Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution

Nature Genetics (2024)Cite this article

Subjects

Abstract

Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K–AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Experimental design and workflow.
Fig. 2: High-grade meningiomas are distinguished by divergent intratumor gene and protein expression programs.
Fig. 3: Spatial expansion of subclonal CNVs underlies high-grade meningioma recurrence.
Fig. 4: Decreased immune infiltration, decreased MAPK signaling, increased PI3K–AKT signaling and increased cell proliferation underlie high-grade meningioma recurrence.
Fig. 5: Regionally distinct subclonal spatial transcriptomes underlie histological heterogeneity in high-grade meningioma.
Fig. 6: High-grade meningiomas are distinguished by regionally distinct intratumor immune infiltration, MAPK signaling, PI3K–AKT signaling and cell proliferation.
Fig. 7: A preclinical platform for testing personalized systemic therapies to overcome intratumor heterogeneity in high-grade meningiomas.

Similar content being viewed by others

Data availability

DNA sequencing and spatial transcriptomic data that support the findings of this study have been deposited in the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under BioProject ID PRJNA950017. DNA methylation and RNA sequencing data that support the findings of this study have been deposited in the NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE228316 (DNA methylation) and GSE228433 (RNA sequencing). The publicly available GRCh38 (hg38) and CRCh37.p13 (hg19) were used in this study. Spatial proteomic data processed counts are provided in Supplementary Table 5. Source data are provided with this paper.

Code availability

No custom software, tools or packages were used. The open source software, tools and packages used for data analysis in this study are referenced in Methods where applicable and include Loupe Browser (10x Genomics, v6.3.0), Space Ranger (10x Genomics, v1.0), 2100 Bioanalyzer Expert Software (vB.02.10), Aggr (v2.0.0), AutoAnnotate (v1.3.5), Burrows–Wheeler aligner (v0.7.17), Clustree (v0.5.0), COMET Control Software (v1.0), Cutadapt (v3.7), Cytoscape (v3.7.2), DESeq2 (v1.36.0), EnrichmentMap (v3.3.4), FASTQC (v0.11.9), featureCounts (v2.0.0), Genome Analysis Toolkit (v4.3.0.0), GSEA (v4.3.2), Harmony (v0.1.1), HISAT2 (v2.2.0), Integrative Genome Viewer (v.2.16.0), inferCNV (1 v.14.0), MultiQC (v1.12), NxClinical (v6.0), Olympus cellSens Standard Imaging (v1.16), Picard (v2.27.5), R (v3.6.1 and v4.2.1), RStudio (v2022.07.02), SeSAMe (v3.10), Seurat (v4.3.0), spatialInferCNV (v0.1.0), SCDC (v 0.0.0.9000) and SpatialFeatureplot (Seurat v3). Code written for this project is available on GitHub (https://doi.org/10.5281/zenodo.10927737)66.

References

  1. Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2015–2019. Neuro. Oncol. 24, v1–v95 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Goldbrunner, R. et al. EANO guideline on the diagnosis and management of meningiomas. Neuro. Oncol. 23, 1821–1834 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lin, D. et al. Trends in intracranial meningioma incidence in the United States, 2004‐2015. Cancer Med. 8, 6458–6467 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sahm, F. et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol. 18, 682–694 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Olar, A. et al. Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma. Acta Neuropathol. 133, 431–444 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Choudhury, A. et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nat. Genet. 54, 649–659 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Patel, A. J. et al. Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors. Proc. Natl Acad. Sci. USA 116, 21715–21726 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Driver, J. et al. A molecularly integrated grade for meningioma. Neuro. Oncol. 24, 796–808 (2021).

    Article  PubMed Central  Google Scholar 

  9. Maas, S. L. N. et al. Integrated molecular-morphologic meningioma classification: a multicenter retrospective analysis, retrospectively and prospectively validated. J. Clin. Oncol. 39, 3839–3852 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nassiri, F. et al. A clinically applicable integrative molecular classification of meningiomas. Nature 597, 119–125 (2021).

    Article  CAS  PubMed  Google Scholar 

  11. Choudhury, A. et al. Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features. Neuro. Oncol. 25, 520–530 (2022).

    Article  PubMed Central  Google Scholar 

  12. Vasudevan, H. N. et al. Intratumor and informatic heterogeneity influence meningioma molecular classification. Acta Neuropathol. 144, 579–583 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Magill, S. T. et al. Multiplatform genomic profiling and magnetic resonance imaging identify mechanisms underlying intratumor heterogeneity in meningioma. Nat. Commun. 11, 1–15 (2020).

    Article  Google Scholar 

  14. Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro. Oncol. 23, 1231–1251 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen, W. C. et al. Radiotherapy for meningiomas. J. Neurooncol. 160, 505–515 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Brastianos, P. K. et al. Advances in multidisciplinary therapy for meningiomas. Neuro. Oncol. 21, i18–i31 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wen, P. Y., Quant, E., Drappatz, J., Beroukhim, R. & Norden, A. D. Medical therapies for meningiomas. J. Neurooncol. 99, 365–378 (2010).

    Article  CAS  PubMed  Google Scholar 

  18. Preusser, M. et al. Trabectedin for recurrent WHO grade 2 or 3 meningioma: a randomized phase II study of the EORTC Brain Tumor Group (EORTC-1320-BTG). Neuro. Oncol. 24, 755–767 (2021).

    Article  PubMed Central  Google Scholar 

  19. Bi, W. L. et al. Activity of PD-1 blockade with nivolumab among patients with recurrent atypical/anaplastic meningioma: phase II trial results. Neuro. Oncol. 24, 101–113 (2021).

    Article  PubMed Central  Google Scholar 

  20. Brastianos, P. K. et al. Phase 2 study of pembrolizumab in patients with recurrent and residual high-grade meningiomas. Nat. Commun. 13, 1325 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen, W. C. et al. Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses. Nat. Med. 29, 3067–3076 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kline, C. N. et al. Targeted next-generation sequencing of pediatric neuro-oncology patients improves diagnosis, identifies pathogenic germline mutations, and directs targeted therapy. Neuro. Oncol. 9, 699–709 (2016).

    Google Scholar 

  24. Youngblood, M. W. et al. Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas. J. Neurosurg. 133, 1345–1354 (2019).

    Article  Google Scholar 

  25. Sahm, F. et al. TERT promoter mutations and risk of recurrence in meningioma. J. Natl Cancer Inst. 108, djv377 (2016).

    Article  PubMed  Google Scholar 

  26. Guyot, A. et al. Analysis of CDKN2A gene alterations in recurrent and non-recurrent meningioma. J. Neurooncol. 145, 449–459 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Youngblood, M. W. et al. Associations of meningioma molecular subgroup and tumor recurrence. Neuro. Oncol. 23, 783–794 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Chen, W. C. et al. Histopathological features predictive of local control of atypical meningioma after surgery and adjuvant radiotherapy. J. Neurosurg. 130, 443–450 (2018).

    PubMed  Google Scholar 

  29. Tang, V. et al. Loss of p16 expression is a sensitive marker of CDKN2A homozygous deletion in malignant meningiomas. Acta Neuropathol. 145, 497–500 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Katz, L. M. et al. Loss of histone H3K27me3 identifies a subset of meningiomas with increased risk of recurrence. Acta Neuropathol. 135, 955–963 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell 40, 639–655.e13 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Couly, G. F. & Douarin, N. M. L. Mapping of the early neural primordium in quail-chick chimeras: II. The prosencephalic neural plate and neural folds: implications for the genesis of cephalic human congenital abnormalities. Dev. Biol. 120, 198–214 (1987).

    Article  CAS  PubMed  Google Scholar 

  34. Simões-Costa, M. & Bronner, M. E. Establishing neural crest identity: a gene regulatory recipe. Development 142, 242–257 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Shankar, G. M. et al. Germline and somatic BAP1 mutations in high-grade rhabdoid meningiomas. Neuro. Oncol. 19, 535–545 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. Vasudevan, H. N. et al. Comprehensive molecular profiling identifies FOXM1 as a key transcription factor for meningioma proliferation. Cell Rep. 22, 3672–3683 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mangelinck, A. et al. MT2A is an early predictive biomarker of response to chemotherapy and a potential therapeutic target in osteosarcoma. Sci. Rep. 9, 12301 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Borchert, S. et al. Impact of metallothionein-knockdown on cisplatin resistance in malignant pleural mesothelioma. Sci. Rep. 10, 18677 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608, 360–367 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Nabors, L. B. et al. Central nervous system cancers, version 3.2020, NCCN clinical practice guidelines in oncology. J. Natl Compr. Canc. Netw. 18, 1537–1570 (2020).

    Article  PubMed  Google Scholar 

  43. Almeida et al. Clinical outcome, tumor recurrence, and causes of death: a long-term follow-up of surgically treated meningiomas. World Neurosurg. 102, 139–143 (2017).

    Article  PubMed  Google Scholar 

  44. Menke, J. R. et al. Somatostatin receptor 2a is a more sensitive diagnostic marker of meningioma than epithelial membrane antigen. Acta Neuropathol. 130, 441–443 (2015).

    Article  PubMed  Google Scholar 

  45. Doki, Y. et al. Nivolumab combination therapy in advanced esophageal squamous-cell carcinoma. N. Engl. J. Med. 386, 449–462 (2022).

    Article  CAS  PubMed  Google Scholar 

  46. Forde, P. M. et al. Neoadjuvant nivolumab plus chemotherapy in resectable lung cancer. N. Engl. J. Med. 386, 1973–1985 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hellmann, M. D. et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl. J. Med. 378, 2093–2104 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Motzer, R. J. et al. Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma. N. Engl. J. Med. 378, 1277–1290 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hellmann, M. D. et al. Nivolumab plus ipilimumab in advanced non–small-cell lung cancer. N. Engl. J. Med. 381, 2020–2031 (2019).

    Article  CAS  PubMed  Google Scholar 

  50. Motzer, R. J. et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373, 1803–1813 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. James, L. et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl. J. Med. 373, 23–34 (2015).

    Article  Google Scholar 

  52. Caroline, R. et al. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 372, 320–330 (2015).

    Article  Google Scholar 

  53. Webb, A. E., Kundaje, A. & Brunet, A. Characterization of the direct targets of FOXO transcription factors throughout evolution. Aging Cell 15, 673–685 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Corniola, M. V., Lemée, J.-M. & Meling, T. R. Histological transformation in recurrent WHO grade I meningiomas. Sci. Rep. 10, 11220 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Nakasu, S., Notsu, A., Na, K. & Nakasu, Y. Malignant transformation of WHO grade I meningiomas after surgery or radiosurgery: systematic review and meta-analysis of observational studies. Neurooncol. Adv. 2, vdaa129 (2020).

    PubMed  PubMed Central  Google Scholar 

  56. Chen, W. C. et al. Salvage therapy outcomes for atypical meningioma. J. Neurooncol. 138, 425–433 (2018).

    Article  PubMed  Google Scholar 

  57. Gauchotte, G. et al. Prognostic value of histopathological features and loss of H3K27me3 immunolabeling in anaplastic meningioma: a multicenter retrospective study. J. Neuropathol. Exp. Neurol. 79, 754–762 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    Article  CAS  PubMed  Google Scholar 

  60. Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Jamal-Hanjani, M. et al. Tracking the evolution of non–small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Wang, L. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat. Cancer 3, 1534–1552 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lee, W. H. Characterization of a newly established malignant meningioma cell line of the human brain: IOMM-Lee. Neurosurgery 27, 389–395; discussion 396 (1990).

    Article  CAS  PubMed  Google Scholar 

  64. Mei, Y. et al. Genomic profile of human meningioma cell lines. PLoS ONE 12, e0178322 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Zhou, W., Triche, T. J., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).

    PubMed  PubMed Central  Google Scholar 

  66. Mdm247. Mdm247/spatial_meningioma: v_1.0.0. Zenodo https://doi.org/10.5281/zenodo.10927737 (2024).

Download references

Acknowledgements

We thank A. Shai and the staff of the UCSF Brain Tumor Center Biorepository and Pathology Core, B. Ndjamen and the staff of the Gladstone Institutes Histology Core, M. Bernardi and H.-R. Lin and the staff of the Gladstone Institutes Genomics Core, E. Chow and the staff of the UCSF Center for Advanced Technology and E. Zacco and the staff of the UCSF Laboratory for Cell Analysis Genome Core. Lunaphore COMET multiplexed seqIF was made possible by a gift from the Stephen M. Coffman trust to the Northwestern University Malnati Brain Tumor Institute of the Lurie Cancer Center. We appreciate the thoughtful comments and critiques from J. Costello and the members of the Raleigh Laboratory during the inception, execution and dissemination of this study. This study was supported by National Institutes of Health (NIH) grants T32 CA15102 and P50 CA097257 to C.-H.G.L.; NIH grant P50 CA097257 to W.C.C.; NIH grants F30 CA246808 and T32 GM007618 and the UCSF Wolfe Meningioma Program Project to A.C.; NIH grant P50 CA097257 to S.J.L. and J.J.P.; NIH grant F32 CA213944, the UCSF Wolfe Meningioma Program Project and the Northwestern University Malnati Brain Tumor Institute to S.T.M.; NIH grants R01 CA120813, R01 NS120547 and P50 CA221747 to A.B.H.; NIH grants R01 NS117104, R01 NS118039 and P50 CA221747 to C.M.H.; and NIH grants R01 CA262311 and P50 CA097257, the UCSF Wolfe Meningioma Program Project and the Trenchard Family Charitable Fund to D.R.R.

Author information

Author notes

  1. These authors contributed equally: Kanish Mirchia, Kyounghee Seo.

Authors and Affiliations

  1. Department of Pathology, Johns Hopkins University, Baltimore, MD, USA

    Calixto-Hope G. Lucas

  2. Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA

    Calixto-Hope G. Lucas

  3. Department of Oncology, Johns Hopkins University, Baltimore, MD, USA

    Calixto-Hope G. Lucas

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

    Kanish Mirchia, Kyounghee Seo, William C. Chen, Naomi Zakimi, Kyla Foster, Charlotte D. Eaton, Martha A. Cady, Abrar Choudhury, S. John Liu, Harish N. Vasudevan & David R. Raleigh

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

    Kanish Mirchia, Kyounghee Seo, William C. Chen, Naomi Zakimi, Kyla Foster, Charlotte D. Eaton, Martha A. Cady, Abrar Choudhury, S. John Liu, Joanna J. Phillips, Arie Perry, Harish N. Vasudevan & David R. Raleigh

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

    Kanish Mirchia, Kyounghee Seo, William C. Chen, Naomi Zakimi, Kyla Foster, Charlotte D. Eaton, Martha A. Cady, Abrar Choudhury, S. John Liu, Joanna J. Phillips, David A. Solomon, Arie Perry & David R. Raleigh

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

    Hinda Najem, Stephen T. Magill, Craig M. Horbinski & Amy B. Heimberger

  8. Department of Pathology, Northwestern University, Chicago, IL, USA

    Craig M. Horbinski

Authors
  1. Calixto-Hope G. Lucas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kanish Mirchia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kyounghee Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hinda Najem
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. William C. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Naomi Zakimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kyla Foster
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Charlotte D. Eaton
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Martha A. Cady
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Abrar Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. S. John Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Joanna J. Phillips
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Stephen T. Magill
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Craig M. Horbinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. David A. Solomon
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Arie Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Harish N. Vasudevan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Amy B. Heimberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. David R. Raleigh
    View author publications

    You can also search for this author in PubMed Google Scholar

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 paper. All authors approved the paper. 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. C.-H.G.L. conceived and designed the study with supervision from D.R.R., analyzed bioinformatic data and performed and analyzed histological analyses with supervision from J.J.P., D.A.S. and A.P. K.S., K.F., C.D.E. and M.A.C. performed molecular biology, cell culture and co-culture experiments. K.M., W.C.C., N.Z., A.C. and S.J.L. performed bioinformatic analyses with supervision from H.N.V. H.N. performed meningioma immunofluorescence microscopy with supervision from C.M.H. and A.B.H. S.T.M. generated primary human meningioma cells and curated archival nucleic acid samples from the validation cohort. D.R.R. conceived, designed and supervised the study.

Corresponding author

Correspondence to David R. Raleigh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Meningioma spatial transcriptome analysis.

a, Uniform manifold approximation and projection (UMAP) analysis of spatial transcriptomes from the 16 meningioma samples in this study shaded by unsupervised hierarchical clusters in each sample. b, Distribution of unsupervised hierarchical meningioma spatial transcriptome clusters overlayed onto H&E-stained sections of each sample. Scale bar, 1mm. c, UMAP analysis of meningioma spatial transcriptomes shaded by sample of origin without Harmony batch correction suggesting significant batch effects across meningioma spatial transcriptomes.

Extended Data Fig. 2 Meningioma spatial transcriptome clusters and spatial protein analysis.

a, Spatial transcriptomes in reduced dimensionality clusters from each meningioma sample after Harmony batch correction. b, Stacked bar showing spatial transcriptomes from each meningioma sample after Harmony batch correction. Colors as in a. c, Top 92 differentially expressed genes (Supplementary Table 4) across 30 unsupervised hierarchical spatial transcriptome clusters from all meningiomas after Harmony batch correction. d, Principal component (PC) analysis of spatial protein profiling from all meningioma samples (72 proteins, 82 regions).

Extended Data Fig. 3 High-grade meningiomas are distinguished by divergent intratumor gene and protein expression programs.

a, Preoperative T1 post-contrast magnetic resonance imaging (MRI) of meningiomas with driver mutations associated with adverse clinical outcomes, such as BAP1 loss (M1), CDKN2A/B loss (M2), or TERT promoter mutation (M3). b, Representative H&E-stained sections and immunohistochemistry (IHC) for Ki-67, p16, or H3K27me3 from M1-3. Scale bar, 1mm. c, Uniform manifold approximation and projection (UMAP) of M1-3 spatial transcriptomes after Harmony batch correction shaded by sample of origin. d, UMAP of M1-3 spatial transcriptomes after Harmony batch correction shaded by unsupervised hierarchical clusters. e, Distribution of unsupervised hierarchical spatial transcriptome clusters from M1-3 after Harmony batch correction.

Extended Data Fig. 4 WHO grade 1 meningiomas demonstrate reduced diversity and number of spatial transcriptomes compared to high-grade meningiomas.

a, Uniform manifold approximation and projection (UMAP) of M1-3 and LGM1-2 spatial transcriptomes after Harmony batch correction shaded by sample of origin. b, UMAP of M1-3 and LGM1-2 spatial transcriptomes after Harmony batch correction shaded by unsupervised hierarchical clusters. c, Distribution of unsupervised hierarchical spatial transcriptome clusters from M1-3 and LGM1-2 after Harmony batch correction. d, Top 101 differentially expressed genes across unsupervised hierarchical spatial transcriptome clusters from M1-3 and LGM1-2 after Harmony batch correction. e, Spatial transcriptome expression of MKI67, FOXM1, or CCN3 across M1-3 and LGM1-2. f, InferCNV traces of copy number variants across unsupervised hierarchical transcriptome clusters from M1-3 and LGM1-2 after Harmony batch correction, revealing no significant CNVs in C2 comprising LGM1, and relative loss of chromosomes 1p and 22q in C1 and C7 comprising LGM2. Loses are shown in blue. Gains are shown in red. g, UMAP of LGM1-2 spatial transcriptomes after Harmony batch correction shaded by sample of origin. h, UMAP of LGM1-2 spatial transcriptomes after Harmony batch correction shaded by unsupervised hierarchical clusters. i, Spatial distribution of unsupervised hierarchical spatial transcriptome clusters from LGM1-2. Scale bar, 1mm. j, Distribution of unsupervised hierarchical spatial transcriptome clusters from LGM1-2 after Harmony batch correction. k, Top 116 differentially expressed genes across unsupervised hierarchical spatial transcriptome clusters from LGM1-2 after Harmony batch correction. l, Representative H&E morphology of spatial transcriptome clusters from LGM1 and LGM2. Colors correspond to spatial transcriptomes from h-j. Scale bars, 1mm and 10μm.

Extended Data Fig. 5 Primary and recurrent meningiomas are distinguished by divergent intratumor gene expression programs.

a, Representative H&E-stained sections and immunohistochemistry (IHC) for Ki-67, p16, or H3K27me3 from matched pairs of primary and recurrent meningiomas. Dots show regions of spatial protein profiling. Scale bar, 1mm. b, UMAP analysis of matched pairs of primary and recurrent meningioma spatial transcriptomes after Harmony batch correction shaded by unsupervised hierarchical clusters. Scale bar, 1mm. c, Spatial distribution of unsupervised hierarchical spatial transcriptome clusters from matched pairs of primary and recurrent meningiomas after Harmony batch correction. Scale bar, 1mm. d, Distribution of unsupervised hierarchical spatial transcriptome clusters from matched pairs of primary and recurrent meningiomas after Harmony batch correction.

Extended Data Fig. 6 Primary and recurrent meningiomas are distinguished by divergent copy number variants.

a, InferCNV traces of copy number variants across unsupervised hierarchical transcriptome clusters from matched pairs of primary and recurrent meningiomas after Harmony batch correction. Loses are shown in blue. Gains are shown in red.

Extended Data Fig. 7 Regionally distinct meningiomas are distinguished by divergent intratumor gene and protein expression programs and copy number variants.

a, Preoperative T1 post-contrast magnetic resonance imaging (MRI) of meningioma M8 from patient 7, which was regionally distinct compared to M7 and M7’ (Fig. 3a). b, Representative H&E-stained sections and immunohistochemistry (IHC) for Ki-67, p16, or H3K27me3 from M8. Dots show regions of spatial protein profiling. Scale bar, 1mm. c, Principal component (PC) analysis of spatial protein profiling from M7, M7’, and M8. d, Uniform manifold approximation and projection (UMAP) of M7, M7’, and M8 spatial transcriptomes after Harmony batch correction shaded by sample of origin. e, UMAP of M7, M7’, and M8 spatial transcriptomes after Harmony batch correction shaded by unsupervised hierarchical clusters. f, Spatial distribution of unsupervised hierarchical spatial transcriptome clusters from M7, M7’, and M8 after Harmony batch correction. Scale bar, 1mm. g, Distribution of unsupervised hierarchical spatial transcriptome clusters from M7, M7’, and M8 after Harmony batch correction. h, Spatial distribution of unsupervised hierarchical transcriptome clusters harboring chromosome 10q loss in M8 from InferCNV (Fig. 3c). Scale bar, 1mm. i, InferCNV traces of copy number variants across unsupervised hierarchical transcriptome clusters from M8 after Harmony batch correction. Loses are shown in blue. Gains are shown in red.

Extended Data Fig. 8 Primary and recurrent meningiomas are distinguished by divergent intratumor protein expression programs.

a and b, Principal component (PC) analysis of spatial protein profiling from matched pairs of primary and recurrent meningiomas. c, Spatial proteins from matched pairs of primary and recurrent meningiomas. d, Quantification of differentially expressed spatial proteins from at least 3 of 4 matched pairs of primary (M4 n = 7, M5 n = 6, M6 n = 5, M7 n = 3) and recurrent (M4’ n = 2, M5’ n = 4, M6’ n = 3, M7’ n = 4) meningiomas. Lines represent means and error bars represent standard error of the means. Student’s t tests, one-sided, * ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.0001. e, Multiplexed seqIF microscopy showing sparse lymphocytes in the meningioma microenvironment. Scale bar, 20μm, similar across 3 high-power field replicates per tumor.

Extended Data Fig. 9 High-grade meningiomas are distinguished by regionally distinct intratumor protein expression programs.

a, Preoperative T1 post-contrast magnetic resonance imaging (MRI) of meningiomas M9 and M10. b, Representative H&E-stained sections and immunohistochemistry (IHC) for Ki-67, p16, or H3K27me3 from regionally distinct meningioma samples. Dots show regions of spatial protein profiling. Scale bar, 1mm. c, Principal component (PC) analysis of spatial protein profiling from M9 and M10.

Extended Data Fig. 10 Validation of pharmacological strategies to overcome intratumor heterogeneity in high-grade meningiomas.

a, M10G patient-derived meningioma cells stably expressing CRISPRi machinery (dCas9-KRAB) and sgRNAs suppressing CDKN2A (sgCDKN2A, n = 3), CDKN2B (sgCDKN2B, n = 4), ARID1A (sgARID1A, n = 6), or non-targeted control sgRNAs (sgNTC, n = 3, 4, 6, respectively). Cells were labeled with red or green fluorescence proteins and integrated into 3D co-cultures for pharmacologic and live cell imaging experiments. b, IOMM-Lee meningioma cells that lack endogenous CDKN2A/B stably expressing CRISPRi machinery (dCas9-Zim3) and sgARID1A (n = 4) or sgNTC (n = 4). Cells were labeled with red or green fluorescence proteins and integrated into 3D co-cultures for pharmacologic and live cell imaging experiments. c, Combination molecular therapy treatments of 3D co-cultures of IOMM-LeedCas9-Zim3 meningioma cells expressing sgARID1A or sgNTC. Scale bar, 100μm. d, Quantification of combination molecular therapy treatments of 3D co-cultures of IOMM-LeedCas9-Zim3 meningioma cells expressing sgARID1A or sgNTC. Representative of 8 biological replicates per condition. e, MTT cell viability results normalized to vehicle control after 5 days of molecular therapy of SF12964, a patient-derived meningioma cell line from a WHO grade 3 meningioma (Hypermitotic DNA methylation group, NF2 p.Q165* mutation, chromosome 1p and 22q deletion, CDKN2A/B homozygous deletion, chromosome 1q amplification) that underwent reoperation after 5 months of abemaciclib treatment following prior surgeries and prior radiotherapy treatments. Representative of 8 biological replicates per condition. Lines represent means and error bars represent standard error of the means. Student’s t tests, one-sided, **P ≤ 0.01, ***P ≤ 0.0001.

Source data

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Table 1. Meningioma discovery cohort clinical data. Supplementary Table 2. Meningioma targeted next-generation DNA sequencing. Supplementary Table 3. Meningioma targeted gene expression profiling. Supplementary Table 4. Meningioma spatial transcriptomic cluster marker genes. Supplementary Table 5. Meningioma spatial protein profiling. Supplementary Table 6. Low-grade meningioma clinical data and targeted next-generation DNA sequencing. Supplementary Table 7. RNA sequencing differential expression analyses across meningiomas with BAP1 inactivation, CDKN2A/B homozygous deletion, or TERT promoter mutation. Supplementary Table 8. Antibodies for multiplexed sequential immunofluorescence. Supplementary Table 9. MAPK and PI3K–AKT target gene expression from RNA sequencing of 502 primary or recurrent meningiomas. Supplementary Table 10. M10GdCas9-KRAB patient-derived meningioma cell RNA sequencing differential expression analyses of protein coding genes.

Source data

Source Data Fig. 7

Numerical values for dot-plot histograms and drug treatment heatmaps/graphs (statistical source data).

Source Data Extended Data Fig. 10

Numerical values for dot-plot histograms and drug treatment heatmaps/graphs (statistical source data).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lucas, CH.G., Mirchia, K., Seo, K. et al. Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01747-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41588-024-01747-1

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer