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:

Drug addiction unveils a repressive methylation ceiling in EZH2-mutant lymphoma

Nature Chemical Biology volume 19, pages 1105–1115 (2023)Cite this article

Subjects

Abstract

Drug addiction, a phenomenon where cancer cells paradoxically depend on continuous drug treatment for survival, has uncovered cell signaling mechanisms and cancer codependencies. Here we discover mutations that confer drug addiction to inhibitors of the transcriptional repressor polycomb repressive complex 2 (PRC2) in diffuse large B-cell lymphoma. Drug addiction is mediated by hypermorphic mutations in the CXC domain of the catalytic subunit EZH2, which maintain H3K27me3 levels even in the presence of PRC2 inhibitors. Discontinuation of inhibitor treatment leads to overspreading of H3K27me3, surpassing a repressive methylation ceiling compatible with lymphoma cell survival. Exploiting this vulnerability, we show that inhibition of SETD2 similarly induces the spread of H3K27me3 and blocks lymphoma growth. Collectively, our findings demonstrate that constraints on chromatin landscapes can yield biphasic dependencies in epigenetic signaling in cancer cells. More broadly, we highlight how approaches to identify drug addiction mutations can be leveraged to discover cancer vulnerabilities.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: CRISPR-suppressor scanning identifies the EZH2 CXC domain as a drug resistance hotspot to PRC2 inhibitors.
Fig. 2: CRISPR-addiction scanning reveals that mutations in PRC2 can confer addiction to PRC2 inhibitors.
Fig. 3: EZH2 CXCdel mutant is addicted to PRC2 inhibitors.
Fig. 4: EZH2 CXC-mutants allosterically activate PRC2 methyltransferase activity by increasing substrate affinity.
Fig. 5: EZH2 CXCdel hyperactivity induces pervasive spreading of H3K27me3.
Fig. 6: SETD2 inhibition induces H3K27me3 spreading and halts lymphoma cell proliferation.

Similar content being viewed by others

Data availability

ChIP–seq and RNA-seq data have been deposited to NCBI GEO (GSE199889). Transformed CRISPR-suppressor scanning reads (log2 + 1) used for Fig. 1 and Extended Data Fig. 1 are supplied in Supplementary Dataset 2. Addiction scores and growth rates used for Fig. 2 and Extended Data Fig. 2 are supplied in Supplementary Dataset 3. Oligonucleotide sequences are provided as Supplementary Information. Unprocessed gel and immunoblot images, as well as additional data generated in this study, are provided as Source Data with this paper. The following publicly available datasets were used Protein Data Bank (PDB) accession codes 6WKR, 5K0M, 5LS6, EMDB-7306; and ENCODE datasets ENCFF932BQZ, ENCFF435PXJ and ENCFF603BHK. Source data are provided with this paper.

Code availability

Computer code employed in Fig. 2 for the calculation of the addiction score is publicly available at https://github.com/skissler/EZH2 under the GNU General Public License, Version 3.0 (details included in code repository). Custom code used to analyze CRISPR-suppressor scanning data is available at https://github.com/liaulab/CRISPR-suppressor_scanning. All other computer codes employed are available upon request.

References

  1. Thakur, M. D. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).

    PubMed  PubMed Central  Google Scholar 

  2. Chen, Z. et al. Signaling thresholds and negative B cell selection in acute lymphoblastic leukemia. Nature 521, 357–361 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Kong, X. et al. Cancer drug addiction is relayed by an ERK2-dependent phenotype switch. Nature 550, 270–274 (2017).

    PubMed  PubMed Central  Google Scholar 

  4. Rajan, S. S. et al. The mechanism of cancer drug addiction in ALK-positive T-cell lymphoma. Oncogene 39, 2103–2117 (2020).

    CAS  PubMed  Google Scholar 

  5. Serrano, M., Lin, A. W., McCurrach, M. E., Beach, D. & Lowe, S. W. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell 88, 593–602 (1997).

    CAS  PubMed  Google Scholar 

  6. Michaloglou, C. et al. BRAFE600-associated senescence-like cell cycle arrest of human naevi. Nature 436, 720–724 (2005).

    CAS  PubMed  Google Scholar 

  7. Reimann, M. et al. Tumor stroma-derived TGF-beta limits myc-driven lymphomagenesis via Suv39h1-dependent senescence. Cancer Cell 17, 262–272 (2010).

    CAS  PubMed  Google Scholar 

  8. Nemazee, D. Mechanisms of central tolerance for B cells. Nat. Rev. Immunol. 17, 281–294 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ecker, V. et al. Targeted PI3K/AKT-hyperactivation induces cell death in chronic lymphocytic leukemia. Nat. Commun. 12, 3526 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Reddy, A. et al. Genetic and functional drivers of diffuse large B cell lymphoma Cell 171, 481–494 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Schmitz, R. et al. Genetics and pathogenesis of diffuse large B cell lymphoma. N. Engl. J. Med. 378, 1396–1407 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Béguelin, W. et al. EZH2 enables germinal centre formation through epigenetic silencing of CDKN1A and an Rb-E2F1 feedback loop. Nat. Commun. 8, 877 (2017).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  14. Qi, W. et al. An allosteric PRC2 inhibitor targeting the H3K27me3 binding pocket of EED. Nat. Chem. Biol. 13, 381–388 (2017).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  16. Vinyard, M. E. et al. CRISPR-suppressor scanning reveals a nonenzymatic role of LSD1 in AML. Nat. Chem. Biol. 15, 529–539 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Bödör, C. et al. EZH2 mutations are frequent and represent an early event in follicular lymphoma. Blood 122, 3165–3168 (2013).

    PubMed  PubMed Central  Google Scholar 

  18. Verma, S. K. et al. Identification of potent, selective, cell-active inhibitors of the histone lysine methyltransferase EZH2. ACS Med. Chem. Lett. 3, 1091–1096 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Brooun, A. et al. Polycomb repressive complex 2 structure with inhibitor reveals a mechanism of activation and drug resistance. Nat. Commun. 7, 11384 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Gibaja, V. et al. Development of secondary mutations in wild-type and mutant EZH2 alleles cooperates to confer resistance to EZH2 inhibitors. Oncogene 35, 558–566 (2016).

    CAS  PubMed  Google Scholar 

  21. Baker, T. et al. Acquisition of a single EZH2 D1 domain mutation confers acquired resistance to EZH2-targeted inhibitors. Oncotarget 6, 32646–32655 (2015).

    PubMed  PubMed Central  Google Scholar 

  22. Poepsel, S., Kasinath, V. & Nogales, E. Cryo-EM structures of PRC2 simultaneously engaged with two functionally distinct nucleosomes. Nat. Struct. Mol. Biol. 25, 154–162 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kasinath, V. et al. JARID2 and AEBP2 regulate PRC2 in the presence of H2AK119ub1 and other histone modifications. Science 371, eabc3393 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Chorin, A. B. et al. ConSurf‐DB: an accessible repository for the evolutionary conservation patterns of the majority of PDB proteins. Protein Sci. 29, 258–267 (2020).

    PubMed  Google Scholar 

  25. Murray, J. D. Mathematical Biology (Springer, 2002).

  26. Gosavi, P. M. et al. Profiling the landscape of drug resistance mutations in neosubstrates to molecular glue degraders. ACS Cent. Sci. 8, 417–429 (2022).

    CAS  PubMed Central  Google Scholar 

  27. Yuan, W. et al. H3K36 methylation antagonizes PRC2-mediated H3K27 methylation. J. Biol. Chem. 286, 7983–7989 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Youmans, D. T., Gooding, A. R., Dowell, R. D. & Cech, T. R. Competition between PRC2.1 and 2.2 subcomplexes regulates PRC2 chromatin occupancy in human stem cells. Mol. Cell 81, 488–501 (2021).

    CAS  PubMed  Google Scholar 

  29. Ernst, T. et al. Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat. Genet. 42, 722–726 (2010).

    CAS  PubMed  Google Scholar 

  30. Calebiro, D. et al. Recurrent EZH1 mutations are a second hit in autonomous thyroid adenomas. J. Clin. Invest. 126, 3383–3388 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. Margueron, R. et al. Ezh1 and Ezh2 maintain repressive chromatin through different mechanisms. Mol. Cell 32, 503–518 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Schmitges, F. W. et al. Histone methylation by PRC2 is inhibited by active chromatin marks. Mol. Cell 42, 330–341 (2011).

    CAS  PubMed  Google Scholar 

  33. Laugesen, A., Højfeldt, J. W. & Helin, K. Molecular mechanisms directing PRC2 recruitment and H3K27 methylation. Mol. Cell 74, 8–18 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Velichutina, I. et al. EZH2-mediated epigenetic silencing in germinal center B cells contributes to proliferation and lymphomagenesis. Blood 116, 5247–5255 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Caganova, M. et al. Germinal center dysregulation by histone methyltransferase EZH2 promotes lymphomagenesis. J. Clin. Invest. 123, 5009–5022 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Lampe, J. W. et al. Discovery of a first-in-class inhibitor of the histone methyltransferase SETD2 suitable for preclinical studies. ACS Med. Chem. Lett 12, 1539–1545 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Lampe, J. et al. Substituted indoles and methods of use thereof. https://patents.google.com/patent/WO2020037079A1/en (2020).

  39. Carvalho, S. et al. Histone methyltransferase SETD2 coordinates FACT recruitment with nucleosome dynamics during transcription. Nucleic Acids Res. 41, 2881–2893 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Leung, W. et al. SETD2 haploinsufficiency enhances germinal center-associated AICDA somatic hypermutation to drive B-cell lymphomagenesis. Cancer Discov. 12, 1782–1803 (2021).

    Google Scholar 

  41. Carvalho, S. et al. SETD2 is required for DNA double-strand break repair and activation of the p53-mediated checkpoint. eLife 3, e02482 (2014).

    PubMed  PubMed Central  Google Scholar 

  42. Pfister, S. X. et al. SETD2-dependent histone H3K36 trimethylation is required for homologous recombination repair and genome stability. Cell Rep. 7, 2006–2018 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kim, K. H. & Roberts, C. W. M. Targeting EZH2 in cancer. Nat. Med. 22, 128–134 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Choi, J. et al. DNA binding by PHF1 prolongs PRC2 residence time on chromatin and thereby promotes H3K27 methylation. Nat. Struct. Mol. Biol. 24, 1039–1047 (2017).

    CAS  PubMed  Google Scholar 

  45. Lee, C.-H. et al. Distinct stimulatory mechanisms regulate the catalytic activity of polycomb repressive complex 2. Mol. Cell 70, 435–448 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Son, J., Shen, S. S., Margueron, R. & Reinberg, D. Nucleosome-binding activities within JARID2 and EZH1 regulate the function of PRC2 on chromatin. Gene Dev. 27, 2663–2677 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Kasinath, V. et al. Structures of human PRC2 with its cofactors AEBP2 and JARID2. Science 359, eaar5700 (2018).

    Google Scholar 

  48. Freedy, A. M. & Liau, B. B. Discovering new biology with drug-resistance alleles. Nat. Chem. Biol. 17, 1219–1229 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Ngan, K. C. et al. CRISPR-suppressor scanning for systematic discovery of drug-resistance mutations. Curr. Protoc. 2, e614 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Joung, J. et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Fellmann, C. et al. An optimized microRNA backbone for effective single-copy RNAi. Cell Rep. 5, 1704–1713 (2013).

    CAS  PubMed  Google Scholar 

  54. Clement, K. et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 37, 224–226 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Davidovich, C., Goodrich, K. J., Gooding, A. R. & Cech, T. R. A dimeric state for PRC2. Nucleic Acids Res. 42, 9236–9248 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Luger, K., Rechsteiner, T. J. & Richmond, T. J. Preparation of nucleosome core particle from recombinant histones. Methods Enzymol. 304, 3–19 (1999).

    CAS  PubMed  Google Scholar 

  57. Dyer, P. N. et al. Reconstitution of nucleosome core particles from recombinant histones and DNA. Methods Enzymol. 375, 23–44 (2003).

    Google Scholar 

  58. Clapier, C. R., Längst, G., Corona, D. F. V., Becker, P. B. & Nightingale, K. P. Critical role for the histone H4 N terminus in nucleosome remodeling by ISWI. Mol. Cell. Biol. 21, 875–883 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Orlando, D. A. et al. Quantitative ChIP–seq normalization reveals global modulation of the epigenome. Cell Rep. 9, 1163–1170 (2014).

    CAS  PubMed  Google Scholar 

  60. Wu, D., Wang, L. & Huang, H. Protocol to apply spike-in ChIP–seq to capture massive histone acetylation in human cells. Star. Protoc. 2, 100681 (2021).

    CAS  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of T. Cech Laboratory (University of Colorado Boulder), especially A. Gooding and Y. Long, and members of B. Kingston Laboratory (Massachusetts General Hospital) especially S. Marr and T. Oei for guidance on protein biochemistry experiments; P. Cole (Harvard Medical School) for providing the nucleosomal DNA sequence for binding studies; K. Ngan for guidance on computational analysis of CRISPR-suppressor scans; S. Araten for aiding in protein conservation analysis; M. Quezada for aiding in genomics analysis; D. Youmans, D. Narducci and A. Hansen for guidance on live-cell imaging experiments; R. Ryan for providing cell lines for SETD2 inhibitor studies; and the Bauer Core Facility at Harvard University, particularly Z. Nizioleck and J. Nelson for their assistance with cell sorting. We thank members of the Liau Laboratory, especially P. Gosavi, for helpful discussions and comments on the paper. This work was supported by the award 1DP2GM137494 (B.B.L.) from the National Institute of General Medical Sciences, American Cancer Society Research Scholar Grant RSG-22-083-01-DMC (B.B.L.), startup funds from Harvard University (B.B.L), award T32GM007753 (A.M.F.) from the National Institute of General Medical Sciences, Charles A. King Trust Postdoctoral Research Fellowship (H.S.K.) from Sara Elizabeth O’Brien Trust/Simeon J. Fortin Charitable Foundation, Bank of America Private Bank and co-trustees.

Author information

Author notes

  1. These authors contributed equally: Hui Si Kwok, Allyson M. Freedy.

Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Hui Si Kwok, Allyson M. Freedy, Allison P. Siegenfeld, Julia W. Morriss, Amanda L. Waterbury & Brian B. Liau

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Hui Si Kwok, Allyson M. Freedy, Allison P. Siegenfeld, Julia W. Morriss, Amanda L. Waterbury & Brian B. Liau

  3. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Stephen M. Kissler

Authors
  1. Hui Si Kwok
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Allyson M. Freedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Allison P. Siegenfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julia W. Morriss
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amanda L. Waterbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stephen M. Kissler
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Brian B. Liau
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.S.K., A.M.F., and B.B.L. conceived the study and designed experiments; H.S.K. and A.M.F. performed and analyzed cell and molecular biology experiments; A.M.F., A.L.W. and J.W.M performed protein purification and biochemical experiments; H.S.K. performed genomics experiments; H.S.K. and A.P.S. analyzed genomics data; S.M.K. performed growth rate calculations for addicted sgRNAs; H.S.K., A.M.F. and B.B.L. edited and wrote the paper, with inputs from all authors; B.B.L. held overall responsibility for the study.

Corresponding author

Correspondence to Brian B. Liau.

Ethics declarations

Competing interests

B.B.L. holds a sponsored research project with AstraZeneca, is a scientific consultant for Imago BioSciences and Exo Therapeutics and is a shareholder and member of the scientific advisory board of Light Horse Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks Jonathan Licht and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 sgRNAs targeting EZH2 CXC domain and the drug-binding pockets are the most enriched in CRISPR-suppressor scanning.

a, Scatter plots showing fitness scores (y-axis) in Karpas-422 under vehicle treatment at week 8. Fitness scores were calculated as the log2(fold-change) sgRNA enrichment under vehicle normalized to the mean of the negative control sgRNAs (n = 58). The PRC2-targeting sgRNAs (n = 650) are arrayed by their predicted cut sites in the coding sequences (x-axis). Data represent mean of n = 3 replicates. b, Structural view of the GSK343 binding site with EZH2 residues I109 and N699 highlighted in red. GSK343 analog is shown in gold. PDB: 5LS6. c, Structural view of the EED226 binding site with EED residue Y365 highlighted in red. EED226 analog is shown in gold. PDB: 5K0M. d, Bar plots of wild-type (gray) and EZH2 I109_M110insM mutant (red) allele frequencies from CRISPR-suppressor scanning under 5-week GSK343 (1 μM) or EED226 (1 μM) treatment. e, Same as in d. but for wild-type (gray) and EED Y365F and Y365F/M366V mutant (red) allele frequencies. f, Schematic showing genotypes and bar plots of allele frequencies for mutations that are observed at frequencies of >2% in the gDNA encoding EZH2 surrounding the CXCdel loop for 6-week GSK343 (1 μM) treatment after transduction with sgA596/D597 in Karpas-422. Allele frequencies under vehicle (gray) or GSK343 (red). (top) Schematic depicts the secondary structure of C-terminal CXC domain and cysteine residues that coordinate the zinc ions. g, Same as in f. but for sgD597 transduction in Karpas-422. h, Same as in f. but for sgD597/H598 transduction in Karpas-422. i, Same as in f. but for sgD597 transduction in Pfeiffer. j, The conservation scores of each amino acid within and surrounding the EZH2 CXCdel loop are shown in colors ranging from variable (blue) to conserved (red) as calculated by ConSurf-DB24. (Top) Schematic depicts the secondary structure of C-terminal CXC domain and cysteine residues that coordinate the zinc ions. k, Comparison of amino acids homologous to the EZH2 CXCdel loop in various model organisms. Data in a, d-i are represented by mean of n = 3.

Source data

Extended Data Fig. 2 CRISPR-addiction scanning reveals drug addiction mutations.

a, Bar plot of wild-type (gray) and CXCdel (red) allele frequencies (y-axis) from CRISPR-addiction scanning. Data are represented by mean of n = 3. b, Histogram showing distribution of addiction scores (x-axis) among the three replicates of the GSK343 (left) and EED226 (right) CRISPR-addiction scanning experiments. c, Graph showing proportions of each sgRNA (y-axis) in each replicate of the GSK343 CRISPR-addiction scan over time (x-axis). Curves were calculated using the estimated intrinsic growth rate r for each sgRNA-containing cell population under the assumption of competitive logistic growth. The reference sgRNA is shown in black, sgRNAs demonstrating pure resistance are in blue and addicted sgRNAs are in red. d, Same as in c. but for each replicate of the EED226 CRISPR-addiction scan. e, Plots showing simulated densities for three hypothetical sgRNA populations. sgRNA densities begin at 1/500. The intrinsic fitness (uninhibited growth rates) for sgRNA populations 1–3 are 1.51, 1.50, and 1.49, respectively. When inhibitor is removed, the fitness of sgRNA populations 1 and 2 remain unchanged, while the fitness of sgRNA 3 decreases to 1.20. The sgRNA densities over time are described by Equation 7 (see Supplementary Note). The total density is equal to the sum of the densities of the three sgRNAs and is described by Equation 2. f, Plots showing simulated sgRNA proportions according to Equation 7. Trajectories are derived by dividing the sgRNA densities depicted in e. by the total population size. g, Plots showing simulated sgRNA proportions according to Equation 9. sgRNA proportions start at 0.33. The intrinsic fitness (uninhibited growth rates) for sgRNA populations 1–3 are 1.51, 1.50, and 1.49, respectively. When inhibitor is removed, the fitness of sgRNAs 1 and 2 remain unchanged, while the fitness of sgRNA 3 decreases to 1.20. h, Plots showing Z-values for simulated sgRNAs relative to sgRNA population 1. The definition of Z is given in Equation 10. The slopes of these lines give may be used to compute the change in intrinsic fitness for each sgRNA when on drug vs. off drug. Calculation of addiction scores is detailed in Supplementary Note.

Source data

Extended Data Fig. 3 CXCdel lymphoma cells are addicted to PRC2 inhibitors.

a, Schematic showing genotypes and allele frequencies of the Karpas-422 CXCdel+/– clonal line. We believe that the over-representation of the CXCdel allele in the clonal cell line can be attributed to PCR bias. The heterozygous nature of Karpas-422 CXCdel is confirmed in Fig. 3a. b, Schematic showing genotypes and allele frequencies for mutations that are observed at frequencies of > 2% in the gDNA encoding the EZH2 CXC domain in Pfeiffer CXCdel. c, Immunoblot showing EZH2 levels in Karpas-422 wild-type, CXCdel+/– and Q575R (left); and Pfeiffer wild-type, CXCdel (right). d, Line plot depicting cumulative growth of Karpas-422 wild-type and CXCdel+/– over 10 days. e, Immunoblot showing H3K27me3 levels in wild-type and CXCdel Pfeiffer cells after treatment with vehicle or 200 nM GSK343 for 24 or 72 hours. f, Dose-response proliferation curves of Karpas-422 wild-type under GSK343 or EED226 treatment for 10 days. Results in a–c, e are representative of two independent experiments. Data in d, f are represented by mean ± s.e.m of n = 3.

Source data

Extended Data Fig. 4 CXCdel lymphoma cells are depleted upon inhibitor withdrawal.

a, Stacked bar plot showing the observed frequencies for out-of-frame, in-frame and no indels in the cDNA encoding EZH2 at week 6 in Karpas-422 and Pfeiffer cells transduced with sgA596/D597, sgD597/H598 or sgD597 under vehicle, GSK343 (1 μM) or EED226 (1 μM) treatment. b, Bar chart showing frequency of insertions or deletions of amino acids in the cDNA encoding EZH2 at week 6 in Karpas-422 transduced with sgD597 under vehicle treatment. Deletions, no change and insertions are shown by red, yellow and blue backgrounds respectively. c, Same as b. but for sgA596/D597 transduction in Karpas-422 under vehicle treatment. d, Same as b. but for sgD597/H598 transduction in Karpas-422 under vehicle treatment. e, Same as b. but for sgD597 transduction in Pfeiffer under vehicle treatment. f, Same as b. but for sgA596/D597 transduction in Karpas-422 under GSK343 (1 μM) treatment. g, Same as b. but for sgD597/H598 transduction in Karpas-422 under GSK343 (1 μM) treatment. h, Same as b. but for sgD597/H598 transduction in Karpas-422 under EED226 (1 μM) treatment. i, Same as b. but for sgD597 transduction in Pfeiffer under EED226 (incremental doses up to 500 nM) treatment. j, Schematic showing genotypes and bar plots of allele frequencies for mutations that are observed at frequencies of >2% in the cDNA encoding EZH2 encompassing the CXC and SET domains for GSK343 treatment at week 6 for sgD597 transduction in Karpas-422. Allele frequencies in the (blue) wild-type allele and (red) Y646N allele. (Top) Schematic depicts the secondary structure of C-terminal CXC domain and cysteine residues that coordinate the zinc ions. k, Same as in j. but for sgA596/D597 transduction in Karpas-422. l, Same as in j. but for sgD597 transduction in Pfeiffer. The wild-type allele is shown in blue and the A682G allele is shown in red. Data in a–l are represented by mean ± s.e.m of n = 3.

Source data

Extended Data Fig. 5 EZH2 CXC-mutants elevate PRC2 methyltransferase activity in vitro and in cells.

a, Immunoblot showing EZH2 levels in K562 wild-type, CXCdel+/+/+ and Q575R. b, Line plot depicting cumulative growth of K562 wild-type and CXCdel+/+/+ over 10 days. c, Immunoblot showing H3K27me1/2/3 levels in K562 wild-type and CXCdel+/+/+ treated with 5 μM GSK343 or EED226 for 72 h. d, Schematic showing genotypes and allele frequencies for mutations that are observed at frequencies of > 2% in the gDNA encoding the EZH2 CXC domain in K562 CXCdel+/+/+. e, Same as d. but for K562 Q575R. f, Same as d. but for Karpas-422 Q575R. g, Purification of recombinant 5-member PRC2 complex. SDS-PAGE stained with Coomassie blue showing purified wild-type, CXCdel, and Q575R PRC2 complexes. h, Representative EMSA showing binding of wild-type, CXCdel and Q575R PRC2 to 185-bp mononucleosomes. The upper band corresponds to the PRC2-bound mononucleosome and bottom band corresponds to the free, unbound mononucleosome. Results in a,c-g are representative of two independent experiments, h is representative of five independent experiments.

Source data

Extended Data Fig. 6 PRC2 hyperactivity leads to H3K27me3 spreading on chromatin.

a, Aggregate profile plots of Karpas-422 ENCODE ChIP-seq signal (y-axis) for the specified histone modifications centered around gene bodies (x-axis). b, Aggregate profile plots of Karpas-422 H3K27me3 ChIP-Rx signal (y-axis, left) and log2(fold-change) H3K27me3 ChIP-Rx signal relative to GSK343-treated CXCdel+/– cells (y-axis, right) centered around H3K36me3 domains in wild-type cells (x-axis). c, Density plot depicting the distribution of H3K27me3 ChIP-Rx signal (x-axis) of K562 wild-type and CXCdel+/+/+ cells for 50-kb genomic bins. d, Line plot depicting cumulative fraction H3K27me3 ChIP-seq coverage relative to that of the bin with the highest coverage (y-axis) as a function of the region’s relative genomic rank (x-axis) for K562 wild-type and CXCdel+/+/+ cells. e, Same as b. but for K562. f, Density plot depicting EZH2 ChIP-seq signal rank for vehicle-treated Karpas-422 wild-type cells (y-axis) relative to H3K27me3 ChIP-Rx signal rank for vehicle-treated wild-type cells (x-axis, left) and EZH2 ChIP-seq signal rank for vehicle-treated CXCdel+/– (x-axis, right). g, Same as f. but for K562. Results in b-c,e-g represent average of two biological replicates.

Extended Data Fig. 7 H3K27me3 overspreading silences genes related to lymphoma growth.

a, Aggregate profile plots of log2(fold-change) H3K27me3 ChIP-Rx signal relative to GSK343-treated Karpas-422 CXCdel+/– (y-axis) centered around gene bodies (x-axis) of down-regulated genes in CXCdel+/– upon inhibitor withdrawal. b, Boxplot depicting log2(fold-change) H3K27me3 ChIP-Rx signal across gene bodies (y-axis) with down-regulated, non-differential and up-regulated genes in Karpas-422 CXCdel+/– upon inhibitor withdrawal annotated (x-axis) (left). Boxplot depicting log2(fold-change) gene expression (y-axis) by change in H3K27me3 (x-axis) for CXCdel+/– vehicle-treated relative to GSK343-treated (right). c, Top 10 enriched pathways within the down-regulated genes upon GSK343 withdrawal in Karpas-422 CXCdel+/– cells. Functional enrichment analysis of differentially expressed genes was performed using DAVID and P values were computed with modified Fisher’s Exact test. d, Volcano plot showing gene expression changes in K562 CXCdel+/+/+ versus wild-type cells. Genes with |log2(fold-change)| > 0.5 and adjusted P-value < 0.05 are defined as differentially expressed. P-values were computed with DESeq2 by Benjamini-Hochberg method, adjusted for multiple testing. Data were from three biological replicates. e, Aggregate profile plots of K562 CXCdel log2(fold-change) H3K27me3 ChIP-Rx signal relative to wild-type cells (y-axis) centered around gene bodies (x-axis) of down-regulated genes in CXCdel+/+/+ cells. f, Same as b. but for K562 CXCdel+/+/+ relative to wild-type K562. Results in a-c, e–f, represent average of two biological replicates. For b, f, the interquartile range (IQR) is depicted by the box with the median represented by the center line. Whiskers maximally extend to 1.5 × IQR (with outliers excluded). P values were calculated by a Mann-Whitney-Wilcoxon two-sided test.

Extended Data Fig. 8 SETD2 depletion halts lymphoma growth.

a, Immunoblot showing H3K27me3 and H3K36me3 levels in lymphoma cell lines treated with 5 μM SETD2-IN-1 for 72 h. Short exposure (SE); long exposure (LE). b, Bar plots showing SETD2 expression at the transcript level after SETD2 knockdown for 72 h. c, Bar plots showing cell proliferation after SETD2 knockdown for 10 days. d, Immunoblot showing γH2AX and H3K36me3 levels in Karpas-422 treated with 5 μM SETD2-IN-1 for 10 days or 10 μM etoposide for 24 h. Results in a, d are representative of two independent experiments. Data in b-c are are represented by mean ± s.e.m of n = 3, except for b. Karpas-422 and SUDHL4 shSETD2 #1 n = 5.

Source data

Extended Data Fig. 9 SETD2 inhibition leads to H3K27me3 spreading on chromatin.

a, Aggregate profile plot of wild-type Karpas-422 H3K36me3 ChIP-Rx signal (y-axis) for SETD2-IN-1 (5 μM) treatment centered around H3K36me3 domains in wild-type cells (x-axis). b, Aggregate profile plot of wild-type Karpas-422 H3K27me3 ChIP-Rx signal (y-axis) for SETD2-IN-1 (5 μM) treatment centered around gene bodies of genes down-regulated upon GSK343 withdrawal in CXCdel+/− cells (x-axis). c, Volcano plot showing gene expression changes in gene expression in Karpas-422 wild-type cells under SETD2-IN-1 (5 µM) treatment for 10 days versus vehicle. Genes with |log2(fold-change)| > 0.5 and adjusted P value < 0.05 are defined as differentially expressed. P values were computed with DESeq2 by Benjamini-Hochberg method, adjusted for multiple testing. Data were from three biological replicates. d, Aggregate profile plot of wild-type Karpas-422 H3K27me3 ChIP-Rx signal (y-axis) for SETD2-IN-1 (5 μM) treatment (left) and for vehicle- or GSK343-treated wild-type and CXCdel+/− cells (right) centered around gene bodies of genes down-regulated upon SETD2-IN-1 (5 µM) treatment (x-axis). e, Boxplot depicting log2(fold-change) H3K27me3 ChIP-Rx signal across gene bodies (y-axis) with down-regulated, non-differential and up-regulated genes in SETD2-IN-1 (5 μM) treatment annotated (x-axis) (left). Boxplot depicting log2(fold-change) gene expression (y-axis) by change in H3K27me3 (x-axis) for Karpas-422 wild-type cells treated with SETD2-IN-1 relative to vehicle (right). f, Density heatmap comparing log2(fold-change) H3K27me3 ChIP-Rx signal of Karpas-422 CXCdel vehicle relative to Karpas-422 CXCdel GSK343 treatment (y-axis) versus log2(fold-change) H3K27me3 ChIP-Rx signal of Karpas-422 SETD2-IN-1 (5 μM) relative to vehicle treatment (x-axis) across all genes (left) and across down-regulated upon SETD2-IN-1 (5 μM) treatment (right). g, Same as f. but for H3K27me3 ChIP-Rx signal of Karpas-422 SETD2-IN-1 treatment relative to vehicle (y-axis) versus log2(fold-change) H3K36me3 ChIP-Rx signal of Karpas-422 SETD2-IN-1 (5 μM) relative vehicle treatment (x-axis). h, Top 10 enriched pathways within the down-regulated genes upon SETD2 inhibition in Karpas-422 wild-type cells. Functional enrichment analysis of differentially expressed genes was performed using DAVID and P values were computed with modified Fisher’s Exact test. Results in a, b, d–h represent average of two biological replicates. For e, the interquartile range (IQR) is depicted by the box with the median represented by the center line. Whiskers maximally extend to 1.5 × IQR (with outliers excluded). P values were calculated by a Mann-Whitney-Wilcoxon two-sided test.

Supplementary information

Supplementary Information

Supplementary Fig. 1, Tables 1 and 2 and Note.

Reporting Summary

Supplementary Data

Supplementary Datasets 1–6 (CRISPR-suppressor and CRISPR-addiction scanning data, gene expression datasets).

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed immunoblots.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

Unprocessed immunoblots and gels.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed immunoblots.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 3

Unprocessed immunoblots.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 5

Unprocessed immunoblots and gels.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed immunoblots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwok, H.S., Freedy, A.M., Siegenfeld, A.P. et al. Drug addiction unveils a repressive methylation ceiling in EZH2-mutant lymphoma. Nat Chem Biol 19, 1105–1115 (2023). https://doi.org/10.1038/s41589-023-01299-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-023-01299-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer