Abstract
RNA polymerase II mediates the transcription of all protein-coding genes in eukaryotic cells, a process that is fundamental to life. Genomic mutations altering this enzyme have not previously been linked to any pathology in humans, which is a testament to its indispensable role in cell biology. On the basis of a combination of next-generation genomic analyses of 775 meningiomas, we report that recurrent somatic p.Gln403Lys or p.Leu438_His439del mutations in POLR2A, which encodes the catalytic subunit of RNA polymerase II (ref. 1), hijack this essential enzyme and drive neoplasia. POLR2A mutant tumors show dysregulation of key meningeal identity genes2,3, including WNT6 and ZIC1/ZIC4. In addition to mutations in POLR2A, NF2, SMARCB1, TRAF7, KLF4, AKT1, PIK3CA, and SMO4,5,6,7,8, we also report somatic mutations in AKT3, PIK3R1, PRKAR1A, and SUFU in meningiomas. Our results identify a role for essential transcriptional machinery in driving tumorigenesis and define mutually exclusive meningioma subgroups with distinct clinical and pathological features.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Wintzerith, M., Acker, J., Vicaire, S., Vigneron, M. & Kedinger, C. Complete sequence of the human RNA polymerase II largest subunit. Nucleic Acids Res. 20, 910 (1992).
Inoue, T., Ogawa, M., Mikoshiba, K. & Aruga, J. Zic deficiency in the cortical marginal zone and meninges results in cortical lamination defects resembling those in type II lissencephaly. J. Neurosci. 28, 4712–4725 (2008).
Choe, Y., Zarbalis, K.S. & Pleasure, S.J. Neural crest-derived mesenchymal cells require Wnt signaling for their development and drive invagination of the telencephalic midline. PLoS One 9, e86025 (2014).
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).
Reuss, D.E. et al. Secretory meningiomas are defined by combined KLF4 K409Q and TRAF7 mutations. Acta Neuropathol. 125, 351–358 (2013).
Schmitz, U. et al. INI1 mutations in meningiomas at a potential hotspot in exon 9. Br. J. Cancer 84, 199–201 (2001).
Abedalthagafi, M. et al. Oncogenic PI3K mutations are as common as AKT1 and SMO mutations in meningioma. Neuro-oncol. 18, 649–655 (2016).
Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).
Kämpjärvi, K. et al. Somatic MED12 mutations in uterine leiomyosarcoma and colorectal cancer. Br. J. Cancer 107, 1761–1765 (2012).
Bhagwat, A.S. & Vakoc, C.R. Targeting transcription factors in cancer. Trends Cancer 1, 53–65 (2015).
Santi, L. et al. Acute liver failure caused by Amanita phalloides poisoning. Int. J. Hepatol. 2012, 487480 (2012).
Ostrom, Q.T. et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2006–2010. Neuro-oncol. 15, ii1–ii56 (2013).
Davies, M.A. et al. A novel AKT3 mutation in melanoma tumours and cell lines. Br. J. Cancer 99, 1265–1268 (2008).
Aavikko, M. et al. Loss of SUFU function in familial multiple meningioma. Am. J. Hum. Genet. 91, 520–526 (2012).
Kirschner, L.S. et al. Mutations of the gene encoding the protein kinase A type I-α regulatory subunit in patients with the Carney complex. Nat. Genet. 26, 89–92 (2000).
Kotani, T. Protein kinase A activity and Hedgehog signaling pathway. Vitam. Horm. 88, 273–291 (2012).
Lin, C.Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).
Tibshirani, R., Hastie, T., Narasimhan, B. & Chu, G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA 99, 6567–6572 (2002).
Whyte, W.A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).
Rifat, Y. et al. Regional neural tube closure defined by the Grainy head-like transcription factors. Dev. Biol. 345, 237–245 (2010).
Wu, X.H., Wang, Y., Zhuo, Z., Jiang, F. & Wu, Y.D. Identifying the hotspots on the top faces of WD40-repeat proteins from their primary sequences by β-bulges and DHSW tetrads. PLoS One 7, e43005 (2012).
Gymnopoulos, M., Elsliger, M.-A. & Vogt, P.K. Rare cancer-specific mutations in PIK3CA show gain of function. Proc. Natl. Acad. Sci. USA 104, 5569–5574 (2007).
Olshen, A.B., Venkatraman, E.S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).
Bilgüvar, K. et al. Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature 467, 207–210 (2010).
Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Cui, Q. A network of cancer genes with co-occurring and anti-co-occurring mutations. PLoS One 5, e13180 (2010).
Sathirapongsasuti, J.F. et al. Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics 27, 2648–2654 (2011).
Chen, K. et al. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat. Methods 6, 677–681 (2009).
O'Roak, B.J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
Thorvaldsdóttir, H., Robinson, J.T. & Mesirov, J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).
Smyth, G.K. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds. Gentleman, R., Carey, V.J., Huber, W., Irizarry, R.A. & Dudoit, S.) 397–420 (Springer, 2005).
Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E. & Storey, J.D. The SVA package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
Lovén, J. et al. Revisiting global gene expression analysis. Cell 151, 476–482 (2012).
Verhaak, R.G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).
De Sousa E Melo, F. et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat. Med. 19, 614–618 (2013).
Sadanandam, A. et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat. Med. 19, 619–625 (2013).
Wilkerson, M.D. & Hayes, D.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).
Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367 (2010).
Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).
Isella, C. et al. Stromal contribution to the colorectal cancer transcriptome. Nat. Genet. 47, 312–319 (2015).
Huang, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Cotney, J. et al. Chromatin state signatures associated with tissue-specific gene expression and enhancer activity in the embryonic limb. Genome Res. 22, 1069–1080 (2012).
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).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Chipumuro, E. et al. CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer. Cell 159, 1126–1139 (2014).
Ross-Innes, C.S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).
Bailey, T.L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).
Acknowledgements
We are grateful to the patients and their families who have contributed to this study. This work was supported by the Gregory M. Kiez and Mehmet Kutman Foundation, the Yale School of Medicine, the NIH (Medical Scientist Training Program grant T32GM00720 to V.E.C. and M.W.Y.; NIH/NCI NRSA F30CA183530-03 to V.E.C.), and the Austrian Science Fund (FWF; Erwin Schrödinger Fellowship J3490 to D.H.). Partial funding for sequencing of the tumor samples was provided through a research agreement between Gilead Sciences, Inc., and Yale University. We gratefully acknowledge T. Boggon for constructive comments regarding the structure of RNA polymerase II and N. Ivanova for providing expertise on the maintenance of mouse embryonic stem cells.
Author information
Authors and Affiliations
Contributions
V.E.C. performed whole-exome sequencing analysis, molecular inversion probe (MIP) sequencing and analysis, amplicon sequencing and analysis, chr22 copy-number assessment via qPCR, structural analysis of mutations, H3K27ac ChIP-seq of POLR2A mutant tumors, TRAF7 splice mutation characterization, clinical correlations, and sample selection. V.E.C., M.W.Y., J.F.B., and D.D. performed Sanger confirmation of detected mutations for the 775-meningioma cohort and sample preparation. A.S.H. performed expression microarray analysis, CNV analysis, and RNA-seq variant calling. B.J.A. called super-enhancers on H3K27ac meningioma ChIP-seq samples. A.S.H., H.B., and V.E.C. performed differential super-enhancer binding analysis. H.B. performed predication analysis of microarray on meningioma samples. V.E.C. and M.W.Y. performed RNA-seq analysis. K.Y. created the bioinformatics pipelines used for DNA-seq experiments (whole-exome, MIPs, and amplicon). O.H. performed KLF4 luciferase experiments. T.I.L., A.G.E.-S., A.S.W., D.H., M.S., B.K., E.Z.E.-O., G.C.-G., K.M.-G., J.E.G., J.S., R.G., J.M.P., A.O.V., J.M.G., K.B., R.A.Y., and M.G. assisted with sample preparation and/or experimental design. V.E.C., M.W.Y., and M.G. wrote the manuscript, and A.S.H., H.B., T.I.L. and R.A.Y. edited the manuscript. M.G. designed and oversaw the project.
Corresponding author
Ethics declarations
Competing interests
Partial funding for sequencing of the tumor samples was provided through a research agreement between Gilead Sciences, Inc., and Yale University.
Integrated supplementary information
Supplementary Figure 1 POLR2A-mutant meningiomas do not have recurrent interchromosomal translocations.
Circos plots generated from somatic whole-genome sequencing data of three POLR2A mutant tumor-blood pairs: (a) MN-60355; (b) MN-63345; (c) MN-63670. Only structural events with a supporting read count >25 were plotted. For breakpoint call coordinates, see Supplementary Table 2.
Supplementary Figure 2 POLR2A-mutant meningiomas do not have increased numbers of somatic DNA mutations or transcriptional errors compared with other subgroups.
(a) Barplots of the number of somatic deleterious coding mutations, somatic silent mutations, and somatic non-coding mutations from 45 meningioma exomes normalized per megabase of sequencing (n=11 POLR2A mutant, orange; n=16 NF2/Chr22 loss, blue; n=18 non-NF2 mutant, green). The number of coding and non-coding somatic mutations from whole-exome sequencing data is not statistically different between POLR2A mutant and other meningioma samples (p=0.08 for deleterious coding; p=0.4 for noncoding; p=0.82 for all mutations). (b, c) Variants were called from RNAseq data for 25 meningiomas (n=8 POLR2A mutant, orange; n=9 NF2/Chr22 loss, blue; n=8 non-NF2 mutant, green). The number of coding (b) or non-coding (c) transcript mutations is not statistically different between POLR2A mutant and other samples (p=0.16 for deleterious coding; p=0.49 for noncoding). Student’s t-test was used to calculate significance.
Supplementary Figure 3 Recurrent TRAF7 splice mutations promote intron inclusion, resulting in a 28-amino-acid in-frame insertion plus an S/C missense.
(a) Location of the recurrent canonical (green) and non-canonical (crimson) TRAF7 splice mutations. The number above the diamond indicates the number of meningiomas mutant at that position. (b) RNAseq read alignments of a TRAF7 splice mutant (top) and a TRAF7 splice wildtype (bottom) reveal that TRAF7 splice mutant meningiomas have reads spanning intron 11 (grey shading above reads). This is not observed in non-splice mutant TRAF7 meningiomas. (c) An enhanced view of the RNAseq reads reveals the reads spanning the intron contain the non-canonical splice mutation (G/A change 5 basepairs intronic). (d) Sanger sequencing of cDNA libraries created from TRAF7 splice wildtype meningiomas reveal that intron 10 is spliced. Top: alignment of the Sanger bases to the human mRNA reference sequence; Bottom: Sanger chromatograms. (e) Sanger sequencing of cDNA libraries created from TRAF7 splice mutant meningiomas reveals mRNA reads spanning intron 10. Top: alignment of the Sanger bases to the human genomic reference sequence; Bottom: Sanger chromatograms.
Supplementary Figure 4 Schematic of Sonic hedgehog (SHH) signaling.
Mutually exclusive somatic mutations in SMO, PRKAR1A and SUFU that activate SHH signaling were identified in the 775 meningioma cohort.
Supplementary Figure 5 Spike-in normalized RNA-seq data from POLR2A-mutant v6.5 mouse ESCs do not reveal evidence for global transcriptional amplification in this model.
(a) The FPKM (fragments per kilobase of exon per million mapped reads) was computed for spike-in control normalized RNAseq data from knock-in POLR2A mutant mES cells (two independent Q403K clones, A7 and B10) and compared to wildtype v6.5 mouse ES cells. Transcripts with an FPKM value of 1 or greater in wildtype cells were selected, and the density of transcripts with a given FPKM was plotted. The FPKM distribution is not significantly different between the samples, consistent with a lack of evidence for global transcriptional amplification. (b) In contrast, identical analysis of previously published spike-in normalized RNAseq data with high- and low- expression of c-Myc reveals a shift in the distribution of normalized FPKM in the cells with high expression of c-Myc, consistent with global transcriptional amplification.
Supplementary Figure 6 Gene expression clustering of benign meningiomas using non-PAM methods.
Clustering using (a) consensus, (b) NMF, (c) hierarchical, or (d) PCA methods resulted in 5 discrete subgroups that were separated by the underlying driver gene mutations. Grade I, genomically unstable NF2 samples were excluded from analysis.
Supplementary Figure 7 Super-enhancers with concordant changes in gene expression that are more active in meningiomas compared to dura.
(a-c) Shading represents high confidence super-enhancers that were detected in at least 2 samples per subgroup. Chromosomal coordinates are hg19. (d-e) Concordant differential gene expression and differentially-bound super enhancers between meningioma subgroups. Red points represent a differentially highly bound super-enhancer (FDR < 0.05) with concordant up-regulation of its nearby target gene (FDR < 0.05). Blue points represent a differentially lower bound super-enhancer that correlates with down-regulation of its target gene.
Supplementary Figure 8 Super-enhancers with concordant changes in gene expression that are more active in specific meningioma subgroups.
Shading represents high confidence super-enhancers that were detected in at least 2 samples per subgroup. Chromosomal coordinates are hg19.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–8 and Supplementary Tables 4 and 5. (PDF 2662 kb)
Supplementary Table 1
Genomic and clinical characterization of 775 meningiomas. (a) Per-sample summary of clinical data, detected mutations, method of sequencing, method of structural variation analysis, method of transcriptomic analysis, and method of ChIPseq analysis (if applicable). Anatomical location key: ASB: Anterior skull base, MSB_med: Middle skull basemedial, MSB_lat: Middle skull baselateral, PF_antmed: Anterior, medial posterior cranial fossa, PF_postlat: Posterior, lateral posterior cranial fossa, Spinal: Spinal, ST_ant: Anterior supratentorial, ST_post: Posterior supratentorial, Ventricular: Intraventricular, Multiple: Multiple. Coordinates are hg19. Exome = Whole-exome sequencing. MIPs = next-generation sequencing screening of the coding sequences of NF2, SMARCB1, TRAF7, PIK3CA, PIK3R1, PRKAR1A, SUFU, SMO, AKT1 p.Glu17Lys and KLF4 p.Lys409Gln. Amplicon = next-generation screening of the coding sequences of NF2, TRAF7, SMO, AKT1 p.Glu17Lys and KLF4 p.Lys409Gln. P = primary; R = Recurrent. M = Male; F = Female; U = Unknown; N/A = Not performed. (b) Cohort summary of genomic and epigenomic approaches for 775 meningiomas. (XLSX 198 kb)
Supplementary Table 2
Somatic breakpoints from three whole-genome-sequenced POLR2A-mutant meningiomas and matching blood pairs. Somatic breakpoints from three whole-genome sequenced POLR2A mutant meningiomas and matching blood pairs called by Breakdancer35 and passing filters as described in the methods. Chromosomal coordinates are hg19. (XLSX 65 kb)
Supplementary Table 3
Somatic variants from whole-exome-sequenced meningiomas. Somatic variants passing quality filters. Coordinates are hg19. (XLSX 456 kb)
Supplementary Table 6
Alternative splicing between meningioma subgroups. Transcripts were assembled from RNA-seq data using the Tuxedo suite, and alternative splicing and differential promoter usage was calculated between subgroups using CummeRbund37,38. There are no significant differences in alternative splicing between meningioma subtypes. (TXT 65341 kb)
Supplementary Table 7
Differential promoter usage between meningioma subgroups. Transcripts were assembled from RNAseq data using the Tuxedo suite, and differential promoter usage was calculated between subgroups using CummeRbund37,38. There are no significant differences in differential promoter usage between meningioma subtypes. (TXT 42857 kb)
Supplementary Table 8
Alternative splicing between v6.5 mESC knock-in models of POLR2AQ403K (clones A7 and B10) and wild-type cells. Transcripts were assembled from RNA-seq data using the Tuxedo suite, and alternative splicing and differential promoter usage was calculated between subgroups using CummeRbund37,38. There are no significant differences in alternative splicing between POLR2A mutant mES cells and wildtype. (TXT 19096 kb)
Supplementary Table 9
Differential promoter usage between v6.5 mESC knock-in models of POLR2AQ403K (clones A7 and B10) and wild-type cells. Transcripts were assembled from RNAseq data using the Tuxedo suite, and differential promoter usage was calculated between subgroups using CummeRbund37,38. There are no significant differences in differential promoter usage between meningioma subtypes. (TXT 10795 kb)
Supplementary Table 10
PAM clustering signature genes for meningioma subgroups. (XLSX 510 kb)
Supplementary Table 11
Concordant differentially bound super-enhancers and differential gene expression. Comparisons are between all meningiomas and dura. (XLSX 873 kb)
Supplementary Table 12
Concordant differentially bound super-enhancers and differential gene expression: comparisons between meningioma subgroups. (XLSX 174 kb)
Supplementary Table 13
Concordant differentially bound super-enhancers in at least two samples and differential gene expression: comparisons between meningioma subgroups. Super-enhancers assigned to a group (e.g. POLR2A) were present in at least two samples. Superenhancers present in dura were excluded from analysis. (XLSX 398 kb)
Supplementary Table 14
Whole-exome sequencing metrics. (a) Mean depth of sequencing coverage for tumor exomes was 205.08. (b) Mean depth of sequencing coverage for matching blood exomes was 115.19. (XLSX 69 kb)
Rights and permissions
About this article
Cite this article
Clark, V., Harmancı, A., Bai, H. et al. Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas. Nat Genet 48, 1253–1259 (2016). https://doi.org/10.1038/ng.3651
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.3651
This article is cited by
-
A novel BRAF::PTPRN2 fusion in meningioma: a case report
Acta Neuropathologica Communications (2023)
-
Respective roles of Pik3ca mutations and cyproterone acetate impregnation in mouse meningioma tumorigenesis
Cancer Gene Therapy (2023)
-
Signaling pathways in brain tumors and therapeutic interventions
Signal Transduction and Targeted Therapy (2023)
