Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the prevalent tumors. Gliomagenesis in NF1 results in a heterogeneous spectrum of low- to high-grade neoplasms occurring during the entire lifespan of patients. The pattern of genetic and epigenetic alterations of glioma that develops in NF1 patients and the similarities with sporadic glioma remain unknown. Here, we present the molecular landscape of low- and high-grade gliomas in patients affected by NF1 (NF1-glioma). We found that the predisposing germline mutation of the NF1 gene was frequently converted to homozygosity and the somatic mutational load of NF1-glioma was influenced by age and grade. High-grade tumors harbored genetic alterations of TP53 and CDKN2A, frequent mutations of ATRX associated with Alternative Lengthening of Telomere, and were enriched in genetic alterations of transcription/chromatin regulation and PI3 kinase pathways. Low-grade tumors exhibited fewer mutations that were over-represented in genes of the MAP kinase pathway. Approximately 50% of low-grade NF1-gliomas displayed an immune signature, T lymphocyte infiltrates, and increased neo-antigen load. DNA methylation assigned NF1-glioma to LGm6, a poorly defined Isocitrate Dehydrogenase 1 wild-type subgroup enriched with ATRX mutations. Thus, the profiling of NF1-glioma defined a distinct landscape that recapitulates a subset of sporadic tumors.

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Data availability

Genomic, epigenomic, and transcriptomic data supporting the findings of this study have been deposited at the European Genome-phenome Archive database (https://ega-archive.org), which is hosted by the EBI and the CRG, under accession number EGAS00001003186. All other data are available within the article, Supplementary Information, and Supplementary Data file.

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Change history

  • 21 December 2018

    The original Nature Research Reporting Summary included with this article at publication was an outdated version. The correct version is now available online.


  1. 1.

    Uusitalo, E. et al. Incidence and mortality of neurofibromatosis: a total population study in Finland. J. Invest. Dermatol. 135, 904–906 (2015).

  2. 2.

    Evans, D. G. et al. Birth incidence and prevalence of tumor-prone syndromes: estimates from a UK family genetic register service. Am. J. Med. Genet. A 152A, 327–332 (2010).

  3. 3.

    Gutmann, D. H. et al. Neurofibromatosis type 1. Nat. Rev. Dis. Primers 3, 17004 (2017).

  4. 4.

    Brems, H., Beert, E., de Ravel, T. & Legius, E. Mechanisms in the pathogenesis of malignant tumours in neurofibromatosis type 1. Lancet Oncol. 10, 508–515 (2009).

  5. 5.

    Philpott, C., Tovell, H., Frayling, I. M., Cooper, D. N. & Upadhyaya, M. The NF1 somatic mutational landscape in sporadic human cancers. Hum. Genomics 11, 13 (2017).

  6. 6.

    Uusitalo, E. et al. Distinctive cancer associations in patients with neurofibromatosis type 1. J. Clin. Oncol. 34, 1978–1986 (2016).

  7. 7.

    Seminog, O. O. & Goldacre, M. J. Risk of benign tumours of nervous system, and of malignant neoplasms, in people with neurofibromatosis: population-based record-linkage study. Br. J. Cancer 108, 193–198 (2013).

  8. 8.

    Blanchard, G. et al. Systematic MRI in NF1 children under six years of age for the diagnosis of optic pathway gliomas. Study and outcome of a French cohort. Eur. J. Paediatr. Neurol. 20, 275–281 (2016).

  9. 9.

    Sellmer, L. et al. Non-optic glioma in adults and children with neurofibromatosis 1. Orphanet J. Rare Dis. 12, 34 (2017).

  10. 10.

    Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016).

  11. 11.

    Neurofibromatosis. Conference statement. National Institutes of Health Consensus Development Conference. Arch. Neurol. 45, 575–578 (1988).

  12. 12.

    Gutmann, D. H. et al. Gliomas presenting after age 10 in individuals with neurofibromatosis type 1 (NF1). Neurology 59, 759–761 (2002).

  13. 13.

    Helfferich, J. et al. Neurofibromatosis type 1 associated low grade gliomas: a comparison with sporadic low grade gliomas. Crit. Rev. Oncol. Hematol. 104, 30–41 (2016).

  14. 14.

    Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. ArXiv, 1207.3907 (2012).

  15. 15.

    Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

  16. 16.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  17. 17.

    Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

  18. 18.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  19. 19.

    Lai, Z. et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 44, e108 (2016).

  20. 20.

    Hiltemann, S., Jenster, G., Trapman, J., van der Spek, P. & Stubbs, A. Discriminating somatic and germline mutations in tumor DNA samples without matching normals. Genome Res. 25, 1382–1390 (2015).

  21. 21.

    Lee, S. et al. NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types. Nucleic Acids Res. 45, e103 (2017).

  22. 22.

    Chapuy, B. et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690 (2018).

  23. 23.

    Evans, D. G. et al. Comprehensive RNA analysis of the NF1 gene in classically affected NF1 affected individuals meeting NIH criteria has high sensitivity and mutation negative testing is reassuring in isolated cases with pigmentary features only. EBioMedicine 7, 212–220 (2016).

  24. 24.

    Hutter, S. et al. No correlation between NF1 mutation position and risk of optic pathway glioma in 77 unrelated NF1 patients. Hum. Genet. 135, 469–475 (2016).

  25. 25.

    Messiaen, L. M. et al. Exhaustive mutation analysis of the NF1 gene allows identification of 95% of mutations and reveals a high frequency of unusual splicing defects. Hum. Mutat. 15, 541–555 (2000).

  26. 26.

    Thomas, L. et al. Exploring the somatic NF1 mutational spectrum associated with NF1 cutaneous neurofibromas. Eur. J. Hum. Genet. 20, 411–419 (2012).

  27. 27.

    Stenson, P. D. et al. The human gene mutation database (HGMD) and its exploitation in the fields of personalized genomics and molecular evolution. Curr. Protoc. Bioinformatics 39, 1.13.1–1.13.20 (2012).

  28. 28.

    Friedman, J. M. Neurofibromatosis 1. in GeneReviews (eds. Adam, M. P. et al., University of Washington, Seattle, 1993).

  29. 29.

    Messiaen, L. et al. Clinical and mutational spectrum of neurofibromatosis type 1-like syndrome. JAMA 302, 2111–2118 (2009).

  30. 30.

    Eisenbarth, I., Beyer, K., Krone, W. & Assum, G. Toward a survey of somatic mutation of the NF1 gene in benign neurofibromas of patients with neurofibromatosis type 1. Am. J. Hum. Genet. 66, 393–401 (2000).

  31. 31.

    Laycock-van Spyk, S., Thomas, N., Cooper, D. N. & Upadhyaya, M. Neurofibromatosis type 1-associated tumours: their somatic mutational spectrum and pathogenesis. Hum. Genomics 5, 623–690 (2011).

  32. 32.

    Pemov, A. et al. The primacy of NF1 loss as the driver of tumorigenesis in neurofibromatosis type 1-associated plexiform neurofibromas. Oncogene 36, 3168–3177 (2017).

  33. 33.

    Upadhyaya, M. et al. Germline and somatic NF1 gene mutation spectrum in NF1-associated malignant peripheral nerve sheath tumors (MPNSTs). Hum. Mutat. 29, 74–82 (2008).

  34. 34.

    Upadhyaya, M. et al. Germline and somatic NF1 gene mutations in plexiform neurofibromas. Hum. Mutat. 29, E103–111 (2008).

  35. 35.

    Grobner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).

  36. 36.

    Yan, H. et al. IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 360, 765–773 (2009).

  37. 37.

    Noushmehr, H. et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17, 510–522 (2010).

  38. 38.

    Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).

  39. 39.

    Killela, P. J. et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc. Natl Acad. Sci. USA 110, 6021–6026 (2013).

  40. 40.

    Heaphy, C. M. et al. Altered telomeres in tumors with ATRX and DAXX mutations. Science 333, 425 (2011).

  41. 41.

    Zhang, Y., Zhou, H., Zhou, J. & Sun, W. Regression models for multivariate count data. J. Comput. Graph. Stat. 26, 1–13 (2017).

  42. 42.

    Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013).

  43. 43.

    Henson, J. D. et al. DNA C-circles are specific and quantifiable markers of alternative-lengthening-of-telomeres activity. Nat. Biotechnol. 27, 1181–1185 (2009).

  44. 44.

    Frattini, V. et al. A metabolic function of FGFR3-TACC3 gene fusions in cancer. Nature 553, 222–227 (2018).

  45. 45.

    Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

  46. 46.

    Yuan, J. et al. Single-cell transcriptome analysis of lineage diversity in high-grade glioma. Genome Med. 10, 57 (2018).

  47. 47.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).

  48. 48.

    Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

  49. 49.

    Aran, D. et al. Reference-based annotation of single-cell transcriptomes identifies a profibrotic macrophage niche after tissue injury. Preprint at bioRxiv, https://doi.org/10.1101/284604 (2018).

  50. 50.

    Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248–262 (2017).

  51. 51.

    Dedeurwaerder, S. et al. DNA methylation profiling reveals a predominant immune component in breast cancers. EMBO Mol. Med. 3, 726–741 (2011).

  52. 52.

    Jeschke, J. et al. DNA methylation-based immune response signature improves patient diagnosis in multiple cancers. J. Clin. Invest. 127, 3090–3102 (2017).

  53. 53.

    Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

  54. 54.

    Harndahl, M. et al. Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays. J. Biomol. Screen. 14, 173–180 (2009).

  55. 55.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

  56. 56.

    Mall, R. et al. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic Acids Res. 46, e39 (2018).

  57. 57.

    Rodriguez, F. J. et al. Gliomas in neurofibromatosis type 1: a clinicopathologic study of 100 patients. J. Neuropathol. Exp. Neurol. 67, 240–249 (2008).

  58. 58.

    Solga, A. C. et al. RNA sequencing of tumor-associated microglia reveals Ccl5 as a stromal chemokine critical for neurofibromatosis-1 glioma growth. Neoplasia 17, 776–788 (2015).

  59. 59.

    Flynn, R. L. et al. Alternative lengthening of telomeres renders cancer cells hypersensitive to ATR inhibitors. Science 347, 273–277 (2015).

  60. 60.

    Koschmann, C. et al. ATRX loss promotes tumor growth and impairs nonhomologous end joining DNA repair in glioma. Sci. Transl. Med. 8, 328ra328 (2016).

  61. 61.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  62. 62.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  63. 63.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  64. 64.

    Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  65. 65.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  66. 66.

    Schwarz, J. M., Cooper, D. N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361–362 (2014).

  67. 67.

    Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. Chapter 7, (2013).

  68. 68.

    Choi, Y. & Chan, A. P. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745–2747 (2015).

  69. 69.

    Sim, N. L. et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40, W452–457 (2012).

  70. 70.

    Ferlaino, M. et al. An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome. BMC Bioinformatics 18, 442 (2017).

  71. 71.

    Hu, J. & Ng, P. C. SIFT Indel: predictions for the functional effects of amino acid insertions/deletions in proteins. PLoS ONE 8, e77940 (2013).

  72. 72.

    Douville, C. et al. Assessing the pathogenicity of insertion and deletion variants with the variant effect scoring tool (VEST-Indel). Hum. Mutat. 37, 28–35 (2016).

  73. 73.

    Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

  74. 74.

    Babadi, M.et al. GATK CNV: copy-number variation discovery from coverage data. Cancer Res. 77, abstr. 3580 (2017).

  75. 75.

    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).

  76. 76.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  77. 77.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  78. 78.

    Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-Seq data. BMC Bioinformatics 12, 480 (2011).

  79. 79.

    Isserlin, R., Merico, D., Voisin, V. & Bader, G. D. Enrichment Map—a Cytoscape app to visualize and explore OMICs pathway enrichment results. F1000Res 3, 141 (2014).

  80. 80.

    Colaprico, A. et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44, e71 (2016).

  81. 81.

    Leiserson, M. D., Wu, H. T., Vandin, F. & Raphael, B. J. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 16, 160 (2015).

  82. 82.

    Zhang, Y., Zhou, H., Zhou, J. & Sun, W. Regression models for multivariate count data. J. Comput. Graph. Stat. 26, 1–13 (2017).

  83. 83.

    Mayakonda, A., Koeffler, H.P. Maftools: efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. Preprint at BioRxiv, https://doi.org/10.1101/052662 (2016).

  84. 84.

    Shukla, S. A., Howitt, B. E., Wu, C. J. & Konstantinopoulos, P. A. Predicted neoantigen load in non-hypermutated endometrial cancers: correlation with outcome and tumor-specific genomic alterations. Gynecol. Oncol. Rep. 19, 42–45 (2017).

  85. 85.

    Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).

  86. 86.

    Bai, Y., Ni, M., Cooper, B., Wei, Y. & Fury, W. Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads. BMC Genomics 15, 325 (2014).

  87. 87.

    Boegel, S. et al. HLA typing from RNA-Seq sequence reads. Genome Med. 4, 102 (2012).

  88. 88.

    Braendstrup, P. et al. Identification and HLA-tetramer-validation of human CD4+ and CD8+ T cell responses against HCMV proteins IE1 and IE2. PLoS One 9, e94892 (2014).

  89. 89.

    Hong, E. et al. Configuration-dependent presentation of multivalent IL-15:IL-15Ralpha enhances the antigen-specific T cell response and anti-tumor immunity. J. Biol. Chem. 291, 8931–8950 (2016).

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This work was supported by the Children’s Tumor Foundation Synodos Glioma Consortium (2015-04-007); NIH R01CA101644, U54CA193313, and R01CA131126 to A.L.; and R01CA178546, U54CA193313, R01CA179044, R01CA190891, R01NS061776, and The Chemotherapy Foundation to A.I. This work benefited from the facilities and expertise of the Onconeurotek Tumor Bank (Pitié-Salpêtrière, Paris, France), the CCBH-M Collection Neurology (University Hospital, Montpellier, France, www.chu-montpellier.fr), the NeuroBioTec Collection (Groupement Hospitalier Est, Bron France), the biobank Tissutheque Beaujon BB-0033-00078 (Pathology Department, Beaujon hospital, Clichy, France), the Centre de Ressource Plurithématique Bordeaux Biothèque Santé, and the TUCERA network (Bordeaux, France). We are particularly grateful to P. Polisi for technical support with the mutation calls on high-performance clusters; to A. Rahimian and I. Detrait for technical support; and to V. Rigau, C. Gozé, A. Vital, S. Elmer, and I. Quintin-Roue for histological analyses.

Author information

Author notes

  1. These authors contributed equally: F. D’Angelo, M. Ceccarelli.

  2. These authors jointly supervised this work: A. Lasorella, A. Iavarone.


  1. Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA

    • Fulvio D’Angelo
    • ,  Tala
    • , Luciano Garofano
    • , Jing Zhang
    • , Véronique Frattini
    • , Genevieve Lewis
    • , Anna Lasorella
    •  & Antonio Iavarone
  2. BIOGEM Istituto di Ricerche Genetiche ‘G. Salvatore’, Ariano Irpino, Italy

    • Fulvio D’Angelo
    • , Michele Ceccarelli
    • , Luciano Garofano
    • , Francesca P. Caruso
    •  & Mario Cangiano
  3. Department of Science and Technology, Università degli Studi del Sannio, Benevento, Italy

    • Michele Ceccarelli
    •  & Francesca P. Caruso
  4. The University of Texas M.D. Anderson Cancer Center John Mendelsohn Faculty Center (FC7.3025) – Neuro-Oncology – Unit 0431, Houston, TX, USA

    • Kristin D. Alfaro
  5. Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France

    • Luc Bauchet
  6. Sorbonne Universités UPMC Université Paris 06, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, APHP, Paris, France

    • Giulia Berzero
    • , Susanna Ronchi
    • , Karima Mokhtari
    •  & Marc Sanson
  7. Department of Neuro-Oncology, Medical University of South Carolina, Charleston, SC, USA

    • David Cachia
  8. Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, USA

    • David Cachia
  9. AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neurochirurgie, Paris, France

    • Laurent Capelle
  10. The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    • John de Groot
    • , Carlos Kamiya-Matsuoka
    • , Ian McCutcheon
    • , John Slopis
    •  & Krishna P. Bhat
  11. Department of Neurological Surgery, Carlo Besta Neurological Institute, Milan, Italy

    • Francesco DiMeco
  12. Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy

    • Francesco DiMeco
  13. Hunterian Brain Tumor Research Laboratory CRB2 2M41, Baltimore, MD, USA

    • Francesco DiMeco
  14. Service de Neuro-Oncologie, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Department of Cancer Cell Plasticity, Cancer Research Center of Lyon, INSERM U1052, CNRS UMR5286, Lyon, France

    • François Ducray
  15. Department of Neurosurgery, CHU, Dijon, France

    • Walid Farah
  16. Unit of Molecular Neuro-Oncology, IRCCS Foundation, Carlo Besta Neurological Institute, Milan, Italy

    • Gaetano Finocchiaro
    •  & Marica Eoli
  17. Service de Neurochirurgie, Hôpital Beaujon, Assistance Publique-Hôpitaux de Paris, Clichy, France

    • Stéphane Goutagny
  18. Developmental Tumor Laboratory, Fundación Sant Joan de Déu, Barcelona, Spain

    • Cinzia Lavarino
  19. Department of Neurosurgery, Bordeaux University Hospital. Labex TRAIL (ANR-10-LABX-57). EA 7435 – IMOTION Bordeaux University, Bordeaux, France

    • Hugues Loiseau
  20. Department of Medical Oncology, Centre GF Leclerc, Dijon, France

    • Véronique Lorgis
  21. Pediatric Neurosurgery Unit, Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children’s Hospital, Rome, Italy

    • Carlo E. Marras
  22. Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    • Do-Hyun Nam
  23. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    • Do-Hyun Nam
  24. Developmental Neurology Unit, IRCCS Foundation, Carlo Besta Neurological Institute, Milan, Italy

    • Veronica Saletti
  25. Service de Neurochirurgie, Hôpital de la Cavale Blanche, CHRU de Brest, Université de Brest, Brest, France

    • Romuald Seizeur
  26. Department of Pathology, Hospital Sant Joan de Déu, Barcelona, Spain

    • Mariona Suñol
  27. Central Laboratory of Pathology, Pasteur I University Hospital, Nice, France

    • Fanny Vandenbos
  28. Department of Neuropathology, Sainte-Anne Hospital, Paris, France

    • Pascale Varlet
  29. IMA-Brain, Inserm U894, Institute of Psychiatry and Neuroscience of Paris, Paris, France

    • Pascale Varlet
  30. EA7331, Université Paris Descartes, France; Service de Génétique et Biologie Moléculaires, Hôpital Cochin, AP-HP, Paris, France

    • Dominique Vidaud
  31. Institute of Cancer and Genomic Sciences University of Birmingham Edgbaston, Birmingham, United Kingdom

    • Colin Watts
  32. Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Viviane Tabar
  33. Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    • David E. Reuss
  34. Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany

    • David E. Reuss
  35. Division of Pediatric Neurosurgery, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea

    • Seung-Ki Kim
  36. Centre de Pathologie Et Neuropathologie Est Hospices Civils de Lyon, Lyon, France

    • David Meyronet
  37. Pediatric Oncology Unit, Hospital Sant Joan de Déu, Esplugues, Barcelona, Spain

    • Hector Salvador
  38. Department of Pediatrics, Columbia University Medical Center, New York, NY, USA

    • Anna Lasorella
  39. Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA

    • Anna Lasorella
    •  & Antonio Iavarone
  40. Department of Neurology, Columbia University Medical Center, New York, NY, USA

    • Antonio Iavarone


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A.I. and A.L. conceived and coordinated the studies and provided overall supervision. F.D'Angelo and M. Ceccarelli developed and performed bioinformatics analyses. L.G., F.P.C., and M. Cangiano conducted gene expression and bioinformatics analyses. J.Z. performed neoantigen identification studies. V.F. and T. performed sequencing and qPCR validation. G.L. and K.M. performed quantitative immunostaining. M. Sanson, K.M., K.D.A., L.B., G.B., D.C., L.C., J.d.G., F. DiMeco, F. Ducray, W.F., G.F., S.G., C.K.-M., C.L., H.L., V.L., C.E.M., I.M., D.-H.N., S.R., V.S., R.S., J.S., M. Suñol, F.V., P.V., D.V. C.W., V.T., D.E.R., S.-K.K., D.M., H.S., K.P.B., and M.E. provided tissues. A.I. and A.L. wrote the manuscript with input from all authors.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Anna Lasorella or Antonio Iavarone.

Extended data

  1. Extended Data Fig. 1 Data analysis workflow.

    Fifty nine tumor samples from 56 NF1-glioma patients with 43 matched normal were profiled with WES, DNA Methylation profiles (31 tumors) and RNA sequencing (29 tumors). WES was used to call NF1 germline mutations using HaplotypeCaller and Somatic-germline log odds filter. Somatic SNVs were called from WES data by integrating the results of five algorithms (Freebayes, MuTect, Strelka, VarDict and VarScan). Recurrent CNVs were detected by GATK and GISTIC2. SNVs and CNVs were validated by Sanger sequencing (93% validation rate) and genomic qPCR (96% validation rate), respectively. Neoantigen prediction was obtained using netMHCpan and HLA genotype was determined by Polysolver, Optitype, Phlat and Seq2hla and validated by affinity binding kinetics. COSMIC cancer mutation signatures were identified by deconstructSig and compared to those occurring in sporadic glioma. DNA Methylation arrays were used to classify NF1 glioma in the methylation subtypes of sporadic glioma form the TCGA pan-glioma dataset (KNN). RNAseq was used to define gene expression clusters and immune subtypes of low-grade NF1-glioma and results were confirmed by RT-qPCR and immunohistochemistry. Integrative analysis of gene expression and DNA Methylation identified epigenetic signatures characterizing immune subtypes of low-grade glioma. A pan-glioma gene regulatory network was used to identify MRs of the ATRX-mutant phenotype in LGm6 sporadic and NF1-glioma (RGBM). Finally, the impact of ATRX mutation on survival was assessed using TCGA pan-glioma and NF1-glioma data.

  2. Extended Data Fig. 2 Fingerprint analysis of WES NF1 samples.

    Dendrogram of hierarchical clustering of 59 tumor and 43 normal samples based on Pearson correlation coefficients of SNPs allele fractions. Case ID and the tissue specimen are indicated (blood DNA, red; tumor with available matched blood DNA, blue; tumor without matched normal DNA, yellow). The analysis confirmed proper matching of samples for each of the 43 tumor-blood DNA pairs. Thirteen tumors without available paired normal DNA (yellow) showed individual branches in the clustering dendrogram.

  3. Extended Data Fig. 3 Validation of recurrent CNVs.

    Genomic qPCR was performed to assay copy number changes for TERT (n = 10 glioma samples), b, IL-15 (n = 8 glioma samples), c, FGF1 (n = 17 glioma samples) and d, CDKN2A (n = 11 glioma samples). Red and blue bars indicate WES-inferred gene gain and loss, respectively. Analysis of normal DNA (green bars) was included to define diploidy (dotted line). Tumor samples diploid for the tested gene were included as control (white bars). Bar graphs show mean ± s.d. of 3 technical replicates. Experiments were repeated three times with similar results. Source data

  4. Extended Data Fig. 4 Somatic mutation burden of NF1-glioma and pediatric and adult cancer genomes.

    Distribution of somatic non-synonymous coding mutation rate is represented on a logarithmic scale for NF1- and sporadic glioma (bold) and other frequent cancer types, including pediatric tumors. Cancer types and subgroups are ordered by increasing mutation frequency median, with the lowest frequencies (left) found in pediatric tumors and low-grade NF1-glioma. Somatic mutations used to calculate the mutational burden for different cancer types were retrieved from TCGA (adult tumors) and TARGET (pediatric tumors) databases.

  5. Extended Data Fig. 5 Mutational clonality.

    Analysis of mutational clonality in 55 NF1-glioma samples. a, Number of mutation clones relative to age (Pearson correlation coefficient = –0.126 and p = 0.363), and b, tumor grade (Pearson correlation coefficient = 0.031 and p = 0.820). Blue line: linear regression; shaded area: 95% confidence interval.

  6. Extended Data Fig. 6 Analysis of DNA Copy Number Variations.

    Schematics of chromosome location peaks (gain, red; loss, blue) identified using GISTIC2. Peaks are designated by candidate targets for each region, selected according to criteria described in Methods. The complete list of chromosome location peaks is included in Supplementary Table 6a, b.

  7. Extended Data Fig. 7 Mutual exclusivity and co-occurrence of genetic alterations in NF1-glioma.

    a, Mutually exclusive and b, co-occurring genetic alterations in NF1-glioma were evaluated using CoMEt and two-sided Fisher’s exact test, respectively. Significant mutual relationships between two gene alterations are indicated by a line (green, exclusion; red, co-occurrence) whose thickness represents -log10 of p-value (reported in Supplementary Table 7).

  8. Extended Data Fig. 8 Distribution of somatic mutation spectrum in NF1-glioma.

    Dirichlet multinomial regression test for ATRX status (n = 10 and n = 46 ATRX mutant and ATRX wild-type samples, respectively), age (n = 22 pediatric glioma; n = 33 adult glioma) and glioma grade (n = 24 high-grade glioma; n = 32 low-grade glioma). b, The relative proportions of the six different possible base-pair substitutions are represented by barplots for ATRX mutant (n = 10, solid fill) and ATRX wild-type (n = 46, patterned fill). The relative frequency of C > T transition was significantly higher in ATRX mutant tumors (p = 5.1 × 10–3, two-sided Fisher’s exact test).

  9. Extended Data Fig. 9 Somatic alterations in PI3K and Transcription/Chromatin regulation pathways in NF1-glioma.

    Integrated matrix of 59 NF1-glioma samples (56 patients) and somatic alterations (SNVs and indels, and significant copy number variations) occurring in genes linked to PI3K and transcription/chromatin regulation pathways (left panel, high-grade glioma; right panels low-grade glioma). Rows and columns represent genes and tumor samples, respectively. NF1-glioma samples are sorted in the same order of Fig. 2. Genes are grouped by PI3K (purple) and transcription/chromatin regulation (blue) pathways. Genomic alterations, age, the histology of glioma and the identification of NF1 germline mutation are shown by the indicated colors. Validation by Sanger sequencing (SNVs) and quantitative-genomic PCR (gains and losses) are indicated by yellow and green triangles, respectively.

  10. Extended Data Fig. 10 Somatic alterations in splicing, MAPK and cilium/centrosome pathways in NF1-glioma.

    Integrated matrix of 59 NF1-glioma (56 patients) and somatic alterations (SNVs and indels, and significant copy number variations) occurring in genes included in splicing, MAPK and cilium/centrosome pathways (left panel, high-grade glioma; right panels low-grade glioma). Rows and columns represent genes and tumor samples, respectively. NF1-glioma samples are sorted in the same order of Fig. 2. Genes are grouped by splicing (red), MAPK (yellow) and cilium/centrosome (green) pathways. Genomic alterations, age, the histology of glioma and the identification of NF1 germline mutation are shown by color as indicated. Validation by Sanger sequencing (SNVs) and quantitative-genomic PCR (gains and losses) are indicated by yellow and green triangles, respectively.

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