Meningiomas are the most common primary intracranial tumour in adults1. Patients with symptoms are generally treated with surgery as there are no effective medical therapies. The World Health Organization histopathological grade of the tumour and the extent of resection at surgery (Simpson grade) are associated with the recurrence of disease; however, they do not accurately reflect the clinical behaviour of all meningiomas2. Molecular classifications of meningioma that reliably reflect tumour behaviour and inform on therapies are required. Here we introduce four consensus molecular groups of meningioma by combining DNA somatic copy-number aberrations, DNA somatic point mutations, DNA methylation and messenger RNA abundance in a unified analysis. These molecular groups more accurately predicted clinical outcomes compared with existing classification schemes. Each molecular group showed distinctive and prototypical biology (immunogenic, benign NF2 wild-type, hypermetabolic and proliferative) that informed therapeutic options. Proteogenomic characterization reinforced the robustness of the newly defined molecular groups and uncovered highly abundant and group-specific protein targets that we validated using immunohistochemistry. Single-cell RNA sequencing revealed inter-individual variations in meningioma as well as variations in intrinsic expression programs in neoplastic cells that mirrored the biology of the molecular groups identified.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Analyzing integrated network of methylation and gene expression profiles in lung squamous cell carcinoma
Scientific Reports Open Access 22 September 2022
The WHO 2021 Classification of Central Nervous System tumours: a practical update on what neurosurgeons need to know—a minireview
Acta Neurochirurgica Open Access 26 July 2022
Journal of Cancer Research and Clinical Oncology Open Access 15 July 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Raw sequencing data for all datatypes have been deposited into public repositories. Proteomic data has been deposited to the Mass Spectrometry Interactive Virtual Environment (MassIVE, https://massive.ucsd.edu/; ID MSV000086901). DNA methylation idat files have been deposited to the Gene Expression Omnibus (GEO; GSE180061). Whole-exome sequencing (fastq), bulk mRNA (fastq) and snRNA (fastq) datasets have been deposited to the European Genome Archive (https://www.ebi.ac.uk/ega/) under study ID EGAS00001004982 and dataset IDs EGAD00001007051, EGAD00001007494 and EGAS00001004982. The processed genomic data has been submitted to cBioportal at https://www.cbioportal.org/study/summary?id=mng_utoronto_2021. Source data are provided with this paper.
Specific code will be made available upon request to G.Z.
Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016. Neuro. Oncol. 21, v1–v100 (2019).
Goldbrunner, R. et al. EANO guidelines for the diagnosis and treatment of meningiomas. Lancet Oncol. 17, e383–e391 (2016).
Sahm, F. et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol. 18, 682–694 (2017).
Clark, V. E. et al. Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO. Science 339, 1077–1080 (2013).
Brastianos, P. K. et al. Genomic sequencing of meningiomas identifies oncogenic SMO and AKT1 mutations. Nat. Genet. 45, 285–289 (2013).
Clark, V. E. et al. Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas. Nat. Genet. 48, 1253–1259 (2016).
Harmancl, A. S. et al. Integrated genomic analyses of de novo pathways underlying atypical meningiomas. Nat. Commun. 8, 14433 (2017).
Nassiri, F. et al. DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management. Neuro. Oncol. 21, 901–910 (2019).
Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304.e6 (2018).
Koboldt, D. C. et al. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481–2498 (2015).
Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).
Reshef, D. N. et al. Detecting novel associations in large data sets. Science 334, 1518–1524 (2011).
Zhou, W. et al. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nat. Genet. 50, 591–602 (2018).
Teschendorff, A. E. A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Med. 12, 56 (2020).
Yang, Z. et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 17, 205 (2016).
Silva, T. C. et al. ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles. Bioinformatics 35, 1974–1977 (2019).
Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1–16 (2017).
Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).
Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).
Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).
Cerami, E. et al. The cBio Cancer Genomics Portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).
Dentro, S. C., Wedge, D. C. & Van Loo, P. Principles of reconstructing the subclonal architecture of cancers. Cold Spring Harb. Perspect. Med. 7, a026625 (2017).
Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).
Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protocols 14, 482–517 (2019).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Langmead, B. & Salzberg, S. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2013).
Kent, W. J. BLAT—the BLAST-Like Alignment Tool. Genome Res. 12, 656–664 (2002).
Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).
Wingender, E., Schoeps, T., Haubrock, M., Krull, M. & Dönitz, J. TFClass: expanding the classification of human transcription factors to their mammalian orthologs. Nucleic Acids Res. 46, D343–D347 (2018).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
The 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
L. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Research 5, 2122 (2016).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Tickle, T. I., Georgescu, C., Brown, M. & Haas, B. Infer copy number variation from single-cell RNA-seq data. https://doi.org/10.18129/B9.bioc.infercnv (2019).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).
Jerby-Arnon, L. et al. Opposing immune and genetic mechanisms shape oncogenic programs in synovial sarcoma. Nat. Med. 27, 289–300 (2021).
Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020).
Izar, B. et al. A single-cell landscape of high-grade serous ovarian cancer. Nat. Med. 26, 1271–1279 (2020).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).
F.N. is supported by the Canadian Institute of Health Research (CIHR) Vanier Scholarship, AANS/CNS Section on Tumors & NREF Research Fellowship Grant, and Hold’em for Life Oncology Fellowship. G.Z is funded by a CIHR Project Grant Award (173241), a Canadian Cancer Society Innovation Grant (706898) as well as a Quest for Cures Grant (GN-000430) and Clinical Biomarkers Grant (GN-000693) from the Brain Tumour Charity UK. P.C.B. was supported by the NIH/NCI under awards P30CA016042, U24CA248265 and P50CA211015. We also thank N. Dewitt for editing, and the Mary Hunter Meningioma Program and Toronto Western Hospital Foundation.
D.D.d.C. and A.C. are listed as inventors on patents filed that are unrelated to this project. D.D.d.C. received research funding from Pfizer and Nektar therapeutics that was not related to this project. D.D.d.C is co-founder, shareholder and CSO of Adela, Inc. P.C.B sits on the Scientific Advisory Boards of BioSymetrics Inc. and Intersect Diagnostics Inc. G.T. has served on advisory boards of AbbVie, Bayer and BMS; received consulting fees from AbbVie, Bayer; received speaker fees from Medac and Novocure; received travel grants from Novocure, Medac and BMS; received research grants from Roche Diagnostics and Medac, all not related to this work.
Peer review information Nature thanks Itay Tirosh, Roel Verhaak 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
a, Violin plots showing the distribution of the normalized mutual information (MI) for each pairwise comparison of datatype. Median is shown as white dot. The number of total genes and number of genes with statistically significant (FDR < 5%) MI values are shown. Below this is a heatmap showing the consensus clustering of genes where MI was significant for at least one datatype pair. Rows represent a gene for which data exists from all data types. b,d,f, Unsupervised consensus hierarchical clustering of (b), 5,000 genes that show that highest median absolute deviation across expression values, (d), 10,000 CpG sites that show that highest median absolute deviation across β-values, (f), 1,000 genes that show that highest median absolute deviation across copy number ratios. Heatmap of consensus matrices with K = 6 groups (b,d,f) are displayed. Overall, six groups were most stable across all platforms. c,e,g, Kaplan Meier-plot displaying recurrence-free survival (RFS) distributions of unsupervised cluster assignments by (c) mRNA data, (e) DNA methylation data, (g) copy number data. The associations with outcomes are unique for the 6 cluster groups obtained on individual platform analyses. h, Average silhouette widths for unsupervised consensus hierarchical clustering from K = 2 to K = 10. The silhouette score is a measure of stability of number of groups. Higher scores indicate greater stability and robustness. Average silhouette width is highest at K = 4 subgroups. i, Alluvial plot demonstrating associations between WHO grade and integrative molecular groups defined in this study. j-l, Kaplan Meier-plot displaying recurrence-free survival (RFS) distributions of patients stratified and colored by molecular group assignments for WHO grade 1 tumors (j), WHO grade 2 tumors (k), and WHO grade 3 tumors (l).
a, Ensemble of Receiver Operating Characteristic (ROC) curves from 50 iterations of trained MG-versus-other models. Overlaid for each model is the mean Area Under the Curve (AUC) and its associated 95% confidence interval for samples in corresponding test sets. b, Heatmap showing results of single-sample Gene-Set Enrichment Analysis (ssGSEA) using mRNA data in an independent cohort of 80 meningioma samples. Each sample in the validation set was assigned a score for molecular groups 1, 2, 3 and 4 using gene-expression based signatures from the discovery cohort. MG designation was determined by highest scores from ssGSEA assignments. Unsupervised hierarchical clustering using scores from MG assignments revealed four distinctive groups of tumors w with 97% of samples having concordant assignment by maximal scores. Samples almost always showed high scores that were distinctive to only a single group, highlighting the robustness of classification in an independent cohort. c, Brier prediction curve for recurrence-free survival comparing molecular group to WHO grade in the generalization cohort. The models tested were those developed on the discovery cohort. Prediction errors are consistently lowest using molecular groups in comparison to the validation cohort. d, Kaplan Meier-plot displaying recurrence-free survival (RFS) distribution of patients stratified and colored by molecular group assignments for generalization set. P value reported is a Log Rank Test. Distributions are highly similar to those obtained in discovery cohort.
a, Lollipop plots showing the distribution of NF2 mutations by genomic regions within each molecular group. b, Mutational burden (nonsynonymous mutations per megabase) of meningiomas stratified by molecular groups in comparison to other TCGA solid cancers. Every dot represents a sample and horizontal lines are median number of mutations in each cancer type. Mutational burden in each cancer is ordered by percentile rank. Cancer types are ordered on the horizontal axis based on their median numbers of somatic mutations. Mutational burden of MG4 tumors is statistically higher than tumours in MG1-3 (P = 1.6 × 10−3, Kruskal Wallis test). c, Distribution of the number of mutations that are considered clonal per each patient sample (column). A total of 26% of tumors exhibited only clonal point mutations. In the median tumor, 75% of single nucleotide variants were clonal. d, Cancer cell fraction of all variants in each patient sample (columns) ordered as in (c). Variants are colored according to the classification in the legend. e, Cancer cell fraction of recurrent oncogenic driver mutations (columns). Variants are colored according to the classification in the legend.
a, Genome-wide copy-number alterations computed from whole-exome sequencing data. Arrangements of copy number profile are matched to the samples from mutation plot above. Only mutations that are relevant to discussion in text are shown.b, Boxplots showing the mRNA expression of NF2 stratified by molecular group. Each dot is a sample. Samples are colored by NF2 mutation status and shapes are according to NF2 deletion status by CNA. Some MG3 and MG4 meningiomas that are NF2 wildtype show silencing of NF2 expression. c, Boxplots comparing the mean methylation level of NF2 wildtype MG3 and MG4 meningiomas with high versus low NF2 expression using all probes (left), those mapping to the promoter region (middle), and those mapping to the gene body (right). d, Circos plot showing the landscape of interchromosomal gene rearrangements detected using a stringent threshold for conservative estimation of fusion events (unique spanning reads ≥ 25) in each molecular group. Total number of interchromsomal fusion in MG1, MG2, MG3 and MG4 are 2, 7,18, and 23, respectively.
a, Hierarchical clustering of the expression of genes from select pathways identified in Fig. 2a. Selected genes have been labeled. Redundancy of genes to pathways is shown in the side bar. b, Boxplots showing the results for estimates of immune and stromal infiltration by DNA methylation (LUMP score on left and methylCIBERSORT in middle) and somatic DNA alterations (right, ABSOLUTE score). c, Scatterplots comparing normalized enrichment scores between molecular groups using Gene Set Variation Analysis (GSVA). Each dot is a pathway. Shown at the top of each panel are Pearson correlations and associated 95%CI. MG2 tumors were divided into tumors that are driven by CNA (MG2-CNA) and tumors that are driven by mutations (MG2-Mut). Correlations were highest when comparing MG2 tumors driven by CNA to MG2 tumors driven but mutations (red box). d, Hierarchical clustering of normalized enrichment scores from (c) identifies MG2-CNA and MG2-Mut tumors as one coherent group. e, Boxplots comparing the activation of molecular signatures of proliferation between MGs. Statistical significance is denoted by asterisks.
a, t-distributed Stochastic Neighbor Embedding (tSNE) plot of genome-wide DNA methylation profiles of patient derived cell lines (red), to meningiomas (blue), and 2798 previously published tumors from 40 other brain tumor types58. b, Heatmap showing results of single-sample Gene-Set Enrichment Analysis (ssGSEA) using mRNA data from cell lines. Each cell line was assigned a score for molecular groups 1, 2, 3 and 4 using gene-expression based signatures from the discovery cohort. Molecular group designation was determined by highest scores from ssGSEA assignments. c, Gross morphological images of a representative MG4-xenografted mice. Extra axial tumor is outlined in dashed yellow lines. Compression on adjacent neural structures is evident after partial (middle panel) and complete (right panel) separation of meningioma from brain. d, Serial sections and immunostaining for MCM2 in representative MG4-xenograted mice. Scale bar is 2mm. Small areas of tumor that have invaded the brain can be seen staining for MCM2.
Extended Data Fig. 7 Proteomic and gene expression data converge to similar biology driving each molecular group.
a, Hierarchical clustering of normalized enrichment scores obtained by Gene-Set Variation Analysis (GSVA) using proteomic data (rows) and mRNA data (columns). b, Distribution of correlation of mRNA expression to protein abundance in all samples (grey), MG1 meningiomas (red), MG2 meningiomas (blue), MG3 meningiomas (green) and MG4 meningiomas (orange). Vertical line indicates overall median correlation across all samples (Spearman’s r = 0.279, 95%CI 0.273-0.284). c, Scatterplots comparing normalized enrichment scores by GSVA using gene expression (x-axis) and protein abundance (y-axis) stratified by MG classifications. Each dot represents a pathway. Pathways that are statistically significant and concordant by protein and mRNA data are colored green while those that are discordant are colored green. Pearson correlations and 95% confidence intervals are indicated at the top of each panel. d, Network of activated gene circuits by proteome data in N = 96 samples. Protein groups were ranked for each subtype by degree of differential expression. Gene-set enrichment analysis was performed on the ranked gene lists and enriched pathways are visualized using the EnrichmentMap plugin in Cytoscape App. Nodes represent pathways and edges represent shared genes between pathways. Pathways above horizontal line are up-regulated (red nodes) in each molecular group while pathways below horizontal line are down-regulated (blue nodes) in each molecular group.
a, Hierarchical clustering of highly differentially methylated CpGs (absolute ∆β > 0.35, FDR < 0.05) between all meningiomas and healthy meninges. Annotations of molecular groups are on the right side of heatmap. b, Boxplots showing the distribution of β values for probes in (a) that are hypomethylated in healthy meninges (left) and hypermethylated in healthy meninges (right). Pairwise comparisons in each boxplot are statistically significant (p < 0.05), unless explicitly stated otherwise (ns, not significant). c, Boxplots showing the distribution of using epigenetic mitotic clocks with epiTOC model (left), epiTOC2 model (middle), and HypoClock model (right). Pairwise comparisons in each boxplot are statistically significant (p < 0.05), unless explicitly stated otherwise (ns, not significant). d, Number of unique and overlapping probes that are differentially methylated (absolute ∆β > 0.1, FDR < 0.05) when comparing each molecular group to healthy meninges. e, Scatterplots comparing master regulator transcription factor expression with average β values at sites enriched for the motif of that transcription factor. Samples are colored according to molecular group. Pearson correlation with 95% confidence intervals are reported. Hypomethylation at motifs of immunological-lineage-specific transcription factors such as PU.1, RUNX1/2 and IRF5/8 were enriched in immunogenic (MG1) tumors (P = 1.05 × 10−8, hypergeometric test) and associated with enhancer hypomethylation. Similarly, master regulators of cell proliferation such as MYBL2, LHX4, and FOXM1 were hypomethylated in proliferative (MG4) tumors and associated with increased abundance of these transcription factors (P = 1.24 × 10−3, hypergeometric test).
Extended Data Fig. 9 Meningiomas show low within patient variation of expression and copy number profile.
a, Pairwise correlations of expression profiles of all cells ordered by hierarchical clustering. Each cell is annotated to tumor of origin from Fig. 4a and cluster assignments from Fig. 4b at top and side bars. b, Inferred genome-wide copy number variations of single nuclei of healthy meninges (reference, top panel), immune cells (middle panel), and neoplastic cells (bottom panel). Sample and cluster annotation are shown on the left. The copy number plot of these tumors are homogenous and subclones of cells within tumors with distinct copy number profiles are not common. Annotation to patient of origin and cluster on the left of each heatmap. c, Scatterplots showing the relationship between arm-level CNA inferred by snRNA-seq (x-axis) to matched CNA by bulk whole exome sequencing (y-axis). Two representative samples are shown.
Extended Data Fig. 10 The transcriptome of MGs is shaped by the expression profiles from both neoplastic and non-neoplastic cells.
a, Bubble plot showing the expression of lineage specific markers for distinct cell types. b, Stacked barplot showing the relationship of samples to clusters. Samples are colored by patient of origin as in Fig. 4a. Barplot to the right shows the number of cells within each cluster. c, The top heatmap shows hierarchical clustering results of single cells by molecular group scores. Each cell was scored for the bulk signature of each molecular group and scores were compared to a permuted random gene set. Shown are cells with at least one score with FDR < 0.2. Scores were scaled such that the sum of all scores for each cell is equal to one. Below is a matched heatmap showing the number of genes detected for each MG signature in each cell. In a subset of cells, low scores are associated with low detection rate of genes (yellow and pink boxes). d, Stacked barplot showing the distribution of immune versus non-immune cells across molecular groups (left) and cycling versus non-cycling neoplastic cells across molecular groups (right) to clusters. Samples are colored by molecular group of tumor as in Fig. 4d. e, Barplot showing the total number of cells that are immune versus non-immune (left) and cycling versus non-cycling (right) by MG status of tumor of origin. f, Boxplots comparing the cell type composition of bulk RNA seq samples after deconvolution using single cell RNA-seq signatures. Pairwise comparisons in each boxplot are statistically significant (p < 0.05), unless explicitly stated otherwise (ns, not significant). g. Heatmap showing the expression of marker genes for single cell clusters (determined by CIBERSORTx) in bulk RNA seq data. Each column represents one tumor. Rows are designated marker genes for each cluster. Tumors were partitioned into 4 partitions by consensus k-means clustering with samples and gene sets clustered by hierarchical clustering using Pearson distance metric.
Extended Data Fig. 11 Discrete and continuous patterns of variability can be identified in meningioma.
a, Hierarchical clustering of similarities between NMF programs. Top panel indicates Pearson correlations between number of mitochondrial and ribosomal genes detected with NMF scores for each program. A cluster of programs (dashed lines) showed positive correlation with the expression of mitochondrial and ribosomal genes (confirmed by manual inspection). These programs were considered to be reflective of technical artifacts and not included in subsequent analyses. b, Violin plots showing the distribution of activation scores for NMF programs across MGs. c, Side-by-side tSNEs showing the relationship of discrete clustering results with activation scores of each NMF program. Shown are four representative samples. Activation scores of cell cycle program are closely associated with discrete clusters, whereas scores of metabolism, inflammatory, and mesenchymal program are not associated with discrete clusters. d, Heatmap showing the average expression of genes defining NMF programs (annotated to left) in representative sample CAM_0071. Cells are ranked and ordered according to the activation score of the metabolism program. There is a continuous pattern of gene expression variability in these programs.
Shown is a schematic representation that summarizes the major molecular findings and conclusions of our study: unsupervised consensus clustering combining DNA copy number, DNA methylation, and mRNA sequencing data revealed four robust groups of tumors with prototypical biology and distinct clinical outcomes.
About this article
Cite this article
Nassiri, F., Liu, J., Patil, V. et al. A clinically applicable integrative molecular classification of meningiomas. Nature 597, 119–125 (2021). https://doi.org/10.1038/s41586-021-03850-3
Clinical significance of NF2 alteration in grade I meningiomas revisited; prognostic impact integrated with extent of resection, tumour location, and Ki-67 index
Acta Neuropathologica Communications (2022)
Acta Neuropathologica Communications (2022)
Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas
Modern Pathology (2022)
Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours
Nature Reviews Neurology (2022)
Analyzing integrated network of methylation and gene expression profiles in lung squamous cell carcinoma
Scientific Reports (2022)