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The expanding landscape of ‘oncohistone’ mutations in human cancers

Nature volume 567, pages 473–478 (2019)Cite this article

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Abstract

Mutations in epigenetic pathways are common oncogenic drivers. Histones, the fundamental substrates for chromatin-modifying and remodelling enzymes, are mutated in tumours including gliomas, sarcomas, head and neck cancers, and carcinosarcomas. Classical ‘oncohistone’ mutations occur in the N-terminal tail of histone H3 and affect the function of polycomb repressor complexes 1 and 2 (PRC1 and PRC2). However, the prevalence and function of histone mutations in other tumour contexts is unknown. Here we show that somatic histone mutations occur in approximately 4% (at a conservative estimate) of diverse tumour types and in crucial regions of histone proteins. Mutations occur in all four core histones, in both the N-terminal tails and globular histone fold domains, and at or near residues that contain important post-translational modifications. Many globular domain mutations are homologous to yeast mutants that abrogate the need for SWI/SNF function, occur in the key regulatory ‘acidic patch’ of histones H2A and H2B, or are predicted to disrupt the H2B–H4 interface. The histone mutation dataset and the hypotheses presented here on the effect of the mutations on important chromatin functions should serve as a resource and starting point for the chromatin and cancer biology fields in exploring an expanding role of histone mutations in cancer.

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Fig. 1: Histones as signal integrators and cancer driver genes.
Fig. 2: Cancer-associated histone mutations occur at sites of known PTMs and in both tail and globular domains.
Fig. 3: Hypothesis generating classes of histone mutations.
Fig. 4: A model for the effect of oncohistones on the chromatin polymer.

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Data availability

All data analysed are included in this Analysis and its Supplementary Information. These data are also available in an interactive format (https://bit.ly/2GXH5Ve) except private institutional data that are currently embargoed, but are slated for release to AACR Genie according to established protocols. Also see the cBioPortal main page (https://www.cbioportal.org) ‘Example Queries’ section to link to a real-time query of histone mutations.

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Acknowledgements

We thank the patients who provided tissue for the sequencing that underlies this work. We also thank members the Allis and Muir laboratories and the P01 team. We acknowledge N. Socci of the MKSCC Bioinformatics Core (funded in part through the NIH/NCI Cancer Center Support Grant P30-CA008748). Funding support includes: P01CA196539 (C.D.A., T.W.M.), F32GM123659 (J.D.B.), U24-CA220457-01(J.G., R.K., N.S.), 2T32CA009512-29A1 (B.A.N.), the C. H. Li Memorial Scholar Fund (L.F.), Damon Runyon Cancer Research Foundation DRG-2185-14 (A.A.S.), the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, National Cancer Institute Cancer Center Core Grant no. P30-CA008748, and STARR Cancer Consortium (I9-A9-062).

Reviewer information

Nature thanks Stefan Pfister and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Lijuan Feng, John D. Bagert, Agata E. Lemiesz, JianJiong Gao

Authors and Affiliations

  1. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Benjamin A. Nacev, JianJiong Gao, Ritika Kundra, Nikolaus Schultz & C. David Allis

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

    Benjamin A. Nacev, Lijuan Feng, Agata E. Lemiesz, Alexey A. Soshnev & C. David Allis

  3. Department of Chemistry, Princeton University, Princeton, NJ, USA

    John D. Bagert & Tom W. Muir

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Contributions

B.A.N. and C.D.A. conceptualized and initiated the project. All authors assisted with data analysis, hypothesis generation, and writing the manuscript.

Corresponding authors

Correspondence to Tom W. Muir or C. David Allis.

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Extended data figures and tables

Extended Data Fig. 1 Sample characteristics.

One sample per patient in which TMB ≤ 10 mutations per Mb is shown, except in c, in which all samples are represented regardless of the TMB. a, Tumour allele frequency distribution for 1,452 out of 1,921 tumours in which allele frequency is available based on publicly available data. b, Detailed tumour allele frequency distribution for the four most frequency mutated residues. Blue bars represent the median. c, TMB distribution. d, Tumour type distribution. For display purposes, the main cancer types are used in place of detailed cancer type. e, f, Oncoprint of the distribution of histone mutations between core families on a per patient level for all TMB values (e) and for TMB ≤ 10 mutations per Mb (f). For display purposes, H2A variants and H3.5 are not shown.

Extended Data Fig. 2 Validation of TMB ≤ 10 as an analysis threshold.

a, For known oncohistones, the number of mutations captured reaches a plateau at a TMB of greater than 10 mutations per Mb. b, c, Histogram plot of H3 mutation distribution on a per patient level without a TMB threshold (b) and with a TMB ≤ 10 threshold (c) shows enrichment of known oncohistones as well as additional mutations compared to background.

Extended Data Fig. 3 Heat map of histone H3 mutations with individual residue labels.

Colour intensity indicates normalized mutation count (number of mutations at residue/number of samples per cancer type). Red labels indicate positions of known oncohistones. Per patient data with all TMB values plotted. The numbers of tumours sequenced are indicated following the tumour type label.

Extended Data Fig. 4 Heat map of histone H4 mutations with individual residue labels.

Colour intensity indicates normalized mutation count (number of mutations at residue/number of samples per cancer type). Per patient data with all TMB values plotted. The numbers of tumours sequenced are indicated following the tumour type label.

Extended Data Fig. 5 Heat map of histone H2A mutations with individual residue labels.

Colour intensity indicates normalized mutation count (number of mutations at residue/number of samples per cancer type). Per patient data with all TMB values plotted. The numbers of tumours sequenced are indicated following the tumour type label.

Extended Data Fig. 6 Heat map of histone H2B mutations with individual residue labels.

Colour intensity indicates normalized mutation count (number of mutations at residue/number of samples per cancer type). Per patient data with all TMB values plotted. The numbers of tumours sequenced are indicated following the tumour type label.

Extended Data Fig. 7 Histogram showing mutational frequency from the dataset in histones across all cancers.

One tumour per patient in which a TMB ≤ 10 mutations per Mb is shown. Boxes in the amino acid sequence show the globular domains of each histone. Amino acids with known post-translational modifications are marked in red, and the type of modification is shown by the bars below the histogram.

Extended Data Fig. 8 Proximity heat map showing distances between the most frequently mutated residues in the nucleosome structure.

Samples with TMB ≤ 10 mutations per Mb and mutation counts ≥2.5-fold the median number of mutations per residue for the histone family are displayed. Plotted residues are shown on the axes. Numbers within the grid indicate distance in angstroms between α-carbons. PDB code 1KX5.

Extended Data Fig. 9 Proximity heat map showing distances between the most frequently mutated residues (horizontal axis) and sites of known PTMs (vertical axis).

Per patient data at TMB ≤ 10 mutations per Mb is shown for samples with mutation counts ≥2.5-fold the median mutations per residue for the histone family.

Extended Data Fig. 10 Frequently mutated residues converge in three-dimensional space.

Examples of residues with α-carbons within 11.4 Å that are mutated ≥2.5-fold over the median counts per residue for each histone family, when a TMB ≤ 10 mutations per Mb threshold is applied. Residues of interest are mapped on the nucleosome structure (PDB code 1KX5).

Supplementary information

Reporting Summary

Supplementary Table 1

This file contains all histone mutations.

Supplementary Table 2

This file contains histone mutations on a per patient basis.

Supplementary Table 3

This file contains histone mutations on a per patient basis where TMB ≤ 10 mutations/Mb.

Supplementary Table 4

This file contains histone mutations on a per patient basis where TMB ≤ 2 mutations/Mb.

Supplementary Table 5

This file contains the list of histone genes used to query tumor mutation databases.

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Nacev, B.A., Feng, L., Bagert, J.D. et al. The expanding landscape of ‘oncohistone’ mutations in human cancers. Nature 567, 473–478 (2019). https://doi.org/10.1038/s41586-019-1038-1

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