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.

  • Article
  • Published:

Systematic characterization of BAF mutations provides insights into intracomplex synthetic lethalities in human cancers

Nature Genetics volume 51, pages 1399–1410 (2019)Cite this article

Subjects

Abstract

Aberrations in genes coding for subunits of the BRG1/BRM associated factor (BAF) chromatin remodeling complexes are highly abundant in human cancers. Currently, it is not understood how these mostly loss-of-function mutations contribute to cancer development and how they can be targeted therapeutically. The cancer-type-specific occurrence patterns of certain subunit mutations suggest subunit-specific effects on BAF complex function, possibly by the formation of aberrant residual complexes. Here, we systematically characterize the effects of individual subunit loss on complex composition, chromatin accessibility and gene expression in a panel of knockout cell lines deficient for 22 BAF subunits. We observe strong, specific and sometimes discordant alterations dependent on the targeted subunit and show that these explain intracomplex codependencies, including the synthetic lethal interactions SMARCA4–ARID2, SMARCA4–ACTB and SMARCC1–SMARCC2. These data provide insights into the role of different BAF subcomplexes in genome-wide chromatin organization and suggest approaches to therapeutically target BAF-mutant cancers.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: An isogenic cell line panel for loss of individual BAF subunits.
Fig. 2: BAF complex composition changes following knockout of single BAF-coding genes.
Fig. 3: Knockout of single BAF-coding genes alters global chromatin accessibility.
Fig. 4: Expression changes correlate with altered chromatin accessibility.
Fig. 5: Systematic targeting of multiple BAF subunits identifies previously unknown intracomplex synthetic lethalities.
Fig. 6: Integrative view of BAF complex subunit dependencies and functional similarity.

Similar content being viewed by others

Data availability

Next-generation sequencing data have been deposited with the NCBI GEO (GSE108390). Mass spectrometry data have been deposited with the PRIDE archive PXD013102. The processed data used for the analyses are available at http://baf-complex.computational-epigenetics.org.

Code availability

Software used for the analyses is available at http://baf-complex.computational-epigenetics.org.

References

  1. Wang, W. et al. Purification and biochemical heterogeneity of the mammalian SWI-SNF complex. EMBO J. 15, 5370–5382 (1996).

    Article  CAS  Google Scholar 

  2. Kadoch, C. & Crabtree, G. R. Mammalian SWI/SNF chromatin remodeling complexes and cancer: mechanistic insights gained from human genomics. Sci. Adv. 1, e1500447 (2015).

    Article  Google Scholar 

  3. Phelan, M. L., Sif, S., Narlikar, G. J. & Kingston, R. E. Reconstitution of a core chromatin remodeling complex from SWI/SNF subunits. Mol. Cell 3, 247–253 (1999).

    Article  CAS  Google Scholar 

  4. Chandler, R. L. et al. ARID1a-DNA interactions are required for promoter occupancy by SWI/SNF. Mol. Cell Biol. 33, 265–280 (2013).

    Article  CAS  Google Scholar 

  5. Hodges, C., Kirkland, J. G. & Crabtree, G. R. The many roles of BAF (mSWI/SNF) and PBAF complexes in cancer. Cold Spring Harb. Perspect. Med. 6, a026930 (2016).

    Article  Google Scholar 

  6. 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).

    Article  CAS  Google Scholar 

  7. 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).

    Article  CAS  Google Scholar 

  8. Wilson, B. G. et al. Residual complexes containing SMARCA2 (BRM) underlie the oncogenic drive of SMARCA4 (BRG1) mutation. Mol. Cell. Biol. 34, 1136–1144 (2014).

    Article  Google Scholar 

  9. Oike, T. et al. A synthetic lethality-based strategy to treat cancers harboring a genetic deficiency in the chromatin remodeling factor BRG1. Cancer Res. 73, 5508–5518 (2013).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Kelso, T. W R. et al. Chromatin accessibility underlies synthetic lethality of SWI/SNFsubunits in ARID1A-mutant cancers. eLife 6, e30506 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  13. Mashtalir, N. et al. Modular organization and assembly of SWI/SNF family chromatin remodeling complexes. Cell 175, 1272–1288.e20 (2018).

    Article  CAS  Google Scholar 

  14. 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).

    Article  CAS  Google Scholar 

  15. Dutta, A. et al. Composition and function of mutant Swi/Snf Complexes. Cell Rep. 18, 2124–2134 (2017).

    Article  CAS  Google Scholar 

  16. Sen, P. et al. Loss of Snf5 induces formation of an aberrant SWI/SNF complex. Cell Rep. 18, 2135–2147 (2017).

    Article  CAS  Google Scholar 

  17. Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).

    Article  CAS  Google Scholar 

  18. Yan, Z. et al. PBAF chromatin-remodeling complex requires a novel specificity subunit, BAF200, to regulate expression of selective interferon-responsive genes. Genes Dev. 19, 1662–1667 (2005).

    Article  CAS  Google Scholar 

  19. 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).

    Article  CAS  Google Scholar 

  20. Pan, J. et al. Interrogation of mammalian protein complex structure, function, and membership using genome-scale fitness screens. Cell Syst. 6, 555–568.e7 (2018).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. 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).

    Article  CAS  Google Scholar 

  23. Raab, J. R., Resnick, S. & Magnuson, T. Genome-wide transcriptional regulation mediated by biochemically distinct SWI/SNF complexes. PLoS Genet. 11, e1005748 (2015).

    Article  Google Scholar 

  24. Valianou, M. et al. Pharmacological inhibition of Polo-like kinase 1 (PLK1) by BI-2536 decreases the viability and survival of hamartin and tuberin deficient cells via induction of apoptosis and attenuation of autophagy. Cell Cycle 14, 399–407 (2015).

    Article  CAS  Google Scholar 

  25. Wang, N. N. et al. Molecular targeting of the oncoprotein PLK1 in pediatric acute myeloid leukemia: RO3280, a novel PLK1 inhibitor, induces apoptosis in leukemia cells. Int. J. Mol. Sci. 16, 1266–1292 (2015).

    Article  CAS  Google Scholar 

  26. Zhao, C. L. et al. Downregulation of PLK1 by RNAi attenuates the tumorigenicity of esophageal squamous cell carcinoma cells via promoting apoptosis and inhibiting angiogenesis. Neoplasma 62, 748–755 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  28. Zhao, K. et al. Rapid and phosphoinositol-dependent binding of the SWI/SNF-like BAF complex to chromatin after T lymphocyte receptor signaling. Cell 95, 625–636 (1998).

    Article  CAS  Google Scholar 

  29. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  Google Scholar 

  30. McFarland, J. M. et al. Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. Nat. Commun. 9, 4610 (2018).

    Article  Google Scholar 

  31. Stransky, N. et al. Pharmacogenomic agreement between two cancer cell line data sets. Nature 528, 84–87 (2015).

    Article  CAS  Google Scholar 

  32. 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).

    Article  CAS  Google Scholar 

  33. 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.e10 (2017).

    Article  CAS  Google Scholar 

  34. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

    Article  CAS  Google Scholar 

  35. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 1–9 (2015).

    Google Scholar 

  36. Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).

    Article  CAS  Google Scholar 

  37. Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).

    Article  CAS  Google Scholar 

  40. Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 1–34 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  42. Hoffman, M. M. et al. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res. 41, 827–841 (2013).

    Article  CAS  Google Scholar 

  43. Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    Article  CAS  Google Scholar 

  44. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  Google Scholar 

  45. Ernst, J. & Kellis, M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817–825 (2010).

    Article  CAS  Google Scholar 

  46. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  47. 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).

    Article  Google Scholar 

  48. Glaus, P., Honkela, A. & Rattray, M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 28, 1721–1728 (2012).

    Article  CAS  Google Scholar 

  49. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  50. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  51. Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2016).

    Article  CAS  Google Scholar 

  52. Sheffield, N. C. et al. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res. 23, 777–788 (2013).

    Article  CAS  Google Scholar 

  53. Sánchez-Castillo, M. et al. CODEX: a next-generation sequencing experiment database for the haematopoietic and embryonic stem cell communities. Nucleic Acids Res. 43, D1117–D1123 (2015).

    Article  Google Scholar 

  54. Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).

    Article  CAS  Google Scholar 

  55. Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83 (2011).

    Article  CAS  Google Scholar 

  56. Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224–226 (2012).

    Article  CAS  Google Scholar 

  57. Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  Google Scholar 

  58. Schep, A. N. et al. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Res. 25, 1757–1770 (2015).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the Biomedical Sequencing Facility, the Proteomics and Metabolomics Facility and the Platform Austria for Chemical Biology at CeMM for their support in generating and analyzing the next-generation sequencing, proteomics or screening data, respectively. We gratefully acknowledge Horizon Discovery for providing the HAP1 cell lines, and Boehringer Ingelheim, the Superti-Furga laboratory (CeMM) and Winter laboratory (CeMM) for providing various cancer cell lines. We acknowledge the experimental support provided by J. Block and D. Donertas. Research in the Kubicek laboratory is supported by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, the Austrian Science Fund (FWF) F4701 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-CoG-772437). C.B. is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by an ERC Starting Grant (European Union’s Horizon 2020 research and innovation programme, grant no. 679146).

Author information

Authors and Affiliations

  1. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Sandra Schick, André F. Rendeiro, Kathrin Runggatscher, Anna Ringler, Bernd Boidol, Melanie Hinkel, Peter Májek, Loan Vulliard, Thomas Penz, Katja Parapatics, Christian Schmidl, Jörg Menche, André C. Müller, Christoph Bock & Stefan Kubicek

  2. Christian Doppler Laboratory for Chemical Epigenetics and Antiinfectives, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Sandra Schick, Bernd Boidol, Melanie Hinkel & Stefan Kubicek

  3. Regensburg Center for Interventional Immunology, University Regensburg and University Medical Center Regensburg, Regensburg, Germany

    Christian Schmidl

  4. Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria

    Guido Boehmelt & Mark Petronczki

  5. Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria

    Christoph Bock

  6. Max Planck Institute for Informatics, Saarbrücken, Germany

    Christoph Bock

Authors
  1. Sandra Schick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. André F. Rendeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kathrin Runggatscher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Ringler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bernd Boidol
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Melanie Hinkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peter Májek
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Loan Vulliard
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Thomas Penz
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Katja Parapatics
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Christian Schmidl
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jörg Menche
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Guido Boehmelt
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mark Petronczki
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. André C. Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Christoph Bock
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Stefan Kubicek
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.S., G.B., M.P. and S.K. planned the study and designed the experiments. S.S., K.R., M.H., T.P., K.P., C.S., A.R. and B.B. performed the experiments. A.F.R., P.M., L.V., S.S. and S.K. analyzed the data. S.S. and S.K. wrote the manuscript. S.K., M.P., G.B., A.C.M., C.B. and J.M. supervised the work. S.K. provided the funding.

Corresponding author

Correspondence to Stefan Kubicek.

Ethics declarations

Competing interests

G.B. and M.P. are employees of Boehringer Ingelheim RCV GmbH & Co KG.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–15 and Supplementary Note

Reporting Summary

Supplementary Table 1

Materials.

Supplementary Table 2

ATAC-seq.

Supplementary Table 3

ChIP-seq.

Supplementary Table 4

RNA-seq.

Supplementary Table 5

Synthetic lethality.

Supplementary Table 6

Statistics.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schick, S., Rendeiro, A.F., Runggatscher, K. et al. Systematic characterization of BAF mutations provides insights into intracomplex synthetic lethalities in human cancers. Nat Genet 51, 1399–1410 (2019). https://doi.org/10.1038/s41588-019-0477-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-019-0477-9

This article is cited by

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