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K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas

Nature Genetics volume 54, pages 1865–1880 (2022)Cite this article

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Abstract

Canonical (H3.1/H3.2) and noncanonical (H3.3) histone 3 K27M-mutant gliomas have unique spatiotemporal distributions, partner alterations and molecular profiles. The contribution of the cell of origin to these differences has been challenging to uncouple from the oncogenic reprogramming induced by the mutation. Here, we perform an integrated analysis of 116 tumors, including single-cell transcriptome and chromatin accessibility, 3D chromatin architecture and epigenomic profiles, and show that K27M-mutant gliomas faithfully maintain chromatin configuration at developmental genes consistent with anatomically distinct oligodendrocyte precursor cells (OPCs). H3.3K27M thalamic gliomas map to prosomere 2-derived lineages. In turn, H3.1K27M ACVR1-mutant pontine gliomas uniformly mirror early ventral NKX6-1+/SHH-dependent brainstem OPCs, whereas H3.3K27M gliomas frequently resemble dorsal PAX3+/BMP-dependent progenitors. Our data suggest a context-specific vulnerability in H3.1K27M-mutant SHH-dependent ventral OPCs, which rely on acquisition of ACVR1 mutations to drive aberrant BMP signaling required for oncogenesis. The unifying action of K27M mutations is to restrict H3K27me3 at PRC2 landing sites, whereas other epigenetic changes are mainly contingent on the cell of origin chromatin state and cycling rate.

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Fig. 1: Unique cell type hierarchies in H3.1 and H3.3K27M HGGs.
Fig. 2: Chromatin architecture of HOX clusters implicates distinct progenitor domain origins.
Fig. 3: H3.3K27M thalamic gliomas arise from the thalamus proper.
Fig. 4: H3.1K27M ACVR1-mutant gliomas mirror a SHH-specified NKX6-1+ progenitor.
Fig. 5: NKX6-1 is activated in H3.1K27M HGG.
Fig. 6: ACVR1 mutations confer oncogenic BMP signaling in H3.1K27M HGG.
Fig. 7: H3K27M and EZHIP converge to restrict H3K27me3 to PRC2 nucleation sites.
Fig. 8: Uncoupling the effect of histone variants from cell of origin chromatin state and cycling rate.

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Data availability

ChIP-seq sequencing data for human cell lines and scRNA-seq sequencing data for normal E10, E13, E16 and E18 murine samples have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE188625, whereas E12, E15, P0, P3 and P6 samples have been previously deposited to GEO under GSE133531. Bulk RNA-seq, ChIP-seq, Hi-C, scRNA-seq, scATAC-seq and scMultiome sequencing data for human tumors have been deposited in the European Genome-phenome Archive (EGA) under accession number EGAS00001005773. Processed data for bulk RNA-seq (counts and differential expression analyses), ChIP-seq (genome-wide H3K27ac/me2/me3 levels) and scRNA-seq/scATAC-seq/scMultiome (counts matrices, cell annotations, and chromatin accessibility bigWig files) have been deposited to GEO under the accession number GSE210568 and Zenodo at https://doi.org/10.5281/zenodo.6773261 (ref. 105). Accession numbers for previously published data used in this study are provided in Supplementary Tables 1, 2, 6 and 11. Source data are provided with this paper.

Code availability

Code to reproduce the main results included in the paper is available at https://github.com/fungenomics/HGG-oncohistones and archived on Zenodo at https://doi.org/10.5281/zenodo.6647837 (ref. 106).

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Acknowledgements

We thank the patients and their families for their invaluable contributions to this research, without whom it would be impossible. This work was supported by funding from A Large-Scale Applied Research Project grant from Genome Quebec, Genome Canada, the Government of Canada, and the Ministère de l'Économie, de la Science et de l’Innovation du Québec, with the support of the Ontario Institute for Cancer Research through funding provided by the Government of Ontario to N.J., M.D.T., C.L.K. Fondation Charles Bruneau to N.J., US National Institutes of Health (NIH) (grant P01-CA196539 to N.J. and grants R01CA148699 and R01CA159859 to M.D.T.); the Canadian Institutes for Health Research (CIHR) (grants MOP-286756 and FDN-154307 to N.J. and grant PJT-156086 to C.L.K.); the Canadian Cancer Society (CCSRI) (grant 705182) and the Fonds de Recherche du Québec en Santé (FRQS) salary award to C.L.K.; NSERC (RGPIN-2016-04911) to C.L.K.; CFI Leaders Opportunity Fund 33902 to C.L.K., Genome Canada Science Technology Innovation Centre, Compute Canada Resource Allocation Project (WST-164-AB). Data analyses were enabled by compute and storage resources provided by Compute Canada and Calcul Québec. N.J. is a member of the Penny Cole Laboratory and the recipient of a Chercheur Boursier, Chaire de Recherche Award from the FRQS. This work was performed within the context of the International CHildhood Astrocytoma INtegrated Genomic and Epigenomic (ICHANGE) consortium with funding from Genome Canada and Genome Quebec. S.J. is supported by a fellowship from CIHR. We also acknowledge support from the We Love You Connie, Poppies for Irini and Kat D Strong Foundations (N.J.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We acknowledge the contributions of D. Marchione and J. Wojcik in MS work.

Author information

Author notes

  1. Benjamin A. Garcia

    Present address: Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA

  2. These authors contributed equally: Selin Jessa, Abdulshakour Mohammadnia, Ashot S. Harutyunyan.

Authors and Affiliations

  1. Quantitative Life Sciences, McGill University, Montreal, Quebec, Canada

    Selin Jessa

  2. Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada

    Selin Jessa, Hussein Lakkis, Zahedeh Bashardanesh, Steven Hébert, Samantha Worme, Marie Coutelier & Claudia L. Kleinman

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

    Abdulshakour Mohammadnia, Maud Hulswit, Hussein Lakkis, Nisha Kabir, Steven Hébert, Marie Coutelier, Brian Krug, Augusto Faria Andrade, Andrea Bajic, Nada Jabado & Claudia L. Kleinman

  4. Department of Pediatrics, McGill University, and The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada

    Ashot S. Harutyunyan, Damien Faury & Nada Jabado

  5. Brain Tumour Program, Children’s Cancer Centre and Department of Paediatrics, Baylor College of Medicine, Houston, TX, USA

    Srinidhi Varadharajan

  6. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

    Maria C. Vladoiu

  7. Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada

    Maria C. Vladoiu & Michael D. Taylor

  8. The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada

    Maria C. Vladoiu & Michael D. Taylor

  9. Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada

    Samantha Worme & Nada Jabado

  10. Department of Oncology and The Milner Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

    Manav Pathania

  11. CRUK Children’s Brain Tumour Centre of Excellence, University of Cambridge, Cambridge, UK

    Manav Pathania

  12. Department of Pediatric Neurosurgery, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Quebec, Canada

    Alexander G. Weil

  13. Department of Pathology, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Quebec, Canada

    Benjamin Ellezam

  14. Department of Pediatric Surgery, Division of Neurosurgery, Montreal Children’s Hospital, McGill University, Montreal, Quebec, Canada

    Jeffrey Atkinson, Roy W. R. Dudley & Jean-Pierre Farmer

  15. Division of Child Neurology, Department of Pediatrics, CHU Sainte-Justine, Université de Montréal, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, Canada

    Sebastien Perreault

  16. Department of Biochemistry and Biophysics and Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin A. Garcia

  17. Department of Pediatrics, Centre mère-enfant Soleil du CHU de Québec-Université Laval, Quebec, Quebec City, Canada

    Valérie Larouche

  18. School of Computer Science, McGill University, Montreal, Quebec, Canada

    Mathieu Blanchette

  19. Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada

    Livia Garzia

  20. Division of Orthopedic Surgery, Faculty of Surgery, McGill University, Montreal, Quebec, Canada

    Livia Garzia

  21. Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA

    Aparna Bhaduri

  22. Department of Pathology, Boston Children’s Hospital and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Keith L. Ligon

  23. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA

    Keith L. Ligon

  24. Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA

    Keith L. Ligon & Pratiti Bandopadhayay

  25. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Pratiti Bandopadhayay

  26. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Pratiti Bandopadhayay

  27. Department of Developmental Neurobiology, Neural Brain Tumor Program, St Jude Children’s Research Hospital, Memphis, TN, USA

    Stephen C. Mack

Authors
  1. Selin Jessa
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  2. Abdulshakour Mohammadnia
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  3. Ashot S. Harutyunyan
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  4. Maud Hulswit
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  5. Srinidhi Varadharajan
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  6. Hussein Lakkis
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  7. Nisha Kabir
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  8. Zahedeh Bashardanesh
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  9. Steven Hébert
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  10. Damien Faury
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  11. Maria C. Vladoiu
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  14. Brian Krug
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  15. Augusto Faria Andrade
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  16. Manav Pathania
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  17. Andrea Bajic
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  18. Alexander G. Weil
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  19. Benjamin Ellezam
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  20. Jeffrey Atkinson
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  21. Roy W. R. Dudley
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  22. Jean-Pierre Farmer
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  23. Sebastien Perreault
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  24. Benjamin A. Garcia
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  25. Valérie Larouche
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  26. Mathieu Blanchette
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  27. Livia Garzia
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  28. Aparna Bhaduri
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  29. Keith L. Ligon
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  30. Pratiti Bandopadhayay
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  31. Michael D. Taylor
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  32. Stephen C. Mack
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  33. Nada Jabado
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  34. Claudia L. Kleinman
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Contributions

S.J., A.M., A.S.H. and C.L.K. designed and coordinated computational analyses. A.M., A.S.H., N.J. and C.L.K conceptualized experimental work. S.J., A.M., A.S.H., M.H., H.L., S.V., N.K., Z.B., S.H., S.W., M.C., M.B., S.C.M. and C.L.K contributed to computational and statistical data analyses. M.V. performed timed mating and tissue isolation of developing mouse embryos. A.M., A.S.H., M.H., D.F., B.K., A.F.A. and A. Bajic performed experimental work in patient-derived cell lines. D.F. performed primary tissue isolation, sample preparation and optimization of single-cell protocols. B.A.G. coordinated MS. A. Bhaduri provided normal human fetal brain data. M.P., A.G.W., B.E., J.A., R.W.R.D., J.-P.F., S.P., V.L., L.G., K.L.L., P.B., M.D.T., C.L.K. and N.J. contributed to acquisition of resources and primary tumor samples. SJ., A.M., A.S.H., M.H., C.L.K. and N.J. wrote the manuscript with input from L.G. C.L.K. and N.J. jointly supervised the project.

Corresponding authors

Correspondence to Nada Jabado or Claudia L. Kleinman.

Ethics declarations

Competing interests

S.P. is a member of the advisory board for Bayer, Novartis and AstraZeneca and has received speaker fees from Bayer and Esai outside of the submitted work. All other authors declare no competing interests.

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Nature Genetics thanks Xiao-nan Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work

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Extended data

Extended Data Fig. 1 Overview of expanded scRNA-seq mouse developmental reference.

a. Schematic of developing mouse brain, sagittal view, indicating regions and time points included in the single-cell reference atlas. Red: data generated in this study; black: data from Jessa et al., Nature Genetics, 201934. b. Number of cells captured in each time point and brain region after quality control and filtering. c. Overview of single-cell populations from the mouse pons. Dendrogram constructed based on pairwise Spearman correlations between mean expression profiles in each cluster. Cell class and time point are annotated. d. Overview of single-cell populations from the mouse forebrain. Dendrogram constructed based on pairwise Spearman correlations between mean expression profiles in each cluster. Cell class and time point are annotated.

Extended Data Fig. 2 Unique cell type hierarchies in H3.1 and H3.3K27M HGGs.

a. Similarity matrix between all Non-negative Matrix Factorization (NMF) programs assigned to modules. Heatmap represents the pairwise overlap (in number of genes) between programs. b. Annotation of NMF programs. Top: Correlation between each program and QC or biological metrics in each cell. One module (M11) that was explained by technical factors (mitochondrial content and coverage), was consequently removed from further analyses. Bottom: overlap between each program and developmental or MSigDB reference signatures, one line per signature. Only significant overlaps (p-value < 0.001) are shown, and a number of significant overlaps is shown in parentheses. c. Top 15 genes associated with each module. Module-associated genes were selected by identifying the most frequent program-associated genes for all programs contained in the module. d. UMAP for H3.3K27M thalamic HGG (malignant cells only), with cells colored by consensus projected cell type based on the normal mouse brain reference (left), or the normal human fetal thalamus reference (right). Cells are colored as in Fig. 1d. e. Confusion matrix comparing projected cell types for H3.3K27M thalamic HGG based on mouse or human reference. Proportions were computed row-wise and represent the fraction of cells from each mouse label which were assigned to each human label. Bubbles are scaled to the number of cells with each combination of labels.

Extended Data Fig. 3 Some H3.1K27M pontine gliomas arbor a malignant ependymal-like component.

a-b. UMAP plots for two individual H3.1K27M pontine gliomas containing ependymal-like cells. Only malignant cells are shown. Cells are colored by consensus projected cell type. c-d. Heatmaps of copy-number signal computed for each individual sample using InferCNV. Row annotations correspond to cell type projections, indicating whether they are projected to ependymal cells (left, red), and the overall projected cell class, with colors as in (a) and normal cells colored in gray. Cells lacking a consensus projection were excluded. e-f. UMAP plots as in (a-b), with cells colored by expression of FOXJ1 (ependymal transcription factor), DNAH12 (ciliary gene), and single-cell gene set enrichment (ssGSEA) score of candidate FOXJ1 targets in the early postnatal mouse brain. List of FOXJ1 targets was obtained from Jacquet et al., Development, 200944. g. NMF programs from Fig. 1c, displaying only the overlap between program-associated genes with ependymal gene signatures, and filtering out all other developmental signatures. Top column annotation shows the driver alteration of the sample in which each program was identified. A second annotation highlighting H3.1/2K27M tumors is included for clarity. Module 10, significantly overlapping ependymal signatures, is enriched for programs from this tumor entity. h-i. Activity of the ependymal-related module 10 in individual samples. Top: UMAP plots as in (a-b), cells are colored by the NMF activity score of the module 10 program from each sample. Bottom: heatmap of NMF score of module 10 program-associated genes; names for selected informative genes are indicated.

Extended Data Fig. 4 H3.1K27M, ACVR1-mutant pontine gliomas arise from an NKX6-1+ ventral brainstem progenitor.

a. Volcano plot of differentially expressed genes between H3.3K27M pons and H3.3K27M thalamus HGG. HOX genes are indicated in purple. Only genes with mean normalized expression > 100 are included. b. Epigenomic state at NKX6-1 and PAX3 in representative H3.3K27M pons HGG primary tumors and cell lines. For scATAC-seq data, each track represents RPKM-normalized aggregated accessibility for one malignant single-cell population. c. Co-expression of NKX6-1 and PAX3 in bulk RNA-seq data for pons HGG with each K27M histone variant.

Extended Data Fig. 5 Assessment of NKX6-1 in brain tumors and normal tissues.

a Immunohistochemistry staining of NKX6-1 protein in normal pancreas tissue as positive control. Arrowhead in left panel indicates region shown at higher magnification in right panel. b-d. Immunohistochemistry staining of NKX6-1 protein in Histone 3 WT, H3.3G34R, and H3.3K27M high-grade glioma patient tumors. e. Antibody staining of NKX6-1 in human tissues from the Human Protein Atlas. Detection levels for each cell type are indicated below, ‘-’ indicates that NKX6-1 was not detected. Image credit: Human Protein Atlas. Images available from http://v21.proteinatlas.org (links provided in Supplementary Table 19). f. Left: In situ hybridization (ISH) in E13.5 mouse brain from the Allen Brain Atlas (© 2008 Allen Institute for Brain Science. Allen Developing Mouse Brain Atlas. Available from: developingmouse.brain-map.org). Right: quantification of ISH expression levels. g. Left: ISH in P56 mouse brain from the Allen Brain Atlas (© 2004 Allen Institute for Brain Science. Allen Mouse Brain Atlas. Available from mouse.brain-map.org). Right: quantification of ISH expression levels. h. Bulk expression levels of NKX6-1 in adult human tissues from GTEx. Sample sizes for brain tissues are indicated.

Extended Data Fig. 6 Nkx6-1/Pax3 expression is mutually exclusive in the normal brain.

a. Expression of Nkx6-1 and Pax3 in cell types of the normal developing mouse pons reference, showing their expression is largely mutually exclusive. The number of cells where both Nkx6-1 and Pax3 are detected out of the total number of Nkx6-1+ or Pax3+ cells of the cell type is indicated in parentheses. b. Expression of Nkx6-1 target genes with high cell type specificity in ependymal cells. Dendrogram represents cell clusters in the single-cell mouse pons reference, as in Extended Data Figure S1. c. Cell type specificity score for inferred targets of Nkx6-1 and Pax3 in the normal mouse pons. For a given gene, score represents the difference between the highest detection rate of the gene in any single-cell cluster in the normal mouse reference, and the detection rate of the gene in all other cells in the same sample (see Methods).

Extended Data Fig. 7 H3K27M and EZHIP converge to restrict H3K27me3 to PRC2 nucleation sites.

a. Percentage of H3K27me3-marked 10 kb bins overlapping CGIs or SUZ12 peaks in cell lines and tumors. Number of biologically independent samples per group is indicated in parentheses. H3K27me3 was quantified in 10 kb bins genome-wide and the top 1% bins with highest H3K27me3 in each sample were intersected with CGIs/SUZ12 peaks. For SUZ12, the union of peaks called from SUZ12 ChIP-seq in BT245 and DIPGXIII were obtained from Harutyunyan et al., Nature Communications, 201938. Crossbar indicates the median. P-values: left panel (H3.1K27M vs WT GBM, p = 0.024; H3.3K27M vs WT GBM, p = 0.026; PFA-EP vs WT GBM, 0.00099); right panel (H3.1K27M vs WT GBM, p = 0.063; H3.3K27M vs WT GBM, p = 0.023; PFA-EP vs WT GBM, p = 0.0016); n.s., not significant; Welch two-sample t-test. b. Scatterplots of H3K27me2 signal over 100 kb bins genome-wide in pairwise group comparisons. X- and Y- axes represent log2 mean RPKM value per group, normalized by input. Marked bins (mean RPKM > 1 in at least one of the groups in each comparison) are shown in black, while unmarked bins are shown in gray. Joint density and marginal distributions are calculated over marked bins only. Red line indicates the diagonal. RPKM values of H3K27me2 were divided by the respective input sample RPKM and averaged for all samples in the same mutation group using a geometric mean. c. H3K27me3 (top) and H3K27me2 (bottom) ChIP-seq enrichment tracks, in representative K27M-mutant and isogenic CRISPR-KO cell lines. d. Mass spectrometry data of H3K27ac in cell. Number of biologically independent samples per group is indicated in parentheses. Error bars represent mean +/− SD. P-values: H3.1K27M vs WT GBM, 0.0074; H3.3K27M vs WT GBM: 5.3×10-5; n.s., not significant; Welch two-sample t-test. e. Enrichment of H3K27ac over different repeat element families in HGG cell lines and isogenic K27M-KO counterparts.

Extended Data Fig. 8 Cell-of-origin chromatin state contributes to the tumor epigenome.

a. Schematic of analysis. Single-cell epigenomic data for normal mouse OPCs and ependymal cells was obtained from Zhu et al., Nature Biotechnology, 202166, and used to extract cell type-specific epigenomic features. Tumors were clustered based on H3K27ac levels at promoters of these genes. b. Hierarchical clustering of H3.1K27M HGG, H3.1K27M PFA-EP, and EZHIP PFA-EP based on OPC and ependymal-specific epigenomic features. Select features are indicated. c. Top: RNA and single-cell epigenomic data for normal mouse OPCs and ependymal cells66 at ependymal and OPC genes. Bottom: H3K27ac ChIPseq tracks for H3.1K27M HGG, H3.1K27M PFA-EP, and EZHIP PFA-EP at the same genes as in the top panel. Chromosome coordinates are indicated in Supplementary Table 15.

Extended Data Fig. 9 Uncoupling the effect of histone variants from cell-of-origin chromatin state and cycling rate.

a. Validation of CRISPR removal of ACVR1 in H3.1K27M ACVR1-mutant cell lines by MiSeq (multiple deletions on both alleles (complete KO)). b. Validation of CRISPR removal of H3K27M in H3.1K27M cell lines DIPGIV and DIPG36 (1 bp deletion on K27M allele (frameshift)) and DIPG21 (2 bp deletion on K27M allele) by MiSeq and Western Blot. For Western Blot, G477, an H3.1 WT HGG patient-derived cell line, was used as control. CRISPR removal of H3K27M in H3.3K27M cell lines has been reported previously for BT245 and DIPGXIII in Krug et al., Cancer Cell, 2019 33; and for HSJ019 in Harutyunyan et al., Cell Reports, 2020 35. c. Doubling time of H3.3K27M and H3.1K27M HGG cell lines (DIPGXIII, N = 4 biological replicates; HSJ019, N = 3; DIPG36, N = 9; DIPGIV, N = 12). Error bars represent mean +/− SD. d. Doubling time of H3.1K27M cell line DIPGIV in ACVR1 mutant and ACVR1-KO conditions. Error bars represent mean +/− SD. e. Schematic of experimental design. f. Heatmap showing distribution of Rx-normalized ChIPseq signal for H3K27me3 in DIPGXIII at CpG islands (CGIs), flanked by 20kbp on either side. g. Rx-normalized H3K27me3 tracks in each condition at a representative genomic region. Y-axis limit is indicated in brackets and identical for all tracks. h. Left: Rx-normalized H3K27me2 tracks in each condition at the same region as in (g). Y-axis limit is indicated in brackets and identical for all tracks. Right: genome-wide distribution of H3K27me2 domain length in each condition (H3.3K27M, N = 16,630 domains; K27M-KO, N = 3388; H3.1K27M, N = 11,568). i. Heatmap showing distribution of Rx-normalized ChIPseq signal for H3K27me2 in DIPGXIII H3K27me2 domains across the genome in each condition. Domains are scaled to 50 kb and flanked by 50 kb on either side. The maximum of the color scale is set to the 90th percentile value across all data points.

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Supplementary Protocol 1.

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Supplementary Table 1

Supplementary Tables 1–20 and associated legends.

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Source Data Extended Data Fig. 9

Uncropped scan of western blot for Extended Data Fig. 9.

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Jessa, S., Mohammadnia, A., Harutyunyan, A.S. et al. K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas. Nat Genet 54, 1865–1880 (2022). https://doi.org/10.1038/s41588-022-01205-w

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