inactive TADs downregulate tumor suppressors.

a, Quantification of H3K27me3 by ChIP-qPCR for genes included in TAD6.139 (FOXO3, n = 3 and SESN1, n = 2) in Toledo (EZH2WT) and WSU-DLCL2 (EZH2Y646F) cells. Data were normalized based on the input, and chromatin immune precipitation of Histone-3 (H3-ChIP) was used as control. Bars indicate mean values and error bars correspond to 1 standard deviation. b, Quantitative mRNA expression analysis of genes included in TAD6.139 (FOXO3, ARMC2, and SESN1) in OCI-Ly19 EZH2WT (n = 3 independent experiments) compared to OCI-Ly19 expressing empty vector (pLVX) (n = 1) or OCI-Ly19 EZH2Y646F (n = 3). Data are normalized to GAPDH. Error bars correspond to 1 standard deviation. P-values were calculated using a two-tailed t-test. c, Hi-C contact map at 20kb resolution of the genomic region Chr.3 32.7-33.8Mb in OCI-Ly19 EZH2WT. TAD boundaries are contoured in black and representative genes within TADs are shown. d, H3K27me3 ChIP-seq tracks (n = 3 experiments) on Chr.3 32.7-33.8Mb in OCI-Ly19 EZH2WT and OCI-Ly19 EZH2Y646. e, Barplot showing mRNA expression fold-changes of individual genes in Chr.3 32.7-33.8Mb between OCI-Ly19-EZH2Y646F and OCI-Ly19-EZH2WT and between n = 12 EZH2Y646X cell lines and n = 4 EZH2WT cell lines.

Supplementary Figure 5 Synergistic downregulation of genes in TAD6.139 promotes lymphomagenesis.

a-c, Differential expression analyses in FL5-12 pro-B cells transduced with vector control or shRNAs targeting (a) Sesn1, (b) Foxo3, or (c) Armc2 in three independent experiments. Bars indicate mean values; error bars correspond to 1 standard deviation. P-values were calculated using a two-tailed t-test. d, Percentage of cells in each sub-population transduced with control vectors at day 4, 8, and 12 relatives to day 0. Mean values (bars) and standard deviations (error bars) were calculated based on 2 independent experiments. n.i.: no infected cells. e-f, Percentage of cells in each sub-population transduced with the indicated shRNAs at day 4, 8, and 12 relatives to day 0. Mean values (bars) and standard deviations (error bars) were calculated based on 2 independent experiments. n.i.: no infected cells. g, Overall survival of lethally irradiated animals transplanted with Eμ-myc HPC expressing vector (n = 14 animals), shFoxo3 (n = 16), shSesn1 (n = 17), or mix population of shSesn1 and shFoxo3 (n = 21). P-values were calculated by Log-rank (Mantel-Cox) test for each population versus the vector. h, Analysis by flow cytometry of CD45-R and CD3 expression in the indicated tumours (n = 2 experiments). i, Quantification of the CD45-R and CD3 positive cells in the indicated tumours.

Supplementary Figure 6 Differential expression analysis after treatment with EZH2 inhibitors leads to TAD reactivation.

a-b, Distributions of gene expression fold-changes between EZH2Y646X cell lines (n = 12) and: EZH2WT cell lines (n = 4), EZH2Y646X cell lines treated with EPZ6438 (n = 12), and EZH2Y646X cell lines treated with GSK126 (n = 12). (a) Full density plot, (b) zoom of the bottom part. c, Comparison of TAD mRNA expression fold-changes (n = 2,024 TADs) obtained by comparing cell lines treated with GSK126 versus vehicle (DMSO) (X-axis) and cell lines treated with EPZ6438 versus vehicle (DMSO) (Y-axis). Pearson’s correlation is reported. Points are colored to reflect point density: cold colors indicate low density; warm colors indicate high density. d-f, TAD mRNA expression fold-changes obtained by comparing EZH2Y646X cell lines (n = 12) and EZH2WT cell lines (n = 4) (X-axis) versus EZH2Y646X cell lines (n = 12) and (d) EZH2Y646X cell lines treated with GSK126 (n = 12) or (e) EPZ6438 (n = 12) (Y-axis). (f) TAD mRNA expression fold-changes obtained by comparing OCI-Ly19-EZH2Y646F and OCI-Ly19-EZH2WT (X-axis) versus OCI-Ly19-EZH2Y646X cells treated with GSK126 (Y-axis, n = 3 cell lines per condition). The number of TADs within each quadrant is reported. Points are colored to reflect point density: cold colors indicate low density; warm colors indicate high density. g, Quantitative mRNA expression analysis of genes in the TAD6.139 (FOXO3, ARMC2 and SESN1) in the indicated cell lines treated with 2 μM GSK126 or 1 μM EPZ6438 or vehicle (DMSO) for 72 hours. Gene expression levels in cells treated with DMSO were used as reference. Mean values of two independent experiments are reported, bars indicate mean values, error bars correspond to 1 standard deviation. h, Quantitative expression analysis of genes in the TAD6.139 (FOXO3, ARMC2 and SESN1) in OCY-Ly19 EZH2WT or OCY-Ly19 EZH2Y646F treated with 2 μM GSK126 (n = 3 experiments) or 1 μM EPZ6438 (n = 2) or vehicle (DMSO) (n = 3) for 72 hours. Bars indicate mean values; error bars correspond to one standard deviation.

Supplementary Figure 7 Intra-TAD structural changes in EZH2WT and EZH2Y646X cells by interactome.

a, Hi-C contact map at 20kb resolution of the genomic region Chr.6 108.3-109.8Mb in Karpas422-EZH2Y646N (left) and Karpas422-EZH2Y646N treated with GSK126 for 72 hours (right). TAD boundaries are contoured in black and representative genes within the TADs are shown. b, Number of normalized paired-end reads between Chr.6 108,860-108,880 kb and Chr.6 109,380-109,400 kb in the indicated Hi-C cell lines and conditions. P-value derived by two-tailed Wilcoxon test comparing normalized read counts in EZH2Y646X (n = 4) cell lines and in EZH2WT or EZH2Y646X cell lines (n = 4 in total) treated with GSK126. c, Significant interactions in WSU-DLCL2-DMSO (top) or WSU-DLCL2-GSK126 (bottom) determined by HiC-DC. Bin-pairs within a 2Mb windows were tested (see Methods). d, Significantly different interactions (q-value < 0.1) between WSU-DLCL2-DMSO and WSU-DLCL2-GSK126 (n = 435 tested interactions). Empirical q-values determined as described in Methods. e, Most significantly different interactions between WSU-DLCL2-DMSO and WSU-DLCL2-GSK126. f, UMI-4C domainogram: mean number of contacts (% of the maximum) in the Chr.6 108.80-109.45kb region in Karpas422-DMSO (top) and Karpas422-GSK126 (bottom) using Primer 2. g, Normalized number of UMI-4C reads in Karpas422-DMSO and Karpas422-GSK126 obtained with Primer 2. Normalized read-counts are binned at 5kb and averaged within a sliding 5 bin-window. h, (Top) Most significant different interactions determined by HiC-DC between Karpas422-GSK126 than Karpas422-DMSO (see Methods). Blue: stronger in Karpas422-GSK126, Red: stronger in Karpas422-DMSO. (Bottom) ChIP-seq tracks for H3K4me3 (green), H3K27Ac (dark blue), H3K4me1 (light blue), and H3K27me3 (violet) in Karpas422 and OCI-Ly7 (n = 2 ChIP-seq experiments per cell line). i, (Left) Significant interactions in Karpas422-EZH2Y646N (top) and OCI-Ly19-EZH2WT (bottom) determined by HiC-DC. Bin-pairs within a 2Mb windows were tested (see Methods). (Right-top) Significantly different interactions (q-value < 0.1) between Karpas422-EZH2Y646N and OCI-Ly19-EZH2WT (n = 435 tested interactions). Empirical q-values determined as described in Methods. (Right-bottom) Most significantly different interactions between Karpas422-EZH2Y646N and OCI-Ly19-EZH2WT. j, Distribution of the number of significant different interactions between Karpas422-EZH2Y646N and OCI-Ly19-EZH2WT cells in in 100 sets of 72 randomly sampled neutral TADs and value observed in 72 inactive TADs (red line). Empirical p-value corresponds to fraction of random sets of neutral TADs with a number of significant interactions equal or greater to the one observed in inactive TADs. k, Venn diagram of the overlap between significant different interactions between Karpas422-EZH2Y646N and OCI-Ly19-EZH2WT. P-value derived by two-tailed Fisher’s exact test, OR: odds ratio.

Supplementary Figure 8 Intra-TAD structural changes in EZH2WT and EZH2Y646X cells by STORM.

a, Representative immunofluorescence images from one experiment with WSU-DLCL2 cells treated with 2 μM of GSK126 or DMSO for 72 hours and stained with H3K27me3 and DAPI. Scale bar = 20 μm. b, Graphical representation of data points retained in STORM analyses using a neighborhood radius equal to 40 nm (left) or 30 nm (right). c, Representative images overlaying wide field (gray-scale) and STORM acquisition of TAD6.139 in WSU-DLCL2 cells treated with 2 μM of GSK126 or DMSO for 72 hours in 4 biologically independent experiments. d, Representative images overlaying wide field and STORM acquisition of TAD6.139 in OCI-Ly19-EZH2Y646F (red) and OCI-Ly19-EZH2WT in 3 biologically independent experiments. e, Eccentricity of TAD6.139 with a (neighborhood radius = 30 nm) in WSU-DLCL2 treated with 2 μM of GSK126 or vehicle (DMSO) for 72 hours (n = 4 biologically independent experiments) and in OCI-Ly19 EZH2WT and OCI-Ly19 EZH2Y646 (n = 3 biologically independent experiments). Dots are means of multiple measurements of independent cells (Supplementary Table 7), error bars are +/-1 standard error. f, Representative images overlaying wide field and STORM acquisition of TAD1.54 in WSU-DLCL2 cells treated with 2 μM of GSK126 or DMSO for 72 hours (n = 2 biologically independent experiments). g, Eccentricity of TAD1.54 with neighborhood radius = 30 nm (left) and 40 nm (right) in 2 biologically independent experiments comparing WSU-DLCL2 treated with 2 μM of GSK126 or vehicle (DMSO) for 72 hours. Dots are means of multiple measurements of independent cells (Supplementary Table 7), error bars are +/- 1standard error.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Note

Reporting Summary

Supplementary Table 1

Hi-C in EZH2 wild-type and mutant cells

Supplementary Table 2

List of TADs with epigenetic and transcriptional annotations in cell lines, patient samples and cell treated with EZH2 inhibitors GSK126 and EPZ6438.

Supplementary Table 3

Gene-set enrichment analysis in inactive TADs

Supplementary Table 4

Percentage of cells expressing single or dual shRNA for the indicated genes in three independent experiments.

Supplementary Table 5

Interactome analysis in EZH2 wild-type and mutated cells and in cell treated with EZH2 inhibitor GSK126.

Supplementary Table 6

Number of mapped reads in UMI-4C seq

Supplementary Table 7

STORM measurements in WSU-DLCL2 cells treated with GSK126 or vehicle (control) for 72 h and in OCI-Ly19 EZH2WT and EZH2Y646F cells for the indicated TAD

Supplementary Table 8

List of primers used in this study

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Donaldson-Collier, M.C., Sungalee, S., Zufferey, M. et al. EZH2 oncogenic mutations drive epigenetic, transcriptional, and structural changes within chromatin domains. Nat Genet 51, 517–528 (2019). https://doi.org/10.1038/s41588-018-0338-y

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