Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Spliceosomal disruption of the non-canonical BAF complex in cancer

Nature volume 574, pages 432–436 (2019)Cite this article

Subjects

Abstract

SF3B1 is the most commonly mutated RNA splicing factor in cancer1,2,3,4, but the mechanisms by which SF3B1 mutations promote malignancy are poorly understood. Here we integrated pan-cancer splicing analyses with a positive-enrichment CRISPR screen to prioritize splicing alterations that promote tumorigenesis. We report that diverse SF3B1 mutations converge on repression of BRD9, which is a core component of the recently described non-canonical BAF chromatin-remodelling complex that also contains GLTSCR1 and GLTSCR1L5,6,7. Mutant SF3B1 recognizes an aberrant, deep intronic branchpoint within BRD9 and thereby induces the inclusion of a poison exon that is derived from an endogenous retroviral element and subsequent degradation of BRD9 mRNA. Depletion of BRD9 causes the loss of non-canonical BAF at CTCF-associated loci and promotes melanomagenesis. BRD9 is a potent tumour suppressor in uveal melanoma, such that correcting mis-splicing of BRD9 in SF3B1-mutant cells using antisense oligonucleotides or CRISPR-directed mutagenesis suppresses tumour growth. Our results implicate the disruption of non-canonical BAF in the diverse cancer types that carry SF3B1 mutations and suggest a mechanism-based therapeutic approach for treating these malignancies.

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

Access options

Buy this article

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

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

Fig. 1: BRD9 mis-splicing causes BRD9 loss and proliferative advantage in SF3B1-mutated cancers.
Fig. 2: Mutant SF3B1 recognizes an aberrant, deep intronic branchpoint within BRD9.
Fig. 3: BRD9 loss perturbs the formation and localization of the ncBAF complex.
Fig. 4: BRD9 is a therapeutically targetable tumour suppressor in melanoma.

Similar content being viewed by others

Data availability

RNA-seq and ChIP–seq data generated as part of this study were deposited in the Gene Expression Omnibus (accession number GSE124720). RNA-seq data from published studies were downloaded from CGHub (TCGA UVM59), EMBL-EBI ArrayExpress (Illumina Human BodyMap 2.0: E-MTAB-513), the Gene Expression Omnibus (accession numbers GSE72790 and GSE114922 for chronic lymphocytic leukaemia15 and myelodysplastic syndromes27, respectively), or directly obtained from the authors (for UVM10). Gel source data can be found in Supplementary Fig. 1. Other data that support the findings of this study are available from the corresponding authors upon reasonable request.

References

  1. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011).

    ADS  CAS  PubMed  Google Scholar 

  2. Papaemmanuil, E. et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl. J. Med. 365, 1384–1395 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Wang, L. et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N. Engl. J. Med. 365, 2497–2506 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Seiler, M., et al. Somatic mutational landscape of splicing factor genes and their functional consequences across 33 cancer types. Cell Rep. 23, 282–296.e284 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Michel, B. C. et al. A non-canonical SWI/SNF complex is a synthetic lethal target in cancers driven by BAF complex perturbation. Nat. Cell Biol. 20, 1410–1420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Gatchalian, J. et al. A non-canonical BRD9-containing BAF chromatin remodeling complex regulates naive pluripotency in mouse embryonic stem cells. Nat. Commun. 9, 5139 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  8. Quesada, V. et al. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat. Genet. 44, 47–52 (2011).

    PubMed  Google Scholar 

  9. Furney, S. J. et al. SF3B1 mutations are associated with alternative splicing in uveal melanoma. Cancer Discov. 3, 1122–1129 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Alsafadi, S. et al. Cancer-associated SF3B1 mutations affect alternative splicing by promoting alternative branchpoint usage. Nat. Commun. 7, 10615 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Harbour, J. W. et al. Recurrent mutations at codon 625 of the splicing factor SF3B1 in uveal melanoma. Nat. Genet. 45, 133–135 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Martin, M. et al. Exome sequencing identifies recurrent somatic mutations in EIF1AX and SF3B1 in uveal melanoma with disomy 3. Nat. Genet. 45, 933–936 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cretu, C. et al. Molecular architecture of SF3b and structural consequences of its cancer-related mutations. Mol. Cell 64, 307–319 (2016).

    CAS  PubMed  Google Scholar 

  14. DeBoever, C. et al. Transcriptome sequencing reveals potential mechanism of cryptic 3′ splice site selection in SF3B1-mutated cancers. PLoS Comput. Biol. 11, e1004105 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Darman, R. B. et al. Cancer-associated SF3B1 hotspot mutations induce cryptic 3′ splice site selection through use of a different branch point. Cell Rep. 13, 1033–1045 (2015).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Gozani, O., Potashkin, J. & Reed, R. A potential role for U2AF-SAP 155 interactions in recruiting U2 snRNP to the branch site. Mol. Cell. Biol. 18, 4752–4760 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Pineda, J. M. B. & Bradley, R. K. Most human introns are recognized via multiple and tissue-specific branchpoints. Genes Dev. 32, 577–591 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  Google Scholar 

  20. Martens, J. A. & Winston, F. Recent advances in understanding chromatin remodeling by Swi/Snf complexes. Curr. Opin. Genet. Dev. 13, 136–142 (2003).

    CAS  PubMed  Google Scholar 

  21. Brien, G. L. et al. Targeted degradation of BRD9 reverses oncogenic gene expression in synovial sarcoma. eLife 7, e41305 (2018).

    PubMed  PubMed Central  Google Scholar 

  22. Moore, A. R. et al. Recurrent activating mutations of G-protein-coupled receptor CYSLTR2 in uveal melanoma. Nat. Genet. 48, 675–680 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Baldi, A. et al. The HtrA1 serine protease is down-regulated during human melanoma progression and represses growth of metastatic melanoma cells. Oncogene 21, 6684–6688 (2002).

    CAS  PubMed  Google Scholar 

  24. Chien, J. et al. Serine protease HtrA1 associates with microtubules and inhibits cell migration. Mol. Cell. Biol. 29, 4177–4187 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Walker, C. J. et al. Genome-wide association study identifies an acute myeloid leukemia susceptibility locus near BICRA. Leukemia 33, 771–775 (2019).

    PubMed  Google Scholar 

  26. Stein, C. A. & Castanotto, D. FDA-approved oligonucleotide therapies in 2017. Mol. Ther. 25, 1069–1075 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Pellagatti, A. et al. Impact of spliceosome mutations on RNA splicing in myelodysplasia: dysregulated genes/pathways and clinical associations. Blood 132, 1225–1240 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Smit, A., Hubley, R. & Green, P. RepeatMasker Open-4.0, http://www.repeatmasker.org (2013–2015).

  29. Cheng, D. T. et al. Memorial Sloan Kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Seiler, M. et al. H3B-8800, an orally available small-molecule splicing modulator, induces lethality in spliceosome-mutant cancers. Nat. Med. 24, 497–504 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Leeksma, A. C. et al. Clonal diversity predicts adverse outcome in chronic lymphocytic leukemia. Leukemia 33, 390–402 (2019).

    CAS  PubMed  Google Scholar 

  32. Kim, E. et al. SRSF2 mutations contribute to myelodysplasia by mutant-specific effects on exon recognition. Cancer Cell 27, 617–630 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Mohammed, H. et al. Endogenous purification reveals GREB1 as a key estrogen receptor regulatory factor. Cell Rep. 3, 342–349 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  35. Pelossof, R. et al. Prediction of potent shRNAs with a sequential classification algorithm. Nat. Biotechnol. 35, 350–353 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  37. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Piva, F., Giulietti, M., Burini, A. B. & Principato, G. SpliceAid 2: a database of human splicing factors expression data and RNA target motifs. Hum. Mutat. 33, 81–85 (2012).

    CAS  PubMed  Google Scholar 

  40. Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Mashtalir, N., et al. Modular organization and assembly of SWI/SNF family chromatin remodeling complexes. Cell 175, 1272–1288.e1220 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Dvinge, H. et al. Sample processing obscures cancer-specific alterations in leukemic transcriptomes. Proc. Natl Acad. Sci. USA 111, 16802–16807 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Meyer, L. R. et al. The UCSC genome browser database: extensions and updates 2013. Nucleic Acids Res. 41, D64–D69 (2013).

    CAS  PubMed  Google Scholar 

  44. Flicek, P. et al. Ensembl 2013. Nucleic Acids Res. 41, D48–D55 (2013).

    CAS  PubMed  Google Scholar 

  45. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  47. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    PubMed  PubMed Central  Google Scholar 

  49. Wagenmakers, E. J., Lodewyckx, T., Kuriyal, H. & Grasman, R. Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method. Cognit. Psychol. 60, 158–189 (2010).

    PubMed  Google Scholar 

  50. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).

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

    PubMed  PubMed Central  Google Scholar 

  53. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    ADS  PubMed Central  Google Scholar 

  54. Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Dharmalingam G. & Carroll, T. soGGi: Visualise ChIP–seq, MNase-seq and motif occurrence as aggregate plots summarised over grouped genomic intervals. R package version 1.14.0, https://rdrr.io/bioc/soGGi/ (2018).

  56. Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    CAS  PubMed  Google Scholar 

  57. Tan, G. & Lenhard, B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555–1556 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Kulakovskiy, I. V. et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res. 46, D252–D259 (2018).

    CAS  PubMed  Google Scholar 

  59. Robertson, A.G., et al. Integrative analysis identifies four molecular and clinical subsets in uveal melanoma. Cancer cell 32, 204–220.e215 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful for the support of the MSK RNAi core facility for help with the CRISPR screens performed in the study, the MSK anti-tumor assessment core facility for help with patient-derived xenograft experiments and the Genomics Shared Resource of the Fred Hutchinson/University of Washington Cancer Consortium (P30 CA015704). D.I., S.C.-W.L., A.Y. and O.A.-W. are supported by the Leukemia & Lymphoma Society. D.I. is supported by grants from Lydia O’Leary Memorial Pias Dermatological Foundation and Kobayashi Foundation for Cancer Research. A.Y. is supported by grants from the Aplastic Anemia and MDS International Foundation (AA&MDSIF) and the Lauri Strauss Leukemia Foundation. S.X.L. is supported by a Conquer Cancer Foundation and ASCO Young Investigator Award, an Aplastic Anemia & Myelodysplastic Syndrome International Foundation research award, as well as an AACR Lymphoma Research Fellowship. G.-L.C. is a Mahan Fellow. O.A.-W. is supported by the Pershing Square Sohn Cancer Research Alliance, the Henry & Marilyn Taub Foundation and the Starr Cancer Consortium. R.K.B. is a Scholar of The Leukemia and Lymphoma Society (1344-18) and is supported in part by the US National Institutes of Health (R01 DK103854). O.A.-W. and R.K.B. are supported by the Evans MDS Foundation, the US National Institutes of Health (R01 HL128239) and the Department of Defense Bone Marrow Failure Research Program (BM150092 and W81XWH-12-1-0041). The results shown here are in part based upon data generated by the TCGA research network (https://cancergenome.nih.gov/).

Author information

Author notes

  1. These authors contributed equally: Daichi Inoue, Guo-Liang Chew

Authors and Affiliations

  1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Daichi Inoue, Bo Liu, Tyler Hitchman, Stanley Chun-Wei Lee, Lillian Bitner, Ariele Block, Amanda R. Moore, Akihide Yoshimi, Luisa Escobar-Hoyos, Hana Cho, Alex Penson, Sydney X. Lu, Justin Taylor, Yu Chen & Omar Abdel-Wahab

  2. Department of Hematology-Oncology, Institute of Biomedical Research and Innovation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Japan

    Daichi Inoue

  3. Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Guo-Liang Chew, Joseph Pangallo, Khrystyna North & Robert K. Bradley

  4. Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Guo-Liang Chew, Joseph Pangallo, Khrystyna North & Robert K. Bradley

  5. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Brittany C. Michel

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

    Brittany C. Michel, Andrew R. D’Avino & Cigall Kadoch

  7. Biomedical and Biological Sciences Program, Harvard Medical School, Boston, MA, USA

    Brittany C. Michel, Andrew R. D’Avino & Cigall Kadoch

  8. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Khrystyna North & Robert K. Bradley

  9. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yu Chen & Omar Abdel-Wahab

Authors
  1. Daichi Inoue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guo-Liang Chew
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brittany C. Michel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joseph Pangallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrew R. D’Avino
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tyler Hitchman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Khrystyna North
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stanley Chun-Wei Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lillian Bitner
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ariele Block
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Amanda R. Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Akihide Yoshimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Luisa Escobar-Hoyos
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hana Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Alex Penson
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sydney X. Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Justin Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Cigall Kadoch
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Omar Abdel-Wahab
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Robert K. Bradley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.I., G.-L.C., B.L., B.C.M., C.K., O.A.-W. and R.K.B. designed the study. D.I., B.L. and J.P. performed minigene assays. D.I., B.C.M. and C.K. performed ChIP–seq experiments. A.R.D., G.-L.C. and C.K. performed ChIP–seq analyses. G.-L.C., K.N., A.P. and R.K.B. performed computational analyses of the CRISPR screen, RNA splicing and gene expression data. D.I. and S.X.L. performed the CRISPR screen. D.I. and B.L. generated and validated the anti-BRD9 PE morpholinos in in vitro assays. D.I., L.B., A.B., S.C.-W.L., A.Y. and H.C. performed the mouse experiments. A.R.M., D.I. and L.E.-H. performed experiments using pancreatic cancer cell lines. T.H. and Y.C. provided melanoma cell lines. D.I., G.-L.C., O.A.-W. and R.K.B. prepared the manuscript with help from all co-authors. J.T. and O.A.-W. provided clinically annotated samples from the MSK Hematology/Oncology Tissue Bank and from the MSK Antitumor Assessment Core Facility. O.A.-W. and R.K.B. provided funding and study supervision.

Corresponding authors

Correspondence to Omar Abdel-Wahab or Robert K. Bradley.

Ethics declarations

Competing interests

C.K. is a Scientific Founder, fiduciary Board of Directors member, Scientific Advisory Board member, consultant and shareholder of Foghorn Therapeutics, none of which are related to the current manuscript. O.A.-W. has served as a consultant for H3 Biomedicine, Foundation Medicine, Merck and Janssen; O.A.-W. has received personal speaking fees from Daiichi Sankyo. O.A.-W. has received previous research funding from H3 Biomedicine unrelated to the current manuscript. D.I., O.A.-W. and R.K.B. are inventors on a provisional patent application submitted by the Fred Hutchinson Cancer Research Center that covers BRD9 activation in cancer.

Additional information

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

Peer review information Nature thanks Boris Bastian, Rotem Karni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 BRD9 is mis-spliced in SF3B1-mutated human cells, and BRD9 loss confers a proliferative advantage.

a, Scatter plots comparing differential splicing (ΔPSI) between patients in the TCGA UVM cohort with SF3B1 mutations or wild-type SF3B159 (x axis) and patients from a myelodysplastic syndromes cohort with SF3B1 mutations or wild-type SF3B127 (y axis). Events were classified as alternative 3′ splice sites or skipped exons. b, Rank plot for the −log10(FDR) associated with each sgRNA in our CRISPR–Cas9 positive-selection screen. sgRNAs targeting the positive control (Pten) and Brd9 are highlighted. For the probe-level (per-sgRNA) analysis, we fitted a negative binomial generalized log-linear model and performed a likelihood ratio test. FDR values were computed using the Benjamini–Hochberg method. c, Read counts for sgRNAs targeting the positive control (Pten) or Brd9. D0 and D7 indicate days following withdrawal of IL-3. d, Heat map summarizing the results of a competition assay to measure the effect of each indicated sgRNA on the growth of Cas9-expressing Ba/F3 cells. Cell growth was computed with respect to cells treated with a non-targeting (control) sgRNA and the percentages of GFP+ cells on day 14 were normalized to the percentages on day 2. The illustrated values correspond to the mean computed value over n = 3 biological replicates. Rpa3 sgRNAs were used as a negative control. e, As in d, but for 32Dcl3 cells. f, As in d, but for the indicated melanoma and pancreatic ductal adenocarcinoma cells. g, As in d, but for RN2 cells. h, Sequence conservation of the BRD9 poison exon locus as estimated by phyloP60. Conservation and repetitive element annotation is from the UCSC Genome Browser43. i, RT–PCR analysis of Brd9 poison exon inclusion using whole bone marrow cells from Mx1-cre Sf3b1WT/WT (WT/WT) and Mx1-cre Sf3b1K700E/WT (K700E/WT) mice. Three weeks after pIpC treatment, RT–PCR was performed with mouse primers corresponding to those used to assay BRD9 poison exon inclusion in human cells. Representative images from n = 2 technically independent replicates. j, RT–PCR analysis to confirm mutant-SF3B1-dependent inclusion of the BRD9 poison exon in isogenic NALM-6 cell lines engineered to contain the indicated mutations. SF3B1K700K is a wild-type control for genome engineering. Representative images from n = 2 technically independent replicates. k, Western blot for Flag, SF3B1 and BRD9 in K562 cells overexpressing N-terminally Flag-tagged wild-type SF3B1 or SF3B1K700E cDNAs, or an empty vector; this panel corresponds to the cells evaluated in m. Representative images from n = 2 biologically independent replicates. l, Western blot for SF3B1 in K562 cells treated with doxycycline-inducible SF3B1-targeting shRNAs or a non-targeting control shRNA (shRen); this panel corresponds to cells evaluated in m. Representative images from n = 2 technically independent replicates. m, RT–PCR illustrating the specificity of BRD9 poison exon inclusion for SF3B1-mutated cells in the indicated cell lines. These include K562 cells treated with control shRNA (shRen) or SF3B1-targeting shRNAs (the columns labelled ‘K562 knock-down’); knock-in of the SF3B1K700K, SF3B1K700E or SF3B1K666N mutation into the endogenous locus of SF3B1 (the columns labelled ‘K562 knock-in’); or overexpression of wild-type SF3B1 or SF3B1K700E cDNA (the columns labelled ‘K562 cDNA’). The two right-most lanes show acute myeloid leukaemia cell lines with wild-type SF3B1 (MV4;11) or a naturally occurring endogenous SF3B1K700E mutation (HNT34 cells; the columns labelled ‘leukaemia cell lines’). Representative images from n = 3 biologically independent experiments. n, As in m, but for the indicated pancreatic ductal adenocarcinoma cell lines (left), UVM cell lines (centre) and a cohort of patients with chronic lymphocytic leukaemia (right). CFPAC1 and MIA PaCa2 cells lack SF3B1 mutations; Panc05;04 cells carry SF3B1Q699H/K700E; UPMD1 and MEL270 cells lack SF3B1 mutations; MEL202 and UPMD2 cells carry SF3B1R625G and SF3B1Y623H mutations, respectively. Sample identifiers for patients with chronic lymphocytic leukaemia correspond to the genotypes shown in Supplementary Table 7. Representative images from n = 2 technically independent experiments (left and centre) and n = 3 biologically independent experiments (right). o, RNA-seq read coverage plots of the BRD9 poison exon locus from patient samples with the indicated SF3B1 genotypes. All SF3B1-mutated samples exhibit BRD9 poison exon inclusion. p, As in o, but for the indicated tissues from healthy donors (from BodyMap 2.0). q, qRT–PCR measurement of the half-lives of the poison exon inclusion (left) and exclusion (right) isoforms in isogenic K562 SF3B1K700E cells treated with the indicated shRNAs and actinomycin D to inhibit transcription. NMD inhibition via UPF1 knockdown stabilizes the inclusion, but not exclusion, isoform. Red arrows indicate primers used to specifically detect the two isoforms. n = 2 biologically independent experiments and n = 2 technically independent experiments for the inclusion isoform; n = 3 technically independent experiments for the exclusion isoform. P value was calculated by two-sided t-test at 8 h. r, Bar graph illustrating the estimated poison exon inclusion isoform half-life in the indicated conditions from the data in q. Error bars, mean + s.d. n = 2 biologically independent experiments and n = 2 technically independent experiments. P value was calculated by two-sided t-test. s, As in r, but for the exclusion isoform. Error bars, mean + s.d. n = 3 technically independent experiments. P value was calculated by two-sided t-test. t, As in q, but for NALM-6 SF3B1K700E cells. n = 3 technically independent experiments for the inclusion isoform and the exclusion isoform. P value was calculated by two-sided t-test at 8 h. u, As in r, but for NALM-6 SF3B1K700E cells. n = 3 technically independent experiments. Error bars, mean + s.d. P value was calculated by two-sided t-test. v, As in s, but for NALM-6 SF3B1K700E cells. Error bars, mean + s.d. n = 3 technically independent experiments. P value was calculated by two-sided t-test. w, Western blot for BRD9 in NALM-6 cells with or without knock-in of an SF3B1 mutation. Actin, loading control. Representative images from n = 3 biologically independent experiments. x, Rank plot of BRD9 poison exon inclusion (scale of 0 to 1; top) and box plot of gene expression (inset) for patients stratified by SF3B1 mutational status (data are from cohorts of patients with myelodysplastic syndromes or UVM, as in Fig. 1a). SF3B1 mutations were strongly associated with high poison exon inclusion and low BRD9 expression. Boxes illustrate 1st and 3rd quartiles, with whiskers extending to 1.5× interquartile range. P value computed with one-sided Mann–Whitney U-test.

Source data

Extended Data Fig. 2 Mutant SF3B1 recognizes an aberrant branchpoint within BRD9 to promote poison exon inclusion, causing loss of full-length BRD9 protein.

a, Schematic illustrating the strategy for knock-in of an HA tag into the endogenous BRD9 locus. The single-stranded donor DNA contained a 197-nt fragment, including 83 nt homologous to the BRD9 5′ UTR (upstream of the HA tag) and 87 nt homologous to BRD9 exon 1 (downstream of the start codon). b, Sanger sequencing of genomic DNA validating successful HA tag knock-in in K562 SF3B1K700E cells. Representative images from n = 2 biologically independent experiments. c, Western blot with anti-BRD9 (left), anti-HA (right, top) or anti-actin (right, bottom) used to probe K562 SF3B1K700E cells carrying an endogenously N-terminally HA-tagged BRD9. White arrows, non-specific bands. Red arrow, expected size of BRD9 protein. Representative images from n = 3 biologically independent experiments. d, Western blot for Flag, SF3B1 and endogenous BRD9 protein in MEL270 cells with doxycycline-inducible Flag-tagged wild-type SF3B1 or Flag-tagged SF3B1(K700E). Representative images from n = 3 biologically independent experiments. e, Sanger sequencing of cDNA arising from reverse transcription of lariats arising from inclusion (top) (exon 14–exon 14a splicing) or exclusion (bottom) (exon 14–exon 15 splicing) of the BRD9 poison exon in MEL270 cells with doxycycline-inducible Flag-tagged wild-type SF3B1 (bottom) or Flag-tagged SF3B1(K700E) (top). The branchpoints are illustrated in Fig. 2a. Representative images from n = 3 biologically independent experiments. f, As in e, but for T47D cells. Representative images from n = 3 biologically independent experiments. g, As in Fig. 2b, but for the indicated minigene mutagenesis in T47D cells with doxycycline-inducible Flag-tagged SF3B1(K700E). Representative images from n = 3 biologically independent experiments. h, Western blot of U2AF2, U2AF1 and histone H3 in K562 cells transfected with siRNAs against U2AF1 and/or U2AF2 (top) and bar plot illustrating mean BRD9 poison exon inclusion as measured by quantitative PCR (qPCR) following siRNA knockdown of U2AF1 and/or U2AF2 (bottom). Experiment performed with n = 1 biologically independent replicate for siRNA transfection, n = 1 technically independent replicate for western blot and n = 3 technically independent replicates for RT–PCR. Poison exon inclusion was computed over all n = 3 × 3 (9) combinations of technical replicates for RT–PCR for the inclusion and exclusion isoforms. Bars illustrate mean inclusion. i, EPB49 cassette exon inclusion as measured by qPCR following siRNA knockdown of U2AF1 and/or U2AF2. As the EPB49 cassette exon is U2AF-dependent, this experiment serves as a positive control for the functional efficacy of U2AF1 and U2AF2 knockdown. n = 3 technically independent experiments. Cassette exon inclusion was computed over all n = 3 × 3 (9) combinations of technical replicates for RT–PCR for the inclusion and exclusion isoforms. Bars illustrate mean inclusion. j, As in Fig. 2b, but for the indicated minigene mutagenesis in T47D cells with doxycycline-inducible Flag-tagged SF3B1(K700E). Representative images from n = 3 biologically independent experiments. k, As in Fig. 2c, but for the indicated minigene mutagenesis in T47D cells with doxycycline-inducible Flag-tagged SF3B1(K700E). Representative images from n = 3 biologically independent experiments. l, Western blot for Flag, SF3B1, BRD9 and actin in MEL270 cells expressing an empty vector or N-terminally Flag-tagged wild-type SF3B1, SF3B1R625H or SF3B1K700E cDNA. Representative images from n = 3 biologically independent experiments. m, RT–PCR analysis of BRD9 splicing in MEL270 cells expressing doxycycline-inducible empty vector, wild-type SF3B1, SF3B1(R625H) or SF3B1(K700E). The left column illustrates minigene splicing and the right column illustrates endogenous BRD9 splicing. Representative images from n = 3 biologically independent experiments. n, As in m, but for the illustrated minigene mutations at the 5′ end of the poison exon. Representative images from n = 3 biologically independent experiments. o, Mutations generated at the 5′ end of the BRD9 poison exon by CRISPR–Cas9-mediated indels in MEL202 cells (SF3B1R625G). The PAM sequence is illustrated with uppercase, underlined nucleotides. Red nucleotides hybridize to the sgRNA. Substitutions are illustrated with lowercase, underlined nucleotides. p, As in o, but for MEL270 cells. Representative images from n = 3 biologically independent experiments. q, As in Fig. 2d top, but for MEL270 cells. Representative images from n = 3 biologically independent experiments. r, As in Fig. 2d bottom, but for MEL270 cells. Representative images from n = 3 biologically independent experiments.

Source data

Extended Data Fig. 3 BRD9 loss impairs ncBAF complex formation.

a, Mutation rate observed across TCGA cohorts for canonical BAF, polybromo-associated BAF and ncBAF components. b, Western blot confirming Flag-tagged BRD9 protein expression in 3×Flag–BRD9-expressing K562 cells. Representative images from n = 3 biologically independent experiments. c, Experimental workflow for using rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME)33 for purification and identification of the chromatin-associated interactions partners of BRD9. d, Cross-linking and immunoprecipitation with IgG or Flag followed by probing with the indicated antibodies. Data from 3×Flag–BRD9-expressing MEL270 cells. Representative images from n = 3 biologically independent experiments. e, Immunoprecipitation of GLTSCR1 followed by western blotting with the indicated antibodies in SF3B1K700E knock-in NALM-6 cells. Representative images from n = 3 biologically independent experiments. f, Immunoprecipitation of GLTSCR1 (top) or BRG1 (bottom) followed by blotting with the indicated antibodies in K562 cells with CRISPR-mediated knockout (KO) of BRD9. Representative images from n = 3 biologically independent experiments. g, Schematic of the BRD9 full-length (FL) protein and the deletion mutants that we constructed. BD, bromodomain; DUF, domain of unknown function; N, amino acids 1–133 of BRD9; N + BD, amino acids 1–242 of BRD9; N + BD + DUF, amino acids 1–505 of BRD9; dN, amino acids 134–597 of BRD9; dBD, bromodomain-deletion mutant of BRD9; dDUF, DUF-domain deletion mutant of BRD9. h, Immunoprecipitation with Flag following by probing (immunoblot, IB) for GLTSCR1 or GLTSCR1L in 293T cells expressing 3×Flag-tagged versions of the indicated deletion mutants. Deletion mutants illustrated in g. Representative images from n = 3 biologically independent experiments.

Extended Data Fig. 4 BRD9 loss drives relocalization of GLTSCR1 away from CTCF-associated loci.

a, As in Fig. 3e, but illustrating relative positions with respect to transcription start sites (TSSs). b, As in Fig. 3f, but for motifs at BRG1-bound loci. n = 401 transcription factors analysed. c, UpSet plots depicting the overlap of consensus GLTSCR1-bound loci in MEL270 cells with the indicated treatments. d, Volcano plot illustrating the difference in the mean motif scores at BRD9-sensitive versus constitutive GLTSCR1-bound loci for the transcription factors in Fig. 3f, as well as associated statistical significance. n = 401 transcription factors analysed. P values computed with a two-sided Mann–Whitney U-test. e, As in c, but for BRG1-bound loci. f, As in d, but for BRG1-bound loci. n = 401 transcription factors analysed. g, Selected enriched annotation terms from a Genomic Regions Enrichment of Annotations Tool (GREAT) analysis61 of genes near BRD9-sensitive and constitutive GLTSCR1-bound loci. Plot illustrates −log10(FDR), computed with a one-sided binomial test and corrected for multiple testing using the Benjamini–Hochberg procedure. O and E, numbers of genes that were observed and expected, respectively. h, Differences in gene expression in SF3B1-mutated versus wild-type samples in the TCGA UVM cohort for genes with GLTSCR1-bound promoters identified in MEL270 cells. Colours indicate the responsiveness of peaks to BRD9 loss. i, Read coverage from GLTSCR1 ChIP–seq (MEL270 cells) and RNA-seq (TCGA UVM cohort) around NFATC2IP. Red trapezoid indicates GLTSCR1 binding in the promoter, with reduced binding upon treatment with BRD9 degrader or expression of SF3B1K700E. NFATC2IP was significantly differentially expressed in UVM samples with SF3B1 mutations relative to wild-type samples. Vertical axis scales were rendered comparable by normalizing ChIP–seq read coverage to mapped library size and RNA-seq read coverage to mapped library size, restricted to coding genes. ChIP–seq experiment performed for n = 1 biologically independent replicate. j, As in i, but for SETD1A. SETD1A was significantly differentially expressed in UVM samples with SF3B1 mutations relative to wild-type samples.

Source data

Extended Data Fig. 5 BRD9 is a potent tumour suppressor in UVM.

a, In vitro growth curves of Melan-a cells treated with two non-targeting shRNAs (against Renilla (shRen) and luciferase (shLuc)) versus parental, un-manipulated Melan-a cells. n = 3 biologically independent experiments per group. Data are presented as mean ± s.d. b, Tumour volume in SCID mice subcutaneously injected with Melan-a cells expressing a control shRNA (against Renilla), shRNA against Brd9 (Brd9 shRNA no. 1 and no. 2), shRNA against Brg1 or cDNA encoding CYSLTR2(L129Q) (n = 8 mice per group). Data are presented as mean ± s.d. P value at day 64 was calculated compared to Renilla shRNA group with a two-sided t-test. c, Representative images of the dissected melanomas from b. d, H&E images of melanomas from b. Scale bars, 100 μm. Representative images from n = 3 biologically independent experiments. e, qRT–PCR measuring expression of Brd9 (left) and melanoma-associated genes (Mitf, Dct, Pmel and Tyrp1) of melanomas from a. n = 4 (Renilla shRNA) and n = 5 (Brd9 shRNA no. 1 and no. 2) biologically independent experiments. Data are presented as mean ± s.d. P value was calculated by two-sided t-test. f, Images of mice transplanted with parental, un-manipulated Melan-a cells or Melan-a cells transduced with a non-targeting shRNA or Brd9-targeting shRNA. Cells were subcutaneously engrafted into SCID mice and tumour volume was estimated 36 days after transplant. g, Volumes of tumours from f at day 36. Data are presented as mean ± s.d. n = 10 tumours per group. P value was calculated relative to the parental group by a two-sided t-test.

Source data

Extended Data Fig. 6 BRD9 is a potent tumour suppressor in UVM.

a, Representative images of pulmonary metastatic foci produced 14 days after intravenous injection of B16 cells with or without Brd9 shRNA (MLS-E vector). Scale bar, 5 mm. b, Western blot of endogenous BRD9 in B16 cells immediately before injection. Actin, loading control. The experiment was repeated three times with similar results. c, H&E sections of lung metastases. Arrows indicate metastatic foci. Scale bar, 100 μm. The experiment was repeated three times with similar results. d, Numbers of pulmonary B16 metastases identified in the experiments from a. n = 6 mice per group. P value was calculated relative to the Renilla shRNA group by a two-sided t-test. e, Relative percentages of GFP+ 92.1 cells with or without BRD9 shRNA (MLS-E vector), assessed by flow cytometric analysis of lung tissue in recipient NSG (NOD–SCID Il2rg−/−) mice 14 days after intravenous injection by tail vein. The signal was normalized by dividing by the average percentage of GFP+ cells in the Renilla shRNA (control) group. n = 5 biologically independent experiments per group. P value was calculated relative to the Renilla shRNA group by a two-sided t-test. f, Anti-GFP immunohistochemistry for sections of lung metastases from the experiment in e. Scale bar, 200 μm. The experiment was repeated three times with similar results. g, Representative images of tumours derived from transplantation of Melan-a cells transduced with doxycycline-inducible Brd9 shRNA. Doxycycline was administered for nine weeks (left) and followed by doxycycline withdrawal for three weeks (right). h, Tumour volume for the experiment in g. n = 4 mice per group. The experiment was repeated twice with similar results. P value was calculated relative to the parental group by a two-sided t-test at day 7, day 14 and day 21. i, Representative images of recipient mice engrafted with MEL270 cells transduced with empty vector, full-length BRD9 (BRD9 WT) or a bromodomain-deletion mutant of BRD9 (BRD9 ΔBD) at day 12. n = 5 mice per group. j, Results of a competition assay to measure the effects of expression of the indicated cDNAs on growth of the indicated melanoma cells. Transduced cells were identified by co-expression of GFP (pMIGII vector). The percentage of GFP+ cells was tracked over 21 days and normalized to the GFP percentage on day 2. Data are presented as mean ± s.d. n = 2 biologically independent experiments per group. k, Results of a competition assay to measure the effects of expression of the indicated cDNAs on growth of the indicated melanoma cells. Transduced cells were identified by the co-expression of GFP (pMIGII vector). The percentage of GFP+ cells was tracked over 10 days and normalized to the GFP percentage on day 3. n = 2 biologically independent experiments per group.

Source data

Extended Data Fig. 7 BRD9 regulates HTRA1 expression to promote UVM tumorigenesis.

a, Rank plot illustrating fold change (expressed in log2) of each significantly differentially expressed gene identified by comparing samples from patients in the TCGA UVM cohort with mutated versus wild-type SF3B1. The plot is restricted to genes with BRD9 ChIP–seq peaks within their promoters or gene bodies in the absence of perturbations to BRD9 (MEL270 cells treated with DMSO or following ectopic expression of wild-type SF3B1). n = 3,122 genes analysed, of which n = 248 met the significance (P < 0.001) and expression (median expression in both wild-type and mutant samples > 2 transcripts per million) thresholds, and so are illustrated here. P value was computed using a two-sided Mann–Whitney U-test. b, As in a, but a volcano plot additionally illustrating the P value associated with the comparison between SF3B1-mutated and wild-type SF3B1 samples. n = 3,122 genes analysed and illustrated. P value computed with two-sided Mann–Whitney U-test. c, HTRA1 expression in samples from patients in the TCGA UVM cohort with (n = 18) or without (n = 62) SF3B1 mutations. Expression is z-score normalized across all samples. Data are presented as mean ± s.d. P value computed with two-sided t-test. d, Western blot for Flag, SF3B1, HTRA1, BRD9 and actin in MEL270 cells (wild-type SF3B1), treated with DMSO, BRD9 degrader, Flag–SF3B1(WT) or Flag–SF3B1(K700E). Representative images from n = 3 biologically independent experiments. e, Western blot for HTRA1, BRD9 and actin in MEL202 cells (SF3B1R625G) following CRISPR–Cas9-mediated mutagenesis of the BRD9 poison exon (as shown in Extended Data Fig. 2o). Representative images from n = 3 technically independent experiments. f, Read coverage for BRG1, BRD9 and GLTSCR1 ChIP–seq at the HTRA1 locus in MEL270 cells (shown in d) treated with an empty vector, BRD9 degrader, or SF3B1WT or SF3B1K700E cDNAs (n = 1 ChIP–seq experiment performed for each condition). g, Western blot for HTRA1 and actin in MEL270 cells treated with shRNAs against HTRA1 or with a non-targeting control shRNA (against Renilla). Representative images from n = 3 biologically independent experiments. h, Heat map summarizing the results of a competition assay to measure the effect of each indicated shRNA on the growth of Cas9-expressing UVM cell lines with wild-type SF3B1. Cell growth was computed with respect to cells treated with a non-targeting control shRNA (against Renilla) and the percentage of GFP+ cells at day 14 was normalized to that at day 2. The illustrated values correspond to the mean values computed over n = 3 biologically independent experiments. i, Western blot for HTRA1 and actin in MEL202 cells (SF3B1R625G) following stable overexpression of an empty vector or HTRA1 (both with an MSCV-IRES-GFP vector). Representative images from n = 3 biologically independent experiments. j, Ratio of GFP+ to GFP− MEL202 cells (SF3B1R625G) from a competition experiment in which GFP+ cells from i were seeded at an initial 1:1 ratio with GFP− control cells. Data are presented as mean of n = 2 biologically independent experiments. k, Colony number of MEL202 cells expressing empty vector or HTRA1 cDNA from i, following 10 days of growth in soft agar. Data are presented as mean of n = 3 biologically independent experiments.

Source data

Extended Data Fig. 8 CRISPR–Cas9-mediated mutagenesis of the BRD9 poison exon corrects BRD9 aberrant splicing and abrogates growth of SF3B1-mutated melanoma.

a, Colony number for MEL270 cells (wild-type SF3B1) without (control) or with (clone 1, clone 2 and clone 3) CRISPR–Cas9-induced indels that disrupted BRD9 poison exon recognition. Data presented as mean ± s.d. n = 3 biologically independent experiments per group. b, Representative images from a. c, Proliferation of the clones described in a. n = 3 biologically independent experiments per group. d, Proliferation of MEL285 cells (wild-type SF3B1) without (control) or with (clones 1, 2 and 3) CRISPR–Cas9-induced indels that disrupted BRD9 poison exon recognition. n = 3 biologically independent experiments per group. e, Mutations generated at the 5′ end of the BRD9 poison exon by CRISPR–Cas9-mediated indels in clones 1, 2 and 3 of MEL285 cells from d. The PAM sequence is illustrated with uppercase, underlined nucleotides. Red nucleotides hybridize to the sgRNA. f, Representative images of dissected tumours from recipient mice transplanted with CRISPR–Cas9-modified MEL202 clones. g, Tumour weight for the tumours illustrated in f. Data presented as mean ± s.d. n = 6 biologically independent experiments per group. P value was calculated relative to the control shRNA group by a two-sided t-test. h, H&E staining, as well as Ki-67 immunohistochemistry images, for the tumours illustrated in f. Representative images from n = 3 independent histological analyses.

Source data

Extended Data Fig. 9 Correcting BRD9 mis-splicing in SF3B1-mutated xenografts with ASOs suppresses tumour growth.

a, Cartoon representation of the BRD9 loci targeted by each designed morpholino. Melting temperature (Tm) is shown. Lengths of target sequences are indicated in parentheses; if these are not indicated, then the length is 25 nt. b, Growth of MEL202 cells (SF3B1R625G) treated with 10 μM of control non-targeting (control) or BRD9 poison-exon-targeting morpholinos (no. 3, no. 6 and no. 7). n = 3 biologically independent experiments per group. P values at day 9 were calculated relative to the control group by a two-sided t-test. c, Representative images of recipient mice xenografted with MEL202 cells and treated with PBS or morpholinos in vivo. Each tumour was analysed after in vivo treatment with PBS, control morpholino or morpholino against the BRD9 poison exon (no. 6) (12.5 mg kg−1, every other day to a total of 8 intratumoral injections). n = 10 tumours per group. d, Representative images of dissected tumours from the experiment described in c. e, RT–PCR results of tumours from c to evaluate BRD9 splicing. The experiment was repeated three times with similar results. f, Representative H&E and Ki-67 staining images of tumours from c. Scale bars, 100 μm (top), 50 μm (middle and bottom). The experiment was repeated three times with similar results. g, Estimated tumour volume for recipient mice transplanted with a patient-derived xenograft model of SF3B1R625C rectal melanoma and treated with in vivo morpholinos (control or morpholino against BRD9 poison exon, no. 6) (12.5 mg kg−1, every other day to a total of 8 intratumoral injections). n = 5 mice per group. Estimated tumour volumes before and after treatment are shown. Data are presented as mean ± s.d. P values were calculated relative to the control group by a two-sided t-test. h, Representative H&E staining images of tumours from g. The experiment was repeated three times with similar results. i, Representative Ki-67 staining images of tumours from g. The experiment was repeated three times with similar results. j, Estimated tumour volume for recipient mice transplanted with a patient-derived xenograft model of UVM (wild-type SF3B1), treated with in vivo morpholinos (control or morpholino against BRD9 poison exon, no. 6) (12.5 mg kg−1, every other day to a total of 8 intratumoural injections). n = 5 mice per group. Estimated tumour volumes before and after treatment are shown. Data are presented as mean ± s.d. P values weres calculated relative to the control group by a two-sided t-test. k, Representative images of dissected tumours from j. l, Tumour weight for tumours from k. n = 5 mice per group. P value was calculated relative to the control group by a two-sided t-test.

Source data

Extended Data Fig. 10 Use of multiple, distinct NMD isoforms of BRD9.

a, BRD9 gene structure illustrating constitutive BRD9 exons and alternative splicing events that are predicted to induce NMD. SF3B1 mutations promote inclusion of the BRD9 poison exon in intron 14. b, Genomic coordinates (hg19/GRCh37 assembly) of each NMD-inducing event illustrated in a, as well as genomic sequence of each alternatively spliced region highlighted in red in a. The third column indicates the specific isoform that is a predicted NMD substrate. Prox, intron-proximal competing 3′ splice site; dist, intron-distal competing 3′ splice site; inc, exon inclusion; exc, exon exclusion. c, Rank plot illustrating levels of each NMD-inducing isoform relative to total BRD9 mRNA levels for each sample in each indicated TCGA cohort. Boxes illustrate first and third quartiles, with whiskers extending to 1.5× interquartile range. d, Box plot illustrating the distribution of coefficients estimated by fitting a linear model to predict BRD9 gene expression on the basis of relative levels of each NMD-inducing isoform. The relative levels of NMD-inducing isoforms illustrated in c, as well as BRD9 gene expression estimates for each sample, were used to construct an independent linear model with robust regression for each TCGA cohort. The coefficients resulting from this model fitting procedure are illustrated in the box plot, in which each dot corresponds to the coefficient associated with the corresponding NMD-inducing event for a single TCGA cohort. Coefficients for the TCGA UVM cohort are highlighted in red. The coefficients are typically negative (as expected for NMD-inducing isoforms), with the exception of constitutive exon 9 skipping, for which the coefficients are generally positive—as expected for an event in which NMD is induced when a constitutive exon is excluded. The SF3B1-mutation-responsive poison exon in intron 14 dominates the fit for UVM, as expected. n = 33 TCGA cohorts analysed and illustrated. e, Scatter plots comparing actual (y axis) and predicted (x axis) BRD9 expression levels for three TCGA cohorts. Each dot corresponds to a single sample. ρ, Spearman’s correlation between actual and predicted values. f, RNA-seq read coverage plots for patient samples from the TCGA cohorts illustrated in e for representative alternative splicing events illustrated in a. Each coverage plot illustrates data averaged over the n = 5 patient samples from the vertically matched cohort in e that exhibit the lowest or highest relative expression of the NMD-inducing isoform. μ, mean relative expression of the illustrated NMD-inducing isoform, computed over each group of samples.

Source data

Supplementary information

Supplementary Figure 1

.Images of uncropped gels presented throughout the paper. The corresponding figure is indicated for each uncropped gel with a red box indicating how the gel was cropped for presentation in the figures. DNA or protein ladders are present to the side. Probes for immunoblots are indicated when relevant

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-11

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Inoue, D., Chew, GL., Liu, B. et al. Spliceosomal disruption of the non-canonical BAF complex in cancer. Nature 574, 432–436 (2019). https://doi.org/10.1038/s41586-019-1646-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1646-9

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Cancer

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

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