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.

Discordant inheritance of chromosomal and extrachromosomal DNA elements contributes to dynamic disease evolution in glioblastoma

Abstract

To understand how genomic heterogeneity of glioblastoma (GBM) contributes to poor therapy response, we performed DNA and RNA sequencing on GBM samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting dataset to show that somatic driver alterations including single-nucleotide variants, focal DNA alterations and oncogene amplification on extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. We infer that ecDNA was unevenly inherited by offspring cells, a characteristic that affects the oncogenic potential of cells with more or fewer ecDNAs. Longitudinal patient tumor profiling found that oncogenic ecDNAs are frequently retained throughout the course of disease. Our analysis shows that extrachromosomal elements allow rapid increase of genomic heterogeneity during GBM evolution, independently of chromosomal DNA alterations.

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: Comprehensive comparison of GBMs with derived neurospheres and PDX models.
Fig. 2: EcDNA in human GBM samples and FISH validation.
Fig. 3: Extrachromosomal MET DNA.
Fig. 4: EcDNA marks subclones driving tumor progression in patient tumors and derived model systems.
Fig. 5: Copy number variant driver genes located on the potential double-minute regions.
Fig. 6: Schematic illustration of ecDNA element contribution to clonal evolution in GBM patient-derived models.

References

  1. 1.

    Roos, W. P., Thomas, A. D. & Kaina, B. DNA damage and the balance between survival and death in cancer biology. Nat. Rev. Cancer 16, 20–33 (2016).

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    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).

    Article  PubMed  Google Scholar 

  3. 3.

    Aparicio, S. & Caldas, C. The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368, 842–851 (2013).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Sequist, L. V. et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci. Transl. Med. 3, 75ra26 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Dolecek, T. A., Propp, J. M., Stroup, N. E. & Kruchko, C. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005–2009. Neuro-oncol. 14, v1–v49 (2012). (Suppl 5).

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

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

    CAS  Article  PubMed  Google Scholar 

  13. 13.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Szerlip, N. J. et al. Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc. Natl. Acad. Sci. USA 109, 3041–3046 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ozawa, T. et al. Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma. Cancer Cell 26, 288–300 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Wang, J. et al. c-Myc is required for maintenance of glioma cancer stem cells. PLoS One 3, e3769 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Annibali, D. et al. Myc inhibition is effective against glioma and reveals a role for Myc in proficient mitosis. Nat. Commun. 5, 4632 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Cox, D., Yuncken, C. & Spriggs, A. I. Minute chromatin bodies in malignant tumours of childhood. Lancet 1, 55–58 (1965).

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Kohl, N. E. et al. Transposition and amplification of oncogene-related sequences in human neuroblastomas. Cell 35, 359–367 (1983).

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Sanborn, J. Z. et al. Double minute chromosomes in glioblastoma multiforme are revealed by precise reconstruction of oncogenic amplicons. Cancer Res. 73, 6036–6045 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Nikolaev, S. et al. Extrachromosomal driver mutations in glioblastoma and low-grade glioma. Nat. Commun. 5, 5690 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Organ, S. L. & Tsao, M. S. An overview of the c-MET signaling pathway. Ther. Adv. Med. Oncol. 3, S7–S19 (2011). (Suppl.).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Storlazzi, C. T. et al. Gene amplification as double minutes or homogeneously staining regions in solid tumors: origin and structure. Genome Res. 20, 1198–1206 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Lundberg, G. et al. Binomial mitotic segregation of MYCN-carrying double minutes in neuroblastoma illustrates the role of randomness in oncogene amplification. PLoS One 3, e3099 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Liu, X. et al. A novel kinase inhibitor, INCB28060, blocks c-MET-dependent signaling, neoplastic activities, and cross-talk with EGFR and HER-3. Clin. Cancer Res. 17, 7127–7138 (2011).

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Tesfay, L., Schulz, V. V., Frank, S. B., Lamb, L. E. & Miranti, C. K. Receptor tyrosine kinase Met promotes cell survival via kinase-independent maintenance of integrin α3β1. Mol. Biol. Cell 27, 2493–2504 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Arena, S., Pisacane, A., Mazzone, M., Comoglio, P. M. & Bardelli, A. Genetic targeting of the kinase activity of the Met receptor in cancer cells. Proc. Natl. Acad. Sci. USA 104, 11412–11417 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Vogt, N. et al. Molecular structure of double-minute chromosomes bearing amplified copies of the epidermal growth factor receptor gene in gliomas. Proc. Natl. Acad. Sci. USA 101, 11368–11373 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Rubio-Perez, C. et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell 27, 382–396 (2015).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Bigner, S. H., Mark, J. & Bigner, D. D. Cytogenetics of human brain tumors. Cancer Genet. Cytogenet. 47, 141–154 (1990).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Nathanson, D. A. et al. Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science 343, 72–76 (2014).

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Chi, A. S. et al. Rapid radiographic and clinical improvement after treatment of a MET-amplified recurrent glioblastoma with a mesenchymal–epithelial transition inhibitor. J. Clin. Oncol. 30, e30–e33 (2012).

    Article  PubMed  Google Scholar 

  35. 35.

    Humphrey, P. A. et al. Amplification and expression of the epidermal growth factor receptor gene in human glioma xenografts. Cancer Res. 48, 2231–2238 (1988).

    CAS  PubMed  Google Scholar 

  36. 36.

    Pandita, A., Aldape, K. D., Zadeh, G., Guha, A. & James, C. D. Contrasting in vivo and in vitro fates of glioblastoma cell subpopulations with amplified EGFR. Genes Chromosom. Cancer 39, 29–36 (2004).

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Schulte, A. et al. Glioblastoma stem-like cell lines with either maintenance or loss of high-level EGFR amplification, generated via modulation of ligand concentration. Clin. Cancer Res. 18, 1901–1913 (2012).

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Giannini, C. et al. Patient tumor EGFR and PDGFRA gene amplifications retained in an invasive intracranial xenograft model of glioblastoma multiforme. Neuro-oncol. 7, 164–176 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Hasselbach, L. A. et al. Optimization of high grade glioma cell culture from surgical specimens for use in clinically relevant animal models and 3D immunochemistry. J. Vis. Exp. 83, e51088 (2014).

    Google Scholar 

  40. 40.

    deCarvalho, A. C. et al. Gliosarcoma stem cells undergo glial and mesenchymal differentiation in vivo. Stem Cells 28, 181–190 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Irtenkauf, S. M. et al. Optimization of glioblastoma mouse orthotopic xenograft models for translational research. Comp. Med. 67, 300–314 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Graveel, C. et al. Activating Met mutations produce unique tumor profiles in mice with selective duplication of the mutant allele. Proc. Natl. Acad. Sci. USA 101, 17198–17203 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Torres-García, W. et al. PRADA: pipeline for RNA sequencing data analysis. Bioinformatics 30, 2224–2226 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Berlin, K. et al. Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. Nat. Biotechnol. 33, 623–630 (2015).

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Delcher, A. L. et al. Alignment of whole genomes. Nucleic Acids Res. 27, 2369–2376 (1999).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Chiang, C. et al. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat. Methods 12, 966–968 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Conway, T. et al. Xenome—a tool for classifying reads from xenograft samples. Bioinformatics 28, i172–i178 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Xi, R. et al. Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc. Natl. Acad. Sci. USA 108, E1128–E1136 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

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

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Robinson, J. T. et al. Integrative Genomics Viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank our colleagues at Henry Ford Hospital: N. Lehman and C. Hao for contributions to pathology reviews; L. Scarpace for clinical information; S. Irtenkauf, L.Hasselbach, K. Nelson, K. Bergman and S. Sobiechowski for cell culture and animal work; and A.Transou, Y. Meng and E. Carlton for histology. We are indebted to M. Wimsatt (JAX) for the creative design in Fig. 6. We thank G. Geneau, S. Roland and PacBio platform personnel of the Génome Québec/Genome Canada–funded Innovation Centre for providing Pacific Biosciences sequencing. AmpliconArchitect analysis of TCGA was made possible through the Cancer Genomics Cloud of the Institute for Systems Biology (ISB-CGC). This work was supported by the LIGHT Research Program at the Hermelin Brain Tumor Center (A.C.d., T.M.); grants from the US National Institutes of Health R01 CA190121 (R.G.W.V.); Cancer Center Support Grant P30CA034196; the Cancer Prevention and Research Institute of Texas (CPRIT) R140606 (R.G.W.V.); and the National Brain Tumor Society (R.G.W.V.). This work was also supported by grant HI14C3418 of the Korea Health Technology R&D project through the Korea Health Industry Development Institute funded by the Ministry of Health & Welfare, Republic of Korea (D.N.). We are hugely indebted to the patients who provided biospecimens for the purpose of this study.

Author information

Affiliations

Authors

Contributions

A.C.d., H.K. and R.G.W.V. led the study and wrote the manuscript. T.M. obtained the patient samples that made the study possible. A.C.d. supervised the establishment of primary cultures and xenografts, prepared samples for molecular profiling, designed all in vitro and in vivo experiments, and performed data analysis. H.K. designed, supervised and performed all bioinformatic analyses. L.M.P., S.Z., S.S. and J.Z. performed data preprocessing and data analysis. T.M. and L.M.P. collected clinical data. J.K. and A.M. performed FISH experiments. Y.J. performed liquid chromatography–mass spectrometry. A.P. supervised and performed all Illumina sequencing studies, including whole-genome, exome and RNA sequencing library preparation and sequencing experiments. M.F. provided clinical and pathology reviews. M.E.W., C.M., D.C., E.F.P. and L.C. provided valuable input regarding study design, data analysis and interpretation of results. D.-H.N., T.M. and R.G.W.V. provided validation datasets. T.M., L.C. and R.G.W.V. provided financial and technical infrastructure and oversaw the project.

Corresponding authors

Correspondence to Ana C. deCarvalho or Tom Mikkelsen or Roel G. W. Verhaak.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Comparison of DNA copy number and loss of heterozygosity.

Genome wide DNA copy number profiles. Left panel: Copy number increases (red) and decreases (blue) are plotted as a function of distance along the normal genome (vertical axis, divided into chromosomes). Right panel represents segmented B-allele-frequencies at heterozygous germline SNPs, which reflects patterns of loss of heterozygosity. Two cases with diploid but homozygous chromosome 10 are highlighted. See also Supplementary Figure 8.

Supplementary Figure 2 Predicted ecDNA elements in primary tumors, neurospheres, and xenografts using whole genome sequencing.

Segmented copy numbers, structural variation (SV) breakpoints, and fusion junctions have been visualized over patient tumor and its derived model systems for each predicted extrachromosomal region that was predicted with the copy number based approach. The boundaries of each predicted ecDNA have been indicated with vertical dots. In cases where one end of a fusion gene junction or SV breakpoint pair does not fall within the predicted ecDNA region, those points have been plotted outside the region.

Supplementary Figure 3 MET amplification and activation.

a, RT–PCR detection of CAPZA2-MET fusion transcripts in HF3035 samples. Band of predicted size for MET transcript (oligos M2F and M8R) were observed for the HF3035 tumor sample (T), neurosphere cells (N, faint band), 3 xenografts tumors (X), and neurospheres derived from xenograft tumors (NX). A band of predicted size for the fusion CAPZA2(exon1) - MET(exon6) transcript was observed for HF3035 T, X and NX samples. HF2303 neurosphere line expressing only wt MET was used as control. The results are representative of 2 independent experiments. b, Genomic breakpoints of the 7q31 amplification detected in HF3035 and HF3077 are similar in samples from the same parental tumor. c, Representative images of 4 samples per group. HF3035: robust MET protein expression in the tumor, greatly decreased in the neurospheres, and recovered in intracranial and subcutaneous xenografts. MET is activated when expressed, as shown by robust p-Met (Y1234/1235) detection in the orthotopic xenograft. FISH image shows increased frequency of MET amplification in the subcutaneous tumors, as observed for the intracranial tumors (Fig. 3a). HF3077: MET expression in the tumor, was undetectable in the neurospheres. In orthotopic xenografts, MET and p-MET positive cells can be observed at an early time point (day 56), before a tumor mass has formed, and persists until tumor has grown (day 160). Arrowheads point to examples of MET or p-MET positive cells. Scale size is indicated in each panel.

Supplementary Figure 4 Structural variations detected using PacBio sequencing.

a, Contig sequence fragments of at least 1kb were aligned to hg19 chr 7. Right and left arrows represent sequence fragments aligned on + and – strands, respectively. A green dotted line between two contigs indicates that the sequence fragment was shared. Copy numbers and fusion junctions are also shown. Red bars represent area of DNA copy number gain. b, Coverage of the PacBio sequencing reads over the METCAPZA2 region.

Supplementary Figure 5 Validation of predicted ecDNA elements in primary and recurrent gliomas using whole genome sequencing, FISH and DNA copy number profiling.

a, Left panels: Segmented copy numbers, and structural variation (SV) breakpoints/fusion junctions have been visualized for primary and recurrent tumors for each predicted extrachromosomal segment that was predicted with the copy number based approach. The boundaries of each predicted ecDNA have been indicated with vertical dots. In cases where one end of a fusion gene junction or SV breakpoint pair does not fall within the predicted ecDNA region, those points have been plotted outside the region. Representative FISH images from FFPE tissue sections showing amplification of PDGFRA, TERT and RPS6 (red) and control chromosomal probes (green). Fifty nuclei were examined per sample.

Supplementary Figure 6 Predicted ecDNA elements in primary and recurrent gliomas using whole genome sequencing and copy number profiling.

a. Segmented copy numbers, and structural variation (SV) breakpoints/fusion junctions have been visualized for primary and recurrent tumors for each predicted extrachromosomal segment that was predicted with the copy number based approach. The boundaries of each predicted ecDNA have been indicated with vertical dots. In cases where one end of a fusion gene junction or SV breakpoint pair does not fall within the predicted ecDNA region, those points have been plotted outside the region. b. Segmented copy numbers have been visualized for primary and recurrent tumors for each predicted extrachromosomal segment that was predicted with the copy number based approach. The boundaries of each predicted ecDNA have been indicated with vertical dots. In cases where one end of a fusion gene junction or SV breakpoint pair does not fall within the predicted ecDNA region, those points have been plotted outside the region.

Supplementary Figure 7 Association of ecDNA-carrying primary tumors with time-to-secondary surgery.

a, Time to second surgery was compared between ecDNA-carrying primary tumors and non-carrying primary tumors. P-values were derived by the log-rank test in R’s survival package. Confidence intervals for the median survival were derived by the survfit function in R’s survival package. In cases where confidence intervals for median survival failed to be estimated, median survival was predicted.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Note

Reporting Summary

Supplementary Table 1

Clinical characteristics of the GBM patients included in this study

Supplementary Table 2

Patient cohort and detected ecDNAs

Supplementary Table 3

FISH signal per nucleus—HF3253

Supplementary Table 4

FISH results for predicted DM and non-amplified control genes in primary–recurrent GBM pairs

Supplementary Table 5

BAC clones used for preparing FISH probes

Supplementary Table 6

Oligo sequences

Supplementary Table 7

Alignment of the selected contigs on hg19 chromosome 7

Supplementary Table 8

Mutation count and tumor purity estimates

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

deCarvalho, A.C., Kim, H., Poisson, L.M. et al. Discordant inheritance of chromosomal and extrachromosomal DNA elements contributes to dynamic disease evolution in glioblastoma. Nat Genet 50, 708–717 (2018). https://doi.org/10.1038/s41588-018-0105-0

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing