Abstract

Mammalian SWI/SNF chromatin remodelling complexes exist in three distinct, final-form assemblies: canonical BAF (cBAF), PBAF and a newly characterized non-canonical complex (ncBAF). However, their complex-specific targeting on chromatin, functions and roles in disease remain largely undefined. Here, we comprehensively mapped complex assemblies on chromatin and found that ncBAF complexes uniquely localize to CTCF sites and promoters. We identified ncBAF subunits as synthetic lethal targets specific to synovial sarcoma and malignant rhabdoid tumours, which both exhibit cBAF complex (SMARCB1 subunit) perturbation. Chemical and biological depletion of the ncBAF subunit, BRD9, rapidly attenuates synovial sarcoma and malignant rhabdoid tumour cell proliferation. Importantly, in cBAF-perturbed cancers, ncBAF complexes maintain gene expression at retained CTCF-promoter sites and function in a manner distinct from fusion oncoprotein-bound complexes. Together, these findings unmask the unique targeting and functional roles of ncBAF complexes and present new cancer-specific therapeutic targets.

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Data availability

The ChIP-seq and RNA-seq datasets generated and/or analysed during the current study have been deposited in the Gene Expression Omnibus repository under accession number GSE113042 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113042). Other datasets that were previously published and used in this study have been deposited in the Gene Expression Omnibus repository under accession numbers GSE90634 and GSE108025 available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90634 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108025, respectively. The fitness data were derived from Project Achilles through the Project Achilles Data Portal (https://portals.broadinstitute.org/achilles/about). The dataset derived from this resource that supports the findings of this study is available at https://portals.broadinstitute.org/achilles/datasets/all. The fitness data were also derived from Project DRIVE. The dataset derived from this resource that supports the findings of this study is available at https://oncologynibr.shinyapps.io/drive/. All proteomics/mass-spectrometry data are deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011103.

Additional information

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

References

  1. 1.

    Narlikar, GeetaJ., Sundaramoorthy, R. & Owen-Hughes, T. Mechanisms and functions of ATP-dependent chromatin-remodeling enzymes. Cell 154, 490–503 (2013).

  2. 2.

    Clapier, C. R. & Cairns, B. R. The biology of chromatin remodeling complexes. Annu. Rev. Biochem. 78, 273–304 (2009).

  3. 3.

    Ho, L. et al. An embryonic stem cell chromatin remodeling complex, esBAF, is essential for embryonic stem cell self-renewal and pluripotency. Proc. Natl Acad. Sci. USA 106, 5181–5186 (2009).

  4. 4.

    Lessard, J. et al. An essential switch in subunit composition of a chromatin remodeling complex during neural development. Neuron 55, 201–215 (2007).

  5. 5.

    Lickert, H. et al. Baf60c is essential for function of BAF chromatin remodelling complexes in heart development. Nature 432, 107–112 (2004).

  6. 6.

    Priam, P. et al. SMARCD2 subunit of SWI/SNF chromatin-remodeling complexes mediates granulopoiesis through a CEBPε dependent mechanism. Nat. Genet. 49, 753–764 (2017).

  7. 7.

    Witzel, M. et al. Chromatin-remodeling factor SMARCD2 regulates transcriptional networks controlling differentiation of neutrophil granulocytes. Nat. Genet. 49, 742–752 (2017).

  8. 8.

    Staahl, B. T. et al. Kinetic analysis of npBAF to nBAF switching reveals exchange of SS18 with CREST and integration with neural developmental pathways. J. Neurosci. 33, 10348–10361 (2013).

  9. 9.

    Yoo, A. S., Staahl, B. T., Chen, L. & Crabtree, G. R. MicroRNA-mediated switching of chromatin-remodelling complexes in neural development. Nature 460, 642–646 (2009).

  10. 10.

    Yoo, A. S. et al. MicroRNA-mediated conversion of human fibroblasts to neurons. Nature 476, 228–231 (2011).

  11. 11.

    Pedersen, T. A., Kowenz-Leutz, E., Leutz, A. & Nerlov, C. Cooperation between C/EBPα TBP/TFIIB and SWI/SNF recruiting domains is required for adipocyte differentiation. Genes Dev. 15, 3208–3216 (2001).

  12. 12.

    Pan, J. et al. Interrogation of mammalian protein complex structure, function, and membership using genome-scale fitness screens. Cell Syst. 6, 555–568 (2018).

  13. 13.

    Alpsoy, A. & Dykhuizen, E. C. Glioma tumor suppressor candidate region gene 1 (GLTSCR1) and its paralog GLTSCR1-like form SWI/SNF chromatin remodeling subcomplexes. J. Biol. Chem. 293, 3892–3903 (2018).

  14. 14.

    Wang, W. et al. Diversity and specialization of mammalian SWI/SNF complexes. Genes Dev. 10, 2117–2130 (1996).

  15. 15.

    Kaeser, M. D., Aslanian, A., Dong, M. Q., Yates, J. R. 3rd & Emerson, B. M. BRD7, a novel PBAF-specific SWI/SNF subunit, is required for target gene activation and repression in embryonic stem cells. J. Biol. Chem. 283, 32254–32263 (2008).

  16. 16.

    Kadoch, C. et al. Proteomic and bioinformatic analysis of mammalian SWI/SNF complexes identifies extensive roles in human malignancy. Nat. Genet. 45, 592–601 (2013).

  17. 17.

    Shain, A. H. & Pollack, J. R. The spectrum of SWI/SNF mutations, ubiquitous in human cancers. PLoS ONE 8, e55119 (2013).

  18. 18.

    Biegel, J. A. et al. Germ-line and acquired mutations of INI1 in atypical teratoid and rhabdoid tumors. Cancer Res. 59, 74–79 (1999).

  19. 19.

    Eaton, K. W., Tooke, L. S., Wainwright, L. M., Judkins, A. R. & Biegel, J. A. Spectrum of SMARCB1/INI1 mutations in familial and sporadic rhabdoid tumors. Pediatr. Blood Cancer 56, 7–15 (2011).

  20. 20.

    Versteege, I. et al. Truncating mutations of hSNF5/INI1 in aggressive paediatric cancer. Nature 394, 203–206 (1998).

  21. 21.

    Jones, S. et al. Frequent mutations of chromatin remodeling gene ARID1A in ovarian clear cell carcinoma. Science 330, 228–231 (2010).

  22. 22.

    Varela, I. et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469, 539–542 (2011).

  23. 23.

    McBride, M. J. et al. The SS18–SSX fusion oncoprotein hijacks BAF complex targeting and function to drive synovial sarcoma. Cancer Cell 33, 1128–1141 (2018).

  24. 24.

    Helming, K. C. et al. ARID1B is a specific vulnerability in ARID1A-mutant cancers. Nat. Med. 20, 251–254 (2014).

  25. 25.

    Hoffman, G. R. et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc. Natl Acad. Sci. USA 111, 3128–3133 (2014).

  26. 26.

    Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

  27. 27.

    Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

  28. 28.

    Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017).

  29. 29.

    McDonald, E. R. 3rd et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170, 577–592 (2017).

  30. 30.

    Cowley, G. S. et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1, 140035 (2014).

  31. 31.

    Bell, A. C. & Felsenfeld, G. Methylation of a CTCF-dependent boundary controls imprinted expression of the Igf2 gene. Nature 405, 482–485 (2000).

  32. 32.

    Bell, A. C., West, A. G. & Felsenfeld, G. The protein CTCF is required for the enhancer blocking activity of vertebrate insulators. Cell 98, 387–396 (1999).

  33. 33.

    Hark, A. T. et al. CTCF mediates methylation-sensitive enhancer-blocking activity at the H19/Igf2 locus. Nature 405, 486–489 (2000).

  34. 34.

    Kanduri, C. et al. Functional association of CTCF with the insulator upstream of the H19 gene is parent of origin-specific and methylation-sensitive. Curr. Biol. 10, 853–856 (2000).

  35. 35.

    Alver, B. H. et al. The SWI/SNF chromatin remodelling complex is required for maintenance of lineage specific enhancers. Nat. Commun. 8, 14648 (2017).

  36. 36.

    Mathur, R. et al. ARID1A loss impairs enhancer-mediated gene regulation and drives colon cancer in mice. Nat. Genet. 49, 296–302 (2017).

  37. 37.

    Wang, X. et al. SMARCB1-mediated SWI/SNF complex function is essential for enhancer regulation. Nat. Genet. 49, 289–295 (2017).

  38. 38.

    Nakayama, R. T. et al. SMARCB1 is required for widespread BAF complex-mediated activation of enhancers and bivalent promoters. Nat. Genet. 49, 1613–1623 (2017).

  39. 39.

    Kadoch, C. & Crabtree, G. R. Reversible disruption of mSWI/SNF (BAF) complexes by the SS18–SSX oncogenic fusion in synovial sarcoma. Cell 153, 71–85 (2013).

  40. 40.

    Clark, J. et al. Identification of novel genes, SYT and SSX, involved in the t(X;18)(p11.2; q11.2) translocation found in human synovial sarcoma. Nat. Genet. 7, 502–508 (1994).

  41. 41.

    Hohmann, A. F. et al. Sensitivity and engineered resistance of myeloid leukemia cells to BRD9 inhibition. Nat. Chem. Biol. 12, 672–679 (2016).

  42. 42.

    Martin, L. J. et al. Structure-based design of an in vivo active selective BRD9 inhibitor. J. Med. Chem. 59, 4462–4475 (2016).

  43. 43.

    Remillard, D. et al. Degradation of the BAF complex factor BRD9 by heterobifunctional ligands. Angew. Chem. Int. Ed. 56, 5738–5743 (2017).

  44. 44.

    Wang, X. et al. Oncogenesis caused by loss of the SNF5 tumor suppressor is dependent on activity of BRG1, the ATPase of the SWI/SNF chromatin remodeling complex. Cancer Res. 69, 8094–8101 (2009).

  45. 45.

    Chun, H. J. et al. Genome-wide profiles of extra-cranial malignant rhabdoid tumors reveal heterogeneity and dysregulated developmental pathways. Cancer Cell 29, 394–406 (2016).

  46. 46.

    Theodoulou, N. H. et al. Discovery of I-BRD9, a selective cell active chemical probe for bromodomain containing protein 9 inhibition. J. Med. Chem. 59, 1425–1439 (2016).

  47. 47.

    Coatham, M. et al. Concurrent ARID1A and ARID1B inactivation in endometrial and ovarian dedifferentiated carcinomas. Mod. Pathol. 29, 1586–1593 (2016).

  48. 48.

    Tauziede-Espariat, A. et al. Loss of SMARCE1 expression is a specific diagnostic marker of clear cell meningioma: a comprehensive immunophenotypical and molecular analysis. Brain Pathol. 2, 466–474 (2017).

  49. 49.

    Naka, N. et al. Synovial sarcoma is a stem cell malignancy. Stem Cells 28, 1119–1131 (2010).

  50. 50.

    Munoz, D. M. et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov. 6, 900–913 (2016).

  51. 51.

    Mashtalir, N. et al. Autodeubiquitination protects the tumor suppressor BAP1 from cytoplasmic sequestration mediated by the atypical ubiquitin ligase UBE2O. Mol. Cell 54, 392–406 (2014).

  52. 52.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  53. 53.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  54. 54.

    Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinform. 11, 237 (2010).

  55. 55.

    Dale, R. K., Pedersen, B. S. & Quinlan, A. R. Pybedtools: a flexible Python library for manipulating genomic datasets and annotations. Bioinformatics 27, 3423–3424 (2011).

  56. 56.

    Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

  57. 57.

    Loven, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).

  58. 58.

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

  59. 59.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  60. 60.

    McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

  61. 61.

    Machanick, P. & Bailey, T. L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).

  62. 62.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  63. 63.

    Feng, J. et al. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics 28, 2782–2788 (2012).

  64. 64.

    Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

  65. 65.

    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).

  66. 66.

    Tripathi, S. et al. Meta- and orthogonal integration of Influenza "OMICs" data defines a role for UBR4 in virus budding. Cell Host Microbe 18, 723–735 (2015).

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Acknowledgements

We are grateful to members of the Kadoch Laboratory for thoughtful discussions and insights relating to this study. We thank F. Winston, R. Kingston and Y. Shi for advice and critical review of the results. We thank A. Kuo and G.R. Crabtree for the homemade BAF155 (SMARCC1) polyclonal antibody used in these studies. We thank Z. Herbert, M. Berkeley and members of the Molecular Biology Core Facility for library preparation and sequencing. We also thank W. Wang, Z. Zhang and B. Li for conducting the CRISPR screening and cellular proliferation experiments. This work was supported in part by the NIH DP2 New Innovator Award no. 1DP2CA195762–01, American Cancer Society Research Scholar Award no. RSG-14-051-01-DMC and the Pew–Stewart Scholars in Cancer Research Grant awarded to C.K. B.C.M. holds the Albert J. Ryan Fellowship granted by the Division of Medical Sciences (Harvard Medical School). In addition, this work was supported in part by NIH Grant no. 5 T32 GM095450-04 (M.J.M.), a Harvard University Graduate School of Arts and Sciences Fellowship (M.J.M.), a Ford Foundation Fellowship (A.M.V), Howard Hughes Medical Institute Gilliam Fellows Program (A.M.V.) and the National Science Foundation Graduate Research Fellowship Program (J.P.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.

Author information

Author notes

    • James E. Bradner

    Present address: Novartis Institutes for Biomedical Research, Cambridge, MA, USA

  1. These authors equally contributed: Brittany C. Michel, Andrew R. D’Avino and Seth H. Cassel.

Affiliations

  1. Department of Pediatric Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    • Brittany C. Michel
    • , Andrew R. D’Avino
    • , Seth H. Cassel
    • , Nazar Mashtalir
    • , Zachary M. McKenzie
    • , Matthew J. McBride
    • , Alfredo M. Valencia
    • , Joshua Pan
    • , Hayley J. Zullow
    • , Nora Fortoul
    •  & Cigall Kadoch
  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Brittany C. Michel
    • , Andrew R. D’Avino
    • , Seth H. Cassel
    • , Nazar Mashtalir
    • , Matthew J. McBride
    • , Alfredo M. Valencia
    • , Joshua Pan
    • , David I. Remillard
    • , Caleb A. Lareau
    • , Hayley J. Zullow
    •  & Cigall Kadoch
  3. Biomedical and Biological Sciences Program, Harvard Medical School, Boston, MA, USA

    • Brittany C. Michel
    • , Seth H. Cassel
    • , Joshua Pan
    • , Caleb A. Lareau
    •  & Hayley J. Zullow
  4. Medical Scientist Training Program, Harvard Medical School, Boston, MA, USA

    • Seth H. Cassel
    •  & Hayley J. Zullow
  5. Chemical Biology Program, Harvard Medical School, Boston, MA, USA

    • Matthew J. McBride
    • , Alfredo M. Valencia
    •  & David I. Remillard
  6. Foghorn Therapeutics, Inc., Cambridge, MA, USA

    • Qianhe Zhou
    • , Michael Bocker
    • , Luis M. M. Soares
    •  & Ho Man Chan
  7. Department of Cancer Biology, Dana–Farber Cancer Institute, Boston, MA, USA

    • Nathanael S. Gray
  8. Department of Medical Oncology, Dana-Farber Cancer Institute , Boston, MA, United States

    • James E. Bradner

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Contributions

B.C.M., A.R.D., S.H.C., and C.K. conceived and designed the study. B.C.M. designed and performed most experiments, A.R.D. performed all bioinformatic analyses and statistical calculations. S.H.C. designed and performed GLTSCR1/1L biochemistry and contributed to ChIP-seq interpretation and analysis, Z.M.M. performed GLTSCR1/1L biochemistry, N.M. and J.P. were involved in the design and execution of experiments pertaining to ncBAF biochemistry, M.J.M, and A.M.V. were involved in the design and execution of synovial sarcoma and MRT experiments, and J.P. contributed to the analysis and interpretation of large-scale dependency data. D.I.R. synthesized dBRD9 and assisted in experimental design using dBRD9, and H.J.Z. and N.F. assisted with conducting GLTSCR1/1L biochemistry experiments. H.M.C., Q.Z. and M.B. directed the CRISPR tiling experiments and L.M.M.S. performed bioinformatic analyses of these datasets. C.A.L contributed important insights and aided in data analysis and interpretation. N.S.G. and J.E.B. supervised the development of the dBRD9 small molecule. B.C.M., A.R.D., S.H.C. and C.K. wrote the manuscript.

Competing interests

C.K. is a scientific founder, fiduciary Board of Directors member, Scientific Advisory Board (SAB) member, shareholder and consultant for Foghorn Therapeutics, Inc. (Cambridge, MA). H.M.C., Q.Z, M.B. and L.M.M.S are employees and shareholders of Foghorn Therapeutics, Inc. (Cambridge, MA). N.S.G. is a scientific founder, SAB member and equity holder in Gatekeeper, Syros, Petra, Soltego and C4 Therapeutics. J.E.B is now an executive and shareholder of Novartis AG. He is a founder and former shareholder of Tensha Therapeutics (a bromodomain company, now Roche) and Syros (a chromatin biotech). The other authors declare no competing interests.

Corresponding author

Correspondence to Cigall Kadoch.

Integrated supplementary information

  1. Supplementary Figure 1 Mammalian SWI/SNF family complexes exist in three distinct classes.

    (a). Heatmap representing correlations of fitness scores between mSWI/SNF complexes genes in genome-scale shRNA-based genetic perturbation screens (Project DRIVE, Novartis). (b). Table of total peptide counts (raw spectral counts) for each mass spectrometry experiment performed on mSWI/SNF complexes purified using HA-tagged baits. (c,d). Immunoprecipitation of endogenous GLTSCR1 (c) and GLTSCR1L (d) followed by immunoblot captures BRD9-specific mSWI/SNF subunits but not canonical BAF- or PBAF- specific subunits. Immunoprecipitations performed in n = 3 biologically independent experiments. See also Supplementary Figure 7j,k. (e). Immunoprecipitation of BRD9 followed by immunoblot for various subunits performed in NCIH-1437, BJ fibroblasts, IMR90, and ES-2 cell lines. Immunoprecipitations performed in n = 2 biologically independent experiments. See also Supplementary Figure 7l.

  2. Supplementary Figure 2 mSWI/SNF complex subtypes differentially localize on chromatin.

    (a). Schematic of subunits selected for ChIP-seq in EoL-1 cells: BRD9 and GLTSCR1 (ncBAF-specific), DPF2 (BAF-specific), BRD7 (PBAF-specific) and SMARCA4 and SMARCC1 (pan-mSWI/SNF) subunits. (b). Pearson correlation of read density between ChIP-seq experiments using two different BRD9 antibodies in EoL-1. ChIP-seq was performed in n = 2 independent samples. (c,d). Venn diagrams representing overlap between SMARCA4 and DPF2 (c) or BRD7 (d) ChIP-seq peaks in EoL-1. (e). Venn diagram of peaks for BRD7 (PBAF), BRD9 (ncBAF), and DPF2 (cBAF) in EoL-1. (f). Distance of each peak to the nearest TSS in indicated ChIP-seq experiments. (g). BAF, PBAF, and ncBAF complex ChIP-Seq read density distribution over the TSS and 2.5kb into the gene body in EoL-1. (h). Localization of CTCF and ncBAF, BAF, and PBAF complexes at the SH2B3 locus. CTCF-BRD9 overlap sites are shaded in gray. ChIP-seq was performed in n = 2 independent samples. (i). Distribution of CTCF, H3K27ac, H3K4me1, and H3K4me3 marks across all mSWI/SNF sites genome-wide in EoL-1, clustered into four groups. (j). ChIP-seq read density summary plots of DFP2-, BRD9-, and BRD7- bound mSWI/SNF complexes over active enhancers, active promoters, CTCF sites, and primed sites in EoL-1. (k). Example track depicting differential mSWI/SNF complex binding at the CMC1 locus. ChIP-seq was performed in n = 2 independent samples for mSWI/SNF subunits and n = 1 for histone marks. (l). Heatmap of CTCF, BRD9, H3K4me3, and H3K4me1 ChIP-seq occupancy over all CTCF sites in EoL-1, split into proximal and distal sites, and ranked by BRD9 density.

  3. Supplementary Figure 3 Synovial sarcoma and malignant rhabdoid tumor cell lines are sensitive to ncBAF perturbation.

    (a). Schematic for CRISPR-Cas9-based synthetic lethal screening. (b). CERES dependency scores for ncBAF subunits BRD9, GLTSCR1, and SMARCD1 across all soft tissue and bone cancers, ranked by BRD9 score. (c). Waterfall plots of ATARIS (Project DRIVE) scores across n = 387 cancer cell lines for indicated subunits; dashed line = −0.75 score. (d). BAF subunit perturbations in WT, SS, and MRT settings. (e). Heatmap of the z-score of CERES scores across all n = 408 cancer cell lines ranked by median z-score. (f). Immunoblot for ncBAF subunits in HEK-293T cells upon 250nM dBRD9 treatment or BRD9 KO (n = 2). See also Supplementary Figure 7m. (g). Immunoblot and proliferation on SYO-1 cells with either SS18-SSX1 (shSSX) or control (shCtrl) (n = 2 biologically independent experiments for each). Each data point represents mean ± SD from n = 3 biologically independent samples, p-value calculated by two-sided t-test on day 20. See also Supplementary Figure 7n, Supplementary Table 2. (h). Immunoblot and proliferation (n = 1 experiment) performed on SYO-1 cells treated with GLTSCR1 (shGLT1) or a non-targeting guide. Each data point is mean ± SD from n = 3 biologically independent samples, p-value calculated by two-sided t-test on day 7. See also Supplementary Table 2 (i, j). FACS-based cell cycle analysis (i) and Annexin V staining (j) for SYO-1 cells after 8 days of compound treatment (n = 1). (k). (Left) Immunoblot on G401 MRT cells treated with DMSO or dBRD9 (250 nM) (n = 2 biologically independent experiments); (Right) Proliferation experiments performed in G401, each data point represents mean ± SD from n = 3 biologically independent samples, p-value calculated by two-sided t-test on day 7. See also Supplementary Figure 7o, Supplementary Table 2. (l). Proliferation experiment in SMARCB1-intact ESX cells treated with DMSO or dBRD9 (250nM). Each data point represents mean ± SD from n = 3 biologically independent samples. (m, n). Colony formation assay (as in Fig. 3i) performed on HSSYII (m) and Aska (n) cells (n = 2 biologically independent experiments). (o, p, q). Colony formation performed on HCT-116 (n), Calu-6 (o), and RD rhabdomyosarcoma (p) cell lines (n = 2 biologically independent experiments). (r). SS18 and SMARCC1 IP/immunoblot in BRD9 KO HEK-293T cells (n = 2 biologically independent experiments). See also Supplementary Figure 7p.

  4. Supplementary Figure 4 CRISPR tiling screening performed across mSWI/SNF subunit genes in SYO-1 cells reveals subunit- and subunit domain- specific dependencies.

    (a). Box plot of dropout scores for guides targeting the SS18 gene or non-targeting control in CRISPR tiling screens in SYO-1. Box represents interquartile range (IQR), bar in center shows data median. Minima and maxima shown extend to from the box +/− 1.5*IQR with data falling outside of that range shown as points. (SS18: n = 166, Control: n = 200). (b). CRISPR tiling screening performed across the SS18 gene in SYO-1 cells. (c,d). CRISPR tiling screening performed across the SMARCB1 (c) and SMARCE1 (d) genes in SYO-1 cells. (e). Box plot of dropout scores for guides to SMARCD family paralogs or non-targeting control in CRISPR tiling screen in SYO-1. Box represents interquartile range (IQR), bar in center shows data median. Minima and maxima shown extend to from the box +/− 1.5*IQR with data falling outside of that range shown as points. (SMARCD1: n = 147, SMARCD2: n = 167, SMARCD3: n = 137, Control: n = 200). (f). CRISPR tiling screening performed across SMARCD1 gene indicating no specific domain dropout. (g). Box plot of dropout score for guides to SMARCC family paralogs or non-targeting control in CRISPR tiling screen in SYO-1. Box represents interquartile range (IQR), bar in center shows data median. Minima and maxima shown extend to from the box +/− 1.5*IQR with data falling outside of that range shown as points. (SMARCC1: n = 314, SMARCC2: n = 430, Control: n = 200). (h). CRISPR tiling screening performed across the SMARCC1 gene indicating no specific domain dropout. (i). Immunoprecipitation of mammalian GLTSCR1 full-length (GLTSCR1-FL) and GLTSCR1 N-terminal deletion (G1-Ndel) followed by immunoblot (n = 2 biologically independent experiments).

  5. Supplementary Figure 5 BRD9 and SS18-SSX regulate distinct gene sets in synovial sarcoma.

    (a). Enriched gene sets for gene groups 1, 2, and 3 from Fig. 5b. (b). Schematic depicting experimental conditions in CRL7250 fibroblast cells used in RNA-seq experiments. (c). GSEA performed on RNA-seq experiments from conditions outlined in Supplementary Figure 5b. (d). Example tracks at an SS18-SSX fusion-dependent site (left) and bar graph of gene expression by RNA-seq (right) in SYO-1 at the FLRT2 locus. n = 2 independent samples for each ChIP-seq experiment. Bar represents mean RPKM of n = 2 RNA replicates for each condition with RPKM for each sample plotted as a dot. (e). Example tracks at SS18-SSX fusion-independent sites (left) and bar graphs of gene expression by RNA-seq (right) in SYO-1 at the SLC7A5 and SRM loci. n = 2 independent samples for each ChIP-seq experiment. Bar represents mean RPKM of n = 2 RNA replicates for each condition with RPKM for each sample plotted as a dot. (f). Violin plot of CERES scores for genes that changed with a significance of p-adjusted<1e-3 after 6 days of dBRD9 treatment in MOLM-13 cells. p-adjusted values are Benjamini-Hochberg adjusted Wald p-values, p-value between sets of genes was calculated by two-sided t-test. Violin plot shows kernel density estimation with data quartiles represented as lines, the data median is shown as a dot.

  6. Supplementary Figure 6 BRD9 maintains gene expression at retained, CTCF-marked promoter sites in BAF-perturbed settings of synovial sarcoma and malignant rhabdoid tumor.

    (a). Hockey stick plot of TTC1240 H3K27ac signal with MRT-specific super enhancers as defined by Chun et al. marked in red. (b). Track showing BRD9 (DMSO), SMARCA4 (DMSO), SMARCA4 (250nM dBRD9), and H3K27ac (empty vector condition) occupancy at the LIF locus in TTC1240. n = 2 independent samples for each ChIP-seq experiment. (c). Boxplots of H3K27ac and BRD9 ChIP occupancy at the promoters of active genes (n = 1064 sig. changing genes, n = 11503 non_changing genes). n = 2 independent samples for each ChIP-seq experiment, p-value calculated using two-sided t-test. Box represents interquartile range (IQR), bar in center shows data median. Minima and maxima shown extend to from the box +/− 1.5*IQR. (d). GREAT analysis of GO Biological Process for genes near SMARCA4 sites lost upon dBRD9 treatment. (e). ChIP-Seq density heatmap of SMARCA4, BRD9, H3K4me3, H3K4me1, H3K27ac, and CTCF over SMARCA4 proximal (<2 kb to TSS) and distal sites (>2 kb to TSS) in TTC1240 Empty sorted by BRD9 density. (f). ChIP-Seq density heatmap of SS18, BRD9, H3K4me3, SYO-1 CTCF, and EOL-1 CTCF over shScr BRD9 sites in Aska, ranked by difference in SS18 density between shScr and shSSX conditions. (g). BRD9 ChIP-seq density over CTCF sites ordered by BRD9 density in shCtrl condition in SYO-1 cells. (h). BRD9 ChIP-seq density before and after SMARCB1 reintroduction in TTC1240 cells over CTCF sites.

  7. Supplementary Figure 7 All raw and unprocessed immunoblots.

    (a). Western blots related to Fig. 1d. (b). Western blots related to Fig. 1e. (c). Western blots related to Fig. 3d. (d). Western blots related to Fig. 3e. (e). Western blots related to Fig. 3h. (f). Western blots related to Fig. 4g. (g). Western blots related to Fig. 4h. (h). Western blots related to Fig. 5a. (i). Western blots related to Fig. 5d. (j). Western blots related to Supplementary Figure 1c. (k). Western blots related to Supplementary Figure 1d. (l). Western blots related to Supplementary Figure 1e. (m). Western blots related to Supplementary Figure 3f. (n). Western blots related to Supplementary Figure 3g. (o). Western blots related to Supplementary Figure 3k. (p). Western blots related to Supplementary Figure 3r. (q). Western blots related to Supplementary Figure 4i.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–7 and Supplementary Table legends.

  2. Reporting Summary

  3. Supplementary Table 1

    Proteomics for BRD9, DPF2, BRD7 purifications in HEK-293T cells (raw peptide counts).

  4. Supplementary Table 2

    Statistics source data for cell line proliferation experiments.

  5. Supplementary Table 3

    CRISPR–Cas9 tiled sgRNA guide information.

  6. Supplementary Table 4

    mSWI/SNF gene CRISPR–Cas9 dropout scores in SYO-1 cells

  7. Supplementary Table 5

    Sequencing statistics and quality control metrics for genomic data.

  8. Supplementary Table 6

    All primer sets used in this study.

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DOI

https://doi.org/10.1038/s41556-018-0221-1

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