Core regulatory transcription factors (CR TFs) orchestrate the placement of super-enhancers (SEs) to activate transcription of cell-identity specifying gene networks, and are critical in promoting cancer. Here, we define the core regulatory circuitry of rhabdomyosarcoma and identify critical CR TF dependencies. These CR TFs build SEs that have the highest levels of histone acetylation, yet paradoxically the same SEs also harbor the greatest amounts of histone deacetylases. We find that hyperacetylation selectively halts CR TF transcription. To investigate the architectural determinants of this phenotype, we used absolute quantification of architecture (AQuA) HiChIP, which revealed erosion of native SE contacts, and aberrant spreading of contacts that involved histone acetylation. Hyperacetylation removes RNA polymerase II (RNA Pol II) from core regulatory genetic elements, and eliminates RNA Pol II but not BRD4 phase condensates. This study identifies an SE-specific requirement for balancing histone modification states to maintain SE architecture and CR TF transcription.
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The data reported herein are made publicly available through the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo). The GEO accession number for all ChIP-seq, ChIP-Rx, AQuA-HiChIP and RNA-seq data is GSE116344. Code is available at https://github.com/GryderArt. A list of related resources and reagents used in our study can be found in Supplementary Table 6, which includes a list of all software and data used herein and their sources.
Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).
Orphanides, G., Lagrange, T. & Reinberg, D. The general transcription factors of RNA polymerase II. Genes Dev. 10, 2657–2683 (1996).
Lee, T. I. & Young, R. A. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).
Boyer, L. A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).
Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
Saint-André, V. et al. Models of human core transcriptional regulatory circuitries. Genome Res. 26, 385–396 (2016).
Durbin, A. D. et al. Selective gene dependencies in MYCN-amplified neuroblastoma include the core transcriptional regulatory circuitry. Nat. Genet. 50, 1240–1246 (2018).
Lin, C. Y. et al. Active medulloblastoma enhancers reveal subgroup-specific cellular origins. Nature 530, 57–62 (2016).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Gryder, B. E. et al. Chemical genomics reveals histone deacetylases are required for core regulatory transcription. Nat. Commun. 10, 3004 (2019).
Allfrey, V., Faulkner, R. & Mirsky, A. Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proc. Natl Acad. Sci. USA 51, 786–794 (1964).
Pogo, B., Allfrey, V. & Mirsky, A. RNA synthesis and histone acetylation during the course of gene activation in lymphocytes. Proc. Natl Acad. Sci. USA 55, 805–812 (1966).
Kanno, T. et al. BRD4 assists elongation of both coding and enhancer RNAs by interacting with acetylated histones. Nat. Struct. Mol. Biol. 21, 1047–1057 (2014).
Moriniere, J. et al. Cooperative binding of two acetylation marks on a histone tail by a single bromodomain. Nature 461, 664–668 (2009).
Gryder, B. E. et al. PAX3-FOXO1 establishes myogenic super enhancers and confers BET bromodomain vulnerability. Cancer Discov. 7, 884–899 (2017).
Stewart, E. et al. Identification of therapeutic targets in rhabdomyosarcoma through integrated genomic, epigenomic, and proteomic analyses. Cancer Cell 34, 411–426.e19 (2018).
Meyers, R. M. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Tenente, I. M. et al. Myogenic regulatory transcription factors regulate growth in rhabdomyosarcoma. eLife 6, e19214 (2017).
Schmidt, K., Glaser, G., Wernig, A., Wegner, M. & Rosorius, O. Sox8 is a specific marker for muscle satellite cells and inhibits myogenesis. J. Biol. Chem. 278, 29769–29775 (2003).
Böhm, M. et al. Helicase CHD4 is an epigenetic coregulator of PAX3-FOXO1 in alveolar rhabdomyosarcoma. J. Clin. Invest. 126, 4237–4249 (2016).
Chu, T. et al. Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme. Nat. Genet. 50, 1553–1564 (2018).
Blumberg, A. et al. Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data. Preprint at bioRxiv https://doi.org/10.1101/690644 (2019).
Sharova, L. V. et al. Database for mRNA half-life of 19 977 genes obtained by DNA microarray analysis of pluripotent and differentiating mouse embryonic stem cells. DNA Res. 16, 45–58 (2009).
Fukaya, T., Lim, B. & Levine, M. Enhancer control of transcriptional bursting. Cell 166, 358–368 (2016).
Greer, C. B. et al. Histone deacetylases positively regulate transcription through the elongation machinery. Cell Rep. 13, 1444–1455 (2015).
Sanchez, G. J. et al. Genome-wide dose-dependent inhibition of histone deacetylases studies reveal their roles in enhancer remodeling and suppression of oncogenic super-enhancers. Nucleic Acids Res. 46, 1756–1776 (2018).
Orlando, D. A. et al. Quantitative ChIP-Seq normalization reveals global modulation of the epigenome. Cell Rep. 9, 1163–1170 (2014).
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).
Dowen, J. M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).
Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).
Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).
Chory, E. J. et al. Nucleosome turnover regulates histone methylation patterns over the genome. Mol. Cell 73, 61–72.e3 (2019).
Stanton, B. Z., Chory, E. J. & Crabtree, G. R. Chemically induced proximity in biology and medicine. Science 359, eaao5902 (2018).
Chiarella, A. M. et al. Dose-dependent activation of gene expression is achieved using CRISPR and small molecules that recruit endogenous chromatin machinery. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0296-7 (2019).
Cho, W.-K. et al. RNA Polymerase II cluster dynamics predict mRNA output in living cells. eLife 5, e13617 (2016).
Cho, W.-K. et al. Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361, 412–415 (2018).
Weider, M. & Wegner, M. SoxE factors: Transcriptional regulators of neural differentiation and nervous system development. Semin. Cell Dev. Biol. 63, 35–42 (2017).
Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).
Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, eaar3958 (2018).
Boulay, G. et al. Cancer-specific retargeting of BAF complexes by a prion-like domain. Cell 171, 163–178.e19 (2017).
Chang, W. et al. Multi-dimensional ClinOmics for precision therapy of children and adolescent young adults with relapsed and refractory cancer: a report from the center for cancer research. Clin. Cancer Res. 22, 3810–3820 (2016).
Barski, A. et al. High-Resolution Profiling of Histone Methylations in the Human Genome. Cell 129, 823–837 (2007).
Yohe, M. E. et al. MEK inhibition induces MYOG and remodels super-enhancers in RAS-driven rhabdomyosarcoma. Sci. Transl Med. 10, eaan4470 (2018).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Hodges, H. C. et al. Dominant-negative SMARCA4 mutants alter the accessibility landscape of tissue-unrestricted enhancers. Nat. Struct. Mol. Biol. 25, 61–72 (2018).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Tarumoto, Y. et al. LKB1, salt-inducible kinases, and MEF2C are linked dependencies in acute myeloid leukemia. Mol. Cell 69, 1017–1027.e6 (2018).
Shi, J. et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Kidder, B. L. & Zhao, K. Efficient library preparation for next-generation sequencing analysis of genome-wide epigenetic and transcriptional landscapes in embryonic stem cells. Methods Mol Biol. 1150, 3–20 (2014).
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
Stanton, B., Khan, J., Gryder, B. Absolute quantification of architecture (AQuA-HiChIP) enables measurement of differential chromatin interactions. Protoc. Exch. https://doi.org/10.1038/protex.2018.130 (2018).
York, A. G. et al. Instant super-resolution imaging in live cells and embryos via analog image processing. Nat. Methods 10, 1122–1126 (2013).
Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol Methods 1, e10 (2014).
We would like to thank M. Yohe, D. Levens, I. Klein, W. Kuehl, B. Hawley, C. Greer and E. Chory for thoughtful discussions regarding experiments and the manuscript. We are grateful to C. Danko and S.-P. Chou for advice, protocols and code involved with performing ChRO-seq. We are indebted to J. Qi and L. Wu for providing selective HDAC inhibitors LW3 and Merck60. We also thank P. Brown of the Structural Genomics Consortium for providing their set of other epigenetic molecules. We thank the CCR Genomics Core at the National Cancer Institute, NIH, Bethesda, MD, USA, for sequencing the single-cell RNA. This work was supported by the NCI, NIH. S.P. is a recipient of a Fondazione Veronesi fellowship. R. Rota was supported by Associazione Italiana Ricerca sul Cancro (AIRC 15312) and Italian Ministry of Health (PE-2013-02355271). N. Hathaway is supported in part by Grant R01GM118653 from the US National Institutes of Health. C. R. Vakoc and X. S. Wu were supported by the Pershing Square Sohn Cancer Research Alliance, Northwell Health Translational Research Award, the Christina Renna Foundation, The Clark Gillies Foundation, the Friends of T.J. Foundation and the Michelle Paternoster Foundation for Sarcoma Research. B.Z.S. is supported as a St. Baldrick’s Foundation Scholar. Finally, we are indebted to H. Vishwasrao and H. Shroff for contributing their expertise on high-resolution imaging. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services. Mention of trade names, commercial products, or organizations does not indicate endorsement by the US Government.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Core regulatory circuitry landscape of RMS reveals a pan-RMS module, a FP-RMS selective module, a FN-RMS selective module, and a normal-muscle selective module of TFs. Red dots indicate the samples with a PAX3-FOXO1 fusion gene, and the gold dot indicates the tumor sample (NCI0082) with a PAX7-FOXO1 fusion gene. b, Gene expression of all SOX family genes in 63 FP-RMS cell lines and tumors, measured by RNA-seq and reported as bar graph of average FPKM with standard deviation. Box plots show the 1st quartile, median and 3rd quartile, and whiskers extend from the min to the max. c, SOX8 expression from FN-RMS cell lines (n = 17), FN-RMS primary tumors (n = 67), FP-RMS cell lines (n = 23), FP-RMS primary tumors (n = 39), compared to myoblasts/myotubes (n = 12) and normal tissue (muscle, n = 14; brain, n = 34; others, n = 158), with a zoom in for fusion gene types. Box plots represent the 1st quartile, median and 3rd quartile, and the whisker show the 1.5 * inter-quartile range. Distribution for large categories is shown as violin plots overlaid with the box plots, while the zoom-in distribution of SOX8 expression in FP-RMS tumors is shown with a dot plot of all points. d, Network analysis of CR TFs in RH4 cells, constructed from H3K27ac defined motif-SE connections (same analysis summarized in Figure 1a). Red circles indicate ChIP-seq validated TFs. Node size is proportional to TF expression. Arrows indicate the presence of a TF’s motif in the enhancer valleys of the SE driving the gene to which the arrow is pointing. e, Western blot to detect SOX8 from two FP-RMS primary tumors. This experiment was performed once. Full uncropped western blot is available in Supplementary Fig. 8. f, Genetic dependency heat map (z-score) from CRISPR screening data across cancer cell lines (n = 391), organized by tumor type (data from Project Achilles, Broad Institute).
Supplementary Figure 2 Core regulatory TFs in RH4 cells with shRNA dissection of transcriptional networks.
a, Core regulatory (CR) SEs and TFs in RH4 cells. Data is from ChIP-seq of H3K27ac, and is representative of more than 10 replicate independent experiments in RH4 cell across different cell passage numbers that all gave very similar results. b, Portion of TFs with SEs are ranked by network connectivity (below); TFs with greater than 50% of max connectivity are classified as CR TFs in RH4 cells. c, Scatter plot of CRISPR dependency score and total connectivity; Top CR TFs are those with ≤-1 dependency. d, Rank order of gene expression plot for RH4 CR TFs, with top CR TFs (essential in RH4) highlighted. e, RNA-seq after depletion of CR TFs P3F, MYOD1, MYOG, SOX8 and MYCN with shRNA for 48 hours in RH4 cells. Each of these RNA-seq experiments was performed once, and similar results were obtained from orthogonal RNA-seq experiments after CRISPR disruption of these factors at multiple time points (see Fig. 2). f, Bubble plot summarizing the RNA-seq results for gene set enrichment with normalized enrichment score (NES, normalized for gene set size) and P-value calculated in GSEA which uses a Kolmogorov-Smirnov test comparing the enrichment score to the null distribution estimated from 1,000 random permutations. g, Example GSEA plots from (f).
a, ChIP-seq of HDAC1 (left), HDAC2 (center) and HDAC3 (right) along the body of genes, categorized as Repressed, Low Expression, Mid Expression, High Expression, or Core Regulatory TF target genes. Shading shows the SEM. b, Peak overlap measurement (top) among HDAC isoforms 1, 2 and 3 (and % overlap with CR TFs) across the RH4 epigenome (top) and visualized via ChIP-seq heatmap (bottom). c, HDAC inhibition with Entinostat (at 1, 6 and 24 hours in RH4 cells) reduced SOX8, MYOD1, MYOG and MYCN. Data shown are from a single experiment, and is representative of similar results obtained in RH41 cells with similar HDAC inhibitors (data not shown). d, Gene set enrichment analysis (GSEA) of time course RNA-seq from Entinostat treatment (1, 6 and 24 hours). P-values for gene sets are indicated by size of the circles, while color indicates both sign and extend of the normalized enrichment score (NES). e, GSEA plots of CR TF transcription after sgRNA disruption of individual HDACs. Data are for 72 hours of CRISPR editing, compared to 6 hours of Entinostat. 2 sgRNAs per HDAC enzyme where delivered to guide CRISPR–cas9 machinery in RH4 cells, targeting the enzymatic active site responsible for deacetylation. Expression changes of HDACs is shown (right). f, Formulas for calculating mRNA decay rates and half-lives from integrated analysis of nascent RNA and total RNA-seq. g, Calculated relative transcript half-lives shown as box (1st quartile, median and 3rd quartile) and whisker (1.5 * interquartile range) plots, overlapping distribution shown as violin plots, for housekeeping and core regulatory TF transcripts. P-value was calculated using a two sided student’s T-test with Welch’s correction (unpaired). h, Hypothetical graph of mRNA decay assuming halted transcription, 100 molecules at time zero, and indicated half-lives. i, Gene set of apoptotic genes activated by HDACi and measured at the single cell level. The sample sizes per condition that were aggregated at the gene level were DMSO (n = 2,925 cells), Entinostat 1 hr (n = 3,805 cells) or 6 hrs (n = 3,240 cells).
a, Spike-in ChIP-seq with drosophila chromatin shows importance of per-cell normalization to account for global changes in the fraction of the ChIP antibody target bound to chromatin. The decrease in dm3 reads, as compared to hg19 reads, reflective of an increase in the total quantity of H3K27ac decorating human the human cancer cell genome (RH4) increasing upon HDAC inhibition (left). The shape of SE metagene plots are shown without normalization to Spike in reads (middle) or with the normalization to Spiked-in chromatin (right; shading indicates the SEM). b, HDACi induced increase of H3K27ac, BRD4 and YY1 (as measured by ChIP-Rx) at CTCF anchors surrounding SEs in RH4 cells. Shading shows the SEM of the ChIP-seq signal. c, HDACi induced increased background levels of BRD4 and YY1 binding, outside called peaks. d, Chromatin-associated factors (RAD21 of cohesin, HDAC2/3, p300, BRD4, YY1), RNA Pol2, accessibility (ATAC-seq) and H3K27ac altered by acute (6 hr) inhibition of HDAC with Entinostat. Ground-state ChIP-Rx levels at insulators, enhancers and gene bodies near super enhancers are indicated with dashed lines (DMSO), and Entinostat treated ChIP-Rx levels shown in solid lines. Details regarding the number of independent replicate experiments is provided in the Statistics and Reproducibility section of the Methods.
Supplementary Figure 5 Disruption of individual HDACs (1, 2 or 3) are insufficient to observe the spreading of histone acetylation seen with simultaneous inhibition of all three HDACs.
a, Top, ChIP-seq data showing HDAC1, HDAC2, and HDAC3 co-localize to the same SE constituents on the MYOD1 super enhancer. Bottom, RNA-seq following 6 hours of treatment with HDAC3 selective inhibitor LW3, HDAC1/2 selective inhibitor Merck60, or both LW3 and Merck60 simultaneously, or HDAC1/2/3 selective inhibitor Entinostat. Log2 fold change in TPM (transcripts per million) of MYOD1 expression was measured by RNA-seq after 6 hours of treatment in RH4 cells. RNA-seq experiments were performed once per drug per time point per concentration, and are representative of similar observations made at the same time 6 hour point from RMS cells treated with related HDAC inhibitors (data not shown). Merck60 only treatments were performed in isogenic and epigenetically/transcriptionally identical cells, RH41. b, ChIP-qPCR of H3K27ac at MYOD1 super enhancer epicenter, boundary, or TSSR (transcriptional start site region) from RH4 cells treated with DMSO (6 hours), LW3 (1 µM, 6 hours), Merck60 (1 µM, 6 hours) of Entinostat (1 µM, 1 hour). Dots show enrichment across technical triplicates. c, ChIP-Rx (top 4 tracks) of H3K27ac in RH4 cells with CRISPR disrupted HDAC1, 2 or 3 for 72 hours, compared to triple inhibition with HDAC1/2/3 selective inhibitor Entinostat for 6 hours. ChIP-Rx experiments were performed once. AQuA-HiChIP is representative of 2 separate biotin-captures, library preparations and sequencing runs that gave similar results. Controls are colored dark blue and overlap treated tracks colored red. Two-dimensionalized AQuA-HiChIP data in RH4 cells treated with DMSO or Entinostat is shown in the last track. Genome tracks are shown at SOX8, MYOD1 and MYOG.
Supplementary Figure 6 Absolute Quantification of Architecture (AQuA-HiChIP) at core regulatory gene loci.
a, Regular HiChIP (contacts per million), H3K27ac ChIP target, in RH4 cells treated with either DMSO or Entinostat (top), compared with AQuA-HiChIP using exogenous reference chromatin spike-in (bottom), at the MYOD1 super enhancer containing locus. b, SE-to-SE contact loss, amidst aberrant SE-contact spreading at SOX8 cis-regulatory elements. Top, AQuA-HiChIP contact heatmaps (DMSO, Entinostat 6 hours, in RH4 cells); bottom, virtual 4C analysis (change in AQuA-HiChIP contact frequency, with increases colored dark pink and decreases in blue) anchored at SE1 upstream or SE3 downstream of SOX8. c, Hyper-acetylation causes expansive gains of aberrant SE contacts, shown by AQuA-HiChIP of H3K27ac in RH4 cells, treated for 6 hours with DMSO (top row) or Entinostat (bottom row), visualized at 7 core regulatory domains. Super enhancers are indicated by red bars above the contact frequency heatmaps. CR TF genes are shown in dark blue.
a, Time-course ChIP-Rx of H3K27ac during Entinostat treatment, at core regulatory TFs (left) compared to all other TFs (right), both along the gene body (top) and the transcriptional end site (TES, bottom). b, Quantification of H3K27ac density along the promoter, transcriptional start site region (TSSR), the gene body, and the transcriptional end site region (TESR), shown for all Pol2 bound coding genes (n = 28,974), all housekeeping genes (n = 5,034), all super enhancer associated genes (n = 1,284), and SE associated TF genes (n = 114). Adjacent box plots are shown for DMSO (6 hours) and Entinostat (1 hour and 6 hours), where the boxes show the 1st quartile, median and 3rd quartile, and the whiskers show the 1.5 * interquartile range. c, Genic spread ratio of H3K27ac (formula above) shown for various gene sets compared to core regulatory TFs, split into gene size categories: less than 10 kb, 10 to 100 kb, and genes greater than 100 kb. Box plots are used to display the median (center thick line) surrounded by the 1st and 3rd quartiles, with whiskers showing 1.5 * the interquartile range. P-values were calculated using a two-sided t-test with no adjustments for multiple comparisons. d, Cumulative distribution of Traveling Ratios for RNA Polymerase 2 (Pol2) showing increased elongation at CR TF genes compared to all genes (left, p < 0.0001), a general increase in pausing induced by Entinostat treatment (center, p < 2.2x10-16), and no statistically significant change in pausing at CR TF genes under DMSO or Entinostat treatment (right, p = 0.5). Traveling Ratio distribution differences were evaluated by Welch’s two-tailed t-test. e, RNA-Pol2 is heavily reduced at SEs by HDAC inhibition. Scatter plots of Pol2 ChIP-Rx signal, log2-fold change vs ChIP-Rx density in RRPM (left) and summarized by box-plots (right; boxes show 1st and 3rd quartiles with center as the median value, while whiskers show 1.5 * the interquartile range; the p-values were calculated by a two-sided unpaired student’s t-test with Welch’s correction). Pol2 sites outside of SEs were (n = 16,268) and Pol2 sites within SEs were (n = 472). Peaks were stitched together when multiple Pol2 peaks were present in a SE. f, Super resolution imaging (iSIM methodology, see Online Methods for details) of RNA-Pol2-GFP clusters in live RH4 cells. Fluorescence images are pseudo-colored green. Individual Pol2 puncta ranked by density (area times brightness) plots reveal asymmetric distribution of Pol2 in control conditions (DMSO) which are reduced by 6 hours of HDACi (Entinostat) treatment. The experiment was repeated twice with similar results.
a, Copped gel in Supplementary Fig. 1e b, Uncropped western blot image of (a) with a box showing the region cropped.
Supplementary Figures 1-8
Primers for ChIP-qPCR and RT-qPCR
Gene Sets used for RNA-seq Analysis
Single-cell RNA-seq Oligos
Transcription Factor dependencies in RMS
Resources, reagents and software
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Gryder, B.E., Pomella, S., Sayers, C. et al. Histone hyperacetylation disrupts core gene regulatory architecture in rhabdomyosarcoma. Nat Genet 51, 1714–1722 (2019). https://doi.org/10.1038/s41588-019-0534-4