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The molecular landscape of glioma in patients with Neurofibromatosis 1

This article has been updated

Abstract

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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Fig. 1: Analysis of germline and somatic mutations in NF1-glioma patients.
Fig. 2: Landscape of somatic genomic alterations in NF1-glioma.
Fig. 3: Analysis of ATRX somatic mutations in NF1-glioma patients.
Fig. 4: Transcriptomic analysis of NF1-glioma.
Fig. 5: T cell infiltration and neoantigen analysis in low-grade NF1-glioma subclusters.
Fig. 6: NF1-gliomas resemble LGm6 subgroup of sporadic gliomas.

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.

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.

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Acknowledgements

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.

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Authors

Contributions

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.

Corresponding authors

Correspondence to Anna Lasorella or Antonio Iavarone.

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The authors declare no competing interests.

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Extended data

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.

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.

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

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.

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.

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.

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).

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).

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.

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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D’Angelo, F., Ceccarelli, M., Tala et al. The molecular landscape of glioma in patients with Neurofibromatosis 1. Nat Med 25, 176–187 (2019). https://doi.org/10.1038/s41591-018-0263-8

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