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Impaired H3K36 methylation defines a subset of head and neck squamous cell carcinomas

Nature Genetics volume 49, pages 180–185 (2017)Cite this article

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Abstract

Human papillomavirus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs) are deadly and common cancers. Recent genomic studies implicate multiple genetic pathways, including cell signaling, cell cycle and immune evasion, in their development. Here we analyze public data sets and uncover a previously unappreciated role of epigenome deregulation in the genesis of 13% of HPV-negative HNSCCs. Specifically, we identify novel recurrent mutations encoding p.Lys36Met (K36M) alterations in multiple H3 histone genes. histones. We further validate the presence of these alterations in multiple independent HNSCC data sets and show that, along with previously described NSD1 mutations, they correspond to a specific DNA methylation cluster. The K36M substitution and NSD1 defects converge on altering methylation of histone H3 at K36 (H3K36), subsequently blocking cellular differentiation and promoting oncogenesis. Our data further indicate limited redundancy for NSD family members in HPV-negative HNSCCs and suggest a potential role for impaired H3K36 methylation in their development. Further investigation of drugs targeting chromatin regulators is warranted in HPV-negative HNSCCs driven by aberrant H3K36 methylation.

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Figure 1: H3K36M and NSD1 alterations define a specific HNSCC subgroup.
Figure 2: Schematic representation of the anatomical locations of NSD1 and H3K36M alterations in HNSCCs.
Figure 3: Representative immunohistochemical staining of H3K36M, NSD1, H3K36me2 and H3K36me3 in H3K36M or NSD1-mutant or wild-type HNSCCs.
Figure 4: H3K36M and NSD1 alterations decrease levels of H3K36me2 in HNSCC.

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Acknowledgements

We thank EMD Millipore for its role in H3.3K36M antibody generation, A. Ponce de Leon for help with figure formatting, and the TCGA group for the wealth of data available and for their rapid and helpful answers to other enquiries we had on this data set. This research was supported by US National Institutes of Health (NIH) grants (P01CA196539 to N.J., J.M., P.W.L., B.A.G. and C.D.A.; K99CA212257 to C.L.; T32GM008275 to D.M.M.), the Rockefeller University (to C.D.A.), startup funding provided by the Wisconsin Institute for Discovery (to P.W.L.), the Greater Milwaukee Foundation (to P.W.L.), the Sidney Kimmel Foundation (Kimmel Scholar Award to P.W.L.), Canadian Institutes for Health Research (CIHR) grants (MOP 142491 to J.S.M. and A.C.N.; MOP 340674 to A.C.N.) and the Cedars Cancer Foundation (N.J.). B.A.G. is funded by a Leukemia and Lymphoma Society Dr. Robert Arceci Scholar Award. This work was performed within the context of the I-CHANGE consortium and supported by funding from Genome Canada, Genome Quebec, the Institute for Cancer Research of the CIHR, McGill University and the Montreal Children's Hospital Foundation. C.L. is a Kandarian Family Fellow supported by the Damon Runyon Cancer Research Foundation (DRG-2195-14). N.J. is a member of the Penny Cole Laboratory and the recipient of a Chercheur Boursier, Chaire de Recherche Award from the Fond de la Recherche du Québec en Santé. J.M. holds a Canada Research Chair (tier 2). S.P.-C. is supported by a studentship from the Fond de la Recherche du Québec en Santé. D.B. and T.G. are supported by a fellowship from the TD Trust/Montreal Children's Hospital Foundation and CIHR, respectively.

Author information

Author notes

  1. Simon Papillon-Cavanagh and Chao Lu: These authors contributed equally to this work.

  2. Jacek Majewski and Nada Jabado: These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Simon Papillon-Cavanagh, Tenzin Gayden, Denise Bechet, Octavia Maria Dancu, Jacek Majewski & Nada Jabado

  2. Laboratory of Chromatin Biology and Epigenetics, The Rockefeller University, New York, New York, USA

    Chao Lu & C David Allis

  3. Department of Pediatrics, McGill University and McGill University Heath Centre Research Institute, Montreal, Quebec, Canada

    Leonie G Mikael & Nada Jabado

  4. Princess Margaret Cancer Centre, Toronto, Ontario, Canada

    Christina Karamboulas & Laurie Ailles

  5. Department of Pathology, Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada

    Jason Karamchandani

  6. Department of Biochemistry and Biophysics, Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Dylan M Marchione & Benjamin A Garcia

  7. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Dylan M Marchione

  8. Department of Pathology, University Health Network, Toronto, Ontario, Canada

    Ilan Weinreb

  9. Department of Otolaryngology and Department of Surgical Oncology, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada

    David Goldstein

  10. Wisconsin Institute for Discovery and Department of Biomolecular Chemistry, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA

    Peter W Lewis

  11. Department of Otolaryngology—Head and Neck Surgery, University of Western Ontario, London, Ontario, Canada

    Sandeep Dhaliwal, John W Barrett & Anthony C Nichols

  12. Department of Oncology, University of Western Ontario, London, Ontario, Canada

    Sandeep Dhaliwal, John W Barrett & Anthony C Nichols

  13. Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada

    William Stecho & Christopher J Howlett

  14. Department of Microbiology and Immunology, Oncology and Otolaryngology—Head and Neck Surgery, University of Western Ontario, London, Ontario, Canada

    Joe S Mymryk

  15. London Regional Cancer Program and Lawson Health Research Institute, London, Ontario, Canada

    Joe S Mymryk, John W Barrett & Anthony C Nichols

Authors
  1. Simon Papillon-Cavanagh
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Contributions

S.P.-C., C.L., J.M. and N.J. conceived and designed the experiments and analyzed data. C.L., T.G., L.G.M., D.B., C.K., J.K., O.M.D., S.D., W.S., C.J.H., J.W.B., D.M.M. and B.A.G. performed experiments and analyzed data. L.A., I.W., D.G., J.S.M., P.W.L., A.C.N. and C.D.A. contributed materials and analyzed data. All authors contributed to the written manuscript.

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Correspondence to Jacek Majewski or Nada Jabado.

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Integrated supplementary information

Supplementary Figure 1 TCGA data set on HNSCCs reanalyzed in this study.

Summary of the TCGA dataset on HNSCCs re-analyzed in this study and the respective numbers of samples available within each.

Supplementary Figure 2 The methylation-defined H3 K36 subgroup consists of samples carrying damaging NSD1 or H3 K36M alterations.

(a) Our analysis reveals that the H3K36 methylation cluster (Fig. 1) is comprised of 61 samples. Ten samples carry H3K36M alterations, while 51 samples have NSD1 alterations, with one carrying both. One sample had no available genomic/transcriptome data. (b) H3K36M alteraions are observed in a wide array of histone H3 genes, supporting the dominant nature of this K-to-M substitution as reported32. (c) Samples in the H3K36 cluster are enriched for nonsense (in red) NSD1 mutations. Missense mutations (in green) are often observed near or at the functional SET domain.

Supplementary Figure 3 IGV screenshots of complex genomic events in NSD1 using RNA-seq data.

(a) A focal deletion of a set of 10 exons in sample BA-4074. (b) Near absent expression of NSD1 in sample CN-4731. (c) Aberrant NSD1 splicing, possibly due to a translocation in sample D6-6823. (d) A Focal deletion of a set of 14 exons in sample UF-A7JH

Supplementary Figure 4 Heat map showing hierarchical clustering of HNSCC samples based on DNA methylation and genetic alterations in other genes associated with HNSSC.

(Top) Previous 4-class, expression-based classification (Atypical (black), Classical (red), Mesenchymal (green), Basal (blue)). (Middle) Other genes involved in the H3K36 methylation pathways are infrequently mutated in HNSCC and do not vary within HPV- HNSCC subgroups. (Bottom) Only TP53, CASP8, NSD1 and H3K36M vary significantly among HPV- HNSCC subgroups. For color coding of methylation groups and anatomical locations, please see Figure 1.

Supplementary Figure 5 Mean sample methylation levels of HNSCC.

Mean sample methylation levels of HNSCC subgroups showing that NSD1 and H3K36M alterations are similarly associated with a DNA hypomethylated phenotype compared to the other four methylation clusters identified in HNSCCs.

Supplementary Figure 6 Global DNA methylation, NSD1 expression level and the types of mutations in the H3 K36M cluster.

(a,b) Samples with NSD1 mutations outside the H3K36M cluster (grey bars) show higher global DNA methylation levels (a) and higher NSD1 gene expression levels (b) compared to samples in the H3K36 cluster (blue bars). (c) Samples in the H3K36 cluster frequently carry NSD1 truncating (nonsense and frameshift) mutations compared to NSD1 mutated samples in the other HNSCC subgroups.

Supplementary Figure 7 Mean somatic mutations per sample of HNSCC subgroups.

Mean somatic mutations per sample of HNSCC subgroups showing that NSD1 mutant samples have higher somatic mutation count than H3K36M samples or other HNSCC subgroups.

Supplementary Figure 8 NSD1 but not H3 K36M mutant samples have smoking-induced somatic mutation signatures.

(a) In NSD1 samples, similar to lung adenocarcinoma (LUAD) and lung squamous cell carcinomas (LUSC), a large proportion of somatic mutations is accounted by the smoking-associated signature S4 (red). In comparison, other HNSCC samples (left) have much less smoking-induced somatic mutations. (b) Unsupervised clustering of mutation frequencies groups NSD1, but not H3K36M, with lung cancers, suggesting the mutational spectrum in NSD1 samples is associated with smoking. (c) A five-signature model identifies UV exposure (S1), Temozolomide (S2) and smoking (S4) as major mutation generation processes.

Supplementary Figure 9 Kaplan–Meier curve for HNSCC patients, grouped by DNA-methylation-defined molecular subgroups.

Supplementary Figure 10 Anatomical distribution of HNSCC samples relative to K36M and NSD1 alterations.

Anatomical distribution of HNSCC samples showing that H3K36M samples are exclusively found in the oral cavity whereas the majority of NSD1 mutated samples are found in the larynx and oral cavity.

Supplementary Figure 11 NSD1 and NSD2 expression in the H3K36 cluster and other HNSCC subgroups

(a) Samples in the H3K36 cluster show lower NSD1 expression levels. (b) NSD2 levels are uniform among al HNSCC subgroups.

Supplementary Figure 12 RNA-expression clustering of HNSCC samples.

Similar to our findings using DNA methylation, we observe a group of samples enriched in NSD1/H3K36M alterations (H3K36 cluster) and another enriched for HPV+ HNSCC. For color coding of methylation groups and anatomical locations, please see Figure 1.

Supplementary Figure 13 Gene ontology enrichment analysis on H3K36-specific differentially expressed genes.

Differential expression analysis performed on H3K36 samples versus all other HNSCCs tumors, controlling for anatomical location.

Supplementary Figure 14 Western blots for H3 K36M and NSD1 in HNSSC cell lines.

Full length immunoblots including molecular weights that were used in Figure 4.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Tables 1–4 and 6 (PDF 2037 kb)

Supplementary Table 5

Complete clinical annotation for the 528 samples used in the methylation clustering. (XLSX 213 kb)

Supplementary Table 7

List of differentially expressed genes in H3K36 versus other HNSCC samples, controlled for anatomical location. (XLSX 51 kb)

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Papillon-Cavanagh, S., Lu, C., Gayden, T. et al. Impaired H3K36 methylation defines a subset of head and neck squamous cell carcinomas. Nat Genet 49, 180–185 (2017). https://doi.org/10.1038/ng.3757

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