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.

Mutational landscape and clonal architecture in grade II and III gliomas

Subjects

Abstract

Grade II and III gliomas are generally slowly progressing brain cancers, many of which eventually transform into more aggressive tumors. Despite recent findings of frequent mutations in IDH1 and other genes, knowledge about their pathogenesis is still incomplete. Here, combining two large sets of high-throughput sequencing data, we delineate the entire picture of genetic alterations and affected pathways in these glioma types, with sensitive detection of driver genes. Grade II and III gliomas comprise three distinct subtypes characterized by discrete sets of mutations and distinct clinical behaviors. Mutations showed significant positive and negative correlations and a chronological hierarchy, as inferred from different allelic burdens among coexisting mutations, suggesting that there is functional interplay between the mutations that drive clonal selection. Extensive serial and multi-regional sampling analyses further supported this finding and also identified a high degree of temporal and spatial heterogeneity generated during tumor expansion and relapse, which is likely shaped by the complex but ordered processes of multiple clonal selection and evolutionary events.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Copy number alterations in grade II and III gliomas.
Figure 2: Landscape of genetic lesions in grade II and III gliomas.
Figure 3: Impact of genetic and histological subtypes on overall survival.
Figure 4: Driver genes and functional pathways involved in grade II and III gliomas.
Figure 5: Correlations and temporal hierarchy of gene alterations in grade II and III gliomas.
Figure 6: Parallel evolution in serially collected samples.
Figure 7: Intratumoral heterogeneity and clonal evolution and expansion in grade II and III gliomas.

References

  1. 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 (suppl. 2), ii1–ii56 (2013).

    PubMed  PubMed Central  Google Scholar 

  2. Claus, E.B. & Black, P.M. Survival rates and patterns of care for patients diagnosed with supratentorial low-grade gliomas: data from the SEER program, 1973–2001. Cancer 106, 1358–1363 (2006).

    Article  PubMed  Google Scholar 

  3. Louis, D.N. et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114, 97–109 (2007).

    PubMed  PubMed Central  Google Scholar 

  4. Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10, 459–466 (2009).

    Article  CAS  PubMed  Google Scholar 

  6. Bauman, G., Fisher, B., Watling, C., Cairncross, J.G. & Macdonald, D. Adult supratentorial low-grade glioma: long-term experience at a single institution. Int. J. Radiat. Oncol. Biol. Phys. 75, 1401–1407 (2009).

    Article  PubMed  Google Scholar 

  7. Smith, J.S. et al. Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. J. Clin. Oncol. 26, 1338–1345 (2008).

    Article  PubMed  Google Scholar 

  8. Omuro, A. & DeAngelis, L.M. Glioblastoma and other malignant gliomas: a clinical review. J. Am. Med. Assoc. 310, 1842–1850 (2013).

    Article  CAS  Google Scholar 

  9. Johnson, B.E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hartmann, C. et al. Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1,010 diffuse gliomas. Acta Neuropathol. 118, 469–474 (2009).

    Article  PubMed  Google Scholar 

  12. Liu, X.Y. et al. Frequent ATRX mutations and loss of expression in adult diffuse astrocytic tumors carrying IDH1/IDH2 and TP53 mutations. Acta Neuropathol. 124, 615–625 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Jiao, Y. et al. Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 3, 709–722 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bettegowda, C. et al. Mutations in CIC and FUBP1 contribute to human oligodendroglioma. Science 333, 1453–1455 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kannan, K. et al. Whole-exome sequencing identifies ATRX mutation as a key molecular determinant in lower-grade glioma. Oncotarget 3, 1194–1203 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Arita, H. et al. Upregulating mutations in the TERT promoter commonly occur in adult malignant gliomas and are strongly associated with total 1p19q loss. Acta Neuropathol. 126, 267–276 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Shiraishi, Y. et al. An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data. Nucleic Acids Res. 41, e89 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 45, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011).

    Article  CAS  PubMed  Google Scholar 

  21. Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cancer Genome Atlas Research Netowrk. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  23. Frattini, V. et al. The integrated landscape of driver genomic alterations in glioblastoma. Nat. Genet. 45, 1141–1149 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Brennan, C.W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhang, J. et al. Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas. Nat. Genet. 45, 602–612 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jones, D.T. et al. Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma. Nat. Genet. 45, 927–932 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Okamoto, Y. et al. Population-based study on incidence, survival rates, and genetic alterations of low-grade diffuse astrocytomas and oligodendrogliomas. Acta Neuropathol. 108, 49–56 (2004).

    Article  PubMed  Google Scholar 

  28. Yip, S. et al. Concurrent CIC mutations, IDH mutations, and 1p/19q loss distinguish oligodendrogliomas from other cancers. J. Pathol. 226, 7–16 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Watanabe, T., Nobusawa, S., Kleihues, P. & Ohgaki, H. IDH1 mutations are early events in the development of astrocytomas and oligodendrogliomas. Am. J. Pathol. 174, 1149–1153 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Vogazianou, A.P. et al. Distinct patterns of 1p and 19q alterations identify subtypes of human gliomas that have different prognoses. Neuro-oncol. 12, 664–678 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  31. van den Bent, M.J. et al. IDH1 and IDH2 mutations are prognostic but not predictive for outcome in anaplastic oligodendroglial tumors: a report of the European Organization for Research and Treatment of Cancer Brain Tumor Group. Clin. Cancer Res. 16, 1597–1604 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Wiestler, B. et al. ATRX loss refines the classification of anaplastic gliomas and identifies a subgroup of IDH mutant astrocytic tumors with better prognosis. Acta Neuropathol. 126, 443–451 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wiestler, B. et al. Integrated DNA methylation and copy-number profiling identify three clinically and biologically relevant groups of anaplastic glioma. Acta Neuropathol. 128, 561–571 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Fontebasso, A.M. et al. Mutations in SETD2 and genes affecting histone H3K36 methylation target hemispheric high-grade gliomas. Acta Neuropathol. 125, 659–669 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mantha, S. et al. Activating Notch1 mutations are an early event in T-cell malignancy of Ikaros point mutant Plastic/+ mice. Leuk. Res. 31, 321–327 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

  38. Agrawal, N. et al. Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 333, 1154–1157 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Weng, A.P. et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269–271 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Dalgliesh, G.L. et al. Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 463, 360–363 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhu, X. et al. Identification of functional cooperative mutations of SETD2 in human acute leukemia. Nat. Genet. 46, 287–293 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bao, Z.S. et al. RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas. Genome Res. 24, 1765–1773 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Di Stefano, A.L. et al. Detection, characterization and inhibition of FGFR-TACC fusions in IDH wild type glioma. Clin. Cancer Res. 10.1158/1078-0432.CCR-14-2199 (21 January 2015).

  44. Lancaster, J.F. & Quade, D. Random effects in paired-comparison experiments using the Bradley-Terry model. Biometrics 39, 245–249 (1983).

    Article  CAS  PubMed  Google Scholar 

  45. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Yachida, S. & Iacobuzio-Donahue, C.A. Evolution and dynamics of pancreatic cancer progression. Oncogene 32, 5253–5260 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. de Bruin, E.C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Hartmann, C. et al. Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol. 120, 707–718 (2010).

    Article  PubMed  Google Scholar 

  52. Haferlach, T. et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 28, 241–247 (2014).

    Article  CAS  PubMed  Google Scholar 

  53. Papaemmanuil, E. et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 122, 3616–3627 quiz 3699 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Nannya, Y. et al. A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays. Cancer Res. 65, 6071–6079 (2005).

    Article  CAS  PubMed  Google Scholar 

  55. Yamamoto, G. et al. Highly sensitive method for genomewide detection of allelic composition in nonpaired, primary tumor specimens by use of Affymetrix single-nucleotide-polymorphism genotyping microarrays. Am. J. Hum. Genet. 81, 114–126 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Niida, A., Imoto, S., Shimamura, T. & Miyano, S. Statistical model-based testing to evaluate the recurrence of genomic aberrations. Bioinformatics 28, i115–i120 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shain, A.H. & Pollack, J.R. The spectrum of SWI/SNF mutations, ubiquitous in human cancers. PLoS ONE 8, e55119 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Santos-Rosa, H. & Caldas, C. Chromatin modifier enzymes, the histone code and cancer. Eur. J. Cancer 41, 2381–2402 (2005).

    Article  CAS  PubMed  Google Scholar 

  60. Katagiri, T. et al. Frequent loss of HLA alleles associated with copy number–neutral 6pLOH in acquired aplastic anemia. Blood 118, 6601–6609 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. Yoshida, K. et al. The landscape of somatic mutations in Down syndrome–related myeloid disorders. Nat. Genet. 45, 1293–1299 (2013).

    Article  CAS  PubMed  Google Scholar 

  62. Shah, S.P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Purdom, P.W. Jr., Bradford, P.G., Tamura, K. & Kumar, S. Single column discrepancy and dynamic max-mini optimizations for quickly finding the most parsimonious evolutionary trees. Bioinformatics 16, 140–151 (2000).

    Article  CAS  PubMed  Google Scholar 

  64. Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Y. Mori, M. Nakamura and H. Higashi for their technical assistance. We gratefully acknowledge the TCGA Consortium and all its members for making their invaluable data publically available. We are grateful to all patients who generously agreed to participate in this study. This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (S.O.; 22134006) and the Funding Program for World-Leading Innovative Research and Development on Science and Technology (S.O.) and by a Grant-in Aid for Scientific Research on Innovative Areas from the Ministry of Education, Culture, Sports, Science and Technology of Japan (A. Natsume; 23107010) and funding from the Japan Neurosurgical Society (A. Natsume; Basic Project Plan FY2012-2014).

Author information

Authors and Affiliations

Authors

Contributions

Experiments and data analysis were performed by H.S., K.A., Y. Sato, A. Natsume, F.O., T. Yamamoto, K.T., M.R., T. Yoshizato, K.K., K.Y., Y.N., A.S.-O., M.S. and Y.K. Specimens were provided by T.W., K.M., H.N., M.M., T.A. and Y.M. Bioinformatics analyses were performed by H.S., K.A., K.C., Y. Shiozawa, Y. Shiraishi, A. Niida, T.S., H.T. and S.M. Histological analysis was performed by R.W. and I.I. H.S., K.A. and A. Natsume contributed to the generation of the figures, and H.S., K.A., A. Natsume and S.O. prepared the manuscript.

Corresponding authors

Correspondence to Atsushi Natsume or Seishi Ogawa.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Depths and coverages in whole-exome and targeted deep sequencing data.

Depth (top) and coverage (bottom) of whole-exome sequencing for 38 independent JPN cases (mean depth = 130) (a), ten serial sampling cases (mean depth = 119) (b), four multi-regional sampling cases (mean depth = 142) (c), 425 TCGA cases (mean depth = 94) (d) and targeted deep sequencing for 332 JPN cases (mean depth = 178) (e). The genetic fractions analyzed by the indicated coverage are shown by color.

Source data

Supplementary Figure 2 Number of somatic mutations detected by whole-exome sequencing in the JPN and TCGA cohorts.

Number of somatic mutations detected by whole-exome sequencing in the JPN and TCGA cohorts. (a) Thirty-eight independent JPN cases, (b) 10 serial sampling cases, (c) 4 multi-regional sampling cases and (d) 425 TCGA cases.

Supplementary Figure 3 Mutational spectra in grade II and III glioma.

Mutational spectra of primary grade II and III glioma cases (n = 476) except for TCGA-DU-6392-01A (a) and relapsed hypermutated cases (n = 2) (b). Each plot organizes the 96 mutational patterns. Colors indicate the base substitution type. Each substitution is divided into the 16 pairs of immediately 5′ and 3′ bases. The height of each block is the frequency.

Supplementary Figure 4 Genetic landscape of 425 grade II and III glioma cases from the TCGA cohort.

Molecular classification, histology types, WHO grades (on top rows), and types of mutations and CNVs are shown by color as indicated. The number of samples that had alterations in frequently mutated genes and CNVs are shown in a bar plot (right). PI3K, phosphatidylinositol 3-kinase; RTK, receptor tyrosine kinase; HMT, histone methyltransferase.

Supplementary Figure 5 Genetic landscape of 757 grade II and III glioma cases from the JPN and TCGA cohorts.

The representation is the same as that used in Supplementary Figure 4.

Supplementary Figure 6 Impact of histopathological subtypes on overall survival.

(a) Kaplan-Meier curves for each histopathological subtype from the combined JPN and TCGA cohort (n = 664). (b) Kaplan-Meier curve for type III tumors separated by histopathological diagnoses (n = 128). As a reference, the corresponding curve for primary GBM is also depicted based on combined JPN (n = 79) and TCGA (n = 583) data. P values were calculated using the log-rank test. DA, diffuse astrocytoma; AA, anaplastic astrocytoma; OA, oligoastrocytoma; AOA, anaplastic oligoastrocytoma; OD, oligodendroglioma; AO, anaplastic oligodendroglioma.

Supplementary Figure 7 DNA methylation analysis of grade II and III gliomas and glioblastomas.

(a) Consensus clustering matrix of 425 grade II and III glioma and 144 glioblastoma samples for k = 3. (b) Cumulative distribution function plots from the consensus matrices for k = 2 to k = 6. (c) Integrated view of DNA methylation clustering combined with genetic subtypes, IDH1 and IDH2 mutations, and histopathological diagnosis. GBM, glioblastoma; NA, not available.

Source data

Supplementary Figure 8 DNA expression analysis of grade II and III gliomas and glioblastomas.

(a) Consensus clustering matrix of 422 grade II and III glioma and 160 glioblastoma samples for k = 4. (b) Cumulative distribution function plots from the consensus matrices for k = 2 to k = 6. (c) Integrated view of expression clustering combined with genetic subtypes, IDH1 and IDH2 mutations, and histopathological diagnosis. GBM, glioblastoma; NA, not available.

Source data

Supplementary Figure 9 The frequency of affected samples.

The frequency of affected samples from the JPN cohort (left) (n = 332) and the TCGA cohort (right) (n = 425). Types of mutation are shown by color as indicated.

Supplementary Figure 10 Mutational patterns of representative genes detected by exome and targeted deep sequencing (n = 757).

Mutation distributions for TP53, ATRX, CIC, FUBP1, NOTCH1, NOTCH2, NOTCH3, NOTCH4, EGFR, PDGFRA, PIK3CA, PIK3R1, PTEN, NF1, ARID1A, ARID1B, SMARCA4, SETD2, MLL2 and MLL3 in 757 grade II and III glioma cases. Types of mutation are distinguished by the indicated colors.

Supplementary Figure 11 Spectrum of genetic alteration in each grade II and III glioma type.

The frequencies of representative gene mutations and CNVs in each grade II and III glioma type are shown by different colors. These mutations and CNVs were globally mutually exclusive among grade II and III glioma types. PI3K, phosphatidylinositol 3-kinase; HMT, histone methyltransferase; RTK, receptor tyrosine kinase.

Source data

Supplementary Figure 12 Temporal patterns of clonal evolution in nine cases with tumor samples collected at multiple time points.

The bar plots show the tumor cell fraction of each somatic mutation and CNV (left). A phylogenetic tree depicts the patterns of clonal evolution inferred from somatic mutations and CNVs (right). HD, homozygous deletion; N, normal tissue; P, primary tumor; R, relapse tumor.

Source data

Supplementary Figure 13 Spatial patterns of clonal evolution in three cases with multi-regional sampling.

Left, the sampling positions (T1–T5 or T6) in three cases—LGG172, LGG173 and LGG175—are overlaid onto a three-dimensional magnetic resonance image. Center left, schematic diagram of spatial clonal evolution. Center right, major driver and parallel mutations are mapped in a phylogenetic tree. Right, landscape of genetic lesions in the 5–6 regional samples, showing mutations shared by all samples (orange), those shared by partial subsets of samples (green) and private mutations (blue). Dom, dominant tumor; min, minor tumor; N, normal tissue; T, tumor sample.

Source data

Supplementary Figure 14 Clonal architecture estimated by PyClone.

Genetic alterations existing in the same clone are shown in the same color by Dinamic Tree Cut. Red clusters indicate the clusters with the highest frequency.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Tables 4, 5, 7, 9, 13 and 15. (PDF 2482 kb)

Supplementary Tables 1–3, 6, 8, 10–12, 14 and 16

Supplementary Tables 1–3, 6, 8, 10–12, 14 and 16. (XLSX 2650 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suzuki, H., Aoki, K., Chiba, K. et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat Genet 47, 458–468 (2015). https://doi.org/10.1038/ng.3273

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3273

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research