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:

Elevated NSD3 histone methylation activity drives squamous cell lung cancer

Nature volume 590, pages 504–508 (2021)Cite this article

Subjects

Abstract

Amplification of chromosomal region 8p11–12 is a common genetic alteration that has been implicated in the aetiology of lung squamous cell carcinoma (LUSC)1,2,3. The FGFR1 gene is the main candidate driver of tumorigenesis within this region4. However, clinical trials evaluating FGFR1 inhibition as a targeted therapy have been unsuccessful5. Here we identify the histone H3 lysine 36 (H3K36) methyltransferase NSD3, the gene for which is located in the 8p11–12 amplicon, as a key regulator of LUSC tumorigenesis. In contrast to other 8p11–12 candidate LUSC drivers, increased expression of NSD3 correlated strongly with its gene amplification. Ablation of NSD3, but not of FGFR1, attenuated tumour growth and extended survival in a mouse model of LUSC. We identify an LUSC-associated variant NSD3(T1232A) that shows increased catalytic activity for dimethylation of H3K36 (H3K36me2) in vitro and in vivo. Structural dynamic analyses revealed that the T1232A substitution elicited localized mobility changes throughout the catalytic domain of NSD3 to relieve auto-inhibition and to increase accessibility of the H3 substrate. Expression of NSD3(T1232A) in vivo accelerated tumorigenesis and decreased overall survival in mouse models of LUSC. Pathological generation of H3K36me2 by NSD3(T1232A) reprograms the chromatin landscape to promote oncogenic gene expression signatures. Furthermore, NSD3, in a manner dependent on its catalytic activity, promoted transformation in human tracheobronchial cells and growth of xenografted human LUSC cell lines with amplification of 8p11–12. Depletion of NSD3 in patient-derived xenografts from primary LUSCs containing NSD3 amplification or the NSD3(T1232A)-encoding variant attenuated neoplastic growth in mice. Finally, NSD3-regulated LUSC-derived xenografts were hypersensitive to bromodomain inhibition. Thus, our work identifies NSD3 as a principal 8p11–12 amplicon-associated oncogenic driver in LUSC, and suggests that NSD3-dependency renders LUSC therapeutically vulnerable to bromodomain inhibition.

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: Deletion of NSD3, but not FGFR1, inhibits LUSC tumorigenesis in vivo.
Fig. 2: Molecular basis of increased H3K36me2 catalysis by NSD3(T1232A).
Fig. 3: Mutant NSD3-mediated H3K36me2 synthesis promotes oncogenic programming and LUSC tumorigenesis.
Fig. 4: NSD3 promotes human lung cell transformation and xenograft tumour growth of LUSC cells and PDXs, and renders PDXs hypersensitive to BETi.

Similar content being viewed by others

Data availability

The CUT&RUN and RNA-seq data from tumour-derived cells have been deposited in the Gene Expression Omnibus (GEO) database under accession number GSE149482, and RNA-seq data from tumour biopsies have been deposited in the GEO database under accession number GSE149272. Full blot data for all the western blots are shown in Supplementary Fig. 1. Source data are provided with this paper.

References

  1. Balsara, B. R. et al. Comparative genomic hybridization analysis detects frequent, often high-level, overrepresentation of DNA sequences at 3q, 5p, 7p, and 8q in human non-small cell lung carcinomas. Cancer Res. 57, 2116–2120 (1997).

    CAS  PubMed  Google Scholar 

  2. Tonon, G. et al. High-resolution genomic profiles of human lung cancer. Proc. Natl Acad. Sci. USA 102, 9625–9630 (2005).

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  3. Rooney, C. et al. Characterization of FGFR1 locus in sqNSCLC reveals a broad and heterogeneous amplicon. PLoS ONE 11, e0149628 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Weiss, J. et al. Frequent and focal FGFR1 amplification associates with therapeutically tractable FGFR1 dependency in squamous cell lung cancer. Sci. Transl. Med. 2, 62ra93 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lim, S. H. et al. Efficacy and safety of dovitinib in pretreated patients with advanced squamous non-small cell lung cancer with FGFR1 amplification: a single-arm, phase 2 study. Cancer 122, 3024–3031 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Yang, Z. Q., Liu, G., Bollig-Fischer, A., Giroux, C. N. & Ethier, S. P. Transforming properties of 8p11-12 amplified genes in human breast cancer. Cancer Res. 70, 8487–8497 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Turner-Ivey, B. et al. Development of mammary hyperplasia, dysplasia, and invasive ductal carcinoma in transgenic mice expressing the 8p11 amplicon oncogene NSD3. Breast Cancer Res. Treat. 164, 349–358 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Travis, W. D. Lung cancer pathology: current concepts. Clin. Chest Med. 41, 67–85 (2020).

    Article  PubMed  Google Scholar 

  9. Husmann, D. & Gozani, O. Histone lysine methyltransferases in biology and disease. Nat. Struct. Mol. Biol. 26, 880–889 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Qiao, Q. et al. The structure of NSD1 reveals an autoregulatory mechanism underlying histone H3K36 methylation. J. Biol. Chem. 286, 8361–8368 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Graham, S. E., Tweedy, S. E. & Carlson, H. A. Dynamic behavior of the post-SET loop region of NSD1: implications for histone binding and drug development. Protein Sci. 25, 1021–1029 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yang, S. et al. Molecular basis for oncohistone H3 recognition by SETD2 methyltransferase. Genes Dev. 30, 1611–1616 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 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).

    Article  CAS  PubMed  Google Scholar 

  16. Bass, A. J. et al. SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas. Nat. Genet. 41, 1238–1242 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhang, Q. et al. Structural mechanism of transcriptional regulator NSD3 recognition by the ET domain of BRD4. Structure 24, 1201–1208 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shen, C. et al. NSD3-short is an adaptor protein that couples BRD4 to the CHD8 chromatin remodeler. Mol. Cell 60, 847–859 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bradbury, R. H. et al. Optimization of a series of bivalent triazolopyridazine based bromodomain and extraterminal inhibitors: the discovery of (3R)-4-[2-[4-[1-(3-methoxy-[1,2,4]triazolo[4,3-b]pyridazin-6-yl)-4-piperidyl]phenoxy]ethyl]-1,3-dimethyl-piperazin-2-one (AZD5153). J. Med. Chem. 59, 7801–7817 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, 7–34 (2019).

    Article  PubMed  Google Scholar 

  21. Cochran, A. G., Conery, A. R. & Sims, R. J. III. Bromodomains: a new target class for drug development. Nat. Rev. Drug Discov. 18, 609–628 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. Lin, K. H. et al. Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer. Nat. Genet. 52, 408–417 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Su, Y. et al. Novel NanoLuc substrates enable bright two-population bioluminescence imaging in animals. Nat. Methods 17, 852–860 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Kuo, A. J. et al. NSD2 links dimethylation of histone H3 at lysine 36 to oncogenic programming. Mol. Cell 44, 609–620 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lowary, P. T. & Widom, J. New DNA sequence rules for high affinity binding to histone octamer and sequence-directed nucleosome positioning. J. Mol. Biol. 276, 19–42 (1998).

    Article  CAS  PubMed  Google Scholar 

  26. Cheema, M. S. & Ausió, J. Analytical ultracentrifuge analysis of nucleosomes assembled from recombinant, acid-extracted, HPLC-purified histones. Methods Mol. Biol. 1528, 75–95 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Luger, K., Rechsteiner, T. J. & Richmond, T. J. Expression and purification of recombinant histones and nucleosome reconstitution. Methods Mol. Biol. 119, 1–16 (1999).

    CAS  PubMed  Google Scholar 

  28. Shi, X. et al. Modulation of p53 function by SET8-mediated methylation at lysine 382. Mol. Cell 27, 636–646 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chen, S. et al. The PZP domain of AF10 senses unmodified H3K27 to regulate DOT1L-mediated methylation of H3K79. Mol. Cell 60, 319–327 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mazur, P. K. et al. Combined inhibition of BET family proteins and histone deacetylases as a potential epigenetics-based therapy for pancreatic ductal adenocarcinoma. Nat. Med. 21, 1163–1171 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Edelman, B. L. & Redente, E. F. Isolation and characterization of mouse fibroblasts. Methods Mol. Biol. 1809, 59–67 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Liu, S. et al. METTL13 methylation of eEF1A increases translational output to promote tumorigenesis. Cell 176, 491–504.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Adams, J. R. et al. Cooperation between Pik3ca and p53 mutations in mouse mammary tumor formation. Cancer Res. 71, 2706–2717 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Ferone, G. et al. SOX2 is the determining oncogenic switch in promoting lung squamous cell carcinoma from different cells of origin. Cancer Cell 30, 519–532 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Krimpenfort, P. et al. p15Ink4b is a critical tumour suppressor in the absence of p16Ink4a. Nature 448, 943–946 (2007).

    Article  CAS  ADS  PubMed  Google Scholar 

  36. Hoch, R. V. & Soriano, P. Context-specific requirements for Fgfr1 signaling through Frs2 and Frs3 during mouse development. Development 133, 663–673 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Skarnes, W. C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Raymond, C. S. & Soriano, P. High-efficiency FLP and PhiC31 site-specific recombination in mammalian cells. PLoS ONE 2, e162 (2007).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  39. Chu, V. T. et al. Efficient generation of Rosa26 knock-in mice using CRISPR/Cas9 in C57BL/6 zygotes. BMC Biotechnol. 16, 4 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Mazur, P. K. et al. SMYD3 links lysine methylation of MAP3K2 to Ras-driven cancer. Nature 510, 283–287 (2014).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  41. Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 541, 359–364 (2017).

    Article  CAS  ADS  PubMed  Google Scholar 

  42. Shultz, L. D. et al. Subcapsular transplantation of tissue in the kidney. Cold Spring Harb. Protoc. 2014, 737–740 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Iwano, S. et al. Single-cell bioluminescence imaging of deep tissue in freely moving animals. Science 359, 935–939 (2018).

    Article  CAS  ADS  PubMed  Google Scholar 

  44. Fushiki, H. et al. Quantification of mouse pulmonary cancer models by microcomputed tomography imaging. Cancer Sci. 100, 1544–1549 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Wang, Z. et al. SETD5-coordinated chromatin reprogramming regulates adaptive resistance to targeted pancreatic cancer therapy. Cancer Cell 37, 834–849.e13 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Salzmann, M., Pervushin, K., Wider, G., Senn, H. & Wüthrich, K. TROSY in triple-resonance experiments: new perspectives for sequential NMR assignment of large proteins. Proc. Natl Acad. Sci. USA 95, 13585–13590 (1998).

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  47. Balwierz, W., Armata, J., Moryl-Bujakowska, A. & Pekacki, A. Is first salvage chemotherapy the last-line chemotherapy in children with Hodgkin’s disease? A tentative answer based on long observation of two patients. Folia Haematol. Int. Mag. Klin. Morphol. Blutforsch. 114, 789–796 (1987).

    CAS  PubMed  Google Scholar 

  48. Li, Y. et al. Backbone resonance assignments for the SET domain of human methyltransferase NSD3 in complex with its cofactor. Biomol. NMR Assign. 11, 225–229 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Shen, Y. & Bax, A. Protein structural information derived from NMR chemical shift with the neural network program TALOS-N. Methods Mol. Biol. 1260, 17–32 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lakomek, N. A., Ying, J. & Bax, A. Measurement of 15N relaxation rates in perdeuterated proteins by TROSY-based methods. J. Biomol. NMR 53, 209–221 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Williamson, M. P. Using chemical shift perturbation to characterise ligand binding. Prog. Nucl. Magn. Reson. Spectrosc. 73, 1–16 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. van Zundert, G. C. P. et al. The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720–725 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Morrison, M. J. et al. Identification of a peptide inhibitor for the histone methyltransferase WHSC1. PLoS ONE 13, e0197082 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46 (W1), W296–W303 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang, Y. et al. Molecular basis for the role of oncogenic histone mutations in modulating H3K36 methylation. Sci. Rep. 7, 43906 (2017).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  56. Tisi, D. et al. Structure of the epigenetic oncogene MMSET and inhibition by N-alkyl sinefungin derivatives. ACS Chem. Biol. 11, 3093–3105 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47 (D1), D766–D773 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. 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  PubMed  PubMed Central  CAS  Google Scholar 

  61. 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).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  62. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    Article  ADS  CAS  Google Scholar 

  63. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  65. Hainer, S. J. & Fazzio, T. G. High-resolution chromatin profiling using CUT&RUN. Curr. Protoc. Mol. Biol. 126, e85 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Zhu, Q., Liu, N., Orkin, S. H. & Yuan, G. C. CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis. Genome Biol. 20, 192 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Gozani and Mazur laboratories for critical reading of the manuscript, and M. Lin for the AkaLuc vector. This work was supported in part by grants from the NIH to O.G. (R01 GM079641 and R35 GM139569), K.F.C. (R01AG050997) and P.K.M. (R00 CA197816), 1U54CA224065 to B.F. and J.A.R., VA Merit Award to K.F.C., intramural funds from KAUST to W.F., Ł.J. and M.J., the P. Neuroendocrine Tumor Research Foundation, AACR, DOD PRCRP Career Development Award (CA181486), Career Enhancement Grant – The University of Texas NIH SPORE in Lung Cancer (P50CA070907), and the Andrew Sabin Family Foundation Scientist and CPRIT Scholar in Cancer Research (RR160078) to P.K.M. N.-Y.S. was supported by SRG2019-00177-FHS and FDCT0038/2020/AFJ, N.M.F. by the American Cancer Society – Joe & Jessie Crump Foundation Fellowship (PF-20-108-01-DMC), X.L. by a CPRIT Research Training Grant (RP170067) and S.H. by a Deutsche Forschungsgemeinschaft Fellowship (HA8434/1-1).

Author information

Author notes

  1. These authors contributed equally: Gang Yuan, Natasha M. Flores

Authors and Affiliations

  1. Department of Biology, Stanford University, Stanford, CA, USA

    Gang Yuan, Deepanwita Sengupta & Or Gozani

  2. Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Natasha M. Flores, Simone Hausmann, Shane M. Lofgren, Xiaoyin Lu & Pawel K. Mazur

  3. Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Vladlena Kharchenko, Iwona Czaban, Dulat Azhibek, Wolfgang Fischle, Mariusz Jaremko & Łukasz Jaremko

  4. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Maria Angulo-Ibanez & Katrin F. Chua

  5. Geriatric Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA

    Maria Angulo-Ibanez & Katrin F. Chua

  6. University of Navarra, Center for Applied Medical Research, Pamplona, Spain

    Silvestre Vicent

  7. Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Bingliang Fang & Jack A. Roth

  8. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ignacio I. Wistuba

  9. Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX, USA

    John D. Minna

  10. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

    John D. Minna

  11. Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    John D. Minna

  12. Faculty of Health Sciences, University of Macau, Macau SAR, China

    Ning-Yi Shao

Authors
  1. Gang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natasha M. Flores
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Hausmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shane M. Lofgren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vladlena Kharchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria Angulo-Ibanez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Deepanwita Sengupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaoyin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Iwona Czaban
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dulat Azhibek
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Silvestre Vicent
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Wolfgang Fischle
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Mariusz Jaremko
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Bingliang Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ignacio I. Wistuba
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Katrin F. Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jack A. Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. John D. Minna
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Ning-Yi Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Łukasz Jaremko
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Pawel K. Mazur
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Or Gozani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.Y. and N.M.F. contributed equally to this work. They were responsible for the experimental design, execution, data analysis, and manuscript preparation. S.H. performed PDX, AALE studies and IHC analysis. D.S., X.L. G.Y. and S.V. helped with PDX and AALE studies. M.A.-I. helped G.Y. with CUT&RUN experiments and K.F.C. supervised. S.M.L. performed bioinformatic analysis of tumour RNA-seq. V.K., I.C., D.A., W.F., M.J. and Ł.J. performed biophysical and NMR experiments. I.I.W. evaluated histopathology. I.I.W., B.F. J.A.R. and J.D.M. provided PDX samples. N.-Y.S. analysed RNA-seq and CUT&RUN data. O.G., P.K.M., Ł.J. and N.Y.S. were equally responsible for supervision of research, data interpretation and manuscript preparation.

Corresponding authors

Correspondence to Ning-Yi Shao, Łukasz Jaremko, Pawel K. Mazur or Or Gozani.

Ethics declarations

Competing interests

O.G. is a co-founder of EpiCypher, Inc. and Athelas Therapeutics, Inc. J.A.R. is a consultant and stock owner (including pending patents) in Genprex, Inc. P.K.M. is a co-founder of Amplified Medicines, Inc. The other authors declare no competing interests.

Additional information

Peer review information Nature thanks Gang Greg Wang, Jon Wilson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Characterization of genetic drivers of LUSC tumorigenesis.

a, Genetic alterations and mRNA expression of genes in the 8p11–12 amplification genomic region of chromosome 8 from the 85 specimens of 8p11–12-amplified human LUSC in the TCGA dataset. b, Genetic alterations and mRNA expression of NSD3, FGFR1, PIK3CA, PTEN, SOX2, CDKN2A and CDKN2B from all 464 of the human LUSC specimens in the TCGA dataset (ID: phs000178) analysed using cBioPortal.

Extended Data Fig. 2 Depletion of NSD3 but not FGFR1 inhibits LUSC tumorigenesis in vivo.

a, Western blot analysis with the indicated antibodies of whole-cell lysates from H520 cells expressing sgControl or sgNSD3. The bands representing NSD3 and NSD3Short are indicated. Tubulin was used as a loading control. b, Western blot analysis with the indicated antibodies of whole-cell lysates from H520 cells expressing sgControl or sgFGFR1. Tubulin was used as a loading control. c, Real-time (RT) qPCR analysis of PLPP5 mRNA expression in H520 cells expressing sgControl or sgPLPP5. Several commercial antibodies against PLPP5 were tested but we could not detect any reproducible band close to the correct size (data not shown). The RT–qPCR data were normalized to Actb and are presented as fold change relative to the sgControl sample; mean ± s.d. from three independent experiments. P value determined by two-tailed unpaired t-test. d, Treatment of the 8p11AMP-positive H520 LUSC cell line with sgRNAs targeting the 8p11-amplified region does not cause an increase in phosphorylated H2AX (γH2AX). Western blot analysis with the indicated antibodies of H520 cells expressing Cas9 and the indicated sgRNAs. DNA damage induced by hydroxyurea (HU) treatment was used as a positive control, H3 was used as a loading control. e, Depletion of NSD3, but not depletion of FGFR1 or PLPP5, attenuates xenograft growth of 8p11AMP H520 LUSC cells. Tumour volume quantification of the indicated H520 xenografts generated in immunocompromised mice (n = 5 mice, for each treatment group). P values determined by two-way ANOVA with Tukey’s post hoc test. Data are represented as mean ± s.e.m. Data for sgControl, sgNSD3 and sgFGFR1 are the same as from Fig. 1b. f, Representative HE-stained sections and IHC staining of lung tissue from PSC mouse model lung tumour showing key diagnostic histological features of LUSC, including positive staining for keratin and p63 and negative staining for TTF1 (representative of n = 8 samples for each group). Human LUSC samples are shown for comparison (representative of n = 8 samples for each group). Scale bars, 50 μm. g, Increased expression of NSD3 in lungs tracks with tumour progression. IHC analysis of NSD3 levels in wild-type lung and tumour biopsies collected from the PSC model at 150 days and at the clinical endpoint when mice developed substantial morbidity (representative of n = 8 samples for each group). Scale bars, 50 μm. h, Quantification of NSD3 levels in tumour biopsies as in g within tumour areas that have high-, low-intensity or negative staining for chromogen. Data are represented as mean ± s.e.m. i, NSD3 mRNA overexpression is common (~60%) and not limited to 8p11–12-amplified tumours. Genetic alterations and mRNA expression of the indicated genes in human LUSC samples from the TCGA dataset showing frequency of overexpression and alternations in NSD3 and known driver mutations PIK3CA, PTEN, SOX2, CDKN2A and CDKN2B. j, Representative HE-stained sections and IHC staining of lung tissue from PSC (control), PSCFGFR1-KO and PSCNSD3-KO mutant mice (representative of n = 8 mice for each experimental group). Scale bars, 50 μm; arrowheads, positive cleaved caspase 3 cells. k, l, Quantification of Ki67 (Ki67+ positive cells; a marker of proliferation) (k) and cleaved caspase 3 (cl. Caspase 3+ cells; a marker of apoptosis) (l) in samples as in j. In k, l and in all subsequent box plots, the line indicates the median, the box marks the 75th and 25th percentiles and the whiskers indicate the minimum and maximum values. All data points are shown. P values determined by one-way ANOVA with Tukey’s post hoc test (k, l).

Source data

Extended Data Fig. 3 Identification of the cancer-associated NSD3(T1232A) variant as a gain-of-function variant.

a, Schematic diagram of the distribution of the different NSD3 candidate mutants tested in b. b, In vitro methylation assay as in Fig. 2b on rNuc with recombinant wild-type NSD3SET or the indicated mutants (1–35) with non-radiolabelled SAM (the T1232A mutant is indicated in red). For each screening panel set, the top row shows the western blot and the bottom row shows Coomassie blue stain of the NSD3 proteins. c, Schematic diagram of NSD3 fragments used in this study. d, In vitro methylation reactions with rNucs and different concentrations of recombinant wild-type NSD3SET or NSD3SET(T1232A) with 3H-SAM. Methylation intensity was measured by liquid scintillation counting. Data shown as mean ± s.d. from three independent experiments. e, Western blot analysis with the indicated antibodies of whole-cell extracts from NSD2-deficient HT1080 cells (to deplete H3K36me2 compared to control HT1080 cells (lanes 1 and 2)) overexpressing the indicated NSD3 variants. Total H3 was used as a loading control. f, Methylation assays as in Fig. 2b with rNuc with linker DNA (187 bp) and increasing concentrations of enzyme as indicated. Western blot shows H3K36me2 generation, H3 is shown as a loading control. Bottom, Coomassie blue stain of NSD3 proteins.

Source data

Extended Data Fig. 4 Biophysical characterization of T1232A substitution on the NSD3SET domain.

a, Kd values (± s.d.) for binding studies of NSD3SET and NSD3SET(T1232A) to recombinant nucleosomes (rNuc) reconstituted on 147 bp and 187 bp 601 Widom DNA as indicated (determined by microscale thermophoresis (MST)) and the cofactor SAM (determined by ITC) (Supplementary Table 1; Methods). Mean ± s.d. from three independent experiments. b, The topology of NSD3SET domain segments of pre-SET (white-grey), SET (cyan), post-SET (dark grey) and regulatory loop (magenta) marked onto the ribbon representations of existing NSD3SET domain crystal structures (PDB 5UPD, 6CEN). The missing residues in the tip of the regulatory loop, not modelled into the electron densities, are denoted with dotted lines. The zinc(II) ions are depicted as grey spheres and the SAM cofactor with sticks. Residues T1232 and V1243 are marked with their side-chains. Bottom, schematic representation of the primary sequence with indicated domains and the location of the T1232A substitution. c, Overlay of the 2D [1H,15N] TROSY–HSQC (transverse relaxation optimized spectroscopy–heteronuclear single quantum coherence) spectra of 250 μM wild-type U-[15N]NSD3SET (dark grey) and U-[15N]NSD3SET(T1232A) (magenta) at pH 7.5 and 25 °C. The 1H/15N backbone amide cross-peaks of T1232 in wild-type NSD3SET and A1232 of NSD3SET(T1232A) are marked. d, The backbone amide chemical shift perturbations (CSPi = [(ΔδH,i)2 + 0.25 × (ΔδN,i)2]0.5) between NSD3SET and NSD3SET(T1232A) are mapped onto the ribbon representations of protein structures; left, NSD3SET with regulatory loop in closed conformation (PDB 5UPD); centre, in open conformation (based on the NSD2SET crystal structure (PDB 5LSU) modelled with SwissModel); and right, the open conformation with the H3.1 A28–R41 substrate docked (Methods). The T1232 and V1243 residues are marked with their side-chains. Residues are coloured as follows: 0.03 ≤ CSPi < 0.05 (yellow), 0.05 ≤ CSPi < 0.11 (light orange), 0.11 ≤ CSPi < 0.75 ppm (orange), CSPi > 4.60 (red), prolines and residues missing amide assignments are grey. e, Overlay of NSD3SET domains with the regulatory loop in open and closed conformations. The open state is the crystal structure of NSD3SET, while the closed state is based on the NSD2SET crystal structure (PDB 5LSU) modelled with SwissModel. Protein sequence graph and colouring as in Fig. 4h. f, Projections of 2D TROSY-type heteronuclear {1H}-15N nuclear Overhauser effect (NOE) experiments collected for 370 μM wild-type U-[15N]NSD3SET (left) and NSD3SET(T1232A) (right) at pH 7.5 and 25 °C with several residues marked (Methods). The higher the NOE ratio value, the more rigid the N–H vector, meaning less dynamic motion, whereas a lower value means more dynamic, indicative of less restricted motions. The tryptophan imidazole side-chain 1H/15N correlations are also detected in this experiment, Hε1/Nε1 from W1235 and W1157. g, The observed changes in {1H}-15N NOE values (ΔNOEi) reporting on ps-ns polypeptide main-chain mobility differences between NSD3SET and NSD3SET(T1232A) are plotted on the 3D static structures of NSD3SET (PDB: 6CEN, left column represented as protein surface, middle column ribbon with H3 peptide, right column ribbon with no substrate). Key features are indicated. Light blue to dark blue indicates the decrease in fast dynamics after T1232A substitution, and yellow to red indicates increased dynamics on ps–ns time scale. h, The T1232A substitution enhances the mobility of the regulatory loop. The observed changes in heteronuclear {1H}-15N NOE mapped on the ribbon representations of the open and closed states of the regulatory loop within the NSD3SET and in Fig. 2e. The structures are the same as in e. i, Methylation assays as in Fig. 2a with rNuc (187bp) and the indicated NSD3 variants.

Extended Data Fig. 5 Generation of PSCN LUSC mouse model and NSD3 coordination of an oncogenic gene expression program in LUSC.

a, Alignment of NSD3 human and mouse residues spanning human T1232 indicates that human NSD3 T1232 corresponds with mouse NSD3 T1242. b, Schematic of the LSL-V5-Nsd3T1242A conditional allele. In the presence of Cre recombinase, a translational stop cassette flanked by LoxP recombination sites is deleted to enable V5-Nsd3T1242A expression. P1 and P2 indicate locations of genotyping primers. c, Confirmation of successful generation of LSL-Nsd3T1242A allele by PCR (P1 + P2 + P3 primers) on DNA isolated from mouse tail biopsies from indicated mouse genotypes; expected product sizes are marked. d, Western blots with the indicated antibodies (V5 antibody detects V5-tagged NSD3(T1242A)) of lysates from LSL-V5-Nsd3T1242A lung fibroblasts transduced ex vivo with Ad-Cre or vehicle (control). Actin is shown as a loading control. e, Left, Sanger sequencing confirmation that the Nsd3T1242A mutation is present in generated conditional LSL-V5-Nsd3T1242A mutant mouse. Right, wild-type NSD3 sequence from control mice. f, Representative lung tissue sections from PSC and PSCN mutant mice stained with HE or IHC with indicated antibodies (representative of n = 8 mice for each group). Scale bars, 50 μm; arrowheads, cleaved caspase 3-positive cells. g, Quantification of cleaved caspase 3, a marker of apoptosis in samples as in f. P values determined by two-tailed unpaired t-test. h, Western blots with indicated antibodies of whole-cell lysates from PSCC or two different PSCN cell lines. Total H3 was used as a loading control. i, Volcano plot of RNA-seq comparison between PSCN and PSC tumour biopsies (three independent biological replicates for each condition) shows higher expression of 891 genes shown in red (fold change log2 ≤ −0.5 and P < 0.05 by Wald test) and decreased expression of 1,839 genes shown in green (fold change log2 ≥ 0.5 and P < 0.05 by Wald test). False discovery rate (FDR) values are provided. j, Volcano plot of RNA-seq comparison of PSCN cells with or without sgNSD3 (three biological replicates for each condition) shows that sgNSD3 caused decreased expression of 234 genes shown in red (adjusted P < 0.001 by likelihood ratio test) and increased expression of 229 genes shown in green (adjusted P < 0.001 by likelihood ratio test). False discovery rate (FDR) values are provided. k, Top hallmark gene sets identified in the GSEA analysis of datasets from i, j shows high overlap. Normalized enrichment scores (NES) provided (Methods). l, Reconstitution of NSD3-deficient PSCN cells with NSD3 and derivatives. Western blot analysis with the indicated antibodies of PSCN whole cell lysates with or without sgNSD3 and complemented with the indicated CRISPR-resistant NSD3 derivatives. H3 and tubulin were used as loading controls. m, Quantitative real-time (RT)-PCR analysis of the transcript levels of the indicated genes in the cells as described in l. The RT–qPCR data for each gene were normalized to Actb and presented as fold change relative to the sgControl sample. Error bars represent mean ± s.d. from three independent experiments. P values determined by one-way ANOVA with Tukey’s post hoc test.

Source data

Extended Data Fig. 6 NSD3(T1242A)-mediated H3K36me2 in LUSC oncogenic reprogramming.

a, Independent CUT&RUN replicate for H3K36me2 as in Fig. 3i. b, Normalized gene expression levels from the indicated groups from PSCN cell line RNA-seq datasets. dDEGs, downregulated DEGs; uDEGs, upregulated DEGs, from Extended Data Fig. 5j. P values determined by two-tailed robust t-test (Methods). dDEGs n = 234, uDEGs n = 229, all genes n = 16,091. Boxes, 25th to 75th percentiles; whiskers, minimum to maximum; centre line, median. c, CUT&RUN profile for H3K36me2 and H3K27me3 in PSCN cells with or without sgNSD3 over average of intergenic regions on a genome-wide scale. d, CUT&RUN profile for a monoclonal IgG (against GFP) as in Fig. 3i for all genes and dDEGs as indicated in PSCN cells with or without sgNSD3 is shown as a negative control. e, Genome browser view of CUT&RUN signals for H3K36me2 and H3K27me3 on the indicated genes and conditions. Arrow indicates the direction of gene transcription. f, Loss of occupancy of NSD3(T1242A) and H3K36me2 at the indicated NSD3 target genes (Irgm1 and Prkaa2) upon NSD3 depletion. Btg2 and Gadd45g are not NSD3 target genes and are shown as control regions that do not change in response to NSD3 depletion. ChIP–qPCR analysis of V5–NSD3(T1242A) (left) and H3K36me2 (right) in the bodies of the indicated genes. The data were plotted as per cent enrichment relative to input. Mean ± s.d. from three independent experiments. P values determined by two-tailed unpaired t-test. g, NSD3(T1242A) and H3K36me2 occupy the promoter and gene body regions of target genes Irgm1 and Prkaa2. Top, schematic of general gene structure and site of primers for Irgm1 (left) and Prkaa2 (right) gene loci. ChIP–qPCR analysis of V5–NSD3(T1242A) (left) and H3K36me2 (right) in promoter (p1), gene body (p2) and transcription end site (TES; p3) regions of target genes. TSS, transcription start site. The data were plotted as per cent enrichment relative to input. Mean ± s.d. from three independent experiments. P values determined by two-tailed unpaired t-test. h, ChIP–qPCR analysis of NSD3(T1242A) (left) and H3K36me2 (right) occupancy at the gene body regions of Irgm1 and Prkaa2 gene loci in reconstituted cells as described in Extended Data Fig. 5l. The enrichment was normalized to the sgControl sample and presented as fold change relative to the sgControl sample. Mean ± s.d. from three independent experiments. P values determined by one-way ANOVA with Tukey’s post hoc test.

Source data

Extended Data Fig. 7 NSD3 depletion attenuates growth of 8p11-amplified and NSD3-overexpressing human LUSC cell lines and ectopic expression of NSD3 cooperates with SOX2 to transform tracheobronchial epithelial cells.

a, Genetic alterations and mRNA expression of LUSC-associated genes in human LUSC cell lines used in this study. b, NSD3 depletion by CRISPR–Cas9 attenuates xenograft tumour growth of 8p11AMP and NSD3-overexpressing LUSC cell lines. Top, tumour volume quantification of human LUSC cell line xenografts described in Fig. 4a and Extended Data Fig. 7a treated with sgControl or sgNSD3 and grown in immunocompromised mice at 28 days after implantation (n = 5 mice for each treatment group). P values determined by one-way ANOVA with Tukey’s post hoc test. Data are represented as mean ± s.e.m. Bottom, western blot analysis with the indicated antibodies of whole-cell lysates from cells in top panel. γH2AX levels are shown to assess whether sgNSD3 expression induces non-specific DNA damage. H3 and actin are used as loading controls. c, NSD3 depletion by shRNA attenuates xenograft tumour growth of 8p11AMP-positive LUSC cell lines. Top, tumour volume quantification of the indicated 8p11AMP-positive and two control 8p11AMP-negative human LUSC cell line xenografts as in a treated with shControl or shNSD3 as indicated and grown in immunocompromised mice at 28 days after implantation (n = 5 mice for each treatment group). P values determined by one-way ANOVA with Tukey’s post hoc test. Data are represented as mean ± s.e.m. Bottom, western blot analysis with the indicated antibodies of whole-cell lysates from cells in top panel. Actin is used as a loading control. d, NSD3-deficient H520 cells reconstituted with the indicated V5-tagged CRISPR-resistant NSD3 derivatives: NSD3(T1232A) (NSD3TA) and NSD3(T1232A/Y1174A) (NSD3TA/YA). Western blots of H520 lysates with indicated antibodies H3 and tubulin were used as loading controls (Fig. 4b). e, Western blots of whole-cell lysate of AALE cells used in transformation assays for Fig. 4c with ectopic expression of SOX2 and the indicated V5-tagged constructs (NSD3WT, NSD3TA, NSD3YA, NSD3TA/YA, NSD3Short or FOXE1). f, Quantification of soft agar colony formation for AALE tracheobronchial epithelial cells with ectopic expression of SOX2 and NSD3WT, NSD3TA, NSD3YA, NSD3TA/YA, NSD3Short or FOXE1 as in e. Data are represented as mean ± s.e.m. of three technical replicates in two independent experiments. g, Representative soft-agar images from AALE transformation assays in f. h, In vivo AALE transformation assay images from Fig. 4c. Optical overlay of bioluminescent signal with X-ray images of mice grafted under the renal capsule with AALE cells expressing plasmids as in e and AkaLuc after substrate (AkaLumine-HCl; Methods) administration (n = 5 for each condition). The colour bar indicates the total bioluminescence radiance (photons/s/cm2/sr).

Source data

Extended Data Fig. 8 Genomic characterization of PDXs from primary LUSC.

a, Distribution of whole-genome copy number alterations in LUSC PDXs used in this study by OncoScan (Methods). Green lines, 3p11 amplification (including PIK3CA and SOX2); red lines, marks, 8p11–12 (for example, NSD3 and FGFR1 as shown); blue lines, biallelic deletion in 9p21.3 (including CDKN2A/B). b, Sanger sequencing analysis of NSD3 SET domain mutation status in collection of 37 LUSC PDXs identified NSD3T1232A mutation in one of the PDX samples (PDXT1232A) and wild-type NSD3 in other analysed PDXs.

Source data

Extended Data Fig. 9 NSD3 dependency renders PDXs from patients with LUSC therapeutically vulnerable to BETi.

a–c, Bottom, tumour volumes of PDXCTR-1 (a), PDXCTR-2 (b) and PDXAMP-2 (c) growing in immunocompromised mice (n = 5 mice, for each treatment group). Top, western blots with the indicated antibodies of lysates from PDXs with or without sgNSD3. Actin is shown as a loading control. d, BRD4 interacts with NSD3 in PSCN cells. Left, western blot of whole-cell lysates from PSCN cells with or without sgNSD3 to show the relative position of NSD3 and NSD3short in PSCN cells. Tubulin used as a loading control. Right, BRD4 interacts with both NSD3 and NSD3short isoforms in PSCN cells. Co-immunoprecipitation (IP) experiments in PSCN cells with indicated antibodies for IPs and western analyses. Input, PSCN cell nuclear extract. e, Proliferation assay of PSCC and PSCN cells treated with vehicle control or 20 nM AZD5153 (BETi). Data represent mean ± s.e.m. of three technical replicates in two independent experiments. f, Treatment of PSCN cells with the BETi JQ1 inhibits expression of the indicated NSD3 target genes. RT–qPCR analysis of the indicated mRNAs from PSCN cells treated with DMSO or 100 nM JQ1. The RT–qPCR data for each target gene were normalized to Actb and presented as fold change relative to DMSO-treated sample. Mean ± s.d. from three independent experiments. P values determined by two-tailed unpaired t-test. g, h, Tumour volumes of PDXCTR-1 (g) and PDXCTR-2 (h) treated with BETi or vehicle (control). Arrowhead indicates start of the treatment. P values determined by two-tailed unpaired t-test (a–c, f) or two-way ANOVA with Tukey’s post hoc test (e, g, h). Data are represented as mean ± s.e.m. (a–c, e–h).

Source data

Extended Data Fig. 10 Model of the role for elevated NSD3 H3K36 methylation activity in LUSC pathogenesis.

LUSC is characterized by a number of driver mutations such as increased expression of PI3K and SOX2, PI3K activating mutations, deletion of PTEN and other deletions and alterations. One of the more common genetic alterations is amplification of the 8p11–12 genomic region (about 20% of patients with LUSC). While increased expression of FGFR1 is postulated to be the causative mutation of the 8p11–12 amplicon, our work implicates amplification of the neighbouring gene NSD3 as the main driving alteration. As shown, NSD3 amplification leads to increased NSD3 expression and hence increased synthesis of H3K36me2, which works with other LUSC driver mutations to promote LUSC pathogenesis. In addition, as shown in Extended Data Fig. 2i, NSD3 overexpression is detected in 60% of patients with LUSC; thus, NSD3 is frequently highly expressed in LUSCs that do not harbour the 8p11–12 amplifications. We also describe NSD3(T1232A) as a gain-of-function (GOF) variant, that while far less common than the 8p11–12 amplicon and NSD3 overexpression, is present in human LUSC. The NSD3(T1232A) variant, because of its enhanced catalytic behaviour, functionally acts like amplified or overexpressed NSD3 in increasing H3K36me2 synthesis and cooperating with other LUSC driver mutations to accelerate tumorigenesis. The increase in H3K36me2 by NSD3 overexpression or NSD3(T1232A) reprograms the chromatin landscape, blocking synthesis of H3K27me3 and increasing H3K36me2, which stimulates transcription of key oncogenic targets including genes involved in mTOR signalling and MYC-associated pathways. We speculate that NSD3-regulated tumours become addicted to H3K36me2-driven transcriptional activation, rendering these tumours particularly vulnerable to BETi as NSD3 and BRD4 interact and cooperate in transcription. This hypersensitivity could potentially be exploited clinically owing to an expanded therapeutic window for BETi and by using these drugs as targeted therapy for the tens of thousands of patients that are 8p11–12 positive. Not shown in the model, NSD3 is overexpressed in many LUSC samples without an underlying known alteration and we speculate that in such cases, increased NSD3 would contribute to tumorigenesis in a similar fashion as the scenarios described above in which NSD3 is hyperactive or overexpressed owing to amplification.

Supplementary information

Supplementary Figure 1

Gel source data for this study: Figures 1c, 1d, 2a, 2b, 2c, 3f, 3g, 4d, 4e and Extended Data Figures 2a, 2b, 2d, 2k, 2k, 3b, 3e, 3f, 4i, 5d, 5h, 5l, 7b, 7c, 7d, 7e, 9a, 9b, 9c, 9d.

Reporting Summary

Supplementary Table 1

Biophysical and NMR-based analyses of the NSD3 T1232A substitution. Tab 1: Nucleosome binding. Tab 2: SAM binding. Tab 3: 13C chemical shifts. Tab 4: Backbone angles. Tab 5: Thermal stability.

Supplementary Table 2

Genes differentially regulated in PSCN versus PSC tumor biopsies. List of upregulated (A) and downregulated (B) genes in RNA-seq analysis of PSCN versus PSC tumor biopsy tumors (n = 3 for each condition). Genes with P value ≤ 0.05 and log2 fold change ≥ 0.5 were defined as up-regulated genes, and genes with P value ≤ 0.05, log2 fold change ≤ -0.5 were defined as down-regulated genes.

Supplementary Table 3

Genes differentially regulated in PSCN cells ± sgNSD3. List of upregulated (A) and downregulated (B) genes in RNA-seq analysis of sgNSD3 versus sgControl PSCN cells (n = 3 for each condition). Genes with adjusted P value < 0.001 (by likelihood ratio test), log2 FC ≥ 0.1 were defined as up-regulated genes, and genes with adjusted P value < 0.001 (by likelihood ratio test), log2 FC ≤ -0.1 were defined as down-regulated genes, FC: fold change.

Supplementary Table 4

A list of compounds and their results used in cell growth inhibition screen.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, G., Flores, N.M., Hausmann, S. et al. Elevated NSD3 histone methylation activity drives squamous cell lung cancer. Nature 590, 504–508 (2021). https://doi.org/10.1038/s41586-020-03170-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-03170-y

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

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing