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.

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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

Author notes

  1. These authors contributed equally: Ana C. deCarvalho and Hoon Kim.

Affiliations

  1. Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA

    • Ana C. deCarvalho
    •  & Tom Mikkelsen
  2. Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    • Hoon Kim
    •  & Roel G. W. Verhaak
  3. Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA

    • Laila M. Poisson
  4. Bioinformatics and Biostatistics Core, Van Andel Research Institute, Grand Rapids, MI, USA

    • Mary E. Winn
    •  & David Cherba
  5. Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA

    • Claudius Mueller
    •  & Emanuel F. Petricoin
  6. Genomics Core, Van Andel Research Institute, Grand Rapids, MI, USA

    • Julie Koeman
  7. Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    • Sahil Seth
    • , Alexei Protopopov
    • , Yongying Jiang
    • , Jianhua Zhang
    •  & Lynda Chin
  8. Department of Pathology, Henry Ford Hospital, Detroit, MI, USA

    • Michelle Felicella
  9. Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    • Siyuan Zheng
    •  & Lynda Chin
  10. Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    • Asha Multani
  11. Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Korea

    • Do-Hyun Nam
  12. Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea

    • Do-Hyun Nam
  13. Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    • Do-Hyun Nam
  14. Department of Neurology, Henry Ford Hospital, Detroit, MI, USA

    • Tom Mikkelsen

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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.

Competing interests

The authors declare no competing interests.

Corresponding authors

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

Integrated supplementary information

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  1. Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 1

    Clinical characteristics of the GBM patients included in this study

  4. Supplementary Table 2

    Patient cohort and detected ecDNAs

  5. Supplementary Table 3

    FISH signal per nucleus—HF3253

  6. Supplementary Table 4

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

  7. Supplementary Table 5

    BAC clones used for preparing FISH probes

  8. Supplementary Table 6

    Oligo sequences

  9. Supplementary Table 7

    Alignment of the selected contigs on hg19 chromosome 7

  10. Supplementary Table 8

    Mutation count and tumor purity estimates

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https://doi.org/10.1038/s41588-018-0105-0

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