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Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity

Nature volume 543, pages 122–125 (2017)Cite this article

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Abstract

Human cells have twenty-three pairs of chromosomes. In cancer, however, genes can be amplified in chromosomes or in circular extrachromosomal DNA (ecDNA), although the frequency and functional importance of ecDNA are not understood1,2,3,4. We performed whole-genome sequencing, structural modelling and cytogenetic analyses of 17 different cancer types, including analysis of the structure and function of chromosomes during metaphase of 2,572 dividing cells, and developed a software package called ECdetect to conduct unbiased, integrated ecDNA detection and analysis. Here we show that ecDNA was found in nearly half of human cancers; its frequency varied by tumour type, but it was almost never found in normal cells. Driver oncogenes were amplified most commonly in ecDNA, thereby increasing transcript level. Mathematical modelling predicted that ecDNA amplification would increase oncogene copy number and intratumoural heterogeneity more effectively than chromosomal amplification. We validated these predictions by quantitative analyses of cancer samples. The results presented here suggest that ecDNA contributes to accelerated evolution in cancer.

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Figure 1: Integrated next-generation DNA sequencing and cytogenetic analysis of ecDNA.
Figure 2: ecDNA is found in nearly half of cancers and contributes to intratumoural heterogeneity.
Figure 3: The most common focal amplifications in cancer are contained on ecDNA.
Figure 4: Theoretical model for focal amplification via extrachromosomal and intrachromosomal mechanisms.

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Acknowledgements

We thank R. Kolodner, W. Mischel, D. Geschwind, members of the Mischel laboratory, A. Akbari, A. Iranmehr and A. Patel for helpful comments. This work was supported by the Ludwig Institute for Cancer Research (P.S.M., B.R., K.A., W.K.C., F.B.F.), Defeat GBM Program of the National Brain Tumor Society (P.S.M., F.B.F.), The Ben and Catherine Ivy Foundation (P.S.M.), generous donations from the Ziering Family Foundation in memory of Sigi Ziering (P.S.M.); The Susan G. Komen Foundation (SAC110036), The Leona M. and Harry B. Helmsley Charitable Trust (2012-PG-MED002) and The Breast Cancer Research Foundation (BCRF) to G.M.W.; CureSearch for Children’s Cancer and a Leadership Award from the California Institute for Regenerative Medicine to R.W.R. This work was also supported by the following NIH grants: NS73831 (P.S.M.), GM114362 (V.B., V.D., D.B.), NS80939 (F.B.F.), CA014195 and CA159859 (G.M.W.) and CA151819 (D.A.N.) and T32CA121938 (K.M.T.) and NSF grants: NSF-IIS-1318386 and NSF-DBI-1458557 (V.B., V.D., D.B.).

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    Authors and Affiliations

    1. Ludwig Institute for Cancer Research, University of California at San Diego, La Jolla, 92093, California, USA

      Kristen M. Turner, Tomoyuki Koga, Bin Li, Karen Arden, Bing Ren, Webster K. Cavenee, Frank B. Furnari & Paul S. Mischel

    2. Department of Computer Science and Engineering, University of California at San Diego, La Jolla, 92093, California, USA

      Viraj Deshpande, Doruk Beyter & Vineet Bafna

    3. Tumor Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, 92037, California, USA

      Jessica Rusert, Catherine Lee & Robert Wechsler-Reya

    4. Department of Molecular and Medical Pharmacology, David Geffen UCLA School of Medicine, Los Angeles, 90095, California, USA

      David A. Nathanson & Harley I. Kornblum

    5. Neuropsychiatric Institute–Semel Institute for Neuroscience and Human Behavior and Department of Psychiatry and Biobehavioral Sciences, David Geffen UCLA School of Medicine, Los Angeles, 90095, California, USA

      Harley I. Kornblum

    6. The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, M5G 1X8, Ontario, Canada

      Michael D. Taylor

    7. Moores Cancer Center, University of California at San Diego, La Jolla, 92093, California, USA

      Sharmeela Kaushal & Paul S. Mischel

    8. Department of Pathology, University of California San Francisco, San Francisco, 94143, California, USA

      Scott R. Vandenberg

    9. Department of Pathology and Laboratory Medicine, David Geffen UCLA School of Medicine, Los Angeles, 90095, California, USA

      P. Nagesh Rao

    10. Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, 92037, California, USA

      Geoffrey M. Wahl

    11. Department of Pathology, University of California at San Diego, La Jolla, 92093, California, USA

      Paul S. Mischel

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    Contributions

    K.M.T., V.D., D.B., V.B. and P.S.M. conceived and designed the study. K.M.T., V.D., D.B., T.K., J.R., C.L. and B.L. performed experiments. D.B., V.D. and V.B. developed the ECdetect software and performed mathematical modelling and simulations. V.D. and V.B. developed the AmpliconArchitect software. K.A., B.R., D.A.N., F.B.F., W.K.C., P.N.R. and G.M.W. provided analytic support. H.I.K., M.D.T., S.K, R.W.-R. and S.R.V. provided additional clinical samples and analytic support. K.M.T., V.D., D.B., V.B. and P.S.M. wrote the manuscript with feedback from all authors.

    Corresponding authors

    Correspondence to Vineet Bafna or Paul S. Mischel.

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    Competing interests

    V.B. is a co-founder, has an equity interest and receives income from Digital Proteomics, LLC. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. Digital Proteomics was not involved in the research presented here.

    Additional information

    Reviewer Information: Nature thanks C. Maley, A. Papenfuss and the other anonymous reviewer(s) for their contribution to the peer review of this work.

    Extended data figures and tables

    Extended Data Figure 1

    Full metaphase spreads corresponding to the partial metaphase spreads shown in Fig. 1a, Images corresponding to Fig. 1b. b, Images corresponding to Fig. 1c. c, Images corresponding to Fig. 1d.

    Extended Data Figure 2 Alternative analysis of ecDNA presence according to varying criteria, stratified by sample type.

    Samples with a minimum number of ecDNA elements per 10 cells in metaphase in average shown in x axis are classified ecDNA positive, and their fraction is displayed on the y axis. The vertical line at x = 4 shows that for a minimum of 4 ecDNA elements per 10 cells in metaphase on average, 0% of normal, 10% of immortalized, 46% of tumour cell line and 89% of PDX samples are classified as ecDNA positive.

    Extended Data Figure 3 ecDNA counts in normal and immortalized cells.

    Extended Data Figure 4 Histogram of depth of coverage for next-generation sequencing of tumour samples.

    We sequenced 117 tumour samples including 63 cell lines, 19 neurospheres (PDX) and 35 cancer tissues with coverage ranging from 0.6× to 3.89× (excluding one sample with 0.06× coverage) with median coverage of 1.19×. 

    Extended Data Figure 5 Full metaphase spreads corresponding to the partial metaphase spreads shown in Fig. 3c.

    Extended Data Figure 6 FISH images displaying both ecDNA elements and HSRs in cells from the same sample.

    Extended Data Figure 7 Copy-number amplification and diversity due to ecDNA.

    To test how much of the copy-number amplification and diversity could be attributed to ecDNA, we chose FISH probes that bind to four of the most commonly amplified oncogenes in our sample set, EGFR, MYC, CCND1 or ERBB2, and quantified the cell-to-cell variability in their DNA copy number in metaphase spreads, from four tumour cell lines: GBM39, MB411FH, SF295 and PC3 cancer cells. For each cell line, only the target oncogene marked in red is known to be amplified on ecDNA (EGFR in GBM39; MYC in MB411FH and PC3, and CCND1 in SF295). The other 3 genes reside on chromosomal loci. The target oncogene shows consistently higher copy numbers (top) and diversity (bottom).

    Extended Data Figure 8 Fine structure analysis of EGFRvIII amplification in extrachromosomal or chromosomal DNA in GBM39 cells.

    a, FISH images showed the EGFR gene on ecDNA (top) and HSRs (bottom) in different passages of the GBM39 cell line. Analysis of the HSR FISH images shows evidence of multiple integration sites on different chromosomes. b, Next-generation sequencing of DNA from 4 independent cultures of GBM39 was used to analyse the fine structure of amplifications (Supplementary Information 4.3). In 3 biological replicates (rows 1–3) of these cultures, EGFRvIII was exclusively on ecDNA, whereas one of the later passage cultures (row 4) was found to contain EGFRvIII entirely on HSRs, with no detectable ecDNA. The DNA derived from different ecDNA cultures shows identical structure with some heterogeneity (P < 2.18 × 10−8 for all pairs), suggesting common origin. However, DNA derived from HSRs show a conserved structure that is identical to the ecDNA structure (P < 1.98 × 10−5, Supplementary Information 2.4), possibly with tandem duplications. c, A possible progression of normal genome to cancer genome with EGFRvIII ecDNA and amplification to a copy count of around 100 copies. The EGFRvIII ecDNA elements possibly aggregate into tandem duplications and reintegrate into multiple chromosomes as HSRs, so that 5–6 HSRs accommodate around 100 copies of EGFRvIII.

    Extended Data Figure 9 Fine structure analysis of EGFRvIII amplification in extrachromosomal or chromosomal DNA in naive GBM39 cells and in response to erlotinib treatment and drug withdrawal.

    a, FISH images of naive GBM39 cells, in response to erlotinib treatment (ERZ) and drug withdrawal (after ERZ is removed) displayed ecDNA amplification, HSR amplification and ecDNA amplification, respectively (top to bottom). b, Next-generation sequencing of DNA from 6 independent cultures of GBM39 was used to analyse the fine structure of amplifications (Supplementary Information 4.3). Average copy numbers of amplified intervals as determined from sequencing analysis in naive samples: 110–150 (biological replicates in rows 1–3), the ERZ sample: 5.4 (row 4) and after drug withdrawal: 100–105 (biological replicates in rows 5–6). All three categories show similar fine structure indicating common origin (Methods). Drug-withdrawal replicates show additional rearrangements and heterogeneity as compared to naive samples. c, Cytogenetic and sequencing progression suggests that the EGFRvIII ecDNA in naive cells get reintegrated into HSRs after drug application and that the copies in the HSRs break off from the chromosomes again to form ecDNA with a copy count similar to naive cells. Drug-withdrawal samples also show additional heterogeneity in structure.

    Extended Data Figure 10 A GBM cell in metaphase with large ecDNA counts (>600), as determined by manual counting and ECdetect.

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    Supplementary information

    Supplementary Information

    This file contains Supplementary sections 1-4 comprising: Section 1 ECDNA count and presence statistics; Section 2 ECdetect - Software for detection of extrachromosomal DNA from DAPI staining metaphase images; Section 3 AmpliconArchitect: Sequence analysis for identification and reconstruction of focal amplifications; Section 4 A theoretical model of extrachromosomal and intrachromosomal duplication and Supplementary References. (PDF 14810 kb)

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    Turner, K., Deshpande, V., Beyter, D. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017). https://doi.org/10.1038/nature21356

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