Three-dimensional chromatin landscapes in T cell acute lymphoblastic leukemia

Abstract

Differences in three-dimensional (3D) chromatin architecture can influence the integrity of topologically associating domains (TADs) and rewire specific enhancer–promoter interactions, impacting gene expression and leading to human disease. Here we investigate the 3D chromatin architecture in T cell acute lymphoblastic leukemia (T-ALL) by using primary human leukemia specimens and examine the dynamic responses of this architecture to pharmacological agents. Systematic integration of matched in situ Hi-C, RNA-seq and CTCF ChIP–seq datasets revealed widespread differences in intra-TAD chromatin interactions and TAD boundary insulation in T-ALL. Our studies identify and focus on a TAD ‘fusion’ event associated with absence of CTCF-mediated insulation, enabling direct interactions between the MYC promoter and a distal super-enhancer. Moreover, our data also demonstrate that small-molecule inhibitors targeting either oncogenic signal transduction or epigenetic regulation can alter specific 3D interactions found in leukemia. Overall, our study highlights the impact, complexity and dynamic nature of 3D chromatin architecture in human acute leukemia.

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Fig. 1: In situ Hi-C analysis identifies genome-wide 3D chromatin differences between normal T cells and T-ALL subtypes.
Fig. 2: Intra-TAD activity changes affect downstream effectors of T-ALL pathogenesis.
Fig. 3: TAD boundary insulation analysis reveals changes in insulation of neighboring TADs.
Fig. 4: CTCF-mediated TAD insulation defines the accessibility of MYC promoter–super-enhancer looping.
Fig. 5: NOTCH1 inhibition affects enhancer–promoter looping, specifically of NOTCH1-dependent enhancers.
Fig. 6: CDK7 inhibition concomitantly reduces H3K27ac levels and associated enhancer–promoter looping.

Data availability

All sequencing data created in this study have been uploaded to the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and are available under accession code GSE115896. Biological material used in this study can be obtained from the authors upon request. Source data for Figs. 26 and Extended Data Figs. 13, 5, 7, 9 and 10 are provided with the paper.

Code availability

All code for Hi-C analysis is available within the previously published Hi-C bench platform (https://github.com/NYU-BFX).

References

  1. 1.

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

  2. 2.

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

  3. 3.

    Spielmann, M., Lupianez, D. G. & Mundlos, S. Structural variation in the 3D genome. Nat. Rev. Genet. 19, 453–467 (2018).

  4. 4.

    Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

    Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

  9. 9.

    Whyte, W. A. et al. Master transcription factors and Mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

  10. 10.

    Gong, Y. et al. Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries. Nat. Commun. 9, 542 (2018).

  11. 11.

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

  12. 12.

    Hnisz, D., Day, D. S. & Young, R. A. Insulated neighborhoods: structural and functional units of mammalian gene control. Cell 167, 1188–1200 (2016).

  13. 13.

    Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944 (2017).

  14. 14.

    Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320 (2017).

  15. 15.

    Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

  16. 16.

    Narendra, V. et al. CTCF establishes discrete functional chromatin domains at the Hox clusters during differentiation. Science 347, 1017–1021 (2015).

  17. 17.

    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).

  18. 18.

    Petrovic, J. et al. Oncogenic Notch promotes long-range regulatory interactions within hyperconnected 3D cliques. Mol. Cell 73, 1174–1190 (2019).

  19. 19.

    Grabher, C., von Boehmer, H. & Look, A. T. Notch 1 activation in the molecular pathogenesis of T-cell acute lymphoblastic leukaemia. Nat. Rev. Cancer 6, 347–359 (2006).

  20. 20.

    Belver, L. & Ferrando, A. The genetics and mechanisms of T cell acute lymphoblastic leukaemia. Nat. Rev. Cancer 16, 494–507 (2016).

  21. 21.

    Wang, H. et al. NOTCH1–RBPJ complexes drive target gene expression through dynamic interactions with superenhancers. Proc. Natl Acad. Sci. USA 111, 705–710 (2014).

  22. 22.

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

  23. 23.

    Weng, A. P. et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269–271 (2004).

  24. 24.

    Liu, Y. et al. The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia. Nat. Genet. 49, 1211–1218 (2017).

  25. 25.

    Coustan-Smith, E. et al. Early T-cell precursor leukaemia: a subtype of very high-risk acute lymphoblastic leukaemia. Lancet Oncol. 10, 147–156 (2009).

  26. 26.

    Zhang, J. et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481, 157–163 (2012).

  27. 27.

    Palomero, T. et al. CUTLL1, a novel human T-cell lymphoma cell line with t(7;9) rearrangement, aberrant NOTCH1 activation and high sensitivity to γ-secretase inhibitors. Leukemia 20, 1279–1287 (2006).

  28. 28.

    Sulis, M. L. et al. NOTCH1 extracellular juxtamembrane expansion mutations in T-ALL. Blood 112, 733–740 (2008).

  29. 29.

    Lazaris, C., Kelly, S., Ntziachristos, P., Aifantis, I. & Tsirigos, A. HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking. BMC Genomics 18, 22 (2017).

  30. 30.

    Kourtis, N. et al. Oncogenic hijacking of the stress response machinery in T cell acute lymphoblastic leukemia. Nat. Med. 24, 1157–1166 (2018).

  31. 31.

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

  32. 32.

    Zhang, Z. et al. Expression of a non-DNA-binding isoform of Helios induces T-cell lymphoma in mice. Blood 109, 2190–2197 (2007).

  33. 33.

    Vilimas, T. et al. Targeting the NF-κB signaling pathway in Notch1-induced T-cell leukemia. Nat. Med. 13, 70–77 (2007).

  34. 34.

    Espinosa, L. et al. The Notch/Hes1 pathway sustains NF-κB activation through CYLD repression in T cell leukemia. Cancer Cell 18, 268–281 (2010).

  35. 35.

    Weng, A. P. et al. c-Myc is an important direct target of Notch1 in T-cell acute lymphoblastic leukemia/lymphoma. Genes Dev. 20, 2096–2109 (2006).

  36. 36.

    Palomero, T. et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl Acad. Sci. USA 103, 18261–18266 (2006).

  37. 37.

    Herranz, D. et al. A NOTCH1-driven MYC enhancer promotes T cell development, transformation and acute lymphoblastic leukemia. Nat. Med. 20, 1130–1137 (2014).

  38. 38.

    Yashiro-Ohtani, Y. et al. Long-range enhancer activity determines Myc sensitivity to Notch inhibitors in T cell leukemia. Proc. Natl Acad. Sci. USA 111, E4946–E4953 (2014).

  39. 39.

    Shi, J. et al. Role of SWI/SNF in acute leukemia maintenance and enhancer-mediated Myc regulation. Genes Dev. 27, 2648–2662 (2013).

  40. 40.

    Bahr, C. et al. A Myc enhancer cluster regulates normal and leukaemic haematopoietic stem cell hierarchies. Nature 553, 515–520 (2018).

  41. 41.

    Deangelo, D. J. et al. A phase I clinical trial of the Notch inhibitor MK-0752 in patients with T-cell acute lymphoblastic leukemia/lymphoma (T-ALL) and other leukemias. J. Clin. Oncol. 24, 6585–6585 (2006).

  42. 42.

    Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

  43. 43.

    Hah, N. et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145, 622–634 (2011).

  44. 44.

    Trimarchi, T. et al. Genome-wide mapping and characterization of Notch-regulated long noncoding RNAs in acute leukemia. Cell 158, 593–606 (2014).

  45. 45.

    Medyouf, H. et al. High-level IGF1R expression is required for leukemia-initiating cell activity in T-ALL and is supported by Notch signaling. J. Exp. Med. 208, 1809–1822 (2011).

  46. 46.

    Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2015).

  47. 47.

    Kwiatkowski, N. et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature 511, 616–620 (2014).

  48. 48.

    Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).

  49. 49.

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

  50. 50.

    Oomen, M. E., Hansen, A. S., Liu, Y., Darzacq, X. & Dekker, J. CTCF sites display cell cycle-dependent dynamics in factor binding and nucleosome positioning. Genome Res. 29, 236–249 (2019).

  51. 51.

    Vian, L. et al. The energetics and physiological impact of cohesin extrusion. Cell 173, 1165–1178 (2018).

  52. 52.

    King, B. et al. The ubiquitin ligase FBXW7 modulates leukemia-initiating cell activity by regulating MYC stability. Cell 153, 1552–1566 (2013).

  53. 53.

    Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572 (2017).

  54. 54.

    Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

  55. 55.

    Donaldson-Collier, M. C. et al. EZH2 oncogenic mutations drive epigenetic, transcriptional, and structural changes within chromatin domains. Nat. Genet. 51, 517–528 (2019).

  56. 56.

    Bellavia, D., Palermo, R., Felli, M. P., Screpanti, I. & Checquolo, S. Notch signaling as a therapeutic target for acute lymphoblastic leukemia. Expert Opin. Ther. Targets 22, 331–342 (2018).

  57. 57.

    Siersbaek, R. et al. Transcription factor cooperativity in early adipogenic hotspots and super-enhancers. Cell Rep. 7, 1443–1455 (2014).

  58. 58.

    Larochelle, S. et al. Cyclin-dependent kinase control of the initiation-to-elongation switch of RNA polymerase II. Nat. Struct. Mol. Biol. 19, 1108–1115 (2012).

  59. 59.

    Chipumuro, E. et al. CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer. Cell 159, 1126–1139 (2014).

  60. 60.

    Christensen, C. L. et al. Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor. Cancer Cell 26, 909–922 (2014).

  61. 61.

    Ambrosini, G., Groux, R. & Bucher, P. PWMScan: a fast tool for scanning entire genomes with a position-specific weight matrix. Bioinformatics 34, 2483–2484 (2018).

  62. 62.

    Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

  63. 63.

    Pitt, L. A. et al. CXCL12-producing vascular endothelial niches control acute T cell leukemia maintenance. Cancer Cell 27, 755–768 (2015).

  64. 64.

    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).

  65. 65.

    Strikoudis, A. et al. Regulation of transcriptional elongation in pluripotency and cell differentiation by the PHD-finger protein Phf5a. Nat. Cell Biol. 18, 1127–1138 (2016).

  66. 66.

    Phanstiel, D. H., Boyle, A. P., Heidari, N. & Snyder, M. P. Mango: a bias-correcting ChIA–PET analysis pipeline. Bioinformatics 31, 3092–3098 (2015).

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Acknowledgements

We thank all members of the Aifantis and Tsirigos laboratories for discussions throughout this project; A. Heguy and the NYU Genome Technology Center (supported in part by National Institutes of Health (NIH)/National Cancer Institute (NCI) grant P30CA016087-30) for expertise with sequencing experiments; the NYU Flow Cytometry facility for expert cell sorting; the Applied Bioinformatics Laboratory for computational assistance; and Genewiz for expertise with sequencing experiments. This work has used computing resources at the High-Performance Computing Facility at the NYU Medical Center. We would also like to acknowledge B. Ren and A. Schmitt for their support on the Hi-C experiments. I.A. is supported by the NCI/NIH (1P01CA229086, 1R01CA228135, R01CA216421, R01CA202025, R01CA133379, R01CA149655 and 1R01CA194923), Alex’s Lemonade Stand Cancer Research Foundation, the Chemotherapy Foundation, the Leukemia and Lymphoma Society and the NYSTEM program of the New York State Health Department. A.T. is supported by the American Cancer Society (RSG-15-189-01-RMC), the NCI/NIH (1P01CA229086), the Leukemia and Lymphoma Society and St. Baldrick’s Foundation. P.T. was previously supported by an AACR Incyte Corporation Leukemia Research Fellowship and is currently supported by a Young Investigator Grant from Alex’s Lemonade Stand Cancer Research Foundation. P.N. was supported by the NCI (R00CA188293), the American Society of Hematology, the Zell Foundation, the H Foundation and a grant (U54CA193419) from the NCI and the Chicago Region Physical Sciences–Oncology Center (CR-PSOC). P.V.V. was supported by a European Research Council Starting Grant (639784). T.L. is supported by the National Institute of General Medical Sciences (NIGMS)/NIH (R01GM127538). F.B. is supported by a Young Investigator Grant from Alex’s Lemonade Stand Cancer Research Foundation. S.N. is supported by the Onassis Foundation (scholarship ID F ZP 036-1/2019-2020).

Author information

A.T. and I.A. conceived, designed and supervised the study with input from A.K., P.T. and P.N. A.K. designed and performed most of the computational analyses with help from A.T., S.N. and C.L. P.T. and P.N. designed and performed most of the experiments with help from Y.G., X.C., H.H., S.B., J.W., T.T., Y.F., F.B., Y.Z., E.P., P.V.V., G.G.I. and T.L. P.T., P.N. and Y.G. performed Hi-C, HiChIP and 4C experiments with help from X.C., S.B., J.W. and Y.Z. P.T. performed DNA FISH with help from Y.F. and T.L. P.T. performed ChIP–seq with help from P.N., S.B. and J.W. P.T. and H.H. performed RNA-seq. T.T. performed GRO-seq. A.T., I.A., A.K. and P.T. wrote the manuscript with input from all authors.

Correspondence to Iannis Aifantis or Aristotelis Tsirigos.

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Competing interests

A.T. is a scientific advisor to Intelligencia.AI. All other authors declare that they have no competing interests.

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Extended data

Extended Data Fig. 1 Hi-C quality control and unsupervised analyses.

a) Read alignment statistics for Hi-C datasets, as absolute reads (left) and relative reads (in %, right). “ds.accepted.intra” are all intra-chromosomal reads used for all downstream analyses. b) Genome-wide stratum-adjusted correlation coefficient (SCC) scores for all pair-wise comparisons of the Hi-C datasets. HiCRep was used to calculate chromosome-wide correlation scores, which were averaged across all chromosomes for each pair-wise comparison. The HiCRep smoothing parameter X was set to 1.0. c) Principal Component Analysis (PCA) of the genome-wide compartment scores for each Hi-C dataset. Number samples: T cells n = 3; T-ALL n = 6, ETP-ALL n = 4. d) Compartment shifts between T cells, T-ALL and ETP-ALL. Assignment of A compartment was done using an average c-score > 0.1 in either all T cell, T-ALL or ETP-ALL samples and B compartment with average c-score < -0.1. Significance for differences between pairwise comparisons of T cells, T-ALL and ETP-ALL was determined using a two-sided t test between c-scores, and compartment shifts were determined using P value < 0.1. e) Integration of gene expression associated with compartment shifts for comparisons of T cell vs T-ALL (left) or T-ALL vs ETP-ALL (right) using RNA-seq (FPKM > 1). For each gene within the respective compartment bin, log2 fold-change between T cells and T-ALL (left) or between T-ALL and ETP-ALL (right) is shown. Significant differences are calculated using an unpaired one-sided t test comparing genes from A to A compartments (that is active compartment) with genes from A to B or B to A compartment shifts, following the hypothesis of a positive correlation between expression and compartment association. Boxplot information can be found as additional Source Data. Source data

Extended Data Fig. 2 Genomic loci displaying differential intra-TAD activity in T-ALL.

a) Hi-C interaction heat maps (first row) showing the IKZF2 locus (black circle). Second row shows heat maps of log2 (fold-change) interactions compared to T cell 1. b) H3K27ac ChIP-seq tracks for IKZF2 locus in T cells and CUTLL1, NOTCH1 ChIP-seq tracks for CUTLL1. Tracks represent fold-enrichment over input where applicable and counts-per-million reads otherwise. Grey area indicates TAD containing IKZF2. Number replicates: T cells H3K27ac n = 2; CUTLL1 H3K27ac n = 2; CUTLL1 NOTCH1 n = 1. c) Quantifications for intra-TAD activity (left; as highlighted in a) and expression of IKZF2 (right). Statistical evaluation for intra-TAD activity was performed using paired two-sided t test of average per interaction-bin for IKZF2 TAD between T cells (n = 3) and T-ALL (n = 6), followed by multiple testing correction. Log2 FPKM of IKZF2 expression for T cells (n = 13) and T-ALL (n = 6) samples; statistical evaluation was performed using edgeR followed by multiple testing correction. d) Hi-C interaction heat maps (first row) showing the CYLD locus (black circle). Second row shows heat maps of log2 (fold-change) interactions when compared to T-cell 1. e) H3K27ac ChIP-seq tracks for CYLD locus in T cells and CUTLL1, NOTCH1 ChIP-seq tracks for CUTLL1. Tracks represent fold-enrichment over input where applicable and counts-per-million reads otherwise. Grey area indicates TAD containing CYLD. Number replicates: T cells H3K27ac n = 2; CUTLL1 H3K27ac n = 2; CUTLL1 NOTCH1 n = 1. f) Quantifications for intra-TAD activity (left; as highlighted in D)) and expression of CYLD (right). Statistical evaluation for intra-TAD activity was performed using paired two-sided t test of average per interaction-bin for CYLD TAD between T cells (n = 3) and T-ALL (n = 6), followed by multiple testing correction (see methods). Log2 FPKM of CYLD expression for T cells (n = 13) and T-ALL (n = 6); statistical evaluation was performed using edgeR followed by multiple testing correction. Boxplot information can be found as additional Source Data. Source data

Extended Data Fig. 3 Intra-TAD activity cross-comparison of T-ALL sub-types.

a) Comparisons of intra-TAD activity between T cells, T-ALL and ETP-ALL samples. b) Overlap of differentially active TADs between the two comparisons of T cells vs T-ALL and T cells vs ETP-ALL, visualized as venn diagram. Red and blue colors correspond to differences as highlighted in a). c, d) Integration of RNA-seq (FPKM > 1) within TADs with decreased / increased intra-TAD activity for ETP-ALL vs T cells (c) and ETP-ALL vs T-ALL (d). For each such gene, the log2 (fold-change) in expression between ETP-ALL and T cells (c) / T-ALL and ETP-ALL (d) taken from RNA-seq is shown. Significant differences are calculated by an unpaired one-sided t test comparing genes from TADs with decreased / increased intra-TAD activity with genes from stable TADs, following the hypothesis of a positive correlation between expression and intra-TAD activity changes. Boxplot information can be found as additional Source Data. Source data

Extended Data Fig. 4 WGS integration with TAD boundaries altered in T-ALL.

a, b) Overlap of altered TAD boundaries as in Fig. 3c, d with genomic inversions (a) or insertions/deletions (indels) (b) from WGS of T-ALL 1 (top) and T-ALL 2 (bottom). Overlap was determined by bedtools intersect, using a 1 bp overlap for indels and 100 kb for individual inversion breakpoints (instead of the entire genomic range affected by the inversion). c) Overlap of individual translocation breakpoints (calculated from T-ALL Hi-C samples as in Supplementary Fig. 1B) with TAD boundaries displaying changes in TAD insulation between T cells and T-ALL. Overlap was determined by bedtools intersect, using a 1 bp overlap.

Extended Data Fig. 5 Difference in CTCF insulation in MYC locus is not due to genomic mutation but potentially regulated by open chromatin.

a) CTCF ChIP-qPCR of the CTCF binding site in the lost MYC TAD boundary, shown as fold-enrichment over input. Significant differences compared to T cells were calculated with an unpaired one-sided t test, following the hypothesis of loss of CTCF binding in T-ALL samples as determined from the genome-wide analysis (n = 3 replicates for T cells, T-ALL 1, T-ALL 2, CUTLL1 and Jurkat; n = 2 replicates for T-ALL 3 and T-ALL 4). Error bars indicate s.d.; center value indicates mean. b) Targeted sanger sequencing indicates no mutation in T-ALL in the CTCF binding site at the MYC TAD boundary. Tracks show chromatogram of individual base calls (left). Whole genome sequencing indicates no mutation in T-ALL in the motif of CTCF binding site. Tracks show (mis-)matches compared to reference sequence in all reads covering the respective genomic position (right). c) CTCF ChIP-qPCR before and after treatment with global DNA-demethylation agent 5-azacytidine (n = 2 replicates). d) ATAC-seq quantification for T cells and Jurkat for the genomic area covering loss of CTCF binding in the downstream TAD boundary of MYC. Data was normalized to the average T cell signal, shown in percent (n = 3 replicates). Statistical evaluation was performed using DiffBind with edgeR-method, following multiple testing correction. Error bars indicate s.d.; center value indicates mean. Source data

Extended Data Fig. 6 4C-Seq validation of MYC super-enhancer interaction in primary T-ALL.

a) 4C-seq analysis using MYC promoter as viewpoint. Positive y-axis shows interactions with the MYC promoter viewpoint as normalized read counts, negative y-axis shows significance of differential interactions between T cells and primary T-ALL samples as log10(P value) derived using edgeR function glmQLFTest. H3K27ac ChIP-seq tracks for T cells and CUTLL1 are represented below as fold-enrichment over input. Grey areas indicate MYC super-enhancer elements. Number replicates: T cells 4 C n = 2; T-ALL 1 4 C n = 1; T-ALL 2 4 C n = 2; T cells H3K27ac n = 2; CUTLL1 H3K27ac n = 2.

Extended Data Fig. 7 CRISPR-Cas9 deletion of CTCF binding site shows loss of insulation around MYC locus.

a) Schematic of Cas9+Synthetic guide transfection of activated T cells. b) Sequence showing CTCF motif in the insulator region in T cells targeted for CRISPR-based deletion. sgRNA targeting sequence within the CTCF motif is highlighted. Sequencing of sgRNA target site indicates various indels along with frequencies observed for each indel. c) CTCF ChIP-qPCR validation of reduced CTCF binding in edited T cells compared to unedited T cells (n = 2 replicates). d) qPCR comparing MYC expression in edited T cells compared to unedited T cells (n = 3 replicates). Statistical significance was determined using unpaired two-sided t test. Error bars indicate s.d.; center value indicates mean. e) 4C-seq analysis using MYC promoter as viewpoint in edited and unedited T cells. Positive y-axis shows interactions with the viewpoint as normalized read counts, negative y-axis shows significance of differential interactions between the two samples as log10(P value) calculated with edgeR function glmQLFTest. Tracks below show CTCF ChIP-seq in CUTLL1 and H3K27ac ChIP-seq in naïve T cells and CUTLL1 as fold-enrichment over input. Grey area indicates deleted CTCF binding site. Number replicates: T cells WT 4 C n = 2; T cells Edited 4 C n = 2; T cells CTCF n = 2; T cells H3K27ac n = 2; CUTLL1 H3K27ac n = 2. Source data

Extended Data Fig. 8 Genome-wide Hi-C analysis in T-ALL following γSI shows no intra-TAD activity differences, but individual promoter-enhancer loops are disrupted.

a) Volcano plot showing differential intra-TAD activity between CUTLL1 DMSO vs CUTLL1 γSI (average activity > 0.58 / < -0.58 and with FDR < 0.05). Statistical evaluation was performed using paired two-sided t test between all per bin-interactions between DMSO and γSI (n = 2 replicates). b) Representation of TAD boundary alteration events (red dots; none identified). Plots depict pair-wise comparisons for TAD boundary losses of adjacent CUTLL1 (untreated, left) TADs and for TAD boundary gains of adjacent CUTLL1 (γSI treated, right) TADs. Dotted line represents outlier threshold as in Fig. 3 c) and d). c) Virtual 4 C of H3K27ac HiChIP in CUTLL1, using MYC promoter as viewpoint (chr8: 128,747,680), showing edgeR-normalized CPM. H3K27ac ChIP-seq track for MYC locus shown as fold-enrichment over input. Detected significant loops as arc-representation (below) from mango pipeline utilizing two-sided binomial test per matrix-diagonal followed by multiple testing correction66 (FDR < 0.1; CPM > 5). Number replicates: CUTLL1 H3K27ac HiChIP n = 1; CUTLL1 H3K27ac ChIP-seq n = 2. d) H3K27ac signal (enrichment over input) (left), chromatin interaction of the highest peak by 4C-seq (center) for the interaction of LUNAR1 promoter with its upstream enhancer and LUNAR1 expression (right). All quantifications are normalized to the respective average T cell signal, shown in percent. Significance of differences was calculated using diffBind with edgeR-method (for H3K27ac ChIP-seq, FDR) and edgeR (for 4C-seq interactions and GRO-seq as P value and FDR respectively). Error bars indicate s.d.; center value indicates mean. Number replicates: CUTLL1 DMSO H3K27ac n = 2; CUTLL1 γSI H3K27ac n = 2; CUTLL1 DMSO 4 C n = 2; CUTLL1 γSI 4 C n = 2; CUTLL1 DMSO GRO-seq n = 2; CUTLL1 γSI GRO-seq n = 2. e) H3K27ac signal (left), chromatin interaction of the highest peak by 4C-seq (center) for the interaction of APCDD1 enhancer with the downstream APCDD1 promoter and APCDD1 expression (right). All quantifications are normalized to the respective average T cell signal, shown in percent. Significance of differences was calculated using diffBind with edgeR-method (for H3K27ac ChIP-seq, FDR) and edgeR (for 4C-seq interactions and GRO-seq as P value and FDR respectively). Error bars indicate s.d.; center value indicates mean. Number replicates: CUTLL1 DMSO H3K27ac n = 2; CUTLL1 γSI H3K27ac n = 2; CUTLL1 DMSO 4 C n = 2; CUTLL1 γSI 4 C n = 2; CUTLL1 DMSO GRO-seq n = 2; CUTLL1 γSI GRO-seq n = 2. f) Schematic of γSI sensitive and insensitive enhancer. g) Peak width of stable (black; n = 111) or decreased H3K27ac signal (green, n = 76) as defined in Fig. 5a. Significant difference between the distributions is estimated by a two-sided Wilcoxon test. Number replicates: CUTLL1 DMSO H3K27ac n = 2; CUTLL1 γSI H3K27ac n = 2.

Extended Data Fig. 9 Treatment with γSI does not alter all NOTCH1 dynamic enhancers.

a) 4C-seq using MYC promoter as viewpoint. Positive y-axis shows interactions with viewpoint as normalized read counts, negative y-axis shows significance of differential interactions as log10(P value) calculated using edgeR function glmQLFTest (CUTLL1 DMSO n = 5; CUTLL1 γSI n = 3). Tracks below show H3K27ac, NOTCH1 ChIP-seq and GRO-seq (positive strand only) as fold-enrichment where applicable, and counts-per-million reads otherwise. Grey areas indicate MYC super-enhancer elements. b) Quantification of H3K27ac signal (enrichment over input), chromatin interactions by 4C-seq for the interactions of MYC promoter and MYC expression. Interaction changes are measured by centering the 40 kb bin on highest peaks within N-Me/NDME, CEE or BDME/BENC elements. MYC expression was measured by qPCR. All quantifications are normalized to CUTLL1 DMSO, shown in percent. Error bars indicate s.d.; center value indicates mean. Significance is shown as false-discovery rate (FDR) for H3K72ac signal change (R package DiffBind with edgeR-method), P value for chromatin interaction change (edgeR function glmQLFTest) or one-tailored t test for qPCR changes. c) Cropped western blot images immunoblotted with MYC antibody. Unprocessed western blots can be found as Source Data. Experiment was repeated twice with similar results. d) CTCF ChIP-qPCR of lost MYC boundary upon γSI in CUTLL1 (n = 3). Error bars indicate s.d.; center value indicates mean. Significance was calculated using unpaired two-sided t test. e) 4C-seq analysis using IKZF2 promoter as viewpoint after γSI treatment. Positive y-axis shows normalized read counts, negative y-axis shows significance of differential interactions as log10(P value) calculated using edgeR function glmQLFTest (CUTLL1 DMSO n = 3; CUTLL1 γSI n = 3). Tracks below show H3K27ac, NOTCH1 ChIP-seq and GRO-seq (negative strand only) as fold-enrichment over input where applicable, and counts-per-million reads otherwise. Grey area indicates IKZF2 enhancer. f) H3K27ac signal is specific for enhancer highlighted in d). Interaction changes are measured by centering the 40 kb bin on the highest enhancer peak. IKZF2 expression after γSI treatment was measured by GRO-seq. All quantifications are normalized to the average T cell signal, shown in percent. Error bars indicate s.d.; center value indicates mean. Significance is shown as false-discovery rate (FDR) for H3K72ac signal (R package DiffBind with edgeR-method), P value for chromatin interaction (edgeR function glmQLFTest) or one-tailored t test for qPCR expression. Source data

Extended Data Fig. 10 Treatment of T-ALL with THZ1 reduces also γSI insensitive promoter-enhancer interactions.

a) H3K27ac signal is specific for N-Me/NDME, CEE and BDME/BENC. Interaction changes are measured by centering the 40 kb bin on highest peaks within N-Me/NDME, CEE or BDME/BENC elements. MYC expression after THZ1 treatment was measured by qPCR. All quantifications are normalized to the average CUTLL1 DMSO signal, shown in percent. Error bars indicate s.d.; center value indicates mean. Significance is shown as false-discovery rate (FDR) for H3K72ac signal (R package DiffBind with edgeR-method), P value for chromatin interaction (edgeR function glmQLFTest) or two-sided t test for qPCR expression. b) Cropped western blot images immunoblotted with MYC antibody. Unprocessed western blots can be found as Source Data. Experiment was repeated twice with similar results. c) CTCF ChIP-qPCR, shown as enrichment over input, of CTCF site in lost boundary in MYC locus (n = 3). Error bars indicate s.d.; center value indicates mean. Significance was calculated using unpaired two-sided t test. d) Inter-probe distance between MYC promoter and MYC-CCE measured by DNA-FISH analysis. Statistical difference between distributions of probe distances was calculated using two-sample one-sided Kolmogorov Smirnov test. Error bars indicate s.d.; center value indicates median. Probe-pairs CUTLL1 DMSO = 2001. Probe-pairs CUTLL1 THZ1 = 1308. Median distance CUTLL1 DMSO = 264.28 µm. Median distance CUTLL1 THZ1 = 321.69 µm. e) 4C-seq using MYC promoter as viewpoint in Jurkat cells. Positive y-axis shows normalized interaction strength with the viewpoint, negative y-axis shows significance of differential interactions as log10(P value) calculated using edgeR function glmQLFTest (n = 3). Grey areas indicate MYC super-enhancer elements. f) Interaction changes are measured by centering the 40 kb bin on N-Me/NDME, CEE or the BDME/BENC. Error bars indicate s.d.; center value indicates mean. Significance is shown as P value for chromatin interaction changes (edgeR function glmQLFTest). g) Quantification of changes in H3K27ac signal (enrichment over input) and chromatin interactions of IKZF2 enhancer in CUTLL1. All quantifications are normalized to the average CUTLL1 DMSO signal, shown in percent. Error bars indicate s.d.; center value indicates mean. Significance is shown as false-discovery rate (FDR) for H3K72ac signal change (R package DiffBind with edgeR-method), P value for chromatin interaction change (edgeR function glmQLFTest). Source data

Supplementary information

Supplementary Information

Supplementary Note, Supplementary Methods and Supplementary Fig. 1

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Supplementary Tables

Supplementary Tables 1–6

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Source Data Extended Data Fig. 1

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Unprocessed CT values of CTCF ChIP-qPCR.

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Unprocessed CT values of expression qPCR and CTCF ChIP-qPCR.

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Unprocessed western blot gel scans.

Source Data Extended Data Fig. 9

Unprocessed CT values of expression qPCR and CTCF ChIP-qPCR.

Source Data Extended Data Fig. 10

Unprocessed western blot gel scans.

Source Data Extended Data Fig. 10

Unprocessed CT values of expression qPCR and CTCF ChIP-qPCR.

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Kloetgen, A., Thandapani, P., Ntziachristos, P. et al. Three-dimensional chromatin landscapes in T cell acute lymphoblastic leukemia. Nat Genet (2020). https://doi.org/10.1038/s41588-020-0602-9

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