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Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities

Nature Genetics volume 54, pages 649–659 (2022)Cite this article

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Abstract

Meningiomas are the most common primary intracranial tumors. There are no effective medical therapies for meningioma patients, and new treatments have been encumbered by limited understanding of meningioma biology. Here, we use DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, proteomic and single-cell approaches to show meningiomas are composed of three DNA methylation groups with distinct clinical outcomes, biological drivers and therapeutic vulnerabilities. Merlin-intact meningiomas (34%) have the best outcomes and are distinguished by NF2/Merlin regulation of susceptibility to cytotoxic therapy. Immune-enriched meningiomas (38%) have intermediate outcomes and are distinguished by immune infiltration, HLA expression and lymphatic vessels. Hypermitotic meningiomas (28%) have the worst outcomes and are distinguished by convergent genetic and epigenetic mechanisms driving the cell cycle and resistance to cytotoxic therapy. To translate these findings into clinical practice, we show cytostatic cell cycle inhibitors attenuate meningioma growth in cell culture, organoids, xenografts and patients.

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Fig. 1: Meningiomas are composed of three DNA methylation groups with distinct outcomes.
Fig. 2: NF2/Merlin drives meningioma apoptosis and susceptibility to cytotoxic therapy.
Fig. 3: HLA expression and meningeal lymphatics underlie meningioma immune enrichment.
Fig. 4: Convergent genetic and epigenetic mechanisms misactivate the cell cycle in meningioma.
Fig. 5: Clinical translation of meningioma DNA methylation groups.
Fig. 6: Genetic, epigenetic and transcriptomic mechanisms distinguishing meningioma DNA methylation groups.
Fig. 7: Molecular, cellular and clinical features distinguishing meningioma DNA methylation groups.

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Data availability

DNA methylation (n = 565), RNA sequencing (n = 185), and single-cell RNA sequencing data (n = 8 meningioma samples, n = 2 dura samples) of new samples reported in this manuscript have been deposited in the NCBI Gene Expression Omnibus under the accession GSE183656. Additional RNA sequencing data from previously reported meningiomas (n = 15) from the discovery cohort are available under the accession GSE101638. Whole-exome sequencing, ChIP sequencing, and additional DNA methylation profiling data incorporated into this study were derived from previously reported and deposited meningiomas in GSE101638, GSE139652. The publicly available GRCh38 (hg38, https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39/) and GRCh37.p13 datasets (hg19, https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.25/) were used in this study. Source data are provided with this paper.

Code availability

The open-source software, tools, and packages used for data analysis in this study, as well as the version of each program, were ImageJ (v2.1.0), R (v3.5.3 and v3.6.1), FASTQC (v0.11.9), HISAT2 (v2.1.0), featureCounts (v2.0.1), Seurat R package (v3.0.1), Harmony R package (v0.1), caret R package (v6.0-90), Rtsne R package (v0.15), PAMES R package (v2.6.2), CONICSmat R package (v1.0), DeepTools (v3.1.2), survival R package (v3.2-13), survAUC R package (v1.0-5), rms R package (v6.2-0), rpart R package (v4.1.16), DynNom R package (v5.0.1), DESeq2 (Bioconductor v3.10), SeSAMe (Bioconductor v3.10), minfi (Bioconductor v3.10), karyoplotR (Bioconductor v3.10), ConsensusClusterPlus (Bioconductor v3.10), and DiffBind (Bioconductor v3.10). No software was used for data collection. A methylation profile multi-class support vector machine (SVM) classifier was generated using the caret R package88, and was deposited in the GitHub repository abrarc/meningioma-svm (ref. 89). In brief, a linear kernel SVM was constructed using training data comprising 75% of randomly selected samples from the discovery cohort (n = 150) with 10-fold cross validation. 2,000 probes from each preprocessing pipeline were used as variables. The remaining 25% of samples from the discover cohort (n = 50) were used to test the model, which performed with 97.9% accuracy when classifying samples into three SeSAMe groups (95% CI 89.2-99.9%, P < 2.2 × 10−16). SVM classifiers for 3, 4, 5, or 6 minfi groups were generated using the same approach and performed with 91.8% (95% CI 80.4%-97.7%, P = 4.69 × 10−9), 91.8% (95% CI 80.4%-97.7%, P = 9.58 × 10−16), 93.8% (95% CI 82.8%–98.7%, P = 2.98 × 10−16), and 93.6% (95% CI 82.5%-98.7%, P < 2.2 × 10−16) accuracy, respectively. SVM classification and K-means consensus clustering of the validation cohort was performed with the same parameters as for the discovery cohort using the same probes in the validation cohort that were identified from the discovery cohort.

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Acknowledgements

We thank A. Abate, A. Bhaduri, A. Tward and B. Tomlin for comments and reagents; K. Probst and N. Sirivansanti for illustrations; A. Shai and the staff of the UCSF Brain Tumor Center Biorepository and Pathology Core; T. Ozawa and the staff of the UCSF Brain Tumor Center Preclinical Therapeutics Core; and E. Chow and the staff of the UCSF Center for Advanced Technology. This study was supported by the UCSF Wolfe Meningioma Program Project and National Institutes of Health (NIH) grants F30 CA246808 and T32 GM007618 to A.C.; NIH grant P50 CA097257 to J.J.P.; the UCSF Wolfe Meningioma Program Project, NIH grant F32 CA213944, and the Northwestern Medicine Malnati Brain Institute of the Lurie Cancer Center to S.T.M.; the UCSF Wolfe Meningioma Program Project (C.D.E., J.E.V-M., H.N.V., S.E.B., N.A.O.B., J.S. and N.B.); NIH grant U54 CA209891 to N.J.K; and the UCSF Physician Scientist Scholar Program, the UCSF Wolfe Meningioma Program Project and NIH grant R01 CA262311 to D.R.R.

Author information

Author notes

  1. These authors contributed equally: Stephen T. Magill, Charlotte D. Eaton.

Authors and Affiliations

  1. Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA

    Abrar Choudhury, Charlotte D. Eaton, William C. Chen, Martha A. Cady, Kyounghee Seo, Calixto-Hope G. Lucas, Tim J. Casey-Clyde, Harish N. Vasudevan, S. John Liu, Michael Y. Zhang, Jason W. Chan, Matthew S. Susko, Steve E. Braunstein, Penny K. Sneed & David R. Raleigh

  2. Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA

    Abrar Choudhury, Charlotte D. Eaton, William C. Chen, Martha A. Cady, Kyounghee Seo, Calixto-Hope G. Lucas, Tim J. Casey-Clyde, Harish N. Vasudevan, S. John Liu, Javier E. Villanueva-Meyer, Michael Y. Zhang, Michael V. Martin, Nancy Ann Oberheim Bush, Nicholas Butowski, Mitchel S. Berger, Arie Perry, Joanna J. Phillips, Joseph F. Costello, Michael W. McDermott & David R. Raleigh

  3. Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA

    Abrar Choudhury

  4. Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Abrar Choudhury

  5. Department of Neurological Surgery, Northwestern University, Chicago, IL, USA

    Stephen T. Magill

  6. Department of Medicine, University of California, San Diego, San Diego, CA, USA

    Briana C. Prager & Zhixin Qiu

  7. Department of Pathology, University of California, San Francisco, San Francisco, CA, USA

    Calixto-Hope G. Lucas, Arie Perry, Joanna J. Phillips & David A. Solomon

  8. Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA

    Javier E. Villanueva-Meyer

  9. Department of Clinical Oncology, The University of Hong Kong, Pokfulam, Hong Kong

    Tai-Chung Lam

  10. Division of Neurosurgery, Department of Surgery, The University of Hong Kong, Pokfulam, Hong Kong

    Jenny Kan-Suen Pu, Lai-Fung Li & Gilberto Ka-Kit Leung

  11. J. David Gladstone Institutes, California Institute for Quantitative Biosciences, San Francisco, CA, USA

    Danielle L. Swaney & Nevan J. Krogan

  12. California Institute for Quantitative Biosciences, San Francisco, CA, USA

    Danielle L. Swaney & Nevan J. Krogan

  13. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA

    Danielle L. Swaney & Nevan J. Krogan

  14. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Nancy Ann Oberheim Bush

  15. Department of Neurosciences, University of California, San Diego, San Diego, CA, USA

    Jessica D. Schulte

  16. Miami Neuroscience Institute, Baptist Health, Miami, FL, USA

    Michael W. McDermott

  17. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA

    Jeremy N. Rich

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Contributions

All authors made substantial contributions to the conception or design of the study; the acquisition, analysis, or interpretation of data; or drafting or revising the manuscript. All authors approved the manuscript. All authors agree to be personally accountable for individual contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved and the resolution documented in the literature. A.C. designed, performed and analyzed most of the experiments and bioinformatic analyses. S.T.M. extracted nucleic acids from all meningiomas, collected clinical data and was involved in the inception, execution and supervision of all aspects of the study. M.S.S. assembled clinical data for the discovery cohort. C.D.E. performed biochemical and apoptosis assays and supervised T.J.C.-C. to perform APEX reactions. B.C.P. analyzed ChIP sequencing data and performed ChIP qPCR experiments with Z.Q. under J.N.R.’s supervision. W.C.C. developed nomograms and performed CNV recursive partitioning analysis. M.A.C. performed immunofluorescence and mouse experiments. K.S. performed organoid experiments. C.H.G.L. analyzed meningioma histology under D.A.S.’s and A.P.’s supervision. H.N.V. and S.J.L. provided bioinformatic guidance. J.E.V.-M. analyzed magnetic resonance imaging studies to define meningioma locations. T.-C.L., J.K.-S.P., L.-F.L., G.K.-K.L. and J.W.C. assembled clinical data and meningiomas for the validation cohort. D.L.S. performed proteomic proximity-label mass spectrometry under N.J.K.’s supervision. M.Y.Z. and M.V.M. performed HLA genotyping and neoantigen prediction under J.F.C.’s supervision. N.A.O.B. treated meningioma patients with CDK4/6 inhibitors. J.J.P. performed immunohistochemistry. S.E.B., J.S., N.B., P.K.S., M.S.B. and M.W.M. provided clinical data and meningiomas for the discovery cohort. D.R.R. conceived, designed and supervised the study.

Corresponding authors

Correspondence to Stephen T. Magill, Jeremy N. Rich or David R. Raleigh.

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The authors declare no competing interests.

Peer review

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Nature Genetics thanks Roel Verhaak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 DNA methylation analysis using SeSAMe to control for the influence of CNVs on β values identifies three groups of meningiomas.

a, Scree/elbow plot of principal component analysis (PCA) of meningioma DNA methylation profiles suggesting differentially methylated DNA probes from the top 3 to 4 principal components (PCs) provide the most information in the discovery cohort (n = 200). b, K-means consensus clustering of meningioma DNA methylation profiles from the discovery cohort (n = 200) using differentially methylated DNA probes from the top 2-4 PCs across k = 2-7 groups, suggesting 3 PCs and k = 3 groups are optimal. c, Continuous distribution functions from K-means consensus clustering of meningiomas from the discovery cohort (n = 200) using differentially methylated DNA probes from the top 2 or 3 PCs across k = 3 groups, validating 3 PCs as the optimal number (p < 2.2 × 10−16, Kolmogorov-Smirnov test). d, Distribution of absolute DNA methylation probe loadings across the top 3 PCs from the discovery cohort (n = 200) for the top 10,000 probes for each PC. Loading distribution plots for each PC were similar, and the top 700 probes for each PC were selected using the elbow method for meningioma clustering. e, Unsupervised hierarchical clustering of meningiomas from the discovery cohort (n = 200) using 500, 1000, or 1500 differentially methylated DNA probes from each PC demonstrating the precise number of probes from each PC does not significantly influence meningioma DNA methylation grouping. In comparison to Fig. 1b, altering the number of probes for meningioma DNA methylation grouping only altered assignments for 3-9 meningiomas (1-4%). Merlin-intact (blue), Immune-enriched (purple), and Hypermitotic (red) DNA methylation group assignments are from Fig. 1b.

Source data

Extended Data Fig. 2 Independent validation of three meningioma DNA methylation groups.

a, K-means consensus clustering of meningioma DNA methylation profiles from the discovery (n = 200, UCSF) and validation (n = 365, HKU) cohorts. b, Sampling distributions of DNA methylation group fractions from the discovery cohort (n = 100 per DNA methylation group), with the observed DNA methylation group fractions from the validation cohort denoted in grey. Lines represent means, and error bars represent standard deviations. The observed fractions of each DNA methylation group from the validation cohort fall within the sampling distributions from the discovery cohort.

Source data

Extended Data Fig. 3 Meningioma DNA methylation grouping using SeSAMe to control for the influence of CNVs on β values compared to approaches that do not control for the influence of CNVs on β values.

a, Unsupervised hierarchical clustering of meningiomas from the discovery cohort (n = 200) using 2,000 differentially methylated DNA probes from the minfi preprocessing pipeline, which does not control for the influence of CNVs on β values. SeSAME meningioma DNA methylation groups (21% altered by minfi) are shown beneath the vertical dendrogram. b, K-means consensus clustering of meningiomas from the discovery and validation cohorts (n = 565) using differentially methylated DNA probes and β values from SeSAMe or minfi. SeSAMe consensus clustering identifies 3 groups as the optimal number, but minfi consensus clustering is unable to discriminate between 3 and 4 clusters. c, Continuous distribution functions (CDFs) from K-means consensus clustering of meningiomas from the discovery and validation cohorts (n = 565) using differentially methylated DNA probes and β values from SeSAMe or minfi. SeSAMe CDFs validated 3 groups as the optimal number, which was quantitatively different from 3 minfi groups (p = 1.341 × 10−4) or 4 minfi groups (p < 2.2 × 10−6) (Kolmogorov-Smirnov test). d, Kaplan–Meier curves for meningioma local freedom from recurrence (n = 565) across minfi DNA methylation groups fails to identify a grouping scheme with non-redundant differences in clinical outcomes, in contrast to SeSAMe DNA methylation groups (Fig. 1c) (Log-rank tests). minfi meningioma DNA methylation grouping schemed comprised of 3, 4, 5, or 6 groups are designated by letters A-C, A-D, A-E, or A-F, respectively.

Source data

Extended Data Fig. 4 Mechanisms of NF2/Merlin tumor suppression in meningioma cells.

a, Meningioma NF2 copy-number deletions containing the entire locus and targeted sequencing of somatic short variants (SSV, n = 65) across DNA methylation groups (Chi-squared test, two-sided). b, QPCR for NF2 in M10GdCas9-KRAB cells expressing a non-targeting control single-guide RNA (sgNTC) or a single-guide RNA suppressing NF2 (sgNF2). 3 biological replicates per condition (Student’s t test, one sided). c, Immunoblot for Merlin or GAPDH in M10GdCas9-KRAB cells expressing sgNTC, sgNF2, or sgNF2 with NF2 rescue (sgNF2 + NF2HA). d, Confocal immunofluorescence microscopy and quantification of Ki-67 in M10GdCas9-KRAB cells from b. DNA is marked with Hoechst 33342. Scale bar 10 μM. From left to right, 123 or 145 cells are shown (Student’s t test, one sided). e, QPCR for NF2 in IOMM-Lee cells stably expressing a non-targeting control shRNA (shNTC) or shRNAs suppressing NF2 (shNF2-1 or shNF2-2). From left to right, 3, 3, or 2 biological replicates are shown (ANOVA, one sided). f, MTT cell proliferation of IOMM-Lee cells from e, normalized to shNTC at 120 h. 4 biological replicates per condition per timepoint. *p = 0.0101, **p≤0.01 (ANOVA, one sided). g, Volcano plots of relative gene expression from RNA sequencing of M10GdCas9-KRAB cells in c. Interferon-regulated genes (including IFIT2, validated in j) are marked in red. h, Gene ontology analysis of differentially expressed genes from RNA sequencing of M10GdCas9-KRAB cells in g. i, QPCR for NF2 in MSC1 cells stably expressing shNTC, shNF2-1, or shNF2-2. 3 biological replicates per condition (ANOVA, one sided). j, QPCR for the IRF target gene IFIT2 in MSC1 cells from i. From left to right, 3, 2, or 3 biological replicates are shown (ANOVA, one sided). Lines represent means, and error bars represent standard error of the means.

Source data

Extended Data Fig. 5 NF2/Merlin drives meningioma apoptosis.

a, Confocal microscopy and quantification of Annexin V in IOMM-Lee cells from Extended Data Fig. 4e treated with actinomycin D or vehicle control for 24 h. DNA is marked with DAPI. Scale bar 10 μM. From left to right, 96, 101, 95, 90, 98, or 75 cells are shown (ANOVA, one sided). b, Immunoblot for Merlin, Caspase-7, cleaved Caspase-7 (cCaspase-7), or GAPDH in IOMM-Lee cells from a. c, Quantification of Annexin V confocal microscopy in MSC1 cells stably expressing sgNTC or sgNF2-2. Cells were treated as in a. From left to right, 29, 19, 40, or 30 cells are shown (ANOVA, one sided). d, Representative images of cleaved Caspase-3 (cCaspase-3) immunohistochemistry from CH-157MN xenografts stably expressing doxycycline-inducible Merlin encoding a FLAG tag (NF2-FLAG) in NU/NU mice after 7 days of doxycycline (n = 6) or vehicle treatment (n = 6), and 24 h after 4 Gy ionizing radiation (n = 6) or control treatment (n = 6). Scale bar 100 μM. e, Immunoblot for Merlin, IRF8, Tubulin, or Histone H3 (HH3) in cytoplasmic or nuclear fractions of M10GdCas9-KRAB cells from Extended Data Fig. 4b. f, Normalized proteomic proximity-labeling mass spectrometry from M10G cells stably expressing Merlin constructs with APEX tags. From left to right, 2 or 3 biological replicates are shown. g, Immunoblot for IRF8 or FLAG after FLAG immunoprecipitation from M10G cells stably expressing Merlin encoding a FLAG tag (NF2FLAG). EV, empty vector. h, QPCR for the glucocorticoid receptor (NR3C1) in IOMM-Lee cells expressing a non-targeting control siRNA (siNTC) or siRNAs suppressing NR3C1 (siNR3C1). 3 biological replicates per condition (Student’s t test, one sided). Lines represent means, and error bars represent standard error of the means.

Source data

Extended Data Fig. 6 Lymphatic vessels underlie meningioma immune infiltration.

a, Fraction of meningioma samples (n = 200) classified meningioma single-cell types across DNA methylation groups, based on single-cell reference transcriptomes. Lines represent means, boxes represent inner quartile ranges, and error bars represent 10th-90th percentiles (ANOVA, one sided). b, c, Meningioma location on preoperative magnetic resonance imaging (n = 169) across DNA methylation groups (Chi-squared test, two-sided). Representative magnetic resonance image shown. d, Meningioma DNA methylation (n = 565) of CCL21 (cg27443224) and TPM expression (n = 200) of CCL21 across DNA methylation groups (ANOVA, one sided). e, Meningioma DNA methylation (n = 565) of CD3E (cg08956138) and TPM expression (n = 200) of CD3E across DNA methylation groups (ANOVA, one sided). f, Representative image of LYVE1 and PROX1 confocal immunofluorescence microscopy in CH157-MN xenografts in NU/NU mice (n = 3). DNA is marked with Hoechst 33342. Scale bars 10 μM. Lines represent means, and error bars represent standard error of the means. *p≤0.05, **p≤0.01, ***p≤0.0001.

Source data

Extended Data Fig. 7 FOXM1 target gene functions in meningiomas and meningioma cells.

a, Predicted network of FOXM1-regulated pathways in Hypermitotic meningiomas based on H3K27ac ChIP sequencing of 25 meningiomas with matched RNA sequencing and DNA methylation profiling (15 Hypermitotic, 10 non-Hypermitotic). b, Immunoblot for Merlin, FOXM1, or GAPDH in IOMM-Lee meningioma cells stably expressing a non-targeting ontrol shRNA (shNTC) or shRNAs suppressing NF2 (shNF2-1 or shNF2-2), after treatment with actinomycin D or vehicle control for 24 h. c, QPCR for FOXM1 in M10G meningioma cells over-expressing FOXM1 or empty vector (EV). 3 biological replicates per condition. ***p≤0.0001 (Student’s t test, one sided). d, Quantification of Annexin V confocal microscopy in M10G cells over-expressing FOXM1 or EV after treatment with actinomycin D or vehicle control for 24 h. From left to right, 57, 58, 65, or 60 cells are shown (ANOVA, one sided). Lines represent means, and error bars represent standard error of the means.

Source data

Extended Data Fig. 8 Cell cycle inhibition blocks meningioma growth in cells, organoids, and xenografts.

a, Relative colony area of M10G, BenMen, CH-157MN, or IOMM-Lee meningioma cells after 10 days of clonogenic growth and treatment with abemaciclib, ribociclib, or palbociclib. 3 biological replicates per condition per timepoint. b, Relative colony area of M10GdCas9-KRAB cells expressing sgNTC, sgCDKN2A, or sgCDKN2B after 10 days of clonogenic growth and treatment with abemaciclib. 3 biological replicates per condition. *p = 0.002, **p = 0.001 (Student’s t test, one sided). Data are normalized to growth with vehicle treatment of each cell lines. c, Relative colony area of CH-157MN cells stably over-expressing USF or empty vector (EV) after 10 days of clonogenic growth and treatment with abemaciclib. 3 biological replicates per condition. **p = 0.001 (Student’s t test, one sided). Data are normalized to growth with vehicle treatment of each cell lines. d, Quantification of BenMen peri-organoid intensity after 10 days of growth and treatment with abemaciclib or vehicle control Representative images of meningioma (red) and organoid (green) cells are shown. Scale bar 100 μM. 5 biological replicates per condition (ANOVA, one sided). e, Representative immunoblots from CH-157MN xenografts in NU/NU mice (left) harvested at intervals after a single treatment of abemaciclib (100 μg/g) via oral gavage (right). f, Representative images of CH-157MN xenograft Ki-67 immunohistochemistry after a daily treatment of abemaciclib or control. Scale bar 1 mm. Lines represent means, and error bars represent standard error of the means.

Source data

Extended Data Fig. 9 Meningioma DNA methylation grouping schemes uncontrolled for the influence of CNVs on β values.

a, Meningioma DNA methylation analysis of copy-number loss at the NF2 locus (n = 565) across different numbers of DNA methylation groups determined by the minfi preprocessing pipeline (Chi-squared tests, two-sided). b, Meningioma DNA methylation estimation of leukocyte fraction (n = 565) across different numbers of DNA methylation groups determined by the minfi preprocessing pipeline (ANOVA, one sided). c, Ki-67 labeling index from meningioma clinical pathology reports (n = 206) across different numbers of DNA methylation groups determined by the minfi preprocessing pipeline (ANOVA, one sided). d, Meningioma genomes (n = 565) with copy-number variations (CNVs) across DNA methylation groups determined by the minfi preprocessing pipeline (ANOVA, one sided). Regardless of the number of groups, meningioma DNA methylation analysis uncontrolled for the influence of CNVs on β values cannot identify a grouping scheme with non-redundant differences in clinical outcomes (Extended Data Fig. 3d), NF2 loss, immune enrichment, cell proliferation, and chromosome instability. Lines represent means, and error bars represent standard error of the means. minfi meningioma DNA methylation grouping schemed comprised of 3, 4, 5, or 6 groups are designated by letters A-C, A-D, A-E, or A-F, respectively.

Source data

Extended Data Fig. 10 Immune-enriched meningiomas display markers of T cell exhaustion and immunoediting.

a, Meningioma transcripts per million (TPM) expression of TIGIT, LAG3, HAVCR2, or PDCD1 (n = 200) T cell exhaustion markers across DNA methylation groups. Lines represent means, and error bars represent standard error of the means (ANOVA, one sided). b, Single-cell RNA sequencing relative expression of immune exhaustion genes in T cells across Immune-enriched (n = 5) and non-Immune-enriched (n = 3) meningioma samples. Circle size denotes percentage of cells. Circle shading denotes average expression. c, Non-synonymous mutations from whole-exome sequencing of Immune-enriched (n = 9) and non-Immune-enriched (n = 16) meningiomas, with paired normal samples, overlapping with the discovery cohort. Lines represent means, and error bars represent standard error of the means (Student’s t test, one sided). d, Neoantigen prediction from whole-exome sequencing of Immune-enriched (n = 5) and Hypermitotic (n = 9) meningiomas, with paired normal samples, overlapping with the discovery cohort. Lines represent means, and error bars represent standard error of the means (Student’s t test, one sided).

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Choudhury, A., Magill, S.T., Eaton, C.D. et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nat Genet 54, 649–659 (2022). https://doi.org/10.1038/s41588-022-01061-8

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