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Mutational landscape and clonal architecture in grade II and III gliomas

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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.

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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.

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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).

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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.

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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)

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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

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