Longitudinal molecular trajectories of diffuse glioma in adults

Article metrics

Abstract

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Temporal changes in glioma mutational burden and processes.
Fig. 2: Quantifying selective pressures during glioma evolution.
Fig. 3: Patterns of glioma driver frequencies over time.
Fig. 4: Neoantigen selection during tumour progression.

Data availability

All de-identified, non-protected access somatic variant profiles and clinical data are accessible via Synapse (http://synapse.org/glass). Raw data of the various sequencing datasets can be obtained in the Supplementary Information.

Code availability

All custom scripts and pipelines are available on the project’s github page (https://github.com/TheJacksonLaboratory/GLASS).

References

  1. 1.

    Barthel, F. P., Wesseling, P. & Verhaak, R. G. W. Reconstructing the molecular life history of gliomas. Acta Neuropathol. 135, 649–670 (2018).

  2. 2.

    Osuka, S. & Van Meir, E. G. Overcoming therapeutic resistance in glioblastoma: the way forward. J. Clin. Invest. 127, 415–426 (2017).

  3. 3.

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

  4. 4.

    Zheng, S. et al. A survey of intragenic breakpoints in glioblastoma identifies a distinct subset associated with poor survival. Genes Dev. 27, 1462–1472 (2013).

  5. 5.

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

  6. 6.

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

  7. 7.

    The Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481–2498 (2015).

  8. 8.

    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).

  9. 9.

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

  10. 10.

    Louis, D. N. et al. International Society of Neuropathology—Haarlem consensus guidelines for nervous system tumor classification and grading. Brain Pathol. 24, 429–435 (2014).

  11. 11.

    Louis, D. N. et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 131, 803–820 (2016).

  12. 12.

    Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).

  13. 13.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

  14. 14.

    Snuderl, M. et al. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 20, 810–817 (2011).

  15. 15.

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

  16. 16.

    Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).

  17. 17.

    Nejo, T. et al. reduced neoantigen expression revealed by longitudinal multiomics as a possible immune evasion mechanism in glioma. Cancer Immunol. Res. 7, 1148–1161 (2019).

  18. 18.

    The GLASS Consortium. Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro-oncol. 20, 873–884 (2018).

  19. 19.

    Hu, H. et al. Mutational landscape of secondary glioblastoma guides met-targeted trial in brain tumor. Cell 175, 1665–1678.e1618 (2018).

  20. 20.

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

  21. 21.

    Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).

  22. 22.

    Kim, H. et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 25, 316–327 (2015).

  23. 23.

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

  24. 24.

    Hunter, C. et al. A hypermutation phenotype and somatic MSH6 mutations in recurrent human malignant gliomas after alkylator chemotherapy. Cancer Res. 66, 3987–3991 (2006).

  25. 25.

    Jolly, C. & Van Loo, P. Timing somatic events in the evolution of cancer. Genome Biol. 19, 95 (2018).

  26. 26.

    Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).

  27. 27.

    Choi, S. et al. Temozolomide-associated hypermutation in gliomas. Neuro-oncol. 20, 1300–1309 (2018).

  28. 28.

    Baumert, B. G. et al. Temozolomide chemotherapy versus radiotherapy in high-risk low-grade glioma (EORTC 22033-26033): a randomised, open-label, phase 3 intergroup study. Lancet Oncol. 17, 1521–1532 (2016).

  29. 29.

    Buckner, J. C. et al. Radiation plus procarbazine, CCNU, and vincristine in low-grade glioma. N. Engl. J. Med. 374, 1344–1355 (2016).

  30. 30.

    Yap, T. A., Gerlinger, M., Futreal, P. A., Pusztai, L. & Swanton, C. Intratumor heterogeneity: seeing the wood for the trees. Sci. Transl. Med. 4, 127ps10 (2012).

  31. 31.

    Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e1021 (2017).

  32. 32.

    Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).

  33. 33.

    Korber, V. et al. Evolutionary trajectories of IDH(WT) glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell 35, 692–704.e612 (2019).

  34. 34.

    deCarvalho, A. C. et al. Discordant inheritance of chromosomal and extrachromosomal DNA elements contributes to dynamic disease evolution in glioblastoma. Nat. Genet. 50, 708–717 (2018).

  35. 35.

    Giam, M. & Rancati, G. Aneuploidy and chromosomal instability in cancer: a jackpot to chaos. Cell Div. 10, 3 (2015).

  36. 36.

    Marty, R., Thompson, W. K., Salem, R. M., Zanetti, M. & Carter, H. Evolutionary pressure against MHC class II binding cancer mutations. Cell 175, 416–428.e413 (2018).

  37. 37.

    McGranahan, N. et al. Allele-specific HLA Loss and immune escape in lung cancer evolution. Cell 171, 1259–1271.e1211 (2017).

  38. 38.

    Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32, 42–56.e46 (2017).

  39. 39.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

  40. 40.

    Dunn, G. P., Bruce, A. T., Ikeda, H., Old, L. J. & Schreiber, R. D. Cancer immunoediting: from immunosurveillance to tumor escape. Nat. Immunol. 3, 991–998 (2002).

  41. 41.

    Hundal, J. et al. pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).

  42. 42.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

  43. 43.

    Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

  44. 44.

    Raub, T. J. et al. Brain exposure of two selective dual CDK4 and CDK6 inhibitors and the antitumor activity of CDK4 and CDK6 inhibition in combination with temozolomide in an intracranial glioblastoma xenograft. Drug Metab. Dispos. 43, 1360–1371 (2015).

  45. 45.

    van den Bent, M. et al. Efficacy of depatuxizumab mafodotin (ABT-414) monotherapy in patients with EGFR-amplified, recurrent glioblastoma: results from a multi-center, international study. Cancer Chemother. Pharmacol. 80, 1209–1217 (2017).

  46. 46.

    Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

  47. 47.

    Schumacher, T. et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature 512, 324–327 (2014).

  48. 48.

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

  49. 49.

    Droop, A. et al. How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: a case study using paired primary and recurrent glioblastoma. Int. J. Cancer 142, 1620–1626 (2018).

  50. 50.

    Mazor, T. et al. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28, 307–317 (2015).

  51. 51.

    Kim, J. et al. Spatiotemporal evolution of the primary glioblastoma genome. Cancer Cell 28, 318–328 (2015)

  52. 52.

    Suzuki, H. et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat. Genet. 47, 458–468 (2015).

  53. 53.

    Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 34, 3600 (2018).

  54. 54.

    Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics. 43, 11.10.11–11.10.33 (2013).

  55. 55.

    Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

  56. 56.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  57. 57.

    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).

  58. 58.

    Beroukhim, R. et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc. Natl Acad. Sci. USA 104, 20007–20012 (2007).

  59. 59.

    Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689.e673 (2018).

  60. 60.

    Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

  61. 61.

    Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal. Cell 173, 581–594.e512 (2018).

  62. 62.

    Ha, G. et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 24, 1881–1893 (2014).

  63. 63.

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

  64. 64.

    Hoof, I. et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61, 1–13 (2009).

Download references

Acknowledgements

This work is made possible by the patients and their families whom generously contributed to this study. This work is supported by the National Brain Tumor Society, Oligo Research Fund; Cancer Center Support grants P30CA16672 and P30CA034196; Cancer Prevention & Research Institute of Texas (CPRIT) grant number R140606; Agilent Technologies (R.G.W.V.); the National Institutes of Health- National Cancer institute for the following grants: NCI CA170278 (L.M.P., T.M.M., H.N.), NCI R01CA222146 (L.M.P., H.K.N.), NCI R01CA230031 (J.H.C., J.N.), NCI R01CA188288 (J.S.B.-S., R.B., P.B., K.L.L., A. Chakravarty., A.E.S.), R01CA179044 (A. Iavarone), U54CA193313 (A. Iavarone). The National Brain Tumor Society (W.K.A.Y., J.F.d.G.). Brain Tumour Northwest tissue bank (including the Walton research tissue bank) is supported by the Sidney Driscol Neuroscience Foundation and part of the Walton Centre and Lancashire Teaching Hospitals NHS Foundation Trusts (A.F.B., M.D.J.). This work was supported by a generous gift from the Dabbiere family (J.F.C.). Support is also provided by a Leeds Charitable Foundation grant (9R11/14-11 to L.F.S.), University of Leeds Academic Fellowship (11001061) (L.F.S.) and Studentship (11061191) (G. Tanner) as well as Leeds Teaching Hospitals NHS Trust (A. Chakravarti, A. Ismail). The Leeds Multidisciplinary Research Tissue Bank staff was funded by the PPR Foundation and The University of Leeds (S.C.S.). Funds were received from The Brain Tumour Charity (C.W., grants 10/136 & GN-000580, B.A.W., 200450). Ghazaleh Tabatabai is funded by EKFS 2015_Kolleg_14. R01CA218144 (P.S.L., E.J.C., J.C., A.K.L.) and Strain for the Brain, Milwaukee, WI (P.S.L., E.J.C., J.C., A.K.L.). E.K. is recipient of an MD-Fellowship by the Boehringer Ingelheim Fonds and is supported by the German National Academic Foundation. The Leeds Multidisciplinary Research Tissue Bank staff was funded by the PPR Foundation and part of the University of Leeds (S.C.S.). GLASS-Austria was funded by the Austrian Science Fund project KLI394 (A.W.). GLASS-Germany was funded by the German Ministry of Education and Research (BMBF) 031A425 (G. Reifenberger, P.L.) and German Cancer Aid (DKH) 70-3163-Wi 3 (M.W.). GLASS-NL receives support from KWF/Dutch Cancer Society project 11026 (M.C.M.K., P.W., R.G.W.V., P.J.F., J.M.N., M. Smits, B.A.W.). We thank the University of Colorado Denver Central Nervous System Biorepository (D.R.O.) for providing tissue samples. Sponsoring was also received from the National Institute of Neurological Disorders and Stroke (NINDS R01NS094615, G. Rao), F.S.V. is supported by a postdoctoral fellowship from The Jane Coffin Childs Memorial Fund for Medical Research. F.P.B. is supported by the JAX Scholar program and the National Cancer Institute (K99 CA226387); K.C.J. is the recipient of an American Cancer Society Fellowship (130984-PF-17-141-01-DMC). We thank the Jackson Laboratory Clinical and Translation Support team for coordinating all data transfer agreements. We thank M. Wimsatt for assistance in graphic design.

Author information

Sequencing data coordination was performed by H.K., F.P.B. and K.C.J., and clinical data coordination was by A.D.M. and O.A. Data analysis was led by F.P.B. and K.C.J. in collaboration with K.J.A., S.B.A., J.H.C., H.K., E.K., J.N., L.F.S., G. Tanner, F.S.V. and R.G.W.V. Clinical analysis was performed by F.P.B., K.C.J., A.D.M., L.M.P. and C.W. Pathology review was completed, in part, by A. Chakrabarty, J.T.H., A. Ismail, A.W, H.K.N., K.L.L., G. Reifenberger and K.A. F.P.B., K.C.J., A.D.M., F.S.V. and R.G.W.V. wrote the manuscript. K.D.A., J.H. and J.F.d.G. coordinated the GLASS-MDACC cohort. L.F.S. was the lead coordinator of the GLASS-Leeds cohort and B.A.W. the lead coordinator of GLASS-Netherlands. D.M.A., D.A., P.B., J.S.B.-S., R.B., C.B., P.K.B., D.J.B., A.R.B. A. Chakrabarty, A. Chakravarti, E.J.C., J.F.C., G.F., M.N.F., A. Iavarone, M.D.J., M.K., P.S.L., M.L., P.L., K.L.L., T.M.M., T.M., A.M.M., D.-H.N., N.N., H.K.N., C.Y.N., S.P.N., H.N., D.R.O., C.-K.P., L.M.P., G. Rao, B.R., J.K.S., S.C.S., A.E.S., M. Schuster, L.F.S., H.S., E.G.V.M., C.W., M.W., G.W. and A.W. contributed to sample acquisition and processing. All co-authors including K.A., P.B., A.F.B., K.R.B., E.B.C., J.C., P.J.F., H.K.G., M. R. Grimmer, P.V.G., M. R. Gilbert, A.K.L., K.L.M., J.M.N., R.R., G. Reifenberger, B.L.S., P.A.S.S., M. Smits, G. Tabatabai, P.W., W.K.A.Y. and G.Z. discussed the results and commented on the manuscript and Supplementary Information. R.G.W.V. was the project lead and coordinator.

Correspondence to Roel G. W. Verhaak.

Ethics declarations

Competing interests

R.G.W.V. declares equity in Boundless Bio, Inc. M.K. receives research grants from BMS and ABBVie. P.K.B. is a consultant for Lilly, Genentech-Roche, Angiochem and Tesaro. P.K.B. receives institutional funding from Merck and Pfizer and honoraria from Merch and Genentech-Roche. W.K.A.Y. serves in a consulting or advisory role at DNAtrix Therapeutics. M.W. receives funding from Acceleron, Actelion, Bayer, Isarna, Merck, Sharp & Dohme, Merck (EMD, Darmstadt), Novocure, OGD2, Pigur and Roche as well as honoraria from BMS, Celldex, Immunocellular Therapeutics, Isarna, Magforce, Merck, Sharp & Dohme, Merck (EMD, Darmstadt), Northwest Biotherapeutics, Novocure, Pfizer, Roche, Teva and Tocagen. G. Reifenberger receives funding from Roche and Merck (EMD, Darmstadt) as well as honoraria from AbbVie. M. Smits is a central reviewer for Parexel Ltd and honoraria are paid to the institution. G. Tabatabai reports personal fees from Bristol-Myers-Squibb, personal fees from AbbVie, personal fees from Novocure, personal fees from Medac, travel grants from Bristol-Myers-Squibb, education grants from Novocure, research grants from Roche Diagnostics, research grants from Medac, membership in the National Steering board of the TIGER NIS (Novocure) and the International Steering board of the ON-TRK NIS (Bayer).

Additional information

Peer review information Nature thanks Kamila Naxerova, Wolfgang Wick and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Sample selection.

a, Quality control workflow steps identifying all GLASS samples available as a resource and the identification of the highest quality set of patient pairs (n = 222) used for the presented mutational and copy number analyses. b, Additional available datasets.

Extended Data Fig. 2 Mutation burden by time point and subtype.

a, Box plots and paired lines depicting coverage-adjusted mutation frequencies in initial and matched recurrent samples across three subtypes. Wilcoxon signed-rank test P values and sample sizes are indicated. b, Bee swarm plot depicting coverage-adjusted mutation frequencies in fractions by subtype. Dashed line indicates the mean. P values comparing three subtypes were determined by one-way analysis of variance (ANOVA). c, Scatter plot showing the relationship between age at diagnosis and coverage adjusted mutation burdens by subtype and fraction. P values were determined by the linear model and adjusted by subtype. d. Similar to the analysis in c but showing the relationship between time to recurrence and coverage-adjusted mutation burdens.

Extended Data Fig. 3 Mutational signatures by fraction and subtype.

a, Correlation plot showing the Pearson’s chi-squared (χ2) residuals for each signature by fraction and subtype. A χ2 test was performed for each subtype and P values are indicated. Positive residuals (blue) indicate a positive correlation, whereas negative residuals (red) indicate an anti-correlation. The point size reflects the contribution to the χ2 estimate. b, Patients were ordered as in Fig. 1a, and relevant clinical information is provided alongside the fraction-specific mutational signatures. PyClone mutational clusters are also presented.

Extended Data Fig. 4 Hypermutator clonality.

a, Bar plots represent counts of recurrence-only mutations per hypermutator tumour that were known to receive treatment alkylating agent and were successfully run through the PyClone algorithm. Colours indicate mutation clonality and colour intensity indicates whether the mutations resulted in coding changes. b, Kaplan–Meier curve comparing the survival of alkylating agent-treated IDH-mutant-noncodel hypermutator tumours that were predominantly clonal (n = 8), predominantly subclonal (n = 7) or non-hypermutator (n = 17). Limited to tumours with available PyClone data. P value determined by log-rank test.

Extended Data Fig. 5 Clonal structure evolution over time.

a, The minimum CCF of the most persistent (shared between initial and recurrence) PyClone cluster. b, Comparison of PyClone clusters ranked by CCF in matched initial and recurrent tumours, as in Fig. 2b, but separated by subtype. c, d, Examples of cluster CCF dynamics over time in three separate samples, including two multi-time point samples (c) and one multi-sector sample (d). These additional data are available in the GLASS resource, but only two time-separated samples were used throughout to ensure clarity.

Extended Data Fig. 6 Distribution of variant allele fraction.

a, Distributions of non-hypermutator variant allele fraction for copy-neutral variants in coding regions (n = 181 patients). Variants are separated by subtype, fraction and also the variant was non-synonymous or synonymous mutation in a coding region. R2 goodness-of-fit measure and associated P values are shown. Note that these data consider only the coding portion of genome, whereas Fig. 2d presents both coding and non-coding data. b, The cumulative distribution of the subclonal mutations in copy-neutral regions for hypermutators (n = 31 patients). For each variant fraction and subtype, the R2 goodness-of-fit measure and P values are shown.

Extended Data Fig. 7 Driver gene nomination.

a, Local (gene-wise) dN/dS estimates by subtype (rows) and fraction (columns). Genes are sorted by Q value and P value. The Q value is shown in colour, whereas the P value is indicated in light grey. The Q value threshold of 0.05 is indicated by a horizontal red line. b, GISTIC significant amplification (red) and deletion (blue) plots in initial (left) and recurrent tumours (right). Chromosomal locations are ordered on the y axis, Q values are shown on the x axis, and selected drivers are indicated by their chromosomal location on the right.

Extended Data Fig. 8 Driver acquisition over time.

a, Tabulated numbers of SNV (top) and CNV (bottom) driver events that were shared, initial-only or recurrence-only. P values were determined by a two-sided Fisher test comparing the initial-only fraction to the recurrence-only fraction testing for acquisition. b, One-sided Fisher test comparing the initial-only fraction to the recurrence-only fraction among previously implicated glioma drivers testing for driver acquisition. P values were adjusted for multiple testing using the false discovery rate (x axis). Hypermutators (red) and non-hypermutators (black) were separately analysed.

Extended Data Fig. 9 Intra-tumour CCF comparison.

Ladder plots comparing the CCF of co-occurring drivers in single tumour samples. The colour of the lines and points indicates whether the sample shown is an initial (brown) or recurrent (green) tumour. P values determined by two-sided Wilcoxon rank-sum test for all initial samples, recurrent samples, as well as all samples (black).

Extended Data Fig. 10 Between time point intra-patient CCF comparison.

a, Driver gene CCF comparison between initial and matched recurrences. Lines are coloured by variant classification. P values determined by two-sided Wilcoxon rank-sum test. b, TP53 CCF by subtype, otherwise as in a. c, IDH1 CCF by subtype, otherwise as in a. d, Ladder plot visualizing change in CCF across all SNVs between initial and recurrent tumours, separated by subtype. P values determined by Wilcoxon rank-sum test. e, Initial and recurrent mutations in each patient were compared using a Wilcoxon rank-sum test. Bar plot with counts of patients in each subtype are shown. Patients lacking significant change are shown in yellow, and those with a significant increase or decrease are shown in dark and light blue, respectively.

Extended Data Fig. 11 Aneuploidy calculation.

a, Heat map displaying the chromosomal arm-level events (x axis) with patients represented in each row. Patients are placed in the same order for both the initial (left) and recurrence (right). White space was inserted as a break between the three subtypes. b, Distribution of total aneuploidy difference. Acquired aneuploidy determination (upper-quartile) indicated with a red line. c, Comparison of aneuploidy score between initial and recurrent tumours separated by subtype d. As in c, comparing aneuploidy value.

Extended Data Fig. 12 Neoantigen evolution and cellular analysis.

a, Bar plots representing the number of shared mutations that give rise to neoantigens (top row, ‘immunogenic’) and those that do not give rise to neoantigens (bottom row, ‘non-immunogenic’) stratified by longitudinal clonality (‘(clonality in initial) − (clonality in recurrence)’) and further separated by subtype. The percentage of longitudinal clonality per subtype and mutation is shown. b, Left, ladder plot depicting the difference in observed-to-expected neoantigen ratio between the initial and recurrent tumours of patients with hypermutated tumours at recurrence. Each set of points connected by a line represents one tumour (n = 70). Right, box plot depicting the distribution of observed-to-expected neoantigen ratios in recurrent tumours stratified by hypermutator status (n = 35 and 183 for hypermutators and non-hypermutators, respectively). Each box spans quartiles, with the lines representing the median ratio for each group. Whiskers represent absolute range, excluding outliers. P values were determined by a paired and an unpaired two-sided t-test, for left and right graphs, respectively. c, Stacked bar plots depicting the average relative fraction of 11 CIBERSORT cell types in the neoantigen depleted (<1) and non-depleted (>1) initial and recurrent tumour subgroups. P values to the right of each plot indicate a significant difference between the depleted and non-depleted groups for the noted cell type at that time.

Supplementary information

Supplementary Information

This file contains a supplementary tables’ guide for tables 1-10, supplementary tables 9 and 10, and some supplementary text.

Reporting Summary

Supplementary Table

Supplementary Table 1: Sequencing Centers and Hospitals with corresponding GLASS barcode designations.

Supplementary Table

Supplementary Table 2: Cox proportional hazards analysis in aklyating agent-treated hypermutators and non-hypermutators.

Supplementary Table

Supplementary Table 3: a) Breakdown of evolution mode subtype b) Breakdown of evolution status at recurrence across subtype.

Supplementary Table

Supplementary Table 4: Multivariate Cox proportional hazards model testing the association between evolution status at recurrence and overall survival (n = 131).

Supplementary Table

Supplementary Table 5: Relationship between selection status at recurrence and therapy.

Supplementary Table

Supplementary Table 6: Neoantigen frequency in the GLASS cohort.

Supplementary Table

Supplementary Table 7: GLASS patient-level summary and clinical characteristics.

Supplementary Table

Supplementary Table 8: Surgery-level clinical data for each GLASS subject.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barthel, F.P., Johnson, K.C., Varn, F.S. et al. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112–120 (2019) doi:10.1038/s41586-019-1775-1

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.