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Hotspot DNMT3A mutations in clonal hematopoiesis and acute myeloid leukemia sensitize cells to azacytidine via viral mimicry response

Nature Cancer volume 2, pages 527–544 (2021)Cite this article

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Abstract

Somatic mutations in DNA methyltransferase 3A (DNMT3A) are among the most frequent alterations in clonal hematopoiesis (CH) and acute myeloid leukemia (AML), with a hotspot in exon 23 at arginine 882 (DNMT3AR882). Here, we demonstrate that DNMT3AR882H-dependent CH and AML cells are specifically susceptible to the hypomethylating agent azacytidine (AZA). Addition of AZA to chemotherapy prolonged AML survival solely in individuals with DNMT3AR882 mutations, suggesting its potential as a predictive marker for AZA response. AML and CH mouse models confirmed AZA susceptibility specifically in DNMT3AR882H-expressing cells. Hematopoietic stem cells (HSCs) and progenitor cells expressing DNMT3AR882H exhibited cell autonomous viral mimicry response as a result of focal DNA hypomethylation at retrotransposon sequences. Administration of AZA boosted hypomethylation of retrotransposons specifically in DNMT3AR882H-expressing cells and maintained elevated levels of canonical interferon-stimulated genes (ISGs), thus leading to suppressed protein translation and increased apoptosis.

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Fig. 1: DNMT3AR882H mutations predict AZA response in human AML and CH.
Fig. 2: Characterization of the steady-state hematopoietic phenotype in DNMT3AR882H mice.
Fig. 3: DNMT3AR882H leads to exhaustion of the murine HSC pool following AZA treatment.
Fig. 4: The DNMT3AR882H mutation sensitizes murine leukemia models to AZA treatment.
Fig. 5: WGBS reveals inflammatory response patterns and DNA hypomethylation of retrotransposon sequences in AZA-treated DNMT3AR882H LSK cells.
Fig. 6: Expression of DNMT3AR882H associates with enhanced proinflammatory pathways and increased expression of ERV transcripts and dsRNA.
Fig. 7: AZA boosts proinflammatory effects and reduces protein synthesis and ribosomal biogenesis in DNMT3AR882H-mutant cells.
Fig. 8: Proposed model of DNMT3AR882-induced sensitivity of hematopoietic cells to AZA treatment via viral mimicry.

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Data availability

The following databases have been used in the study: LOLA (http://big.databio.org/regiondb/LOLACore_180412.tgz), MSigDB (http://www.gsea-msigdb.org/gsea/msigdb/collections.jsp) and HOMER (http://homer.ucsd.edu/homer/). RNA-seq and mouse WGBS data are stored at NCBI’s Gene Expression Omnibus (GEO) data repository with the accession code GSE146907. Human WGBS raw data have been deposited in the European Genome Archive (EGA; accession codes EGAD00001007504 (data set ID) and EGAS00001004825 (study ID)). Data cannot be made publicly available due to data protection regulations and will be shared upon reasonable request to the authors, with clearance by the ethics committee of University Heidelberg in charge of overseeing participant data-sharing requests

The human AML RNA-seq data were derived from the TCGA Research Network (http://cancergenome.nih.gov/). The compiled files consisting of normalized expression values from TCGA for selected DNMT3AWT and DNMT3AR882-mutant participant samples and the respective categorical class file (*.cls), which has been used for upload to GSEA, are available from the corresponding author upon request. Imaging source data are uploaded to figshare (microscopy data of Fig. 6b and Extended Data Fig. 7a (ref. 68); microscopy data of Extended Data Fig. 3b (ref. 69)). All other data are available from the corresponding author upon request. Source data are provided with this paper.

Code availability

All unpublished software packages and workflows used for the methylome analysis described in this paper are available on GitHub at https://github.com/stephenkraemer/ (bistro (for methylation calling), dss_workflow (for DMR calling), gtfanno (for gene annotation) and region_set_profiler (for enrichment analysis)) and https://github.com/DKFZ-ODCF/AlignmentAndQCWorkflows (bisulfite core workflow for WGBS alignment and methylation calling (methylctools was replaced by bistro)). The bistro code repository is currently private; access will be granted upon request. Jupyter notebooks with the code to reproduce the data analysis are available from the authors upon request.

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Acknowledgements

We thank V. Eckstein for supporting the FACS sorting procedure, S. Garg for help in experiments with NSG mice and J. Kollan, K. Nerger, B. Besenbeck, M. Lotze and M. Horn for technical assistance. We thank S. Serba, K. Hillesheim and all animal keepers from the animal facility for supporting the mouse work. We gratefully acknowledge the data storage service SDS@hd supported by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK) and the German Research Foundation (DFG) through grant INST 35/1314-1 FUG. Research reported in this publication was (in part) supported by funds from the German Research Foundation (DFG; MU1328/13-1, MU1328/15‐1 and MU1328/18‐1 to C.M.-T.; DFG Forschergruppe FOR2674 to M.D.M., D.B.L. and A.T.; and SFB873, Project B04 to A.T.), the German Cancer Aid (DKH; Projects 70112974 and 70113908 to C.M.-T. and Project 70112574 to D.B.L.), the Helmholtz Zukunftsthema ‘Aging and Metabolic Programming’ (AMPro) to M.D.M. and D.B.L., the German Jose-Carreras Leukemia Foundation (DJCLS; 22R/2017 to C.M.-T. and 04R/2017 to N.B.), the Innovative Medical Research of the University of Münster Medical School (IMF grant no. 121314 to N.B.), BMBF 031L0212A to C.M.-T., the RiskY-AML Joint Funding program of the German Cancer Consortium (DKTK) to C.M.-T. and A.T., and the Dietmar Hopp Foundation to A.T. We thank the High Throughput Sequencing Unit of the Genomics & Proteomics Core Facility, German Cancer Research Center (DKFZ), for providing excellent NGS services. We further thank the Omics IT and Data Management Core Facility (ODCF) for providing excellent data storage and computing infrastructure.

Author information

Author notes

  1. These authors jointly supervised this work: Daniel B. Lipka, Carsten Müller-Tidow.

Authors and Affiliations

  1. Department of Medicine, Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany

    Marina Scheller, Anne Kathrin Ludwig, Stefanie Göllner, Christian Rohde, Maike Janssen, James-Arne Müller, Lixiazi He, Caroline Pabst & Carsten Müller-Tidow

  2. Molecular Medicine Partnership Unit, European Molecular Biology Laboratory (EMBL), University of Heidelberg, Heidelberg, Germany

    Marina Scheller, Anne Kathrin Ludwig, Christian Rohde, Maike Janssen, Judith Zaugg, Caroline Pabst & Carsten Müller-Tidow

  3. Bioinformatics and Omics Data Analytics Group, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Stephen Krämer & Matthias Schlesner

  4. Section Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Stephen Krämer, Sina Stäble, Maximilian Schönung & Daniel B. Lipka

  5. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

    Stephen Krämer & Maximilian Schönung

  6. National Center for Tumor Diseases (NCT), Heidelberg, Germany

    Stephen Krämer, Sina Stäble, Maximilian Schönung, Daniel B. Lipka & Carsten Müller-Tidow

  7. Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany

    Stephen Krämer & Matthias Schlesner

  8. Department of Medicine A, University Hospital Münster, Münster, Germany

    Nicole Bäumer & Wolfgang E. Berdel

  9. European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    Christian Arnold & Judith Zaugg

  10. Institute of Biostatistics and Clinical Research, WWU Münster, Münster, Germany

    Joachim Gerß

  11. Department of Medicine, University Hospital Dresden, Dresden, Germany

    Christian Thiede

  12. Interdisziplinäre Klinik und Poliklinik für Stammzelltransplantation, University Hospital Hamburg-Eppendorf, Hamburg, Germany

    Christian Niederwieser

  13. Department of Medicine I, University Hospital Leipzig, Leipzig, Germany

    Dietger Niederwieser

  14. Department of Medicine II, University of Frankfurt, Frankfurt, Germany

    Hubert Serve

  15. Geriatrics and Gerontology, University of Hamburg, Hamburg, Germany

    Ulrich Thiem

  16. Department of Medicine, Cardiology, University Hospital of Heidelberg, Heidelberg, Germany

    Inga Hemmerling & Florian Leuschner

  17. Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Christoph Plass

  18. Division of Experimental Hematology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Michael D. Milsom

  19. Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), Heidelberg, Germany

    Michael D. Milsom & Andreas Trumpp

  20. Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany

    Andreas Trumpp

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  1. Marina Scheller
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M. Scheller, N.B. and C.M.-T. designed the DNMT3A KI mouse model. M. Scheller and J.-A.M. performed and analyzed all mouse experiments. L.H., A.K.L. and C. Pabst performed NSG experiments. M. Scheller performed cell culture experiments, western blotting, immunofluorescence staining, flow cytometry and cell sorting. M. Scheller and J.-A.M. performed RT–qPCR. M. Scheller, J.-A.M. and A.K.L. prepared samples for RNA-seq. A.K.L. performed fluorescence imaging and Rnasel knockdown. S.S. and D.B.L. established the 32D Dnmt3aR878H cell line and prepared RNA-seq and WGBS libraries. I.H. prepared samples for histological analysis. C.R., C.A., S.K., M. Schönung, S.S., D.B.L., M. Schlesner and J.Z. performed bioinformatic analysis of RNA-seq and WGBS data. C.T. performed diagnostic sequencing of primary AML samples. M.J., H.S., W.E.B., U.T., J.G., C.N., D.N. and C.M.-T. analyzed the clinical trial data. C. Plass, F.L., A.T., M.D.M., I.H. and F.L. contributed to study conception. M. Scheller and C.M.-T. designed the study. M. Scheller, D.B.L., S.G. and C.M.-T. analyzed the data and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Marina Scheller or Carsten Müller-Tidow.

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Competing interests

The original AML-AZA clinical trial was partially supported by Celgene and Amgen (principal investigator, C.M.T.). The Department of Medicine V is further supported by multiple biopharmaceutical companies for clinical trials and translational research projects. C.T. is co-owner and CEO of AgenDix GmbH, a company performing molecular diagnostics. C.T. has served as an adviser and provided educational support for Celgene, JAZZ, Novartis and Astellas. All other authors declare no competing interests.

Additional information

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Extended data

Extended Data Fig. 1 DNMT3A-R882 mutations predict response to AZA in human AML and clonal hematopoiesis.

a and b, Kaplan Meier plots depict all patients of the AML-AZA trial cohort for whom DNA was available to determine DNMT3A-mutation status and who received at least one day of chemotherapy within the trial (n = 166). Patients were not censored for allogeneic transplantation. Results are similar to the results for the entire patient cohort(n = 214) of the AML-AZA trial as described26. a, Event Free Survival (EFS) of all patients with AZA+ ‘7+3’ (n = 76 patients) versus ‘7+3’ (n = 90 patients). EFS times were 5.1 months for AZA+ ‘7+3’ compared to 5.8 months for the ‘7+3’ treatment group (P = 0.86). b, Overall Survival (OS) of all patients with AZA+ ‘7+3’ versus ‘7+3’. OS trended to be lower in patients receiving AZA+ ‘7+3’ (median OS=11.4 months) compared to 7+3 therapy only (median OS=21.4 months, P = 0.067). Survival probabilities for panels a and b were assessed using the Kaplan-Meier method and evaluated by log-rank Mantle- Cox test. c, Influence of DNMT3A-R882 mutation and AZA treatment on survival as calculated using univariate analysis and Cox proportional hazards regression model. The following parameters were included: treatment (AZA+ ‘7+3’ (n = 76) vs. ‘7+3’ (n = 90)) for all patients, DNMT3A-R882 status (yes (n = 34) vs. no (n = 132)) and DNMT3A-R882 mutation in AZA+ ‘7+3’ treatment (n = 13) vs. all others (n = 153). All patients from the AML-AZA trial with known DNMT3A mutation status and at least one day of received chemotherapy were included into these analyses. d, EFS of patients with DNMT3A-mutations outside of exon 23 treated with ‘7+3’ (n = 10) or AZA+ ‘7+3’ (n = 10) (P = 0.21). e, OS of patients with DNMT3A-mutations outside of exon 23 treated with ‘7+3’ (n = 10) or AZA+ ‘7+3’ (n = 10) (P = 0.36). Survival probabilities for panels d and e were assessed using the Kaplan-Meier method and then evaluated by log-rank Mantle- Cox test. A two-sided α of 0.05 was chosen for all comparisons using patient’s data. f, Variant allele frequency (VAF) of DNMT3A-R882H in bone marrow cells one, four and eight weeks after AZA or 0.9%NaCl control treatment as analyzed by Droplet Digital PCR (ddPCR). Data are presented as mean ± s.e.m. n.s. P = 0.3 for control mice (week 4 vs. week 10 post-transplantation; n = 11 mice), *P = 0.0193 for AZA-treated mice (week 7 vs. week 10 post-transplantation; n = 11 mice). Statistical significance was assessed using two-tailed Student´s unpaired t-test.

Source data

Extended Data Fig. 2 Characterization of the steady-state hematopoietic phenotype in DNMT3AR882H mice and effects of AZA treatment.

a, RNA-seq analyses of cDNA generated from peripheral blood/BM cells of +/m animals demonstrating the CGC to CAC mutation (the thymidine in the genome browser screenshot relates to the antisense sequence represented in the reads). b, Genotyping PCR showing DNMT3A wild-type mice (denoted as ‘+/+’), non-recombined floxed (fl) mice (functions as a null allele; denoted as ‘+/R882Hfl’) and Cre-mediated recombined mice with excised allele (denoted as ‘+/m’) as analyzed from peripheral blood and tail cut cells. c, Gating strategy applied for analyzing hematopoietic stem and progenitor cells from +/+ and +/m mice. d, Apoptotic cells in the peripheral blood of +/+ and +/m mice ten days after NaCl control- or AZA treatment (n = 5 mice per genotype and treatment). Percentages are shown for early apoptotic (AV+DAPI-) and late apoptotic/ necrotic (AV+DAPI+) cells. Data shown are the mean ± s.e.m. Myeloid cells: *P = 0.024 for +/+ AZA vs. +/m AZA. Statistical significance was assessed using a two-tailed Student´s unpaired t-tests. e, Number of donor-derived LMPPs in the bone marrow of recipients 32 weeks after treatment (n = 4 mice per group). Data are presented as mean ± s.e.m. +/+ NaCl vs. +/m NaCl: **P = 0.0044. Statistical significance was assessed using a two-tailed Student´s unpaired t-tests. All P-values not considered significant can be found in the Source Data.

Source data

Extended Data Fig. 3 Survival of leukemic mice after treatment.

a, Representative FACS profiles of leukemia in spleen from +/+:myc-bcl2 and +/m: myc-bcl2 mice. Data are representative for n = 9 mice. b, Hematoxylin and eosin (H&E)–stained spleen and liver from +/+, +/+: myc-bcl2 and +/m: myc-bcl2, +/+:FLT3-ITD and +/m:FLT3-ITD mice. Histologically, leukemic splenic tissue has a markedly distorted organization compared to tissue from healthy C57BL/6N mice (+/+), which’h has organized areas of white pulp (WP) and red pulp (RP). (b, top) staining of spleen (original magnification of 5x), and (b, bottom) staining of liver (original magnification of x10). n = 3 mice for each genotype. c, Representative FACS profiles of leukemia in peripheral blood of +/+:FLT3-ITD (n = 5) and +/m:FLT3-ITD (n = 11) mice.

Extended Data Fig. 4 TWGBS reveals preferential DNA hypomethylation of endogenous retroviral elements and interferon response genes in AZA treated DNMT3AR882H mutant mice.

a, Correlation analysis of DNA methylation levels in all 500 bp tiles using Pearson correlation comparing NaCl-treated +/+ with +/m LSK cells. PCC *100 = Pearson Correlation Coefficient multiplied by 100. b, Principal component analysis based on DNA methylation levels in genome-wide 500 bp tiling windows. red: +/+ NaCl control (n = 5 mice), blue: +/+ AZA (n = 5 mice), green: +/m NaCl control (n = 3 mice), purple: +/m AZA (n = 2 mice). c, Heatmap showing hierarchical clustering of mean beta values (i.e. % DNA methylation) for all 15,468 DMRs across all independent experiments (mice) analyzed. For mouse numbers per genotype and treatment see panel b. d and e, IGV browser tracks displaying DNA methylation data for all independent experiments analyzed by TWGBS in the present study. For mouse numbers per genotype and treatment see panel b. Each vertical line represents a CpG dinucleotide and the height of the line represents the DNA methylation in percent. Depicted is the (d) Ikzf1 gene locus which features 6 DMRs and the (e) Irf8 gene locus which features 8 DMRs (track ‘all_DMRs’). f-h, Enrichment analysis: depicted are –log10(qvalues)* sign(log_odds_ratio) from dark blue (depleted feature) to red (enriched feature) indicating the strength of enrichment of a given feature in a particular cluster as compared to all other clusters. (f) defined genomic regions, i.e. GENCODE transcription start sites (‘gencode_all_tss’), CpG islands (‘island’), CpG island shores (‘shore’), CpG island shelves (‘shelve’), intragenic regions (‘intragenic’), and intergenic regions (‘intergenic’), (g) hematopoietic enhancers as defined by published ChIP-seq data on primary murine hematopoietic cell types, and (h) transcription factor (TF) ChIP-seq peaks from hematopoietic cell types using the LOLA database (http://databio.org/regiondb).

Extended Data Fig. 5 DNA methylation data of individual DMRs at specific gene loci.

a-b, IGV browser tracks displaying DNA methylation data for all independent experiments (mice) analyzed by TWGBS in the present study. +/+ NaCl control (n = 5 mice), +/+ AZA (n = 5 mice), +/m NaCl control (n = 3 mice), +/m AZA (n = 2 mice). Each vertical line represents a CpG dinucleotide and the height of the line represents the DNA methylation in percent. Depicted are DMRs overlapping with LTR-ERV1 elements at the (a) Camk2b locus and at the (b) Cep85 locus.

Extended Data Fig. 6 DNA methylation changes upon AZA treatment in human AML patients.

a, WGBS was performed from human AML samples which either carried a DNMT3AR882H mutation (DNMT3A-R882H, n = 1) or which were wildtype for DNMT3A (WT, n = 2). For each patient a sample was available at diagnosis (Control) and at day 18 of treatment with AZA. Pairwise DMRs were called across genotypes and timepoints and categorized as having high or low levels of DNA methylation. DMRs from human AML samples were tested for enrichment of murine DMR clusters as described in Figure 5. Depicted are log10(qvalues)*sign(log_odds_ratio) from dark-blue (depleted features) to red (enriched features) indicating the strength of enrichment of a given murine DMR cluster as compared to all other clusters. Only DMR categories with significant enrichments are shown. b, DMRs from human AML samples were tested for enrichment of MSigDB hallmark gene sets. Depicted are –log10(q-values)*sign(log_odds_ratio) from dark-blue (depleted features) to red (enriched features) indicating the strength of enrichment of a given feature in a particular cluster as compared to all other clusters. Only DMR categories with significant enrichments are shown. c, ERV loci shown to be affected by AZA in human colorectal cancer cells6. IGV browser tracks display DNA methylation data for MER4A1 locus, MER50 locus, MER57 locus and MLT1C locus. Each vertical line represents a CpG dinucleotide and the height of the line represents the DNA methylation in percent.

Extended Data Fig. 7 AZA induces dsRNA and ERV expression and reduces protein synthesis in DNMT3AR882H mutant cells.

a, Confocal immunofluorescence images of bone marrow cells from n = 1 mouse per genotype incubated with 2°AB (donkey anti mouse)-only as used for background normalization. Nuclei were counterstained with DAPI (blue). Scale bar 10 μm. b, Experimental scheme of tamoxifen treatment of primary mice.

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Scheller, M., Ludwig, A.K., Göllner, S. et al. Hotspot DNMT3A mutations in clonal hematopoiesis and acute myeloid leukemia sensitize cells to azacytidine via viral mimicry response. Nat Cancer 2, 527–544 (2021). https://doi.org/10.1038/s43018-021-00213-9

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